# The Lyman $\alpha$ Reference Sample XIV:
Lyman $\alpha$ imaging of 45 low redshift star-forming galaxies and inferences
on global emission.

Jens Melinder Stockholm University, Department of Astronomy and Oskar Klein
Centre for Cosmoparticle Physics, AlbaNova University Centre, SE-10691,
Stockholm, Sweden  Göran Östlin  Stockholm University, Department of Astronomy
and Oskar Klein Centre for Cosmoparticle Physics, AlbaNova University Centre,
SE-10691, Stockholm, Sweden  Matthew Hayes Stockholm University, Department of
Astronomy and Oskar Klein Centre for Cosmoparticle Physics, AlbaNova
University Centre, SE-10691, Stockholm, Sweden  Armin Rasekh  Stockholm
University, Department of Astronomy and Oskar Klein Centre for Cosmoparticle
Physics, AlbaNova University Centre, SE-10691, Stockholm, Sweden  J. Miguel
Mas-Hesse Centro de Astrobiologia (CAB), CSIC–INTA, ESAC, 28692 Villanueva de
la Cañada, Spain  John M. Cannon Department of Physics & Astronomy, Macalester
College, 1600 Grand Avenue, Saint Paul, MN 55105, USA  Daniel Kunth Institut
d’Astrophysique, Paris, 98 bis Boulevard Arago, F-75014 Paris, France  Peter
Laursen Cosmic Dawn Center (DAWN)  Niels Bohr Institute, University of
Copenhagen, Jagtvej 128, DK-2200, Copenhagen N, Denmark  Axel Runnholm
Stockholm University, Department of Astronomy and Oskar Klein Centre for
Cosmoparticle Physics, AlbaNova University Centre, SE-10691, Stockholm, Sweden
E. Christian Herenz European Southern Observatory, Av. Alonso de Córdova 3107,
763 0355 Vitacura, Santiago, Chile  Matteo Messa Stockholm University,
Department of Astronomy and Oskar Klein Centre for Cosmoparticle Physics,
AlbaNova University Centre, SE-10691, Stockholm, Sweden  Geneva Observatory,
University of Geneva, 51 Chemin des Maillettes, CH-1290 Versoix, Switzerland
Daniel Schaerer Geneva Observatory, University of Geneva, 51 Chemin des
Maillettes, CH-1290 Versoix, Switzerland  Anne Verhamme  Geneva Observatory,
University of Geneva, 51 Chemin des Maillettes, CH-1290 Versoix, Switzerland
T. Emil Rivera-Thorsen Stockholm University, Department of Astronomy and Oskar
Klein Centre for Cosmoparticle Physics, AlbaNova University Centre, SE-10691,
Stockholm, Sweden  Lucia Guaita Departamento de Ciencias Fisicas, Facultad de
Ciencias Exactas, Universidad Andres Bello, Fernandez Concha 700, Las Condes,
Santiago, Chile  Núcleo de Astronomía, Facultad de Ingeniería y Ciencia,
Universidad Diego Portales, Av. Ejército 441, Santiago, Chile  Thomas Marquart
Department of Astronomy and Space Physics, Box 515, 75120 Uppsala, Sweden
Johannes Puschnig Universität Bonn, Argelander-Institut für Astronomie, Auf
dem Hügel 71, D-53121 Bonn, Germany  Alexandra Le Reste Stockholm University,
Department of Astronomy and Oskar Klein Centre for Cosmoparticle Physics,
AlbaNova University Centre, SE-10691, Stockholm, Sweden  Andreas Sandberg
Stockholm University, Department of Astronomy and Oskar Klein Centre for
Cosmoparticle Physics, AlbaNova University Centre, SE-10691, Stockholm, Sweden
Emily Freeland  Stockholm University, Department of Astronomy and Oskar Klein
Centre for Cosmoparticle Physics, AlbaNova University Centre, SE-10691,
Stockholm, Sweden  Joanna Bridge

###### Abstract

We present Ly$\alpha$ imaging of 45 low redshift star-forming galaxies
observed with the Hubble Space Telescope. The galaxies have been selected to
have moderate to high star formation rates using far-ultraviolet (FUV)
luminosity and H$\alpha$ equivalent width criteria, but no constraints on
Ly$\alpha$ luminosity. We employ a pixel stellar continuum fitting code to
obtain accurate continuum subtracted Ly$\alpha$, H$\alpha$ and H$\beta$ maps.
We find that Ly$\alpha$ is less concentrated than FUV and optical line
emission in almost all galaxies with significant Ly$\alpha$ emission. We
present global measurements of Ly$\alpha$ and other quantities measured in
apertures designed to capture all of the Ly$\alpha$ emission. We then show how
the escape fraction of Ly$\alpha$ relates to a number of other measured
quantities (mass, metallicity, star formation, ionisation parameter, and
extinction). We find that the escape fraction is strongly anti-correlated with
nebular and stellar extinction, weakly anti-correlated with stellar mass, but
no conclusive evidence for correlations with other quantities. We show that
Ly$\alpha$ escape fractions are inconsistent with common dust extinction laws,
and discuss how a combination of radiative transfer effects and clumpy dust
models can help resolve the discrepancies. We present a star formation rate
calibration based on Ly$\alpha$ luminosity, where the equivalent width of
Ly$\alpha$ is used to correct for non-unity escape fraction, and show that
this relation provides a reasonably accurate SFR estimate. We also show
stacked growth curves of Ly$\alpha$ for the galaxies that can be used to find
aperture loss fractions at a given physical radius.

cosmology: observations — galaxies: evolution — galaxies: starburst —
radiative transfer — ultraviolet: galaxes — techniques: image processing —
methods: data analysis

††facilities: Hubble Space Telescope (ACS, WFC3), MAST, IRSA††software:
Astropy (Astropy Collaboration et al., 2013), Drizzlepac (Gonzaga et al.,
2012), Matplotlib (Hunter, 2007), SCOOP (Hold-Geoffroy et al., 2014), LACosmic
(van Dokkum, 2001), TinyTim (Krist et al., 2011), LaXS (Hayes et al., 2014;
Östlin et al., 2014), Photutils (Bradley et al., 2022)

##  1 Introduction

Photons emitted in the Ly$\alpha$ line at 1216 Å (carrying an energy of 10.2
eV) come from a radiative transition in the hydrogen (H i) atom from the
second lowest energy state to the ground state ($2p$ to $1s$). The flux
emitted in this line comes from two processes, recombination and collisional
excitation. Recombination, when ionized atoms recombine with free electrons
which go through a cascade of radiative transitions to reach the ground state,
is the more important of these (responsible for $\sim$90% of the line flux).
In this way ionized gas will emit photons in a number of emission lines at
different wavelengths, with energies corresponding to the energy levels
involved. Ly$\alpha$ is a resonance line and has the highest transition
probability of all radiative transitions in the hydrogen atom — roughly 68% of
all recombination events result in the emission of Ly$\alpha$ photon (assuming
case B recombination and a gas temperature of $10^{4}$ K, Dijkstra, 2014).
Because of the resonant nature of the line a large majority of the Ly$\alpha$
photons emitted from the nebular regions will typically be re-absorbed and re-
emitted many times by the neutral interstellar medium (ISM) on their way out
from the galaxies. This means that ISM properties affecting radiative transfer
— such as kinematics, neutral gas geometry, and dust column densities — will
be very important, and needs to be taken into account when estimating the
output Ly$\alpha$ luminosity (e.g. Laursen et al., 2009; Verhamme et al.,
2012; Gronke & Dijkstra, 2016).

Given that hydrogen is the most common element in the Universe, and is the
fundamental ingredient in the star formation process, studying molecular,
neutral, and ionized hydrogen in both absorption and emission is an important
window to the astrophysics of galaxies. Young and massive stars dominate the
radiation output of star-forming galaxies, and are mostly distributed in
stellar clusters. The regions close to these intense sites of star formation
are mostly ionized (H ii regions), and thus emit strongly in recombination
lines. This radiation is also known as nebular line emission, and for non-
resonant hydrogen transitions (such as the optical Balmer recombination lines
H$\alpha$ and H$\beta$) measurements of the nebular line strength can be used
to accurately determine the luminosity of ionizing photons from massive stars
in star-forming regions inside galaxies, and thereby infer the star formation
rate (SFR) in them.

Because of the strength and rest wavelength of the Ly$\alpha$ line it was
suggested as a probe of high redshift galaxies already by Partridge & Peebles
(1967). However, the first searches for Ly$\alpha$ emission in nearby star-
forming galaxies were largely unsuccessful. Instead of a strong emission line,
Ly$\alpha$ was found to be in absorption or much weaker than expected (Meier &
Terlevich, 1981). Absorption of continuum and Ly$\alpha$ photons by the
surrounding neutral medium is of course expected, and even in strong emitters
the line is often superposed on an absorption trough. The measured Ly$\alpha$
flux from these galaxies did thus not match the expectations from the photon
production as estimated from stellar continuum or H$\alpha$ observations.
Furthermore, the unexpected weakness of the Ly$\alpha$ emission was also
observed for active galactic nuclei (Davidsen et al., 1977).

With more sensitive space-based instruments (i.e., International Ultraviolet
Explorer — IUE, Hopkins Ultraviolet Telescope — HUT, Galaxy Evolution Explorer
— GALEX, Hubble Space Telescope — HST) and larger galaxy samples extragalactic
spectroscopic Ly$\alpha$ detections in star-forming galaxies became common-
place (e.g., Giavalisco et al., 1996). These single aperture/slit observations
did not have any information on the spatial distribution of Ly$\alpha$. For
the HST Cosmic Origins Spectrograph (COS) observations the physical size of
the aperture was also quite small and aperture losses of scattered Ly$\alpha$
emission were thus likely large and impossible to constrain. The first
Ly$\alpha$ imaging at high resolution of low redshift galaxies showed the
effects of scattering clearly (Kunth et al., 2003; Hayes et al., 2005; Östlin
et al., 2009), but the observations were hampered by the strong background of
geocoronal Ly$\alpha$ emission present in the line filter. The _Lyman-
$\alpha$ Reference Sample_ (LARS) was designed to simultaneously measure
Ly$\alpha$ emission on small scales (using HST/COS spectroscopy) and large
scales (using far-ultraviolet —FUV— imaging) for an unbiased sample of highly
star-forming galaxies. The first part of the sample, which contains 14
galaxies, was presented in Hayes et al. (2014) and Östlin et al. (2014). By
combining data from many different instruments and wavelength ranges this
study made it possible to perform a systematic investigation of global
Ly$\alpha$ emission and how it relates to other galaxy properties. In
addition, the relatively large field of view of the FUV imaging data allowed
measuring and characterizing the Ly$\alpha$ halos (scattered emission) in a
sample of well-characterized galaxies for the first time (Guaita et al., 2015;
Rasekh et al., 2022). These studies, and others (e.g., Yang et al., 2017a),
have strongly confirmed the theoretical predictions (e.g., Laursen et al.,
2009) that the radiative transfer causes Ly$\alpha$ radiation to be spatially
scattered and thus significantly more extended than both the stellar continuum
and the non-resonant nebular line emission.

Studies of star-forming galaxies in the nearby universe have shown that strong
net Ly$\alpha$ emission is associated with: high specific SFRs (e.g., Cowie et
al., 2011; Hayes et al., 2014), low extinction (e.g., Giavalisco et al., 1996;
Hayes et al., 2014), low metallicity (e.g., Giavalisco et al., 1996), low
neutral gas covering fraction (e.g., Rivera-Thorsen et al., 2015), high
ionisation parameter (e.g., Yang et al., 2017a), turbulent ionised gas
kinematics (e.g., Herenz et al., 2016), and significant outflow velocities
(e.g., Kunth et al., 1998; Heckman et al., 2011). However, while these
conclusions hold when comparing strong emitters to non-emitters, they should
not be mistaken for direct correlations. Comparisons of Ly$\alpha$ relative
output (equivalent widths, $W_{\mathrm{Ly\alpha}}$, or escape fractions,
$f_{\mathrm{esc}}$) with these observables for galaxies with non-zero escape
show either no (sSFR, metallicity), weak (H i covering fraction, outflow
velocity), or fairly strong correlations (extinction) — and with significant
scatter that cannot be explained by measurement uncertainties (Hayes, 2015).
Recently, Runnholm et al. (2020) published a statistical study of the LARS
galaxies. By using multivariate regression techniques they show that a
combination of observables can be used to predict Ly$\alpha$ output of star-
forming galaxies to within a factor of $\sim$50% (0.19 dex). In this study the
most important observables for predicting Ly$\alpha$ luminosity were found to
be FUV luminosity and extinction. The distribution and surface brightness
profile derived properties of Ly$\alpha$ for the sample used in this paper
were studied in Rasekh et al. (2022). The main results from this paper were
that brighter Ly$\alpha$ emitters have smaller halo fractions (a smaller
fraction of their Ly$\alpha$ emission is detected far from star-forming
regions) and that the spatial distribution of Ly$\alpha$ from them is more
circularly symmetric. However, the correlations found in this work are quite
weak and show significant scatter. Investigations of the neutral gas (H i) in
the LARS galaxies are currently on-going and results from part of the sample
have been published in Pardy et al. (2014) and Le Reste et al. (2022). A
comparison of the neutral gas and Ly$\alpha$ properties of the full sample
will be presented in Le Reste et al. (in prep.).

The dependence on extinction has been further explored by, e.g., Scarlata et
al. (2009), Atek et al. (2014), and Bridge et al. (2018). These studies show
that even with radiative transfer effects a simple dust screen model is
insufficient to explain the observations. Most galaxies have escape fractions
that are lower than expected, which is a natural consequence of resonant
radiative scattering increasing the path lengths, and thus causing Ly$\alpha$
radiation to suffer from more extinction than nebular extinction measurements
using other (non-resonant) recombination lines would indicate. However, many
galaxies with quite substantial nebular extinction have Ly$\alpha$ escape
fractions that far exceed the fractions inferred from observed
H$\alpha/$H$\beta$ ratios and using standard extinction curves.

At high redshift ($z=2-7$) Ly$\alpha$ can be used to detect galaxies, and to
spectroscopically confirm their redshifts (e.g., Malhotra & Rhoads, 2004). The
first samples of Ly$\alpha$ emitters (LAEs) were found using narrow-band
techniques (e.g., Cowie & Hu, 1998), which enables surveys covering large
areas at specific high redshift slices, but provides no information on the
Ly$\alpha$ line profile. Narrow band Ly$\alpha$ observations — combined with
broad band imaging to reject low redshift interlopers — thus provides a
powerful and secure method to find high redshift galaxies (e.g., Ouchi et al.,
2018), but it is very challenging to characterize them further using only low
spatial resolution imaging. Follow-up Ly$\alpha$ spectroscopy of high-$z$
galaxies (e.g., Erb et al., 2014; Sobral et al., 2018) and direct Ly$\alpha$
spectroscopy using Integral Field Unit (IFU) spectroscopy (e.g., Adams et al.,
2011; Herenz et al., 2017) have provided a wealth of data on high redshift
LAEs. Trainor et al. (2019) present optical spectroscopy for a large sample
($\sim 700$) of redshift 2–3 LAEs, finding that the equivalent width of metal
line absorption (related to neutral gas density or geometry) and the [O
iii]/H$\beta$ ratio (indicating overall ionisation state) can be used to
predict $W_{\mathrm{Ly\alpha}}$, albeit with large scatter. With simple
assumptions on the stellar population age and star formation history, and
without accounting for radiative scattering in a dusty medium, the escape
fraction of Ly$\alpha$ should be directly proportional to the equivalent width
(Sobral & Matthee, 2019). Sobral & Matthee (2019) use this to construct an
empirical estimator of escape which relies only on $W_{\mathrm{Ly\alpha}}$.
However, many of the high redshift Ly$\alpha$ surveys are limited to either
massive galaxies (when pre-selected from broad-band surveys) or to strong
Ly$\alpha$ emitters (narrow-band or direct spectral detections), and it is
uncertain if the same correlations and conditions also apply to low-mass
galaxies or galaxies with low $f_{\mathrm{esc}}$ and high extinction.

The first observations of large scale Ly$\alpha$ halos at high redshift were
done by Steidel et al. (2011) in a stack of Lyman break galaxies at $z\sim 3$.
The stacked narrow-band images showed extended Ly$\alpha$ halos surrounding
the galaxies. The spatial scales are very large compared to observations of
extended Ly$\alpha$ in nearby galaxies (Guaita et al., 2015; Rasekh et al.,
2022), but the sample properties and resolution/field-of-view are very
different. Other narrow-band and IFU spectroscopy surveys also show the same,
Ly$\alpha$ is invariably more extended than the FUV (e.g., Momose et al.,
2014; Leclercq et al., 2017) or H$\alpha$ emission (e.g., Matthee et al.,
2016).

With observations of low redshifts LAEs giving a more comprehensive
understanding of the effects that radiative transfer has on the Ly$\alpha$
output, current and future Ly$\alpha$ surveys will give important constraints
on re-ionisation and the very first galaxies (e.g., Jensen et al., 2013; Smith
et al., 2015; Runnholm et al., 2020). In this work, we present the extension
of LARS (eLARS), increasing the number of nearby star-forming galaxies imaged
in Ly$\alpha$ from 14 to 45 and widening the sample window to include also
galaxies with lower star formation rates. This paper is organized as follows.
In Section 2 we describe the data set used in this work along with details on
the data reduction. In Section 3 we outline the Ly$\alpha$ continuum
subtraction method used and also specify the assumptions and limitations of
the code. In addition, this section contains a description on how we determine
sizes and perform photometry on the various line, continuum, and property
maps. In Section 4 we present maps of Ly$\alpha$, FUV continuum and H$\alpha$
for the 45 galaxies, along with surface brightness profiles, and demonstrate
that the Ly$\alpha$ is more extended than FUV/H$\alpha$ emission in all
galaxies with significant Ly$\alpha$ escape. Section 5 presents global
measurements of Ly$\alpha$ and other quantities from our data and also
investigates possible correlations between them. In Section 6 we discuss some
implications of our results and how they compare to previous work. Finally,
Section 7 summarizes our conclusions and results. Together with this paper we
also release Ly$\alpha$ images and accompanying reduced HST data for the 45
galaxies to the community.

Throughout this paper we use a $\Lambda$CDM cosmology with
$\\{H_{0},\Omega_{m},\Omega_{\Lambda}\\}=\\{70\mbox{km
s}^{-1}\mbox{Mpc}^{-1},0.3,0.7\\}$.

##  2 The Data

###  2.1 Overall survey strategy

The observational strategy for LARS is described in detail by Östlin et al.
(2014), and the same strategy for imaging Ly$\alpha$ emission from a
combination of broad and narrow-band HST filters is employed for the extended
sample. For clarity we give a brief description of the strategy here. The
basic idea is to use a combination of FUV filters of the Solar Blind Channel
of the Advanced Camera for Surveys (ACS/SBC) on the Hubble Space Telescope
(HST) to measure the flux emitted in nebular Ly$\alpha$ emission and the FUV
stellar continuum, and then isolate the emission from the Ly$\alpha$ emission
line using continuum subtraction methods. This strategy was employed by Kunth
et al. (2003) to obtain the first HST Ly$\alpha$ image of a low redshift
galaxy, and later for a sample of six blue compact galaxies (Hayes et al.,
2005, 2007; Östlin et al., 2009). These earlier surveys used the F122M filter
in the SBC as the Ly$\alpha$ on-line filter. For the LARS galaxies, which are
at slightly higher redshift, it was possible to use a different filter setup,
utilizing multiple long-pass filters of the SBC. In addition to the much lower
geocoronal background the higher throughput of the long-pass filters combined
with having sharp cut-on profiles on the blue side, means that continuum
subtraction using these filters are more contrast sensitive and can reach
lower $W_{\mathrm{Ly\alpha}}$ levels.

The slope of the stellar continuum spectrum at the wavelength of Ly$\alpha$ is
highly sensitive to the age of the stellar population and effective
attenuation from dust (to a lesser extent, also to stellar metallicity). The
slope for a stellar population producing Ly$\alpha$ is not very sensitive to
age, but the continuum subtraction needs to be perfomed also in regions that
do not produce Ly$\alpha$ photons (due to scattering). Hence, without access
to multiple filters spanning both blue- and redward of the Ly$\alpha$ line,
spectral energy distribution (SED) fitting is needed to accurately estimate
the FUV continuum flux. Hayes et al. (2009) showed that to securely constrain
the effect on the FUV continuum of stellar ages, mass, and stellar attenuation
at least four continuum data points are needed, spanning the FUV to optical
wavelength range. A further refinement to the method is possible by adding
narrow-band observations of H$\alpha$ which makes it possible to quantify the
contribution of nebular continuum to the SED (along with H$\beta$ to
extinction correct the H$\alpha$ map). The details of SED fitting and
continuum subtraction is further described in Sec. 3.1

To summarize, the imaging observations obtained for the original 14 galaxies
and the extended sample are three FUV filters (one containing Ly$\alpha$
emission), three ultraviolet/optical broad band filters, and two optical
narrow-band filters (for H$\alpha$ and H$\beta$). For three of the galaxies
(LARS 13, LARS 14, and Tololo 1247-232) only one FUV continuum filter was
used.

###  2.2 Sample selection

Figure 1: Left panel: Emission line diagnostics for the sample. The grey-
shaded region shows the density of galaxies from SDSS DR8. All but one of the
galaxies fall in the region dominated by emission from H ii regions. Right
panel: Galaxy main sequence diagram using data from the JHU-DR8 derivation of
stellar masses and SFRs. Note that Tol 1214 and Tol 1247 do not have SDSS data
and emission line data from other sources (see Table 1) are used. LARS 9 is
not included in the MPA-JHU DR8 catalogues, for this galaxy we use SFR and
stellar mass from the Granada Flexible Stellar Population Synthesis analysis
of DR14 data (Montero-Dorta et al., 2016). Note the majority of galaxies in
the sample are not included in the DR9+ stellar mass and emission line
property catalogues, which is why we chose to use DR8 for this comparison.

The availability of FUV long-pass filters in SBC sets the upper limit on
redshift of the galaxies that can be targeted with our technique. The longest
wavelength SBC long-pass filter is F165LP, and at least one continuum FUV
filter is needed for accurate continuum subtraction. This means that the
reddest Ly$\alpha$ on-line filter that can be used is F150LP, giving an upper
redshift limit of 0.3 (but note that the longer wavelength SBC filters are
less efficient, which makes it hard to target faint galaxies at high
redshift). Geocoronal Ly$\alpha$ emission and Milky Way absorption of
Ly$\alpha$ puts a lower limit to the redshift range at around 0.01. The
selection of galaxies included in the original LARS sample is described in
detail by Östlin et al. (2014). The main astrophysical selection criteria are
star formation rate (based FUV luminosity) and stellar age (using H$\alpha$
equivalent width, $W_{\mathrm{H\alpha}}$). Given that the H$\alpha$ line
fluxes are measured in SDSS apertures they will underestimate the total SFR of
larger galaxies. The $W_{\mathrm{H\alpha}}$ cut of 100 Å in particular means
that the sample only includes galaxies with young stellar populations that
produce Ly$\alpha$ photons. This selection also leads to the sample being
biased towards high specific SFR (irregular and merging systems), and against
the inclusion of high mass galaxies.

For the extended sample we decided to broaden the criteria and include
galaxies with lower specific star formation rate ($W_{\mathrm{H\alpha}}$ $>40$
Å). Most of these galaxies are also thus naturally fainter in the FUV and to
reach acceptable signal to noise while optimizing the use of HST exposure time
we elected to observe targets in the continuous viewing zone (CVZ) (at
$\delta\sim 60\deg$). The longer HST orbit time makes the observations more
efficient, but has the drawback that contamination from the [O i] geocoronal
emission is harder to avoid (see Section 2.3). Furthermore, given the overall
lower surface brightness of the galaxies we also chose to only include lower
redshift galaxies ($z\lesssim 0.05$) in the extended sample. The lower
redshift cut-off was 0.028 which was selected to get a throughput for the
Ly$\alpha$ line of $>85$% in the F125LP filter. The redshift band
$0.036<z<0.044$ was excluded to avoid contamination of the [O iii] 4959+5007 Å
lines in the H$\beta$ narrow-band filter.

With these considerations taken into account we started off by selecting
galaxies from the Sloan Digital Sky Survey (SDSS, DR8) with
$W_{\mathrm{H\alpha}}$ $>40$Å and matched the coordinates to the GALEX (DR3)
catalogues to find FUV fluxes for the sources. Similar to the original LARS
selection we excluded active galactic nucleus (AGN) galaxies by rejecting
galaxies with H$\alpha$ line widths (FWHM) larger than 300 km s-1 (or line of
sight velocity dispersion larger than 130 km s-1), and with emission line
ratio diagnostics from SDSS spectra ([O iii]/H$\beta$ and [N ii]/H$\alpha$).
In addition we required the galaxies to have Milky Way extinction of less than
0.03. We then selected galaxies from the remaining list trying to cover a
range of FUV luminosities — going down to
$\log(L_{\mathrm{FUV}}/L_{\odot})=9.0$ (with $L_{\mathrm{FUV}}$ from the GALEX
FUV channel, defined as $\lambda\times L_{\lambda}$ at a rest wavelength of
1524 Å $/(1+z)$), and morphologies — compactness, clumpiness, disk
inclination, and spiral arm structure. The final number of galaxies selected
using this method is 26 and are all part of the extended LARS observational
programme. Compared to the original 14 galaxies this includes more
continuously star-forming spiral galaxies.

In addition to these 40 galaxies we include five more galaxies in the sample,
which all have the HST imaging required to perform the continuum subtraction.
ELARS 1 (NGC 6090) is a luminous infrared galaxy which was also part of the
Östlin et al. (2009) sample, but was missing H$\beta$ narrow-band imaging.
This galaxy and ELARS 2 (Markarian 65), a compact face-on spiral starburst
galaxy, both fulfil the selection criteria and were already observed in the
SBC long-pass filters as part of HST program 11110 (PI: S. McCandliss). Adding
these galaxies to the sample was thus a cheap addition in terms of
observational time, and they were both part of the extended LARS observational
program. Tololo 1247-232 is a Lyman continuum (LyC) leaking galaxy (Puschnig
et al., 2017) and Tololo 1214-277 is a LyC leaker candidate (Östlin et al., in
prep.). They were both observed with the LARS filter setup to study the
connection between the escape of ionizing photons and Ly$\alpha$. SDSS
J115630.63+500822.1 is an extreme star burst galaxy at higher redshift than
the rest of the sample and was observed in multiple FUV and optical filters by
Hayes et al. (2016) in order to study extended O vi emission.

In Table 1 we show the basic properties that were used to select the sample.
This table also includes the Galactic extinction (Schlafly & Finkbeiner, 2011)
from the Infrared Science Archive, NASA/IPAC (DOI:10.26131/IRSA537) along the
sightline to the galaxies, these are very low for all of the galaxies by
design. The physical properties of the galaxies, derived from the SDSS optical
spectra, are given in Table 2. The SDSS star formation rates (SFR) and masses
are taken from the MPA–JHU catalogue (Brinchmann et al., 2004; Salim et al.,
2007). These are derived from H$\alpha$ and continuum spectral fitting,
respectively, and we show the total estimates for SFR and stellar mass. For
the emission line derived quantities (oxygen abundance and [O iii] $\lambda
5007/$[O ii] $\lambda 3727$ ratio), we use the SDSS based estimates from
Runnholm et al. (2020). For the three galaxies not included there we use the
references given in the table. Note that, apart from O32 and metallicity, we
do not use the SDSS measurements for the analysis and comparisons done in this
paper. They are presented here for completeness and to help characterize the
sample properties.

The left panel of Figure 1 shows the location of the complete sample of
galaxies in a standard BPT diagram (Baldwin et al., 1981). With the exception
of one galaxy, the chosen sources all lie along the star-forming locus. The
outlier (ELARS05) lies in a region of the diagram where galaxies with a low-
ionisation nuclear emission-line region (LINER) normally fall. The inclusion
of this source in the sample was not intended, and was likely caused by
uncertain line fluxes in the selection process. The right panel of Figure 1
shows where the sample falls in a galaxy main sequence diagram (e.g.,
Brinchmann et al., 2004; Rodighiero et al., 2011). The grey-shaded region is a
density map of SFR and stellar mass for nearby galaxies ($z\lesssim 0.2$) from
the MPA-JHU analysis of emission line data in SDSS DR8. As noted above, the
values for the LARS galaxies were pulled from the same catalogue (with a few
exceptions given in the figure caption). While the final sample is not
randomly selected it should be noted that no information on actual Ly$\alpha$
emission was used in the process. The complete sample thus provides an
unbiased data set of extended Ly$\alpha$ emission from star forming galaxies.

Table 1: Sample characteristics

ID | SDSS ID | Redshift | $W_{\mathrm{H\alpha}}$ | log(LHα) | log($L_{\rm FUV}$) |  $E(B-V)$(MW) | Common name
---|---|---|---|---|---|---|---
|  |  | (Å) | (erg/s) | (L⊙) |  |
(1) | (2) | (3) | (4) | (5) | (6) | (7) | (8)
LARS01 | SDSS-J132844.05+435550.5 | 0.028 | 560 | 41.43 | 9.92 | 0.0144 | Mrk 259
LARS02 | SDSS-J090704.88+532656.6 | 0.030 | 312 | 40.77 | 9.48 | 0.0152 | Shoc 240
LARS03 | SDSS-J131535.06+620728.6 | 0.031 | 238 | 41.53 | 9.52 | 0.0193 | Arp 238
LARS04 | SDSS-J130728.45+542652.3 | 0.033 | 234 | 40.93 | 9.93 | 0.0168 |
LARS05 | SDSS J135950.92+572622.9 | 0.034 | 333 | 41.36 | 10.01 | 0.0099 | Mrk 1486
LARS06 | SDSS-J154544.52+441551.8 | 0.034 | 455 | 40.56 | 9.20 | 0.0164 | KISSR 2019
LARS07 | SDSS-J131603.91+292254.0 | 0.038 | 423 | 41.50 | 9.75 | 0.0090 | IRAS 13136+2938
LARS08 | SDSS-J125013.50+073441.5 | 0.038 | 167 | 40.78 | 10.15 | 0.0288 |
LARS09 | SDSS-J082354.96+280621.6 | 0.047 | 505 | 41.81 | 10.46 | 0.0275 | IRAS 08208+2816aaData from SDSS (DR12)
LARS10 | SDSS-J130141.52+292252.8 | 0.057 | 99 | 41.15 | 9.74 | 0.0101 | Mrk 0061
LARS11 | SDSS-J140347.22+062812.1 | 0.084 | 105 | 41.54 | 10.70 | 0.0216 |
LARS12 | SDSS J093813.44+542824.9 | 0.102 | 408 | 42.11 | 10.53 | 0.0154 | LEDA 27453
LARS13 | SDSS-J015028.39+130858.4 | 0.147 | 201 | 42.07 | 10.60 | 0.0619 | IRAS 01477+1254
LARS14 | SDSS-J092600.41+442736.1 | 0.181 | 578 | 42.21 | 10.69 | 0.0161 |
ELARS01 | SDSS-J161140.81+522727.0 | 0.029 | 215 | 41.68 | 10.08 | 0.0172 | NGC 6090
ELARS02 | SDSS-J132109.07+590605.3 | 0.043 | 92 | 41.05 | 10.03 | 0.0091 | Mrk 65
ELARS03 | SDSS-J114511.42+614230.6 | 0.035 | 65 | 40.99 | 10.17 | 0.0186 | UGC 06727
ELARS04 | SDSS-J172823.84+573243.4 | 0.029 | 103 | 41.06 | 10.08 | 0.0333 | NGC 6387
ELARS05 | SDSS-J110501.98+594103.5 | 0.034 | 41 | 40.95 | 9.986 | 0.0062 | SBS 1102+599A
ELARS06 | SDSS-J115840.58+645753.0 | 0.034 | 47 | 40.34 | 9.680 | 0.0132 |
ELARS07 | SDSS-J101242.97+613302.8 | 0.035 | 268 | 40.92 | 9.595 | 0.0105 |
ELARS08 | SDSS-J103020.91+611549.3 | 0.031 | 48 | 40.49 | 9.568 | 0.0088 |
ELARS09 | SDSS-J132734.70+664516.6 | 0.030 | 94 | 40.68 | 9.563 | 0.0126 |
ELARS10 | SDSS-J110504.41+593957.3 | 0.033 | 75 | 40.66 | 9.556 | 0.0062 | SBS 1102+599B
ELARS11 | SDSS-J092715.48+583654.4 | 0.030 | 58 | 40.55 | 9.507 | 0.0300 | MRK 1417
ELARS12 | SDSS-J130607.16+591302.4 | 0.032 | 46 | 40.57 | 9.494 | 0.0136 | SBS 1304+594
ELARS13 | SDSS-J105100.65+655940.5 | 0.032 | 125 | 40.85 | 9.468 | 0.0103 | VII Zw 348
ELARS14 | SDSS-J112722.07+604454.1 | 0.033 | 82 | 40.58 | 9.367 | 0.0103 | SBS 1124+610
ELARS15 | SDSS-J134437.82+611424.3 | 0.035 | 54 | 40.44 | 9.391 | 0.0148 | MCG +10-20-017
ELARS16 | SDSS-J135209.13+560631.4 | 0.035 | 64 | 40.24 | 9.333 | 0.0091 |
ELARS17 | SDSS-J102656.29+584941.6 | 0.031 | 44 | 40.19 | 9.243 | 0.0061 |
ELARS18 | SDSS-J152053.59+571122.1 | 0.029 | 59 | 39.93 | 9.029 | 0.0105 |
ELARS19 | SDSS-J120506.35+563330.9 | 0.031 | 133 | 40.30 | 9.028 | 0.0131 |
ELARS20 | SDSS-J133858.25+614957.6 | 0.031 | 73 | 40.34 | 9.021 | 0.0156 |
ELARS21 | SDSS-J141911.37+654946.3 | 0.033 | 61 | 39.82 | 9.005 | 0.0110 |
ELARS22 | SDSS-J170912.73+604950.0 | 0.047 | 153 | 41.10 | 10.09 | 0.0213 | SBS 1708+608
ELARS23 | SDSS-J144827.20+630210.4 | 0.051 | 49 | 40.79 | 10.08 | 0.0179 |
ELARS24 | SDSS-J144305.31+611838.6 | 0.048 | 83 | 41.51 | 9.996 | 0.0193 |
ELARS25 | SDSS-J101201.93+603720.3 | 0.045 | 56 | 40.48 | 9.833 | 0.0100 | KUG 1008+608
ELARS26 | SDSS-J115241.69+661827.2 | 0.046 | 43 | 40.50 | 9.693 | 0.0100 |
ELARS27 | SDSS-J150247.62+622018.9 | 0.045 | 47 | 40.32 | 9.734 | 0.0106 |
ELARS28 | SDSS-J115218.74+585657.0 | 0.046 | 131 | 40.86 | 9.710 | 0.0255 |
Tol1214 | — | 0.026bbFricke et al. (2001) | 1644ccGuseva et al. (2011) | 40.74 | 9.008 | 0.0555 | Tololo 1214-277
Tol1247 | — | 0.049ddPuschnig et al. (2017) | 530eeTerlevich et al. (1991) | 42.44 | 10.36 | 0.0750 | Tololo 1247-232
J1156 | SDSS-J115630.63+500822.1 | 0.236 | 323 | 42.36 | 11.05 | 0.0187 |

Note. — (3) SDSS spectroscopic redshift (DR8). (4–5) SDSS (DR8). (6) GALEX
FUV. $L_{\rm FUV}$ $=\lambda\times L_{\lambda}$/L⊙ at $\lambda=1524/(1+z)$ Å.
(6) Galactic extinction from the Infrared Science Archive, NASA/IPAC
(DOI:10.26131/IRSA537) and (Schlafly & Finkbeiner, 2011)

Table 2: SDSS derived properties

ID | log(M⋆) | SFR(H$\alpha$) | E(B-V)neb | O32 | $12+\mathrm{log}(O/H)$
---|---|---|---|---|---
| (M⊙) | (M⊙ yr-1) |  |  |
(1) | (2) | (3) | (4) | (5) | (6)
LARS01 | 8.34 | 2.15 | 0.149 | $4.1215\pm 0.0006$ | $8.2182\pm 0.0001$
LARS02 | 8.56 | 1.17 | 0.058 | $4.5481\pm 0.0004$ | $8.2186\pm 0.0040$
LARS03 | 10.4 | 5.37 | 0.656 | $1.3374\pm 0.0001$ | $8.4163\pm 0.00001$
LARS04 | 8.79 | 2.43 | 0.116 | $4.3706\pm 0.0002$ | $8.1787\pm 0.0003$
LARS05 | 8.37 | 1.90 | 0.085 | $6.5808\pm 0.0008$ | $8.0747\pm 0.0004$
LARS06 | 8.47 | 0.731 | 0.010 | $4.9972\pm 0.0009$ | $8.0638\pm 0.0351$
LARS07 | 8.82 | 7.04 | 0.235 | $4.3694\pm 0.0003$ | $8.3396\pm 0.0001$
LARS08 | 9.23 | 2.61 | 0.310 | $1.8424\pm 0.0009$ | $8.5055\pm 0.00003$
LARS09 | 10.1 | 15.8 | 0.231 | $5.9102\pm 0.0003$ | $8.3679\pm 0.00003$
LARS10 | 9.82 | 3.69 | 0.237 | $1.4772\pm 0.0006$ | $8.5049\pm 0.0001$
LARS11 | 10.6 | 18.5 | 0.330 | $0.8637\pm 0.0017$ | $8.4277\pm 0.0004$
LARS12 | 9.37 | 23.7 | 0.152 | $5.6296\pm 0.0004$ | $8.3121\pm 0.0006$
LARS13 | 10.3 | 16.6 | 0.298 | $2.6281\pm 0.0032$ | $8.5019\pm 0.0003$
LARS14 | 9.13 | 11.5 | 0.084 | $8.0942\pm 0.0039$ | $7.9919\pm 0.0416$
ELARS01 | 10.6 | 10.1 | 0.446 | $0.6193\pm 0.0114$ | $8.2278\pm 0.0054$
ELARS02 | 9.67 | 4.01 | 0.342 | $1.3851\pm 0.0371$ | $8.5022\pm 0.0009$
ELARS03 | 10.3 | 7.91 | 0.381 | $0.6950\pm 0.0254$ | $8.3670\pm 0.0056$
ELARS04 | 9.61 | 6.36 | 0.277 | $0.8170\pm 0.0144$ | $8.4838\pm 0.0015$
ELARS05 | 10.4 | 3.06 | 0.300 | $1.3531\pm 0.0520$ | $8.4560\pm 0.0031$
ELARS06 | 9.50 | 1.37 | 0.249 | $0.6497\pm 0.0255$ | $8.4442\pm 0.0047$
ELARS07 | 8.51 | 1.44 | 0.131 | $5.7574\pm 0.1313$ | $8.0152\pm 0.0103$
ELARS08 | 10.0 | 2.01 | 0.365 | $0.3456\pm 0.0266$ | $8.1079\pm 0.0195$
ELARS09 | 8.96 | 1.56 | 0.109 | $2.3410\pm 0.0458$ | $8.3176\pm 0.0069$
ELARS10 | 9.89 | 2.14 | 0.536 | $0.4988\pm 0.0201$ | $8.3688\pm 0.0071$
ELARS11 | 9.50 | 1.52 | 0.129 | $1.1299\pm 0.0262$ | $8.5014\pm 0.0010$
ELARS12 | 10.1 | 2.07 | 0.436 | $0.3831\pm 0.0171$ | $8.2901\pm 0.0091$
ELARS13 | 9.31 | 2.65 | 0.154 | $1.4057\pm 0.0265$ | $8.4589\pm 0.0016$
ELARS14 | 9.13 | 1.09 | 0.201 | $1.4566\pm 0.0340$ | $8.4981\pm 0.0012$
ELARS15 | 9.75 | 1.48 | 0.235 | $1.0751\pm 0.0416$ | $8.4984\pm 0.0017$
ELARS16 | 9.37 | 0.836 | 0.205 | $1.0130\pm 0.0398$ | $8.5064\pm 0.0002$
ELARS17 | 9.57 | 0.726 | 0.257 | $0.7062\pm 0.0369$ | $8.4000\pm 0.0075$
ELARS18 | 8.86 | 0.330 | 0.123 | $1.6726\pm 0.0727$ | $8.4300\pm 0.0076$
ELARS19 | 8.37 | 0.451 | 0.164 | $3.7944\pm 0.0864$ | $8.2358\pm 0.0077$
ELARS20 | 9.44 | 0.577 | 0.217 | $0.5578\pm 0.0186$ | $8.4096\pm 0.0052$
ELARS21 | 8.66 | 0.323 | 0.120 | $2.2992\pm 0.1015$ | $8.3374\pm 0.0159$
ELARS22 | 9.11 | 4.37 | 0.108 | $2.0325\pm 0.0376$ | $8.3930\pm 0.0042$
ELARS23 | 10.1 | 4.40 | 0.310 | $0.5118\pm 0.0210$ | $8.3488\pm 0.0086$
ELARS24 | 10.6 | 10.0 | 0.296 | $0.9139\pm 0.0146$ | $8.4588\pm 0.0018$
ELARS25 | 9.78 | 2.45 | 0.150 | $0.6726\pm 0.0230$ | $8.4685\pm 0.0038$
ELARS26 | 10.1 | 2.65 | 0.437 | $0.5232\pm 0.0358$ | $8.2764\pm 0.0132$
ELARS27 | 9.61 | 1.34 | 0.072 | $0.6335\pm 0.0266$ | $8.4553\pm 0.0051$
ELARS28 | 9.73 | 4.38 | 0.211 | $1.0799\pm 0.0310$ | $8.5035\pm 0.0008$
Tol1214 | 8.90aaFrom HST SED fitting (LaXs) | 0.298 | 0.000bbFricke et al. (2001) | 16.607$\pm 0.0421$ | 7.2102$\pm 0.0200$ccbased on line fluxes from Guseva et al. (2011)
Tol1247 | 10.3aaFrom HST SED fitting (LaXs) | 21.6 | 0.158ddPuschnig et al. (2017) | 3.7224$\pm 0.0209$ | 8.3983$\pm 0.0239$eebased on line fluxes from Terlevich et al. (1993)
J1156 | 9.49 | 36.1 | 0.130 | 2.1096$\pm 0.0191$ | 8.4451$\pm 0.0076$

Note. — (2–3) Total estimates from the SDSS MPA/JHU catalogues (Brinchmann et
al., 2004; Salim et al., 2007). (4) Derived using H$\alpha/$H$\beta$ from SDSS
(DR8). (5) O32 = [O iii] $\lambda$5007Å/([O ii] $\lambda$3727Å+ [O ii]
$\lambda$3729Å) from Runnholm et al. (2020). (6) Derived using O3N2 strong
line metallicity calibration of Yin et al. (2007) (Runnholm et al., 2020).

###  2.3 Observations

All observations were carried out using HST with three different cameras:
ACS/SBC, ACS/Wide Field Camera (WFC), and Wide Field Camera 3 (WFC3)/UVIS. A
list of which filters were used for the individual targets can be found in
Table 7. While we used archival data when available the bulk of the data set
consists of observations from HST program 12310 (Östlin et al., 2014, the
original LARS, observed from 2010–2012), and HST program 13483 (ELARS,
observed from 2013–2014). The observations for Tol 1214-277 come from HST
program 14923 (Östlin et al., in prep.), for Tol 1247-232 from HST program
13027 (Puschnig et al., 2017), and for J1156 from HST program 13656 (Hayes et
al., 2016, Rutkowski et al., in prep). All of the data presented in this paper
were obtained from the Mikulski Archive for Space Telescopes (MAST) at the
Space Telescope Science Institute. The specific observations analyzed can be
accessed here: http://dx.doi.org/10.17909/4s6m-7255 (catalog
10.17909/4s6m-7255).

The FUV continuum and Ly$\alpha$ images were obtained with SBC. The FUV filter
set was chosen to avoid contamination from geocoronal Ly$\alpha$, but the O i
$\lambda\,1302$ Å line is strong enough to severely compromise the F125LP
observations. The exposures in this filter were thus done with the telescope
in Earth shadow. For the extended sample, which used a CVZ observational
setup, this is harder to manage but the timing of the F125LP exposures was
planned to mimimize the time spent observing outside of shadow. We do not
detect any significant change in the background between the original and the
extended sample.

The typical exposure times in the Ly$\alpha$ filter were $3\times 600$s, and
$3\times 400$ in the continuum filters. Using three sub-exposures with small
offsets for the SBC observations was done to cover the gap in the middle of
the detector, and, to a smaller extent, limiting the effect of bad pixels in
the detector.

The optical continuum of the galaxies was imaged in three filters with
WFC3/UVIS (for some of the archival observations the ACS/WFC was used
instead), in general using two blue (F336W and F438W) and one red (F775W)
filter. For the ELARS observations with UVIS post-flash was used to limit the
effect of charge transfer efficiency (CTE) losses. The typical exposure time
for the optical continuum filters was $2\times 400$s. The choice of using only
two dithers was motivated by data transfer limitations when packing
observations in multiple filters into a single HST orbit. It should be noted
that this limits what pixel scale can be achieved when drizzling the data, and
how well cosmic rays are rejected (see Section 2.4). Given that the individual
galaxies are at different redshifts the narrow-band filters used to capture
H$\alpha$ and H$\beta$ photons vary. When possible we used UVIS narrow-band
filters, but for some of the targets (the higher redshift galaxies in
particular) ACS/WCS ramp filters were used instead. The typical exposure time
in H$\alpha$ is $2\times 400$s., and $2\times 600$s. in H$\beta$.

Most of the galaxies are at similar redshift, which means that the
cosmological surface brightness dimming is a small effect. The necessary
exposure times are thus very similar despite a quite large range in distances.
However, three of the targets (LARS 13, 14, and J1156) are at significantly
higher redshift. For these galaxies the exposure times were scaled up to
account for part of the surface brightness dimming, but the rest-frame depth
reached is somewhat reduced.

###  2.4 Data reduction

Figure 2: Color composites for the LARS 1–10 galaxies. Two panels are shown
for each galaxy with the ID number connecting the corresponding panels. The
left sub-panel for each galaxy shows a stellar continuum composite using the
HST I, B, and U band images for the RGB channels. The scale bar in these
panels has a length of 5 kpc. The right panel for each galaxy shows a
composite of red (H$\alpha$), blue (Ly$\alpha$), and green (stellar continuum
in the FUV at the wavelength of Ly$\alpha$). The Ly$\alpha$ maps have been
adaptively smoothed to enhance the contrast and be able to show the low
surface brightness parts of the galaxy.  Figure 3: Color composites for the
LARS 11–14 and ELARS 1–6 galaxies. See the caption for Figure 2 for details.
Figure 4: Color composites for the ELARS 7-16 galaxies. See the caption for
Figure 2 for details.  Figure 5: Color composites for the ELARS 17-26
galaxies. See the caption for Figure 2 for details.  Figure 6: Color
composites for the ELARS 27–28, Tol 1214, Tol 1247, and J1156 galaxies. See
the caption for Figure 2 for details.

Basic image reduction (bias and dark current subtraction, flat-fielding, and
post-flash subtraction where applicable) was done with the standard HST
reduction pipelines, and individual reduced image frames were downloaded from
the Mikulski Archive for Space Telescopes (MAST). In Östlin et al. (2014)
full-frame CTE correction was done with the stand-alone tool available at the
time, and only for the ACS/WFC images. From 2016 full-frame CTE corrected data
has been available directly from MAST and we used this corrected data for both
WFC3/UVIS and ACS/WFC data (ISR WFC3 2016-002).

The optical observations use only two dithers per filter which has one major
consequence for data reduction. The cosmic ray rejection performed during
drizzling of the data fails to mask out many of the affected pixels. Before
drizzling we thus performed an additional rejection using the LACosmic
software (van Dokkum, 2001). We implemented the LACosmic routines in a python
wrapping script that flags the found pixels in the data quality layer of each
individual frame. The flags are then recognized during drizzling and data
affected by cosmic rays are excluded. The individual filters were registered
and stacked using the standard HST drizzling software (Drizzlepac; Gonzaga et
al., 2012) to a common pixel scale of 0.04 ″/pxl. We also produced drizzled
weight maps for each filter that correctly reflect the total noise per pixel
(inverse variance maps).

Sky subtraction is normally handled by the drizzle routine, but for the
ACS/SBC data this subtraction is not able to fully remove large scale
backgrounds. For the FUV filters we thus remove sky from the drizzled frames
by estimating an average sky level well outside the galaxy (usually in the
corners of the images). In some cases the sky background in the F125LP filter
(because of geocoronal [O i] emission, see Section 2.3) varies substantially
(by factors up to 10) between exposures, in this case we subtract the sky from
each individual frame before drizzling. It should be noted that we perform
this background subtraction also for the galaxies that may have Ly$\alpha$
emission to the edge of the chip (discussed further in Section 3.1.3). For the
optical images, the sky subtraction done during drizzling is normally
sufficient. However, for some of the galaxies the ACS/WFC ramp filters — and
the WFC3/UVIS quad filters — show a residual background gradient which may be
caused by problems due to reflected background light in the detector. For the
frames affected by this we fitted and removed a spatially varying background
from each drizzled frame.

The observations in the different filters for each galaxy are done in multiple
HST orbits at different times (however, all observations for a given galaxy
and filter are observed in the same orbit). Because of this the guide star
used is not necessarily the same for all filters which leads to world
coordinate system (WCS) offsets of up to 0.5″ between the drizzled images.
Registering images to a common WCS before drizzling using standard techniques
(e.g., Drizzlepac TweakReg) does not work for the SBC images. Instead we chose
to register the images by using cross-correlation techniques
(pyraf/xregister). The reference image for the registration was the F438W
filter (or equivalent). The average registration uncertainty is around 0.2
pixels ($\sim 0.008$″), but never exceeds 0.5 pixels. After registration we
also inspect all of the images to make sure that the reduction steps have been
successful, and to construct a mask for each galaxy which marks areas not
covered by all filters (particularly important for SBC which has a smaller
field of view than the optical filters). These masks are then used for the
continuum subtraction and photometry. Color composites of the galaxies using
the U, B, and I (HST equivalent bands), showing the stellar continuum are
shown in Figures 2, 3, 4, 5, and 6.

In order to perform accurate continuum subtraction from pixel-based SED
fitting) the effect of the points spread function (PSF) varying between
filters needs to be handled. While the PSF differences in the optical filters
are quite small, the PSFs of the SBC filters (among these the Ly$\alpha$
filter) have very extended wings (see, e.g. Hayes et al., 2016, ISR ACS
2016-05) which makes them differ substantially from the optical filters. If
PSF matching is performed without including the extended wings, the stellar
continuum from the optical filters is under-estimated relative to the FUV
filters. When subtracting the continuum from the Ly$\alpha$ filter too little
flux will be subtracted in the wings, causing the Ly$\alpha$ surface
brightness to be over-estimated in regions of the galaxy not dominated by a
strong point-like source. To study low surface brightness Ly$\alpha$ halos it
is thus critical to correctly constrain not only the core of the PSF but also
the wings, and match the PSFs of all filters to a common reference.

We start by constructing 2D PSF models for all filters and galaxies. For the
optical filters we use TinyTim (Krist et al., 2011) to make models, but for
the SBC images these models are not accurate enough. The SBC PSFs were instead
constructed by stacking stars observed in the stellar cluster NGC 6681 during
calibrations. For each galaxy we then made a maximum width 2D PSF model by
normalizing the models for each filters to the same peak flux and then
stacking them by maximum pixel value. Because of the non-standard shape of the
SBC PSF matching cannot be done with a gaussian-based convolution kernel,
which makes this a non-trivial task. Instead we match the images in individual
filters to the maximum width model by convolving with a delta-function-based
kernel (similar to the technique described in Becker et al., 2012). A detailed
description of how we modeled the PSFs for the different cameras and filters,
and how we performed PSF matching for the data is given in Appendix B.

In addition to the drizzled science images in each filter, the data reduction
pipeline also produces weight frames. We use these weight frames to estimate
the noise per pixel in each filter: $\sigma=1/\sqrt{(}W)$, with $\sigma$
denoting the standard deviation map (i.e., noise) and $W$ the weight map.
Because of the drizzling the pixel-to-pixel noise in the images is correlated
(Fruchter & Hook, 2002), but the noise map still represents the best possible
estimate of the uncertainty for each individual pixel we can find.
Nevertheless, it is important to note that errors on spatially integrated
quantities estimated directly from the noise maps will underestimate the true
noise. We perform a full Monte Carlo (MC) simulation of the continuum
subtraction and photometry, using the noise maps as input (see Section 3.1.2).
However, it should be noted that Voronoi binning will create binned noise maps
that will underestimate the noise per bin and that the resulting errors used
in the MC are thus still underestimated. Because of the complicated nature of
the how the Voronoi variable binning and noise correlations interact we have
not tried to rescale the noise, thus the resulting uncertainties are
underestimated by factors ranging between $\sim 0$ (for the maximum $25^{2}$
Voronoi binsize) to $\sim 40$% (for the minimum binsize of 1 pixel). The
$\chi^{2}$ fitting performed by LaXs (see Section 3.1) will be affected by
this, with the effect that the fits appear to be slightly worse than they are.
The global apertures (and annuli used for surface brightness profiles) are
much larger than the maximum bin size and the error estimates for them will
thus not be directly affected by the correlated noise.

##  3 Analysis methods

###  3.1 Ly$\alpha$ continuum subtraction

With the drizzled and PSF-matched images in hand we can perform the pixel SED
fitting necessary to obtain emission line maps for Ly$\alpha$, H$\alpha$, and
H$\beta$. In addition to modeling the continuum underneath these lines the
fitting software also produces maps of the best-fit parameters (stellar mass,
age, and stellar extinction) and derived properties (e.g., nebular extinction,
mechanical energy yields, ionizing flux) for the stellar populations and gas
in each pixel. The continuum subtraction is done with the Lyman $\alpha$
eXtraction Software, written in python. This code was used for the results
presented in Hayes et al. (2014) and Östlin et al. (2014), but development of
the code has continued since then and we therefore describe the code in detail
below. LaXs is run on both un-binned and Voronoi tessellated data but below we
will use the term pixel also to describe the individual bins.

####  3.1.1 The Lyman $\alpha$ eXtraction Software, LaXS

We fit models to the measured continuum using $\chi^{2}$ minimisation (i.e.,
weighted least-squares minimisation) on a semi-discrete grid of four
parameters.

$\chi^{2}_{j}=\sum_{i}^{N}\frac{\left(F_{i,j}-M_{i}(m_{y},m_{o},t,E(B-V))\right)^{2}}{\sigma_{i,j}^{2}}$
(1)

For each pixel, denoted by $j$ above, we have measured continuum fluxes
($F_{i,j}$) and associated errors ($\sigma_{i,j}$) from four or five filters,
denoted by $i$ with $N=4$ or 5. The model fluxes for each of these filters
($M_{i}$) are calculated from stellar population models that depend on a
number of parameters, some that are varied in the fitting, and some that are
not.

The standard models used here are Starburst99 (Leitherer et al., 1999) single
stellar population (SSP) templates which use a Kroupa IMF (Kroupa et al.,
1993). For the model SEDs we use the low resolution ($\Delta\lambda\sim 10$ Å)
templates at an assumed metallicity and without nebular continuum. The
metallicity used is limited by the available Starburst99 templates and we
choose the one closest to the measured SDSS gas metallicities (given in Table
2). We then calculate a grid of template SEDs for a discrete set of stellar
ages ($t$) and extinction ($E(B-V)_{s}$). We perform testing to find the most
appropriate extinction curve for each galaxy (further detailed in Section
3.1.3). The parameter ranges and step sizes can be found in Table 3. The
spectral templates for each age step is first reddened using the $E(B-V)_{s}$
steps and the flux in each observed filter calculated by using pysynphot and
filter transmission curves for each continuum filter. The resulting template
library contains $\mathrm{N}_{\mathrm{age}}\times\mathrm{N}_{\mathrm{ext}}$
SEDs that are normalized to a stellar mass of $10^{6}M_{\odot}$. The observed
fluxes in the Balmer line filters are also corrected for stellar absorption
depending on the age of the models. This absorption is calculated using the
Starburst99 high resolution stellar templates.

The model SED that is compared to the observations, $M_{i}$, is built from two
stellar populations of this type, one old with fixed age of 1 Gyr and no
extinction, and one young with a varying age and extinction. The final two
free parameters in the fitting are the masses for the two components which are
continuous parameters. The full model SED is thus:

$M_{i}=m_{o}\times S(t_{o},0)+m_{y}\times S(t,E(B-V)_{s}),$ (2)

where $m_{o}$ and $m_{y}$ are the old and young population masses,
respectively, in units of $10^{6}M_{\odot}$, $S(t,E(B-V))$ is the individual
SED template for a given age and extinction, and $t_{o}$ is the age of the old
stellar population.

The minimisation is then performed by calculating the $\chi^{2}$ for each SED
model (each $\\{t,E(B-V)_{s}\\}$ pair) where we also solve a linear least
squares problem to get the optimum normalisation (i.e., stellar masses). The
best-fit stellar masses are thus not restricted to a discrete grid. The code
is parallellized using the python package SCOOP
111https://github.com/soravux/scoop (Hold-Geoffroy et al., 2014), which — due
to each pixel being independent from another — offers a speedup in execution
time close to the number of threads available. The initial best-fitting SED
model is then selected as the one which minimizes the $\chi^{2}$.

From this initial run we use the H$\alpha$ and H$\beta$ line maps to produce a
dust corrected H$\alpha$ map. The nebular continuum and line contribution to
all observed continuum filters and pixels is then estimated by scaling the
nebular continuum spectrum from Aller (1984) combined with nebular lines from
Anders & Fritze-v. Alvensleben (2003) with this H$\alpha$ map, again using
pysynphot to calculate the flux in each filter. The resulting nebular
continuum fluxes are subtracted from the observed filters and the entire
fitting redone. The fraction of nebular continuum to observed flux in
H$\alpha$-detected regions range from $\sim 1$% in the FUV to $\sim 10$% in
the red optical filter, but for the brightest line emitting regions (and
particularly in the galaxies with highest specific SFR) the nebular
contribution can be even higher. By forcing the nebular continuum to scale
with the dust corrected H$\alpha$ this method provides the most secure
determination of nebular continuum for the limited wavelength range.
Furthermore, by virtue of directly observing both H$\alpha$ and H$\beta$ we
can treat the nebular continuum as a semi-independent quantity that does not
have to be directly determined from the stellar continuum fitting (but note
that the H$\alpha$ +H$\beta$ continuum does come from the fitting).

Table 3: LaXs parameters

Type | Parameter | Range | Steps/# tests | Uncertainty, Ly$\alpha$/FUV
---|---|---|---|---
(1) | (2) | (3) | (4) | (5)
Free | $m_{o}$ | $>0$ | Continuous | N/A
| $m_{y}$ | $>0$ | Continuous | N/A
| $t$ |  $10^{6}$–$10^{10}$ yrs | 200 | N/A
| $E(B-V)$ | 0–1.0 | 200 | N/A
Fixed | Attenuation | CCM, SMC, Calz.aaCCM — Cardelli et al. (1989), SMC — Prevot et al. (1984), Calz. — Calzetti et al. (2000). | 3 | 10/1 %
| Z⋆ | 0.001, 0.004, 0.008, 0.020bbThe stellar metallicities tested are given by the available stellar population spectra in Starburst 99. For most galaxies we test three different metallicities, the nominal one (the closest match between Starburst 99 models and SDSS gas metallicities), and the ones just below and above. | 2/3 | 5/0.5 %
| $t_{o}$ |  $10^{9}$–$10^{10}$ yrs | 10 |  $\ll$1/0.1 %
| SFH | SSP+SSP, CSF+SSP, SSP+CSF, CSF+CSF | 4 |  $\ll$1/0.1 %
| SP library | Starburst99, BPASS | 2 |  $\ll$1/0.1 %

Note. — (1) The type is either Free, for parameters that are fitted, or Fixed,
for the parameters which are kept constant. (3) For the Free parameters this
is the number of steps in the fitting grid, for the Fixed parameters it is the
number of tests performed on the systematic uncertainties. (5) This is the
standard deviation of the total flux in the high-SNR region for Ly$\alpha$ and
the Ly$\alpha$ continuum. We have not explored the systematic uncertainties on
using a particular range for the free parameters and they are thus listed as
not available. Statistical uncertainties of the free parameters are available
from the Monte Carlo runs.

####  3.1.2 Statistical error estimates from Monte Carlo simulations

To investigate the statistical errors of the continuum subtracted emission
line maps (and the SED fit parameter maps) we perform Monte Carlo simulations
using 100 realisations. The input data for each of these (fluxes in all
filters and pixels) are generated from a Gaussian distribution with a standard
deviation from the noise maps. LaXs is then run for each of these realisations
and produces cubes of maps (containing the full output of all the 100 Monte
Carlo realisations), along with maps of standard deviation, skewness and other
statistical diagnostics. As previously mentioned, the continuum subtraction is
computationally expensive, which means that we can only estimate statistical
uncertainties for the binned maps, and that the number of Monte Carlo
realisations that can be used is limited. However, tests on individual
galaxies show that increasing the number of realisations to 1000 does not
change the estimated uncertainties significantly. The full Monte Carlo cubes
are used to estimate uncertainties in all integrated quantities presented in
this paper (see Sections 4.2 and 5).

####  3.1.3 Systematic error estimates

In addition to statistical errors the continuum subtraction will also suffer
from systematic uncertainties which originate in the assumptions made when
building the SED templates. In addition to the four free parameters, there are
five major fixed parameters (or assumptions) that go into the modeling. These
are star formation histories (SFHs) for the two populations, stellar
attenuation law, age of the old stellar population, metallicity, and stellar
population template library.

We estimate the systematic uncertainties from these five fixed parameters by
running LaXs in a high signal-to-noise region in each galaxy (SNR$>10$ in all
filters). We vary each of these parameters on a grid while keeping all the
other parameters fixed. In Table 3 we show the test ranges. Old stellar
population age ($t_{o}$) can be chosen freely but only ages that are older
than the young population component are tested. It should be noted that
because the UV brightest parts of the galaxies are used as test regions the
old population age assumption will have very little effect on the output, but
given that we are interested in the FUV and Ly$\alpha$ systematic
uncertainties this is the chosen method. The nominal SFHs used are two single
stellar populations (burst models, SSPs) for both the young (with varying age)
and old populations. We also test combinations of a burst model and a constant
star formation (CSF) model. The extinction curves tested are CCM (Cardelli et
al., 1989), SMC (Prevot et al., 1984), and Calzetti (Calzetti et al., 2000).

The resulting uncertainties are reported in Table 3, where the numbers given
are the relative standard deviations (i.e., the standard deviation divided by
the nominal case fluxes), averaged over the full sample, of the total FUV and
Ly$\alpha$ flux in the high SNR region for the different assumed cases. We
find that the assumption giving rise to the highest systematic uncertainty is
the choice of extinction curve, which has an associated uncertainty of $\sim
10$% and $\sim 1$% for Ly$\alpha$ and the modeled FUV continuum, respectively.
The assumed metallicity is the second most important assumption with
associated Ly$\alpha$/FUV uncertainties of $\sim 5$% and $\sim 0.5$%. It
should be noted that this error is a conservative upper limit, given that the
metallicity grid is very sparse and that we have a robust metallicity
determination from the SDSS spectroscopy. Of course, this case could also be
interpreted as testing the effects of metallicity gradients within the
galaxies on the continuum subtraction. The remaining fixed parameters show
much smaller Ly$\alpha$ systematic uncertainties ($\ll 1$%).

The systematic uncertainty tests are also used to choose the extinction curve
to use for each galaxy. For each galaxy, the results for the three curves are
compared by co-adding the best-fit $\chi^{2}$ inside the high SNR region in
each case. We then do a simple model selection, choosing the attenuation law
that has the lowest co-added $\chi^{2}$.

For the faintest levels of Ly$\alpha$ emission there is an additional
systematic error resulting from the subtraction of geocoronal emission in the
F125LP filter (see Section 2.4). For galaxies with wide-spread Ly$\alpha$
emission that fills the SBC chip the estimated background will thus include
Ly$\alpha$, which causes our maps to systematically underestimate the flux to
an uncertain degree. However, comparing the removed background to average
levels of geocoronal emission (using the ACS/SBC exposure time calculation
tool and assuming a low level of geocoronal contamination) shows that
geocoronal [O i] emission is dominating the background for most of the
galaxies (31 out of 45). The remaining 14 galaxies have larger (and unknown)
systematic uncertainties on Ly$\alpha$ luminosities, $W_{\mathrm{Ly\alpha}}$,
and $f_{\mathrm{esc}}$; and have been marked in Table 5. However, it should be
noted that inspecting the individual raw SBC data frames shows that most of
these images have a flat background that varies significantly from frame to
frame, which would not be the case for true Ly$\alpha$ emission from the
galaxy. Three of the galaxies (LARS04, ELARS04, and ELARS08) are large, have a
subtracted background significantly higher than the ETC estimate, and do not
show any variation in the background. These three galaxies could thus suffer
from substantial oversubtraction of Ly$\alpha$. Assuming that 100%/10% of the
subtracted background is Ly$\alpha$ emission for these three galaxies results
in the Ly$\alpha$ fluxes (as well as $W_{\mathrm{Ly\alpha}}$ and
$f_{\mathrm{esc}}$) being underestimated by factors 2.43/1.14, 1.86/1.09, and
3.94/1.29 for LARS04, ELARS04, and ELARS08, respectively. More details on how
the unknown background affects size and Ly$\alpha$ distribution measurements
in galaxies is given in Rasekh et al. (2022). Furthermore, the uncertainty of
the removed background is also a source of systematic error but is negligible
compared to the statistical errors in the binned maps.

Figure 7: Compilation of measurements on LARS02 (example of a strong
Ly$\alpha$ emitter). The maps and profiles are all constructed from Voronoi
tesselated data. The top three panels show maps of (from left to right):
stellar FUV continuum, H$\alpha$ emission, and Ly$\alpha$ emission. In the FUV
map the dashed grey box shows the cut-out box used for the line ratio maps and
the dark-green contour shows the Petrosian isophotal limit (corresponding to
$r_{\mathrm{p,FUV}}$). The purple contours in the H$\alpha$ map shows three
logarithmically spaced Ly$\alpha$ levels. The purple dashed circle in the maps
indicate the aperture (with radius $r_{\mathrm{tot}}$ used to obtain the total
estimates of fluxes and other properties. The center of this circle is the
central point for the surface brightness profile. The color bar for these
panels is in units of $10^{-17}$ erg/s/cm2/Å (for FUV) and erg/s/cm2 (for line
maps). The middle left panel shows surface brightness profiles in FUV,
H$\alpha$, and Ly$\alpha$. The upper limit symbols are placed at a surface
brightness of two times the positive error (estimated from the Monte Carlo
generated maps) for each data point. The middle right panel shows line ratio
profiles. Note that these are cumulative profiles in all cases but for
Ly$\alpha$/ H$\alpha$. The lower panels contain line ratio maps for a cut-out
region, from left to right: Ly$\alpha$/ H$\alpha$, Ly$\alpha$/ FUV
($W_{\mathrm{Ly\alpha}}$), and H$\alpha$/ H$\beta$. Bins with SNR$<2$ in the
input line maps have been masked in the ratio maps and are shown in black. The
complete figure set (45 images) is available in the online journal. Figure 8:
Compilation of measurements on ELARS08 (example of an intermediate Ly$\alpha$
emitter). A description of the elements in this figure is given in Figure 7.
The complete figure set (45 images) is available in the online journal. Figure
9: Compilation of measurements on ELARS03 (example of a weak Ly$\alpha$
emitter). A description of the elements in this figure is given in Figure 7.
The complete figure set (45 images) is available in the online journal.

###  3.2 Photometry and size measurements

We measure the fluxes in the Ly$\alpha$, H$\alpha$, and FUV maps, as well as
in some of the parameter maps output from LaXs (young population stellar mass,
young population stellar age, $E(B-V)_{s}$). The parameter maps do strictly
speaking not contain fluxes, but in the following we will refer to them as
such to facilitate the description. All of the measurements are done on the
Voronoi binned maps. To define the regions used for the surface brightness
profile (or local estimates of the parameters above) we use two different
methods.

####  3.2.1 Circular photometry

The first method is to use circular annuli centered on the brightest pixel in
the FUV map. The largest radii for this estimate is given by the largest
possible circle that can be fit within the field-of-view (also using the mask
described in 2.4), and the smallest radius is 0.5 kpc. The number of bins is
set to 30 which is a good compromise between SNR per bin and resolution. For
the most distant galaxies (LARS13, LARS14, and J1156) we use logarithmic
spacing of the bins, while for the rest of the galaxies a linear spacing is
used. We have varied the number of bins (between 25 and 35 bins) to check the
systematic errors on sizes and other spatial measurements, but the changes are
small. Using a significantly smaller number of bins will of course increase
the SNR per bin greatly, but this comes at the cost of not being able to
characterize the spatial variation.

With the bins defined we use the python package photutils to measure fluxes in
the annuli of all maps. The measured fluxes are all converted into
luminosities using the redshifts from SDSS (see Table 1). We compute rest
frame surface brightness for Ly$\alpha$, H$\alpha$, and FUV in each bin by
dividing the measured luminosity by the annulus area. For the surface
brightness profiles the radius associated to each bin is the radius that
divides the annulus into two equal areas
($r=\sqrt{r_{\mathrm{inner}}^{2}+r_{\mathrm{outer}}^{2}}/2$). For the
measurements in the stellar age and $E(B-V)_{s}$ maps we use luminosity
weighted (from the FUV images) averages to make sure that low-SNR Voronoi bins
are down-weighted. Note that this weighting scheme means that there is a bias
towards lower average ages and extinction.

We also compute curves (annulus and aperture) for derived quantities
$W_{\mathrm{Ly\alpha}}$, Ly$\alpha/$H$\alpha$, $E(B-V)_{n}$, and
$f_{\mathrm{esc}}$. Nebular extinction is calculated assuming an intrinsic
H$\alpha/$H$\beta$ ratio of 2.86 (case B recombination, $T=10^{4}K$,
$n_{e}=100$cm-3), and using the Cardelli et al. (1989, hereafter CCM)
extinction law. The escape fraction is calculated by assuming an intrinsic
Ly$\alpha/$H$\alpha$ ratio of 8.7 (Henry et al., 2015, and references therein)
and using the measured $E(B-V)_{n}$ for the same region:

$f_{\mathrm{esc}}=\frac{F_{\mathrm{Ly\alpha}}}{8.7F_{\mathrm{H\alpha}}\cdot
10^{-0.4k(\mathrm{H\alpha})E(B-V)_{n}}},$ (3)

where $k(\mathrm{H\alpha})$ is the extinction coefficient at the H$\alpha$
wavelength The CCM extinction curve assumes a dust screen model for the
nebular emission. Opting to use the attenuation curve of Calzetti et al.
(2000) instead for both the calculation and application of $E(B-V)_{n}$ in the
expression above leads to somewhat lower escape fractions. For example, a
H$\alpha/$H$\beta$ ratio of $\sim$4 (corresponding to a Calzetti $E(B-V)_{n}$
of 0.29) leads to a factor 1.06 lower escape fraction, but this rises with
H$\alpha/$H$\beta$ ratio. The difference is quite small for our galaxy sample
and we adopt CCM extinction for all nebular line measurements in this paper.
Note that the estimated $f_{\mathrm{esc}}$ (or $E(B-V)_{n}$) does not change
if the SMC extinction curve is used because the CCM and SMC curves are very
similar at the H$\alpha$ and H$\beta$ wavelengths.

For determining the global nebular extinction for the galaxies the H$\beta$
image limits how large an aperture can be used. Because of the dependence on
the H$\alpha/$H$\beta$ ratio of $E(B-V)_{n}$, including non-detected data
points for H$\beta$ leads to huge errors on the extinction. To obtain a safer
estimate of $E(B-V)_{n}$ we find the radius where the H$\beta$ SNR drops below
one. For calculating cumulative $f_{\mathrm{esc}}$ profiles we set the
$E(B-V)_{n}$ outside of this radius to the value at that radius. For the edge-
on galaxy LARS11, H$\beta$ is too faint all over the galaxy, and we cannot
obtain a meaningful $E(B-V)_{n}$ from HST imaging. For this galaxy we instead
use a constant nebular extinction from SDSS (see Table 1) for all radii.

The local escape fraction (i.e. escape fraction within an annulus) of
Ly$\alpha$ photons is very hard to interpret because of the resonant
scattering of the photons. The same applies to $W_{\mathrm{Ly\alpha}}$,
although this is a direct ratio of observed quantities and is thus less model-
dependent. Hence we do not measure $f_{\mathrm{esc}}$ and
$W_{\mathrm{Ly\alpha}}$ for each annulus but only for the apertures. However,
we do measure Ly$\alpha/$H$\alpha$ ratios for each annulus which can be used
to show where scattered Ly$\alpha$ dominates.

####  3.2.2 Isophotal photometry

We also perform isophotal photometry on the binned Ly$\alpha$, H$\alpha$, and
FUV maps. The number of isophotes used is 30 (variations between 25 and 35
still show little effect on the found size estimates), and we use logarithmic
spacing from the maximum flux in the map to the 1$\sigma$ surface brightness
limit. The 1$\sigma$ limit is computed from the Monte Carlo error simulations
for each map using a sigma-rejection averaging routine (to find the limiting
surface brightness well outside the source).

Each isophotal bin is then treated as a radial bin for surface brightness
profiles with the effective radius for the region calculated as the radius of
a circle with the same area as the isophotal region (i.e. the area for all
pixels with flux higher than the lower limit of the current bin). The
isophotal measurements are used to measure the Petrosian radii in the maps,
but are otherwise not used in this paper.

####  3.2.3 Size measurements

Table 4: Size measurements ID | $r_{tot}$ | $r_{p,FUV}$ | $r_{p,\mathrm{H}\alpha}$ | $r_{p,\mathrm{Ly}\alpha}$
---|---|---|---|---
| (kpc) | (kpc) | (kpc) | (kpc)
(1) | (2) | (3) | (4) | (5)
LARS01 |  $\geq$6.70 | 1.490${}^{0.006}_{0.005}$ | 1.570${}^{0.008}_{0.006}$ | 3.520${}^{0.038}_{0.072}$
LARS02 |  $\geq$8.13 | 1.230${}^{0.019}_{0.021}$ | 1.620${}^{0.406}_{0.022}$ | 5.560${}^{0.171}_{0.388}$
LARS03 |  $\geq$7.63 | 1.180${}^{0.097}_{0.054}$ | 1.100${}^{0.004}_{0.004}$ | 8.610${}^{0.197}_{0.346}$
LARS04 | 3.28 | 4.200${}^{0.056}_{0.063}$ | 2.000${}^{0.021}_{0.017}$ | 1.680${}^{2.502}_{0.174}$
LARS05 |  $\geq$8.37 | 1.090${}^{0.002}_{0.003}$ | 1.490${}^{0.009}_{0.006}$ | 3.140${}^{0.092}_{0.138}$
LARS06 |  $5.45$aaLy$\alpha$ is undetected in all circular annuli in this galaxy, the radius used for photometry and shown here is the maximum radius to the edge of the chip. | 3.660${}^{0.293}_{0.144}$ | 2.630${}^{0.737}_{0.074}$ | 0.540${}^{0.488}_{0.854}$
LARS07 |  $\geq$7.75 | 1.080${}^{0.008}_{0.011}$ | 1.060${}^{0.005}_{0.004}$ | 4.250${}^{1.186}_{0.130}$
LARS08 |  $\geq$7.45 | 5.230${}^{0.088}_{0.052}$ | 4.460${}^{0.069}_{0.069}$ | 10.100${}^{0.563}_{4.616}$
LARS09 |  $\geq$12.86 | 6.840${}^{0.055}_{0.042}$ | 5.590${}^{0.041}_{0.038}$ | 10.740${}^{0.202}_{0.825}$
LARS10 | 5.72 | 2.770${}^{0.234}_{0.192}$ | 2.680${}^{0.037}_{0.049}$ |  $\geq$10.960
LARS11 | 20.22 | 9.010${}^{0.160}_{0.069}$ | 8.770${}^{0.050}_{0.044}$ | 19.170${}^{6.353}_{6.154}$
LARS12 | 19.39 | 2.000${}^{0.009}_{0.006}$ | 2.520${}^{0.035}_{0.027}$ | 5.720${}^{0.434}_{0.333}$
LARS13 |  $\geq$26.59 | 0.800${}^{0.172}_{0.048}$ | 7.190${}^{0.016}_{0.017}$ | 28.050${}^{4.534}_{7.637}$
LARS14 |  $\geq$37.62 | 0.930${}^{0.010}_{0.018}$ | 2.370${}^{0.012}_{0.010}$ | 4.340${}^{0.270}_{0.607}$
ELARS01 |  $\geq$6.85 | 1.530${}^{0.004}_{0.004}$ | 2.510${}^{0.017}_{0.023}$ | 2.070${}^{0.061}_{0.054}$
ELARS02 | 7.40 | 4.190${}^{0.018}_{0.022}$ | 3.310${}^{0.051}_{0.065}$ | 4.060${}^{0.313}_{0.337}$
ELARS03 | 10.03 | 7.180${}^{0.035}_{0.046}$ | 6.660${}^{0.020}_{0.023}$ | 8.340${}^{0.276}_{0.334}$
ELARS04 |  $\geq$9.65 | 3.170${}^{0.017}_{0.012}$ | 3.000${}^{0.007}_{0.008}$ | 7.430${}^{0.082}_{0.123}$
ELARS05 |  $\geq$10.74 | 8.280${}^{0.149}_{0.134}$ | 8.600${}^{0.000}_{0.001}$ | 8.850${}^{0.129}_{0.079}$
ELARS06 | 9.08 | 5.360${}^{0.179}_{0.167}$ | 6.960${}^{0.120}_{0.198}$ | 9.120${}^{0.105}_{0.303}$
ELARS07 | 10.23 | 3.700${}^{0.104}_{0.121}$ | 0.430${}^{0.021}_{0.019}$ | 8.050${}^{0.913}_{3.982}$
ELARS08 | 9.00 | 5.940${}^{0.095}_{0.161}$ | 7.660${}^{0.033}_{0.031}$ | 8.420${}^{0.506}_{0.386}$
ELARS09 | 5.63 | 1.390${}^{0.014}_{0.013}$ | 1.360${}^{0.020}_{0.015}$ | 7.270${}^{0.440}_{0.135}$
ELARS10 |  $\geq$8.59 |  $\geq$9.340 | 3.720${}^{0.020}_{0.020}$ | 8.860${}^{3.757}_{0.270}$
ELARS11 | 5.52 | 2.770${}^{0.053}_{0.017}$ | 2.650${}^{0.011}_{0.013}$ | 6.530${}^{0.546}_{0.285}$
ELARS12 | 6.28 | 4.100${}^{0.170}_{0.069}$ | 4.140${}^{0.005}_{0.003}$ | 6.870${}^{0.638}_{0.133}$
ELARS13 | 3.40 | 0.110${}^{0.000}_{0.000}$ | 0.530${}^{0.014}_{0.010}$ | 0.210${}^{0.018}_{0.011}$
ELARS14 | 4.14 | 2.080${}^{0.054}_{0.079}$ | 1.750${}^{0.002}_{0.003}$ |  $\geq$7.280
ELARS15 | 8.07 |  $\geq$9.460 | 8.790${}^{0.000}_{0.000}$ | 8.610${}^{4.129}_{1.398}$
ELARS16 | 4.13 | 5.180${}^{1.371}_{0.541}$ | 7.300${}^{0.000}_{0.000}$ | 0.280${}^{1.944}_{0.780}$
ELARS17 | 7.49 | 6.090${}^{0.000}_{0.000}$ | 6.850${}^{0.000}_{0.000}$ | 8.480${}^{0.121}_{0.267}$
ELARS18 | 5.77 | 2.560${}^{0.143}_{0.058}$ | 6.240${}^{0.000}_{0.000}$ |  $\geq$6.750
ELARS19 | 4.33 | 1.800${}^{0.082}_{0.077}$ | 6.700${}^{0.038}_{0.163}$ | 7.030${}^{0.417}_{0.099}$
ELARS20 | 4.04 | 1.890${}^{0.025}_{0.023}$ | 7.510${}^{0.000}_{0.000}$ |  $\geq$6.400
ELARS21 | 3.23 | 3.360${}^{0.000}_{0.000}$ | 7.220${}^{0.000}_{0.000}$ |  $\geq$6.660
ELARS22 | 9.74 | 4.090${}^{0.138}_{0.134}$ | 10.100${}^{0.000}_{0.000}$ | 10.230${}^{0.232}_{1.546}$
ELARS23 | 12.87 | 10.640${}^{0.298}_{0.207}$ | 10.680${}^{0.071}_{0.090}$ | 11.780${}^{0.406}_{0.219}$
ELARS24 | 11.05 | 2.720${}^{0.077}_{0.057}$ | 2.070${}^{0.258}_{0.028}$ | 5.390${}^{0.262}_{0.567}$
ELARS25 |  $\geq$12.31 | 8.830${}^{0.415}_{0.270}$ | 9.110${}^{0.000}_{0.000}$ | 11.980${}^{0.356}_{2.611}$
ELARS26 | 11.87 | 8.190${}^{0.460}_{0.198}$ | 14.320${}^{0.000}_{0.000}$ | 11.040${}^{0.389}_{0.709}$
ELARS27 |  $\geq$8.18 | 6.620${}^{0.150}_{0.214}$ | 9.110${}^{0.000}_{0.000}$ | 9.200${}^{0.336}_{0.230}$
ELARS28 | 7.38 | 6.170${}^{0.084}_{0.087}$ | 9.040${}^{0.000}_{0.000}$ | 9.730${}^{1.437}_{0.154}$
Tol1214 |  $\geq$5.92 | 0.470${}^{0.007}_{0.008}$ | 0.520${}^{0.013}_{0.010}$ | 1.960${}^{0.805}_{0.070}$
Tol1247 |  $\geq$10.50 | 0.690${}^{0.003}_{0.004}$ | 2.460${}^{0.008}_{0.007}$ | 4.560${}^{0.117}_{0.099}$
J1156 | 26.64 | 1.470${}^{0.020}_{0.248}$ | 5.930${}^{0.078}_{0.022}$ | 26.060${}^{6.400}_{0.540}$

We measure the sizes of the galaxies in two different ways. Apart from giving
information on the spatial extent of the emission, these sizes can also be
used to define global apertures. The size measurements for all galaxies are
shown in Table 4.

Petrosian radii, $r_{\mathrm{p}}$: We calculate 20% Petrosian radii
(Petrosian, 1976) in Ly$\alpha$, H$\alpha$, and FUV using the SDSS definition
from Blanton et al. (2001). To get a more precise measurement of the radius we
perform spline interpolation on the surface brightness profiles. As mentioned
above these radii are calculated using isophotal photometry and are used to
compare the size of Ly$\alpha$ emission and FUV stellar continuum of the
galaxies, and to estimate specific star formation rates and star formation
rate surface densities.

Total Ly$\alpha$ aperture, $r_{\mathrm{tot}}$: Given that Ly$\alpha$ is often
more extended than H$\alpha$ and FUV, global apertures based on petrosian
radii in those maps fail to capture all of the Ly$\alpha$ emission from the
galaxies. Apertures based on the isophotal photometry are problematic to use
when based on Ly$\alpha$, because of Ly$\alpha$ absorbed regions in the
galaxies (see Section 4.1). Another option would be to use elliptical
apertures which would possibly capture more of the Ly$\alpha$ emission from
galaxies with asymmetric Ly$\alpha$ halos. We have performed some tests, but,
because of the irregular nature of the Ly$\alpha$ spatial distribution in the
galaxies, getting acceptable elliptical annuli requires extensive finetuning
of the fitted elliptical isophotes. Also, given that we want to compare
Ly$\alpha$ to other flux maps which often show quite different morphology,
measurements from elliptical apertures will have have similar systematic
uncertainties as circular apertures. We thus conclude that the safest estimate
of global Ly$\alpha$ flux is found by using a large circular aperture centered
on the FUV brightest pixel as described above.

The radius of this global aperture is defined as the radius where the annulus
signal-to-noise ratio (SNR) of Ly$\alpha$ surface brightness drops below 1. If
this happens at more than one radii we chose the largest radii where this
condition is fulfilled. This condition is necessary because of the patchiness
of Ly$\alpha$ emission and the problems with continuum absorption described
above. Furthermore, if the Ly$\alpha$ SNR stays above one all the way out to
the maximum radius, the total aperture is defined as the circle with this
radius. In these cases the total radius is a lower limit to how far out the
detectable Ly$\alpha$ extends. Given that the Monte Carlo derived
uncertainties are used to derive $r_{\mathrm{tot}}$ we cannot estimate errors
for this radius.

###  3.3 Line ratio maps

For the discussion on spatial Ly$\alpha$ properties of the sample we also make
Ly$\alpha/$H$\alpha$, $W_{\mathrm{Ly\alpha}}$, and H$\alpha/$H$\beta$ maps for
the galaxies. These are created by using the 2D binned line maps from LaXs.
None of the used line maps are extinction corrected. By using the Monte Carlo
derived uncertainties for each line map we also create masks marking bins
where SNR$<2$ in any of the involved line maps. These masks are used to
indicate non-detected regions in the line ratio plots below. Note that,
because of non-zero extinction, the masked region sometimes extend into the
center of the galaxies when H$\beta$ becomes undetected. In this situation we
display an upper limit to the ratio using the $1\sigma$ error on H$\beta$ in
the ratio plots. Further out in the galaxies, both H$\alpha$ and H$\beta$
becomes too faint and the ratio is unconstrained.

##  4 The spatial distribution of Ly$\alpha$ emission, dust and stars

The continuum subtraction code produces line maps for Ly$\alpha$, H$\alpha$,
and H$\beta$, along with best fit stellar continuum maps at the respective
wavelengths. In addition to the flux maps the code also outputs maps of the
best fit parameters (stellar masses, $E(B-V)_{s}$, and stellar population
age). In the following sections we use the map of stellar continuum at
Ly$\alpha$ as the nominal FUV image. While the Voronoi tesselated maps yield
the most secure representation of line fluxes and stellar population
properties (and also include the full error estimate), we also run LaXs on the
un-binned data.

For the descriptions of the binned maps we also divide up the galaxies into
three sub-samples and define: i) _weak Ly $\alpha$ emitters_, with
$W_{\mathrm{Ly\alpha}}$ $<5$ Å; _intermediate Ly $\alpha$ emitters_, with
$5\leq$$W_{\mathrm{Ly\alpha}}$ $\leq 20$ Å; _strong Ly $\alpha$ emitters_,
with $W_{\mathrm{Ly\alpha}}$ $>20$ Å. The $W_{\mathrm{Ly\alpha}}$ values used
here are the global measurements (see Section 5), and the weak Ly$\alpha$
emitters also include net absorbers. With this definition there are 9 weak
Ly$\alpha$ emitters among the galaxies (LARS04, LARS06, LARS10, LARS13,
ELARS03, ELARS12, ELARS14, ELARS16, ELARS28), 21 intermediate Ly$\alpha$
emitters (LARS08-09, LARS12, ELARS02, ELARS04, ELARS06-11, ELARS15,
ELARS17-23, ELARS25, ELARS27), and 15 strong Ly$\alpha$ emitters (LARS01-03,
LARS05, LARS07, LARS11, LARS14, ELARS01, ELARS05, ELARS13, ELARS24, ELARS26,
Tol 1214, Tol 1247, J1156).

###  4.1 Ly$\alpha$, H$\alpha$, and FUV color composites

In Figures 2, 3, 4, 5, and 6 we show color composites of H$\alpha$ (red), FUV
(green), and Ly$\alpha$ (blue) for all of the galaxies. These composites are
constructed from un-binned data, but applying mild smoothing with a bilateral
denoising filter (python/scikit-image van der Walt et al., 2014). The
smoothing makes it possible to show the extended, but somewhat patchy,
Ly$\alpha$ emission together with the more concentrated emission from stars
and H$\alpha$. The scaling (arcsinh) and cut-levels for the respective color
channels and different galaxies have been chosen to increase contrast. From
inspecting these images it is clear that Ly$\alpha$ is almost always more
extended than the H$\alpha$ emission and stellar continuum. In many of the
galaxies the central parts are dominated by red and green, indicating that
Ly$\alpha$ is weak or even in absorption there. The main limitation of the
pseudo-narrowband technique used to isolate the Ly$\alpha$ emission is that it
does not resolve emission and absorption of the line. In regions where the FUV
continuum is strong and the column density of H i high, absorption may very
well dominate the measurement which results in negative Ly$\alpha$ fluxes in
the maps. As seen in the color composites (as well as in the binned images and
surface brightness profiles) this turns out to happen in many of the galaxies
in our sample.

The Ly$\alpha$-FUV-H$\alpha$ color composites for LARS01-LARS14 differs from
the previously published images in Hayes et al. (2013). Most of this comes
from the change in calibration of the ACS/SBC instrument, but also from
updates to the reduction process and continuum subtraction, and the choice of
channel scaling in putting the new images together. We note that the earlier
images and measurements were obtained with an incorrect calibration for the
SBC and blue UVIS filters 222The changes in calibration are documented in ISR
ACS 2019-05, and ISR 2021-04. The updated results in this paper thus replaces
the earlier measurements (see also Section 5). Because of the arbitrary nature
of how the smoothing and scaling was done we do not perform any detailed
measurements on these images, but instead work with the binned maps for the
following analysis.

###  4.2 Spatial Ly$\alpha$ properties of the sample.

In this paper we present detailed measurements of surface brightness profiles
of the galaxies in the sample, and give an overall description of the profile
properties for the different $W_{\mathrm{Ly\alpha}}$ groups. An extensive
statistical study of the Ly$\alpha$ profiles and how they relate to other
properties of the galaxies is given in Rasekh et al. (2022). In the following
section we will describe the spatial Ly$\alpha$ properties of the three sub-
samples. How the global properties relate to one another is described in
Section 5. Figures 7, 8, and 9 show maps of the fluxes and derived quantities,
as well as surface brightness profiles of these. These three galaxies are
shown as examples of the three sub-sample groups, figures for the full sample
are available in the online journal. Surface brightness profiles measured in
Ly$\alpha$ and FUV for the full sample are given in Table 8 in Appendix C.

####  4.2.1 Weak Ly$\alpha$ emitters

This subgroup is characterized by weak and very patchy Ly$\alpha$ emission.
The small amounts of emission that manages to escape comes out in select
regions. The effect of continuum absorption is also strong in these galaxies,
the Ly$\alpha$ flux measured in larger apertures tend to be close to zero or
even negative. This has the effect that the radial profiles are quite messy,
they jump up and down as single spots of emission enters the annuli.

Most of these galaxies are quite dusty (with the exception of LARS04), and
from inspecting the H$\alpha/$H$\beta$ maps, the regions where Ly$\alpha$
escapes seems to have lower nebular extinctions. The $W_{\mathrm{Ly\alpha}}$
maps are noisy and shows low values all over the galaxies, with the possible
exception of ELARS03, which does show somewhat higher $W_{\mathrm{Ly\alpha}}$
on the side inclined towards the observer, and particularly close to a star-
forming knot in one of spiral arms. The Ly$\alpha/$H$\alpha$ maps show a
similar picture with values predominantly $\ll$ 8\. It should be noted that
the intrinsic ratios may be larger than this, but obtaining an accurate local
extinction corrected Ly$\alpha$ flux is not possible, because of the longer
path lengths for the resonantly scattered Ly$\alpha$ photons. We use the non-
extinction corrected Ly$\alpha/$H$\alpha$ maps to signify where in the
galaxies Ly$\alpha$ has been scattered away from the production sites. The
specific distribution of dust may also cause variations of the
Ly$\alpha/$H$\alpha$ ratios, but unless there are significant dust column
densities outside the H$\alpha$ or UV bright region this is unlikely to cause
the high Ly$\alpha/$H$\alpha$ ratios we observe in the outskirts of some of
the galaxies from the stronger LAEs. In addition, obtaining dust corrected
Ly$\alpha/$H$\alpha$ maps that cover the full galaxies is not possible,
because of low S/N in the H$\alpha/$H$\beta$ map.

In general these galaxies also lack Ly$\alpha$ halo emission (meaning emission
visible outside the FUV/H$\alpha$ emitting areas), the emission is quite
concentrated to small patches close to the UV center of the galaxies. Two of
the galaxies (ELARS03 and ELARS28) do have measurable halo emission, and for
ELARS03 this might extend outside the field-of-view.

####  4.2.2 Intermediate Ly$\alpha$ emitters

The intermediate emitters also have patchy Ly$\alpha$ emission, but the
emission is stronger (or continuum absorption weaker). Some of these galaxies
show quite asymmetric emission when inspecting the Ly$\alpha$ map (ELARS19–21,
ELARS23). This is particularly true for the galaxies with a visibly inclined
disk, where the emission comes out from the side closest to us (determined
from the H$\alpha/$H$\beta$ ratio map). Also galaxies without a well-defined
disk show very little Ly$\alpha$ emission in the regions with high
H$\alpha/$H$\beta$ ratio.

Inspection of the surface brightness profiles show that Ly$\alpha$ is more
extended than FUV/H$\alpha$ in all but a few cases (exceptions are LARS08,
ELARS02, ELARS06, and ELARS18). The other galaxies have extended Ly$\alpha$
halos, and six of them have emission extending outside the field-of-view. In
the inclined disk cases, the emission is more extended in the off-axis
direction (which is not visible in the profile plots).

The Ly$\alpha/$H$\alpha$ ratio is below 8.7 in the galaxies, but higher than
for the weak emitter sub-group. The strongest spots of Ly$\alpha$ in some of
the galaxies do reach this limit, and are situated outside the FUV emitting
part of the galaxies. ELARS04 and 11 shows some annulus regions with
Ly$\alpha/$H$\alpha$ $>8.7$, but with large errors.

####  4.2.3 Strong Ly$\alpha$ emitters

The strong emitters show Ly$\alpha$ in large portions of the image, although
some absorption is visible, in particular in regions with high FUV surface
brightness and/or high H$\alpha/$H$\beta$ ratio. The spatial distribution of
Ly$\alpha$ is mostly symmetric, but in some galaxies there appears to be
directions that are unfavored for escape (LARS01, LARS03, LARS11, ELARS01,
ELARS24, Tol 1214).

Most of the galaxies in this group have low global $E(B-V)_{n}$, but, somewhat
surprisingly, a few dusty galaxies are also included. Even though
$W_{\mathrm{Ly\alpha}}$ is higher than 20 Å globally the escape fractions are
very low, indicating that we see Ly$\alpha$ emission from low-dust regions
where the relative escape of the photons can be quite high. In these galaxies
(LARS03, LARS11, ELARS01, and ELARS24) the Ly$\alpha$ emission shows up in
distinct regions that likely indicates channels where the dust content is
lower.

The profile plots show the that the fraction of Ly$\alpha$ emission coming out
in the halo varies among the galaxies. A majority of them (11 out of 15) show
Ly$\alpha/$H$\alpha$ $>8.7$ in parts of the image, preferably in the halo,
indicating that resonant scattering of the Ly$\alpha$ photons is in full
effect. Most (13) also have Ly$\alpha$ emission extending all the way to the
edge of the field-of-view.

####  4.2.4 Escape fraction and equivalent width variations with aperture size

The cumulative escape fraction profiles generally rise as you go further out,
but many galaxies in all sub-groups show a dip in the profile. One explanation
to this behaviour is strong gradients in the extinction profile (which
directly affects the $f_{\mathrm{esc}}$ profile), but could also be due to
Ly$\alpha$ continuum absorption in off-center regions with strong FUV emission
and high H i column densities. The $f_{\mathrm{esc}}$ profile shape of these
galaxies thus indicates that that both direct (with weak resonant scattering)
and indirect (via strong scattering) escape takes place in the galaxies.

In twelve of the galaxies we find that the shapes of the $f_{\mathrm{esc}}$
and $W_{\mathrm{Ly\alpha}}$ cumulative profile differ from one another. This
is somewhat unexpected given that they both provide a normalized estimate of
escaping Ly$\alpha$. In some of these cases (e.g., LARS01, LARS03) the escape
fraction is highest in the center of the galaxies, decreases as the aperture
grows bigger, and then starts to increase again further out. This is very
different from the $W_{\mathrm{Ly\alpha}}$ cumulative profile in these
galaxies that just increases with radius. The cause of this difference lies in
that $W_{\mathrm{Ly\alpha}}$ provides an estimate of Ly$\alpha$ escape
relative to the non-extinction corrected FUV luminosity. The measured FUV
luminosity will thus not give the total budget of ionizing radiation, and will
in particular differ from an absolute measure ($f_{\mathrm{esc}}$) in dusty
regions of the galaxies. We see this effect in dusty galaxies and in galaxies
with strong extinction gradients in the sample. The specific case of high
central escape with a sharp drop is likely the effect of choosing the
brightest FUV coordinates to be the centrum for the apertures. This point is
likely to have low extinction which leads to a high escape fraction. If the
extinction then goes up with radius $f_{\mathrm{esc}}$ will drop. This effect
is strongest in the dusty galaxies that often only have small relatively dust-
free regions,

###  4.3 Average Ly$\alpha$ growth curves

Figure 10: Average growth curve of Ly$\alpha$ luminosity for the galaxies in
the sample with $W_{\mathrm{Ly\alpha}}$ $>0$ Å. The dark grey shaded area
shows the standard deviation (capped at a ratio of 1) over the sample at each
radius and the light grey shade shows the full envelope of the sample.

Spectroscopic measurements of the Ly$\alpha$ line using a limited slit or
aperture size can potentially miss part of the extended emission. Given the
effects of resonant scattering this impacts Ly$\alpha$ much more than the
other nebular lines or stellar continuum. With our Ly$\alpha$ imaging data we
can follow the spatial emission profile further out and estimate slit losses
for an aperture of a given physical size. It should be noted that observations
of high redshift LAEs with VLT/MUSE (see Section 5.1) do not suffer from any
slit losses given that this instrument is an integral field unit spectrograph.
The same is true for slitless spectroscopy (e.g., HST/WFC3 or JWST
NIRCAM/NIRISS grism spectroscopy).

In Figure 10 we show the average growth curve of Ly$\alpha$ luminosity for the
galaxies with confirmed global Ly$\alpha$ emission ($W_{\mathrm{Ly\alpha}}$
$>0$ Å) together with physical sizes for three different spectroscopic slit
(or aperture) sizes. Depending on the redshift and angular slit width these
physical sizes will vary. The total luminosity used for this plot is measured
within the total Ly$\alpha$ aperture described above. It should be noted that
many of the galaxies (20/45) have detectable Ly$\alpha$ all the way out to the
edge of the detector, which means that the total luminosity is a lower limit.
The average L${}_{\mathrm{Ly}\alpha}(r)/$LLyα,tot curve thus shows an upper
limit to the flux recovered at a given physical aperture size.

We have estimated slit losses (defined as
$1-\mathrm{L}_{\mathrm{Ly}\alpha}(r)/\mathrm{L}_{\mathrm{Ly}\alpha,tot}$) for
three typical cases of Ly$\alpha$ spectroscopy. For these estimates we use the
average curve shown in Figure 10 (with the exception of our own sample where
we use the individual growth curves). We also provide estimates using infinite
aperture Ly$\alpha$ luminosities (LLyα,exp), obtained by fitting an
exponential function for the surface brightness profile in the halo and
extrapolating, from Rasekh et al. (2022). These slit loss estimates avoid the
problem of Ly$\alpha$ extending outside the detector but suffer from unknown
systematic uncertainties due to the assumption of purely exponential halos.
HST/COS aperture: For the data presented in this paper (with an average
redshift of 0.05) the COS aperture suffers from quite severe losses. The
average aperture loss for COS for our galaxies compared to LLyα,tot (LLyα,exp)
is $0.5\pm 0.2$ ($0.6\pm 0.3$), where the uncertainty is the standard
deviation for the sample. Inspecting the growth curves of our sample and
looking at more distant galaxies at $z\sim 0.3$ (by calculating the physical
aperture size at that redshift) we find that COS is able to recover more of
the Ly$\alpha$ flux. The average aperture loss is then $0.27\pm 0.23$ and
$0.40\pm 0.26$ for LLyα,tot and LLyα,exp total flux estimates, respectively.
High redshift LAE observed with a 1″slit: For galaxies at $z=3.0$ and assuming
a typical slit width of 1″for ground-based multi object spectroscopy (MOS)
studies of LAEs we find that the slit loss is $0.12\pm 0.18$ and $0.25\pm
0.27$ for LLyα,tot and LLyα,exp total flux estimates, respectively. The slit
losses from pure Ly$\alpha$ scattering are not very significant in this case,
but the issue could be further compounded by PSF losses which need to be
computed assuming an extended source rather than a point source.
Extreme high redshift LAEs observed with JWST/NIRSPEC: With JWST/NIRSPEC it is
now possible to observe extreme redshift LAEs ($z\gtrsim 7$) using the NIRSPEC
MOS mode. However, the slit width of a single micro-shutter array (MSA)
shutter is very narrow (0.2″), which means that slit losses may be very high.
Indeed, calculating the average slit loss at $z=7.0$ within a single MSA
shutter we find losses of $0.80\pm 0.21$ and $0.82\pm 0.21$ for LLyα,tot and
LLyα,exp total flux estimates, respectively.

##  5 Global measurements of Ly$\alpha$ and other properties

Table 5: Global Ly$\alpha$, H$\alpha$, and FUV measurements ID | $L_{\mathrm{FUV}}$ | $L_{\mathrm{H}\alpha}$ | $L_{\mathrm{Ly}\alpha}$ | $W_{\mathrm{Ly\alpha}}$ | $f_{\mathrm{esc}}$
---|---|---|---|---|---
| (1040 erg/s/cm2/Å) | (1041 erg/s/cm2) | (1041 erg/s/cm2) | (Å) |
(1) | (2) | (3) | (4) | (5) | (6)
LARS01 | 1.978${}^{0.004}_{0.004}$ | 5.126${}^{0.010}_{0.014}$ | 8.345${}^{0.089}_{0.089}$ | 42.195${}^{0.397}_{0.458}$ | 0.134${}^{0.002}_{0.002}$
LARS02 | 0.733${}^{0.004}_{0.003}$ | 1.590${}^{0.011}_{0.012}$ | 4.154${}^{0.093}_{0.116}$ | 56.661${}^{1.304}_{1.946}$ | 0.299${}^{0.010}_{0.010}$
LARS03 | 0.561${}^{0.003}_{0.003}$ | 6.091${}^{0.025}_{0.021}$ | 1.917${}^{0.076}_{0.095}$ | 34.164${}^{1.348}_{1.848}$ | 0.005${}^{0.000}_{0.000}$
LARS04aaThe Ly$\alpha$ emission from these galaxies are potentially underestimated due to uncertain background subtraction, see Section 3.1.3. | 1.338${}^{0.002}_{0.003}$ | 3.831${}^{0.009}_{0.011}$ | 0.276${}^{0.062}_{0.058}$ | 2.065${}^{0.467}_{0.435}$ | 0.006${}^{0.001}_{0.001}$
LARS05 | 2.613${}^{0.006}_{0.005}$ | 4.254${}^{0.018}_{0.013}$ | 6.516${}^{0.107}_{0.107}$ | 24.935${}^{0.471}_{0.467}$ | 0.126${}^{0.002}_{0.002}$
LARS06 | 0.375${}^{0.004}_{0.003}$ | 0.648${}^{0.007}_{0.006}$ |  $\leq$0.089 |  $\leq$2.374 |  $\leq$0.012
LARS07 | 1.717${}^{0.006}_{0.008}$ | 3.789${}^{0.016}_{0.015}$ | 6.661${}^{0.132}_{0.144}$ | 38.801${}^{0.935}_{1.000}$ | 0.111${}^{0.002}_{0.002}$
LARS08 | 2.316${}^{0.009}_{0.016}$ | 11.072${}^{0.345}_{0.124}$ | 4.017${}^{0.267}_{0.193}$ | 17.345${}^{1.302}_{0.915}$ | 0.006${}^{0.000}_{0.000}$
LARS09 | 5.913${}^{0.015}_{0.011}$ | 18.962${}^{0.035}_{0.027}$ | 5.810${}^{0.284}_{0.272}$ | 9.827${}^{0.477}_{0.474}$ | 0.016${}^{0.001}_{0.001}$
LARS10 | 1.120${}^{0.005}_{0.007}$ | 2.005${}^{0.012}_{0.022}$ |  $\leq$0.235 |  $\leq$2.100 |  $\leq$0.003
LARS11 | 8.725${}^{0.051}_{0.055}$ | 9.929${}^{0.113}_{0.079}$ | 17.899${}^{1.304}_{0.973}$ | 20.515${}^{0.976}_{0.710}$ | 0.065${}^{0.009}_{0.009}$
LARS12 | 8.502${}^{0.043}_{0.037}$ | 13.904${}^{0.144}_{0.092}$ | 15.396${}^{0.896}_{0.758}$ | 18.108${}^{1.045}_{1.041}$ | 0.027${}^{0.001}_{0.001}$
LARS13 | 11.956${}^{0.229}_{0.273}$ | 20.978${}^{0.084}_{0.074}$ |  $\leq$2.989 |  $\leq$1.774 |  $\leq$0.004
LARS14 | 11.358${}^{0.194}_{0.129}$ | 18.908${}^{0.239}_{0.238}$ | 55.602${}^{1.914}_{1.982}$ | 48.955${}^{2.281}_{2.102}$ | 0.263${}^{0.010}_{0.010}$
ELARS01 | 2.443${}^{0.003}_{0.003}$ | 13.134${}^{0.016}_{0.017}$ | 5.169${}^{0.063}_{0.064}$ | 21.156${}^{0.288}_{0.297}$ | 0.012${}^{0.000}_{0.000}$
ELARS02 | 2.660${}^{0.005}_{0.004}$ | 6.188${}^{0.036}_{0.035}$ | 3.311${}^{0.116}_{0.082}$ | 12.444${}^{0.450}_{0.327}$ | 0.061${}^{0.002}_{0.002}$
ELARS03aaThe Ly$\alpha$ emission from these galaxies are potentially underestimated due to uncertain background subtraction, see Section 3.1.3. | 2.779${}^{0.008}_{0.007}$ | 10.127${}^{0.032}_{0.025}$ | 1.227${}^{0.145}_{0.119}$ | 4.414${}^{0.535}_{0.428}$ | 0.005${}^{0.001}_{0.001}$
ELARS04 | 2.587${}^{0.005}_{0.005}$ | 4.223${}^{0.015}_{0.017}$ | 4.419${}^{0.121}_{0.107}$ | 17.078${}^{0.511}_{0.449}$ | 0.070${}^{0.002}_{0.002}$
ELARS05 | 2.217${}^{0.009}_{0.009}$ | 3.679${}^{0.023}_{0.018}$ | 5.881${}^{0.217}_{0.180}$ | 26.523${}^{1.006}_{0.890}$ | 0.148${}^{0.006}_{0.006}$
ELARS06 | 1.029${}^{0.006}_{0.007}$ | 1.312${}^{0.018}_{0.025}$ | 1.297${}^{0.142}_{0.096}$ | 12.610${}^{1.402}_{0.956}$ | 0.060${}^{0.005}_{0.005}$
ELARS07aaThe Ly$\alpha$ emission from these galaxies are potentially underestimated due to uncertain background subtraction, see Section 3.1.3. | 1.148${}^{0.009}_{0.006}$ | 2.537${}^{0.028}_{0.032}$ | 0.811${}^{0.129}_{0.159}$ | 7.062${}^{1.137}_{1.383}$ | 0.032${}^{0.006}_{0.006}$
ELARS08aaThe Ly$\alpha$ emission from these galaxies are potentially underestimated due to uncertain background subtraction, see Section 3.1.3. | 0.821${}^{0.005}_{0.004}$ | 1.740${}^{0.019}_{0.016}$ | 1.375${}^{0.116}_{0.113}$ | 16.738${}^{1.413}_{1.446}$ | 0.017${}^{0.002}_{0.002}$
ELARS09aaThe Ly$\alpha$ emission from these galaxies are potentially underestimated due to uncertain background subtraction, see Section 3.1.3. | 0.817${}^{0.004}_{0.002}$ | 0.756${}^{0.015}_{0.012}$ | 0.461${}^{0.079}_{0.083}$ | 5.648${}^{0.978}_{1.029}$ | 0.054${}^{0.010}_{0.010}$
ELARS10 | 0.552${}^{0.005}_{0.007}$ | 1.379${}^{0.016}_{0.017}$ | 0.929${}^{0.121}_{0.104}$ | 16.825${}^{2.425}_{1.890}$ | 0.016${}^{0.002}_{0.002}$
ELARS11aaThe Ly$\alpha$ emission from these galaxies are potentially underestimated due to uncertain background subtraction, see Section 3.1.3. | 0.697${}^{0.003}_{0.003}$ | 0.911${}^{0.013}_{0.011}$ | 0.621${}^{0.070}_{0.062}$ | 8.913${}^{1.046}_{0.916}$ | 0.065${}^{0.007}_{0.007}$
ELARS12aaThe Ly$\alpha$ emission from these galaxies are potentially underestimated due to uncertain background subtraction, see Section 3.1.3. | 0.635${}^{0.002}_{0.004}$ | 2.311${}^{0.019}_{0.015}$ |  $\leq$0.082 |  $\leq$1.272 |  $\leq$0.002
ELARS13 | 0.672${}^{0.005}_{0.004}$ | 0.886${}^{0.006}_{0.007}$ | 2.532${}^{0.092}_{0.087}$ | 37.700${}^{1.627}_{1.384}$ | 0.207${}^{0.008}_{0.008}$
ELARS14aaThe Ly$\alpha$ emission from these galaxies are potentially underestimated due to uncertain background subtraction, see Section 3.1.3. | 0.515${}^{0.003}_{0.003}$ | 1.080${}^{0.009}_{0.011}$ |  $\leq$0.066 |  $\leq$1.270 |  $\leq$0.005
ELARS15 | 0.404${}^{0.004}_{0.005}$ | 0.646${}^{0.020}_{0.018}$ | 0.807${}^{0.116}_{0.104}$ | 19.969${}^{3.002}_{2.643}$ | 0.124${}^{0.018}_{0.018}$
ELARS16 | 0.267${}^{0.002}_{0.003}$ | 0.345${}^{0.009}_{0.012}$ |  $\leq$0.063 |  $\leq$2.377 |  $\leq$0.009
ELARS17 | 0.399${}^{0.005}_{0.006}$ | 0.571${}^{0.018}_{0.015}$ | 0.774${}^{0.117}_{0.097}$ | 19.403${}^{3.108}_{2.470}$ | 0.082${}^{0.012}_{0.012}$
ELARS18 | 0.220${}^{0.003}_{0.002}$ | 0.344${}^{0.012}_{0.012}$ | 0.173${}^{0.059}_{0.075}$ | 7.877${}^{2.730}_{3.330}$ | 0.030${}^{0.013}_{0.013}$
ELARS19aaThe Ly$\alpha$ emission from these galaxies are potentially underestimated due to uncertain background subtraction, see Section 3.1.3. | 0.270${}^{0.002}_{0.002}$ | 0.430${}^{0.008}_{0.009}$ | 0.270${}^{0.053}_{0.043}$ | 9.996${}^{2.028}_{1.559}$ | 0.064${}^{0.010}_{0.010}$
ELARS20 | 0.273${}^{0.002}_{0.002}$ | 0.522${}^{0.009}_{0.006}$ | 0.215${}^{0.049}_{0.052}$ | 7.867${}^{1.844}_{1.964}$ | 0.031${}^{0.008}_{0.008}$
ELARS21 | 0.124${}^{0.002}_{0.002}$ | 0.118${}^{0.010}_{0.008}$ | 0.111${}^{0.042}_{0.039}$ | 8.961${}^{3.473}_{3.117}$ | 0.047${}^{0.033}_{0.033}$
ELARS22aaThe Ly$\alpha$ emission from these galaxies are potentially underestimated due to uncertain background subtraction, see Section 3.1.3. | 2.723${}^{0.011}_{0.006}$ | 3.644${}^{0.054}_{0.055}$ | 1.854${}^{0.139}_{0.152}$ | 6.808${}^{0.530}_{0.562}$ | 0.056${}^{0.005}_{0.005}$
ELARS23 | 2.295${}^{0.014}_{0.011}$ | 4.222${}^{0.081}_{0.073}$ | 1.493${}^{0.214}_{0.241}$ | 6.507${}^{0.958}_{1.073}$ | 0.040${}^{0.007}_{0.007}$
ELARS24 | 2.007${}^{0.010}_{0.010}$ | 6.929${}^{0.064}_{0.071}$ | 4.472${}^{0.180}_{0.184}$ | 22.283${}^{0.982}_{0.927}$ | 0.004${}^{0.000}_{0.000}$
ELARS25aaThe Ly$\alpha$ emission from these galaxies are potentially underestimated due to uncertain background subtraction, see Section 3.1.3. | 1.718${}^{0.011}_{0.011}$ | 1.954${}^{0.040}_{0.060}$ | 1.643${}^{0.183}_{0.203}$ | 9.564${}^{1.098}_{1.173}$ | 0.094${}^{0.012}_{0.012}$
ELARS26aaThe Ly$\alpha$ emission from these galaxies are potentially underestimated due to uncertain background subtraction, see Section 3.1.3. | 1.037${}^{0.008}_{0.009}$ | 3.305${}^{0.048}_{0.053}$ | 2.233${}^{0.176}_{0.171}$ | 21.547${}^{1.689}_{1.829}$ | 0.059${}^{0.005}_{0.005}$
ELARS27aaThe Ly$\alpha$ emission from these galaxies are potentially underestimated due to uncertain background subtraction, see Section 3.1.3. | 1.089${}^{0.009}_{0.010}$ | 1.865${}^{0.038}_{0.045}$ | 2.051${}^{0.159}_{0.156}$ | 18.832${}^{1.638}_{1.491}$ | 0.115${}^{0.011}_{0.011}$
ELARS28 | 1.180${}^{0.006}_{0.008}$ | 2.326${}^{0.041}_{0.048}$ | 0.370${}^{0.150}_{0.133}$ | 3.138${}^{1.285}_{1.128}$ | 0.013${}^{0.005}_{0.005}$
Tol1214 | 0.304${}^{0.002}_{0.002}$ | 0.925${}^{0.015}_{0.016}$ | 2.180${}^{0.049}_{0.045}$ | 71.715${}^{2.008}_{1.887}$ | 0.271${}^{0.007}_{0.007}$
Tol1247 | 10.381${}^{0.022}_{0.019}$ | 25.306${}^{0.066}_{0.062}$ | 24.794${}^{0.391}_{0.322}$ | 23.883${}^{0.403}_{0.351}$ | 0.059${}^{0.001}_{0.001}$
J1156 | 24.012${}^{0.423}_{0.429}$ | 31.913${}^{0.763}_{0.706}$ | 63.696${}^{4.799}_{4.555}$ | 26.527${}^{2.124}_{2.230}$ | 0.115${}^{0.009}_{0.009}$
Table 6: Derived galaxy properties

ID | SFRHα | SFRFUV | sSFRHα | $\Sigma_{\mathrm{SFR}}$ | Mass | Age | $E(B-V)_{s}$ | $E(B-V)_{n}$
---|---|---|---|---|---|---|---|---
| (M${}_{\odot}/\mathrm{yr}$) | (M${}_{\odot}/\mathrm{yr}$) | ($10^{-10}$yr-1) | (M${}_{\odot}/\mathrm{yr}/\mathrm{kpc}^{2}$) | ($10^{10}\mathrm{M}_{\odot}$) | (Myr) |  |
(1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9)
LARS 1 | 3.86${}^{0.01}_{0.01}$ | 3.04${}^{0.01}_{0.01}$ | 2.83${}^{0.03}_{0.03}$ | 0.472${}^{0.003}_{0.003}$ | 1.65${}^{0.04}_{0.04}$ | 7.95${}^{0.71}_{0.50}$ | 0.09${}^{0.00}_{0.00}$ | 0.140${}^{0.002}_{0.002}$
LARS 2 | 0.86${}^{0.01}_{0.01}$ | 2.43${}^{0.06}_{0.06}$ | 2.44${}^{0.06}_{0.06}$ | 0.110${}^{0.001}_{0.001}$ | 0.57${}^{0.03}_{0.03}$ | 19.64${}^{1.37}_{1.74}$ | 0.17${}^{0.00}_{0.00}$ | 0.002${}^{0.009}_{0.006}$
LARS 3 | 25.52${}^{0.09}_{0.09}$ | 16.68${}^{0.65}_{0.44}$ | 26.70${}^{2.19}_{2.19}$ | 8.566${}^{0.297}_{0.284}$ | 2.15${}^{0.10}_{0.10}$ | 77.46${}^{1.37}_{1.14}$ | 0.39${}^{0.00}_{0.00}$ | 0.850${}^{0.011}_{0.015}$
LARS 4 | 3.06${}^{0.01}_{0.01}$ | 1.97${}^{0.01}_{0.01}$ | 2.72${}^{0.03}_{0.03}$ | 0.326${}^{0.003}_{0.003}$ | 1.77${}^{0.02}_{0.02}$ | 11.42${}^{0.58}_{0.61}$ | 0.09${}^{0.00}_{0.00}$ | 0.164${}^{0.003}_{0.004}$
LARS 5 | 3.19${}^{0.01}_{0.01}$ | 3.35${}^{0.01}_{0.01}$ | 4.05${}^{0.04}_{0.04}$ | 0.455${}^{0.003}_{0.002}$ | 0.84${}^{0.01}_{0.01}$ | 4.38${}^{0.61}_{0.49}$ | 0.07${}^{0.00}_{0.00}$ | 0.138${}^{0.002}_{0.002}$
LARS 6 | 0.40${}^{0.00}_{0.00}$ | 0.39${}^{0.00}_{0.00}$ | 1.47${}^{0.09}_{0.09}$ | 0.024${}^{0.001}_{0.001}$ | 0.39${}^{0.02}_{0.02}$ | 13.38${}^{1.86}_{1.01}$ | 0.05${}^{0.00}_{0.00}$ | 0.053${}^{0.010}_{0.012}$
LARS 7 | 3.70${}^{0.01}_{0.01}$ | 2.06${}^{0.01}_{0.01}$ | 6.97${}^{0.07}_{0.07}$ | 1.086${}^{0.004}_{0.008}$ | 0.86${}^{0.02}_{0.02}$ | 24.68${}^{0.74}_{0.90}$ | 0.06${}^{0.00}_{0.00}$ | 0.248${}^{0.003}_{0.002}$
LARS 8 | 41.73${}^{0.47}_{0.47}$ | 4.63${}^{0.07}_{0.07}$ | 4.68${}^{0.70}_{0.70}$ | 0.799${}^{0.105}_{0.165}$ | 10.01${}^{0.35}_{0.35}$ | 72.89${}^{8.50}_{8.41}$ | 0.12${}^{0.00}_{0.00}$ | 0.806${}^{0.006}_{0.008}$
LARS 9 | 23.08${}^{0.03}_{0.03}$ | 9.71${}^{0.13}_{0.09}$ | 4.59${}^{0.04}_{0.04}$ | 0.299${}^{0.002}_{0.002}$ | 5.85${}^{0.07}_{0.07}$ | 20.40${}^{6.72}_{4.24}$ | 0.10${}^{0.00}_{0.00}$ | 0.338${}^{0.002}_{0.002}$
LARS 10 | 5.72${}^{0.06}_{0.06}$ | 4.78${}^{0.09}_{0.10}$ | 5.22${}^{0.30}_{0.30}$ | 0.378${}^{0.024}_{0.019}$ | 2.35${}^{0.09}_{0.09}$ | 38.16${}^{1.49}_{1.27}$ | 0.19${}^{0.00}_{0.00}$ | 0.691${}^{0.019}_{0.022}$
LARS 11 | 16.90${}^{0.13}_{0.13}$ | 36.45${}^{1.79}_{1.82}$ | 1.36${}^{0.05}_{0.05}$ | 0.090${}^{0.001}_{0.001}$ | 15.24${}^{0.37}_{0.37}$ | 32.26${}^{0.52}_{0.50}$ | 0.19${}^{0.00}_{0.00}$ | 0.477${}^{0.050}_{0.050}$
LARS 12 | 34.98${}^{0.23}_{0.23}$ | 9.18${}^{0.08}_{0.08}$ | 12.19${}^{0.20}_{0.20}$ | 1.835${}^{0.027}_{0.021}$ | 3.20${}^{0.08}_{0.08}$ | 14.59${}^{0.92}_{1.60}$ | 0.05${}^{0.00}_{0.00}$ | 0.639${}^{0.004}_{0.005}$
LARS 13 | 40.47${}^{0.14}_{0.14}$ | 65.27${}^{1.30}_{1.99}$ | 28.27${}^{4.72}_{4.72}$ | 0.528${}^{0.009}_{0.008}$ | 3.83${}^{0.45}_{0.45}$ | 35.42${}^{1.92}_{2.72}$ | 0.22${}^{0.00}_{0.01}$ | 0.529${}^{0.008}_{0.007}$
LARS 14 | 13.03${}^{0.16}_{0.16}$ | 16.47${}^{0.21}_{0.17}$ | 13.29${}^{0.35}_{0.35}$ | 0.701${}^{0.007}_{0.006}$ | 1.18${}^{0.08}_{0.08}$ | 11.89${}^{2.59}_{2.41}$ | 0.08${}^{0.00}_{0.00}$ | 0.103${}^{0.003}_{0.003}$
ELARS 1 | 25.71${}^{0.03}_{0.03}$ | 6.18${}^{0.02}_{0.02}$ | 3.74${}^{0.05}_{0.05}$ | 1.329${}^{0.009}_{0.011}$ | 7.85${}^{0.08}_{0.08}$ | 21.52${}^{0.66}_{0.27}$ | 0.14${}^{0.00}_{0.00}$ | 0.535${}^{0.002}_{0.003}$
ELARS 2 | 3.32${}^{0.02}_{0.02}$ | 3.53${}^{0.02}_{0.02}$ | 1.39${}^{0.02}_{0.02}$ | 0.100${}^{0.001}_{0.001}$ | 3.08${}^{0.02}_{0.02}$ | 36.77${}^{0.49}_{0.53}$ | 0.07${}^{0.00}_{0.00}$ | 0.000${}^{0.000}_{0.000}$
ELARS 3 | 13.80${}^{0.03}_{0.03}$ | 29.22${}^{0.40}_{0.41}$ | 0.67${}^{0.01}_{0.01}$ | 0.044${}^{0.000}_{0.000}$ | 8.96${}^{0.11}_{0.11}$ | 26.60${}^{0.50}_{0.61}$ | 0.29${}^{0.00}_{0.00}$ | 0.385${}^{0.004}_{0.005}$
ELARS 4 | 3.89${}^{0.02}_{0.02}$ | 4.08${}^{0.02}_{0.01}$ | 1.47${}^{0.02}_{0.02}$ | 0.143${}^{0.001}_{0.001}$ | 3.32${}^{0.03}_{0.03}$ | 39.32${}^{0.71}_{0.54}$ | 0.09${}^{0.00}_{0.00}$ | 0.223${}^{0.002}_{0.003}$
ELARS 5 | 2.45${}^{0.01}_{0.01}$ | 7.30${}^{0.19}_{0.16}$ | 0.33${}^{0.01}_{0.01}$ | 0.010${}^{0.000}_{0.000}$ | 7.08${}^{0.07}_{0.07}$ | 49.82${}^{0.57}_{0.76}$ | 0.17${}^{0.00}_{0.00}$ | 0.088${}^{0.009}_{0.009}$
ELARS 6 | 1.34${}^{0.03}_{0.03}$ | 1.49${}^{0.01}_{0.02}$ | 1.44${}^{0.04}_{0.04}$ | 0.012${}^{0.000}_{0.000}$ | 1.53${}^{0.03}_{0.03}$ | 42.80${}^{1.86}_{1.76}$ | 0.08${}^{0.00}_{0.00}$ | 0.265${}^{0.008}_{0.012}$
ELARS 7 | 1.56${}^{0.02}_{0.02}$ | 1.40${}^{0.01}_{0.01}$ | 34.57${}^{1.00}_{1.00}$ | 6.469${}^{0.106}_{0.135}$ | 1.44${}^{0.08}_{0.08}$ | 30.81${}^{3.70}_{3.24}$ | 0.07${}^{0.00}_{0.00}$ | 0.055${}^{0.004}_{0.004}$
ELARS 8 | 4.94${}^{0.05}_{0.05}$ | 4.53${}^{0.15}_{0.15}$ | 0.65${}^{0.05}_{0.05}$ | 0.015${}^{0.001}_{0.001}$ | 4.55${}^{0.10}_{0.10}$ | 45.16${}^{1.05}_{1.07}$ | 0.22${}^{0.00}_{0.00}$ | 0.689${}^{0.025}_{0.026}$
ELARS 9 | 0.52${}^{0.01}_{0.01}$ | 3.00${}^{0.09}_{0.09}$ | 1.68${}^{0.70}_{0.70}$ | 0.096${}^{0.008}_{0.028}$ | 0.54${}^{0.04}_{0.04}$ | 42.33${}^{1.70}_{1.92}$ | 0.18${}^{0.00}_{0.00}$ | 0.105${}^{0.010}_{0.008}$
ELARS 10 | 3.50${}^{0.04}_{0.04}$ | 3.42${}^{0.17}_{0.16}$ | 3.01${}^{0.15}_{0.15}$ | 0.138${}^{0.006}_{0.005}$ | 2.02${}^{0.08}_{0.08}$ | 61.42${}^{3.19}_{3.53}$ | 0.23${}^{0.01}_{0.01}$ | 0.642${}^{0.021}_{0.023}$
ELARS 11 | 0.59${}^{0.01}_{0.01}$ | 2.25${}^{0.05}_{0.05}$ | 0.28${}^{0.01}_{0.01}$ | 0.027${}^{0.001}_{0.001}$ | 1.90${}^{0.07}_{0.07}$ | 35.56${}^{0.81}_{1.18}$ | 0.17${}^{0.00}_{0.00}$ | 0.075${}^{0.011}_{0.010}$
ELARS 12 | 2.68${}^{0.02}_{0.02}$ | 7.13${}^{0.28}_{0.26}$ | 1.15${}^{0.07}_{0.07}$ | 0.060${}^{0.004}_{0.003}$ | 3.14${}^{0.11}_{0.11}$ | 41.93${}^{1.87}_{1.66}$ | 0.29${}^{0.01}_{0.00}$ | 0.319${}^{0.022}_{0.019}$
ELARS 13 | 0.76${}^{0.01}_{0.01}$ | 1.40${}^{0.02}_{0.03}$ | 1.64${}^{0.03}_{0.03}$ | 0.849${}^{0.013}_{0.014}$ | 1.17${}^{0.08}_{0.08}$ | 3.29${}^{0.58}_{0.62}$ | 0.12${}^{0.00}_{0.00}$ | 0.191${}^{0.007}_{0.008}$
ELARS 14 | 0.72${}^{0.01}_{0.01}$ | 3.66${}^{0.11}_{0.12}$ | 1.91${}^{0.18}_{0.18}$ | 0.091${}^{0.007}_{0.004}$ | 2.40${}^{0.30}_{0.30}$ | 22.85${}^{2.18}_{1.65}$ | 0.25${}^{0.00}_{0.00}$ | 0.089${}^{0.009}_{0.009}$
ELARS 15 | 0.40${}^{0.01}_{0.01}$ | 2.31${}^{0.15}_{0.11}$ | 1.31${}^{0.13}_{0.13}$ | 0.005${}^{0.000}_{0.000}$ | 1.26${}^{0.06}_{0.06}$ | 74.92${}^{2.75}_{3.58}$ | 0.22${}^{0.01}_{0.00}$ | 0.059${}^{0.024}_{0.023}$
ELARS 16 | 0.41${}^{0.01}_{0.01}$ | 1.67${}^{0.12}_{0.13}$ | 2.64${}^{0.01}_{0.01}$ | 0.010${}^{0.000}_{0.000}$ | 0.86${}^{0.03}_{0.03}$ | 41.02${}^{2.86}_{4.05}$ | 0.23${}^{0.01}_{0.01}$ | 0.333${}^{0.018}_{0.023}$
ELARS 17 | 0.58${}^{0.01}_{0.01}$ | 1.34${}^{0.08}_{0.08}$ | 0.38${}^{0.00}_{0.00}$ | 0.003${}^{0.000}_{0.000}$ | 1.36${}^{0.04}_{0.04}$ | 60.70${}^{3.13}_{2.03}$ | 0.17${}^{0.01}_{0.01}$ | 0.263${}^{0.028}_{0.026}$
ELARS 18 | 0.36${}^{0.01}_{0.01}$ | 0.84${}^{0.04}_{0.04}$ | 8.83${}^{3.10}_{3.10}$ | 0.030${}^{0.001}_{0.001}$ | 0.53${}^{0.18}_{0.18}$ | 73.24${}^{7.94}_{7.09}$ | 0.18${}^{0.01}_{0.01}$ | 0.271${}^{0.015}_{0.022}$
ELARS 19 | 0.26${}^{0.01}_{0.01}$ | 0.35${}^{0.01}_{0.00}$ | 1.73${}^{0.27}_{0.27}$ | 0.003${}^{0.000}_{0.000}$ | 0.35${}^{0.02}_{0.02}$ | 23.29${}^{3.52}_{3.24}$ | 0.07${}^{0.00}_{0.00}$ | 0.047${}^{0.011}_{0.011}$
ELARS 20 | 0.42${}^{0.00}_{0.00}$ | 2.26${}^{0.14}_{0.13}$ | 0.39${}^{0.09}_{0.09}$ | 0.003${}^{0.000}_{0.000}$ | 4.28${}^{0.20}_{0.20}$ | 22.97${}^{3.78}_{2.60}$ | 0.26${}^{0.01}_{0.01}$ | 0.169${}^{0.024}_{0.024}$
ELARS 21 | 0.15${}^{0.01}_{0.01}$ | 0.16${}^{0.01}_{0.00}$ | 0.86${}^{0.00}_{0.00}$ | 0.002${}^{0.000}_{0.000}$ | 0.44${}^{0.05}_{0.05}$ | 77.33${}^{6.75}_{6.08}$ | 0.07${}^{0.00}_{0.01}$ | 0.348${}^{0.199}_{0.249}$
ELARS 22 | 2.05${}^{0.03}_{0.03}$ | 13.17${}^{0.26}_{0.20}$ | 1.97${}^{0.13}_{0.13}$ | 0.008${}^{0.000}_{0.000}$ | 1.27${}^{0.06}_{0.06}$ | 16.49${}^{1.68}_{1.37}$ | 0.21${}^{0.00}_{0.00}$ | 0.019${}^{0.008}_{0.006}$
ELARS 23 | 2.31${}^{0.04}_{0.04}$ | 14.29${}^{0.34}_{0.52}$ | 0.54${}^{0.21}_{0.21}$ | 0.007${}^{0.001}_{0.001}$ | 5.58${}^{0.11}_{0.11}$ | 38.63${}^{1.13}_{1.19}$ | 0.23${}^{0.00}_{0.00}$ | 0.007${}^{0.022}_{0.017}$
ELARS 24 | 67.01${}^{0.69}_{0.69}$ | 22.14${}^{0.63}_{0.65}$ | 6.80${}^{0.45}_{0.45}$ | 3.181${}^{0.204}_{0.164}$ | 8.97${}^{0.15}_{0.15}$ | 24.97${}^{1.32}_{1.11}$ | 0.29${}^{0.00}_{0.00}$ | 1.196${}^{0.024}_{0.024}$
ELARS 25 | 1.07${}^{0.03}_{0.03}$ | 5.87${}^{0.16}_{0.16}$ | 0.70${}^{0.21}_{0.21}$ | 0.006${}^{0.001}_{0.000}$ | 3.26${}^{0.20}_{0.20}$ | 22.96${}^{2.07}_{2.15}$ | 0.17${}^{0.00}_{0.00}$ | 0.009${}^{0.008}_{0.010}$
ELARS 26 | 2.32${}^{0.04}_{0.04}$ | 1.32${}^{0.03}_{0.02}$ | 0.58${}^{0.00}_{0.00}$ | 0.004${}^{0.000}_{0.000}$ | 4.51${}^{0.06}_{0.06}$ | 87.91${}^{5.14}_{5.07}$ | 0.07${}^{0.00}_{0.00}$ | 0.111${}^{0.020}_{0.019}$
ELARS 27 | 1.10${}^{0.03}_{0.03}$ | 1.31${}^{0.03}_{0.02}$ | 0.73${}^{0.01}_{0.01}$ | 0.005${}^{0.000}_{0.000}$ | 2.00${}^{0.03}_{0.03}$ | 62.93${}^{2.67}_{2.30}$ | 0.07${}^{0.00}_{0.00}$ | 0.041${}^{0.032}_{0.025}$
ELARS 28 | 1.74${}^{0.04}_{0.04}$ | 7.73${}^{0.42}_{0.27}$ | 11.11${}^{0.56}_{0.56}$ | 0.012${}^{0.000}_{0.000}$ | 1.41${}^{0.06}_{0.06}$ | 25.65${}^{2.64}_{2.28}$ | 0.24${}^{0.01}_{0.00}$ | 0.136${}^{0.011}_{0.020}$
Tol 1214 | 0.50${}^{0.01}_{0.01}$ | 0.41${}^{0.01}_{0.01}$ | 109.13${}^{6.02}_{6.02}$ | 0.574${}^{0.004}_{0.004}$ | 0.04${}^{0.01}_{0.01}$ | 34.90${}^{7.52}_{7.20}$ | 0.08${}^{0.00}_{0.00}$ | 0.000${}^{0.000}_{0.000}$
Tol 1247 | 25.76${}^{0.06}_{0.06}$ | 16.65${}^{0.07}_{0.05}$ | 21.26${}^{0.54}_{0.54}$ | 1.355${}^{0.008}_{0.008}$ | 1.41${}^{0.04}_{0.04}$ | 4.20${}^{0.75}_{0.72}$ | 0.09${}^{0.00}_{0.00}$ | 0.265${}^{0.002}_{0.002}$
J1156 | 34.22${}^{0.76}_{0.76}$ | 30.03${}^{0.47}_{0.45}$ | 1.62${}^{0.01}_{0.01}$ | 0.316${}^{0.003}_{0.002}$ | 3.45${}^{0.30}_{0.30}$ | 13.58${}^{3.32}_{5.34}$ | 0.06${}^{0.00}_{0.00}$ | 0.286${}^{0.008}_{0.011}$

Note. — (1–3) Extinction corrected SFR. (4–5) Based on extinction corrected
H$\alpha$ luminosity within a radius of $r_{\mathrm{p,H}\alpha}$.

Global luminosities and derived quantities are measured within
$r_{\mathrm{tot}}$, with three exceptions. The first is $E(B-V)_{n}$, which
uses the smaller aperture size given by H$\beta$ (see Section 3.2.1). The
total Ly$\alpha$ aperture is sometimes quite small (in particular for weak or
non-emitters) and measured masses are therefore underestimated. To obtain
total masses for the galaxies we thus re-measure global masses within a
circular aperture of size determined from mass growth curves in the same way
as for H$\beta$ (but with a SNR threshold of 1). The HST imaging derived
masses are in general much higher than the masses from SDSS
spectroscopy/imaging SED fits. This difference come from the apertures used
for the HST imaging being much larger than the standard SDSS aperture.

The SFR density and specific SFR is determined within an isophotal region
determined from the H$\alpha$ map with an effective radius of
$r_{\mathrm{p,H}\alpha}$ (given in Table 4). This aperture is in general
smaller than the Ly$\alpha$ total aperture, but provides a much safer estimate
of these observables due to large uncertainties in mass and SFR for large
apertures. It should be noted that the measurements from the HST H$\alpha$
observations sometimes show significantly higher SFR than the SDSS
measurements given in Table 2. This difference comes from the larger apertures
used in our imaging measurements, when comparing measurements for the same
aperture size the HST SFR estimates are conistent (within the errors) with the
SDSS measurements. For consistency we use the imaging derived masses,
extinctions and SFRs throughout this paper.

In addition to the measurements obtained from HST maps we also use SDSS
derived values of $12+\mathrm{log}(O/H)$ and [O iii] $\lambda 5007/$[O ii]
$\lambda 3727$ ratio (O32) from Runnholm et al. (2020). For the oxygen
abundance the strong line N2P calibration (Yin et al., 2007) is used, which
can be estimated for all of the galaxies in the sample. The O32 ratio provides
a diagnostic on the overall ionisation state of the nebular gas (e.g., Kewley
& Dopita, 2002). The luminosities and derived quantities for the full sample
are given in Tables 5 and 6.

For the comparisons shown in this section we use the censored Kendall-$\tau$
test (Akritas et al., 1995, as implemented in the R package CENKEN) to check
for correlations or anti-correlations. The censored test allows the inclusion
of upper limits in the calculations, so that also the Ly$\alpha$ non-detected
galaxies can be included. We use limits of $\left|\tau\right|>0.3$ and
$p_{0}<0.003$ to signify a strong correlation (or anticorrelation), but also
discuss weaker trends.

###  5.1 The luminosity, equivalent width, and escape fraction of Ly$\alpha$

Figure 11: Left panel: The Ly$\alpha$ versus the FUV luminosity of the
galaxies in this sample. Diagonal dashed lines show different limits of
$W_{\mathrm{Ly\alpha}}$. Right panel: $f_{\mathrm{esc}}$ vs
$W_{\mathrm{Ly\alpha}}$ for the galaxies. In addition to a linear fit to the
current data, the relation from Sobral & Matthee (2019) is shown.

We detect global Ly$\alpha$ emission from 39 out of 45 galaxies at a signal-
to-noise ratio of 2 or more (the median ratio for the detected galaxies is
$\sim$13). Three of the six non-detections are net absorbers and we measure a
negative Ly$\alpha$ flux for them.

The luminosity of Ly$\alpha$ is expected to scale with FUV luminosity,
primarily through the connection to star formation with the production of
Ly$\alpha$ photons. In Figure 11 we show total luminosity of Ly$\alpha$ versus
the FUV luminosity. There is indeed a strong correlation between the two
($\tau=0.51$, $p_{0}=7.3\cdot 10^{-7}$), but also substantial scatter. The
spread seen here can be explained by the combination of dust extinction and
resonant scattering of the Ly$\alpha$ photons (see Section 6.1). For an
individual source this effect makes it hard to use Ly$\alpha$ observations as
a direct probe of star formation. The plot also shows the range of
$W_{\mathrm{Ly\alpha}}$ the sample covers, from a few to $\sim$100 Å.

Figure 11 also shows the relation between $f_{\mathrm{esc}}$ and
$W_{\mathrm{Ly\alpha}}$. Given that both of these quantities give Ly$\alpha$
luminosity normalized with measurements that are directly coupled to the star
formation rate they are expected to correlate, which is also what we find
($\tau=0.55$, $p_{0}=8.3\cdot 10^{-8}$). However, also this relation shows
significant scatter, in particular at low $f_{\mathrm{esc}}$. The main
difference between the two is that $f_{\mathrm{esc}}$ is an absolute estimate
(based on extinction corrected H$\alpha$) of the fraction of Ly$\alpha$
photons that escapes the galaxy, while $W_{\mathrm{Ly\alpha}}$ provides a
relative estimate of the escape as compared to observed FUV continuum flux. In
galaxies where much of the star formation is hidden by dust, the observed
$W_{\mathrm{Ly\alpha}}$ can still be quite high because the global aperture
measurements are dominated by the non-dusty regions. The extinction corrected
H$\alpha$ does not suffer from this (modulo extinction uncertainties) and thus
yields a more complete picture of the ionizing photon budget. In these
galaxies $f_{\mathrm{esc}}$ will sit below the locus in the figure. In Section
6.2 we fit $f_{\mathrm{esc}}$ as a linear function of $W_{\mathrm{Ly\alpha}}$
and show how this allows Ly$\alpha$ to be used as a rough SFR diagnostic.
Interestingly, all but one of the galaxies that fall well below the linear fit
of $f_{\mathrm{esc}}$ vs $W_{\mathrm{Ly\alpha}}$ in the figure have high
nebular and stellar extinction. In the single case which seems to have low
nebular extinction but still outlie the locus (ELARS22) the galaxy is inclined
towards us and has a higher stellar than nebular extinction (0.23 and 0.05,
respectively), perhaps indicating that the measured nebular extinction for
this galaxy is under-estimated.

Another explanation that will cause a shift in this relation is the hardness
of the stellar continuum (Sobral & Matthee, 2019). A stellar population
generating more ionizing flux (due to, e.g., younger age, lower metallicity,
higher stellar rotation) will show a higher Ly$\alpha$ equivalent width, at
constant $f_{\mathrm{esc}}$. This effect can be measured by computing the
ionizing production efficiency, $\xi_{ion}$, which is the ratio of ionizing
photons to the dust corrected UV luminosity. For galaxies with multiple
stellar populations a high $\xi_{ion}$ value indicates that they are currently
undergoing a starburst phase, while lower values indicates a less active star
formation phase. For our sample we find an average
$\log(\xi_{ion}/[\mathrm{Hz}\,\mathrm{erg}^{-1}])=24.9$ which is $\sim$0.5 dex
lower than the typical value for high redshift LAEs (e.g., Nakajima et al.,
2018). This difference is not surprising given that our sample includes many
galaxies that have lower SFR than the high redshift LAEs, and could explain
the scattering of data points to lie to the left of the fitted line (i.e.
galaxies with lower than average $\xi_{ion}$). As opposed to the effect of
extinction, inspecting $\xi_{ion}$ for individual galaxies to see where they
lie in the plot does not show any clear effects.

Figure 12: Histograms of $L_{\mathrm{Ly\alpha}}$ (upper panel), $L_{\rm FUV}$
(middle panel), and $W_{\mathrm{Ly\alpha}}$ (lower panel). In addition we also
show histograms for data on high redshift LAEs from VLT/MUSE observations
(Leclercq et al., 2017). Thick dotted vertical lines show the median
$L_{\mathrm{Ly\alpha}}$, $L_{\rm FUV}$, and $W_{\mathrm{Ly\alpha}}$ values for
the net emitters in our data set and the thin dashed lines show the medians
for the MUSE LAEs.

In Figure 12 we show the range of Ly$\alpha$ luminosities, FUV luminosities,
and $W_{\mathrm{Ly\alpha}}$ covered by our sample. Most of our galaxies have
$40<\log$($L_{\mathrm{Ly\alpha}}$)$<42$ with a tail towards higher
luminosities. Note that the non-emitters are included in the lowest bin.
Comparing our data to the high redshift LAEs from Leclercq et al. (2017) it is
clear that the LARS galaxies are in general fainter in Ly$\alpha$ (median
$\log$($L_{\mathrm{Ly\alpha}}$) for LARS and MUSE LAEs are 41.3, and 42.3,
respectively), but with some overlap. This difference could be due to
luminosity bias, but is more likely to be caused by the quite different sample
selection in this case. The MUSE LAEs are selected based on their Ly$\alpha$
emission, while the LARS galaxies were selected based on SFR and stellar
population age. Indeed, when comparing the FUV luminosities of the two samples
the difference is markedly lower (median $\log$($L_{\rm FUV}$) for LARS and
MUSE LAEs are 40.1, and 40.3, respectively).

The same difference is seen in the $W_{\mathrm{Ly\alpha}}$ histogram where the
two samples are quite distinct (median $W_{\mathrm{Ly\alpha}}$ for LARS and
MUSE are 17 and 92 Å, respectively). At high redshift finding the most extreme
LAEs is more likely because of the larger volume covered, but also due to
observing galaxies at the peak of star formation. It should thus be noted that
the results from this study have to be treated with some care when comparing
to high redshift studies, in particular if only the highest
$W_{\mathrm{Ly\alpha}}$ emitters are observed. However, the LARS+ELARS
galaxies should be more representative of the Ly$\alpha$ properties of the
general star forming galaxy population, with more dust and weaker Ly$\alpha$
emission.

For the 14 galaxies previously presented in Hayes et al. (2014) we note that
the Ly$\alpha$ luminosities, equivalent widths, and escape fractions have
changed compared to the previous values. This is mainly due to the updated
photometric calibration of ACS/SBC (10-30% change depending on filter) and
WFC3/UVIS (10% for the F336W filter), but is also affected by the difference
in global aperture between the earlier paper and this work.

###  5.2 The relation between $f_{\mathrm{esc}}$ and other properties

Figure 13: Scatter plots of $f_{\mathrm{esc}}$ versus various properties of
the galaxies.

In Figure 13 we show the relation of $f_{\mathrm{esc}}$ to various properties
of the galaxies that have been suggested to affect the escape of Ly$\alpha$
photons.

The stellar age and star formation quantities (global SFR, SFR density and
specific SFR) are all related to ISM feedback and will strongly affect the
kinematics of the ISM, both in terms of bulk motions and turbulence. With high
relative (to the Ly$\alpha$ production sites) bulk motion of the neutral ISM
Ly$\alpha$ photons will be able to escape in the wings of the ISM absorption
profile (e.g., Dijkstra, 2017). Feedback induced turbulence and shocks will
cause instabilities in the ISM that can create channels of low H i column
density where Ly$\alpha$ photons can travel relatively unimpeded, also causing
galaxies to have dispersion-dominated kinematics (Herenz et al., 2016). In the
upper left and three middle row panels of the figure we show scatter plots of
$f_{\mathrm{esc}}$ versus these quantities and find no strong correlations.
For stellar age this may be because we use a global average (FUV-weighted)