# Local Analogs to High-Redshift Galaxies:
I. Characterization of Dust Emission and Star Formation History

Skarleth M. Motiño Flores  Physics Department, The Catholic University of
America, 620 Michigan Ave. NE, Washington, DC 20064, USA  Tommy Wiklind
Physics Department, The Catholic University of America, 620 Michigan Ave. NE,
Washington, DC 20064  Rafael T. Eufrasio  Department of Physics, University of
Arkansas, 248 Physics Building, 825 West Dickson Street, Fayetteville, AR
72701, USA

# Bayesian Estimation of Black-body Models Best Fits

Skarleth M. Motiño Flores  Physics Department, The Catholic University of
America, 620 Michigan Ave. NE, Washington, DC 20064, USA  Tommy Wiklind
Physics Department, The Catholic University of America, 620 Michigan Ave. NE,
Washington, DC 20064  Rafael T. Eufrasio  Department of Physics, University of
Arkansas, 248 Physics Building, 825 West Dickson Street, Fayetteville, AR
72701, USA

###### Abstract

Star-forming dwarf galaxies have properties similar to those expected in high-
redshift galaxies. Hence, these local galaxies may provide insights into the
evolution of the first galaxies, and the physical processes at work. We
present a sample of eleven potential local analogs to high-$z$ (LAHz)
galaxies. The sample consists of blue compact dwarf galaxies, selected to have
spectral energy distributions that fit galaxies at $1.5<z<4$. We use SOFIA-
HAWC+ observations combined with optical and near-infrared data to
characterize the dust properties, star formation rate (SFR) and star formation
histories (SFH) of the sample of LAHz. We employ Bayesian analysis to
characterize the dust using two component black-body models. Using the
Lightning package we fit the spectral energy distribution of the LAHz galaxies
over the FUV-FIR wavelength range, and derive the SFH in five time-steps up to
a look-back time of 13.3 Gyr. Of the eleven LAHz candidates, six galaxies have
SFH consistent with no star formation activity at look-back times beyond $1$
Gyr. The remaining galaxies show residual levels of star formation at ages
$\gtrsim$1 Gyr, making them less suitable as local analogs. The six young
galaxies stand out in our sample by having the lowest gas-phase metallicities.
They are characterized by warmer dust, having the highest specific SFR, and
the highest gas mass fractions. The young age of these six galaxies suggests
that merging is less important as a driver of the star formation activity. The
six LAHz candidates are promising candidates for studies of the gas dynamics
role in driving star formation.

galaxies: blue compact dwarf galaxies, local analogs, high-redshift -
infrared: photometry - infrared: ISM, dust - SFH: Star Formation History -
SFR: Star Formation Rate.

††journal: ApJ

##  1 Introduction

The high-redshift (high-$z$) universe is very different from our local
environment: galaxies are physically small with irregular morphologies; they
are metal-poor, star formation rates (SFRs) are high and growing
exponentially, and the interstellar gas constitutes a major component of the
total baryonic mass. However, the faintness and the small sizes make it
extremely difficult to study the physical processes in these galaxies in
detail. Instead, a significant effort over the last decade has been devoted to
identifying low-redshift (low-$z$) analogs to high-$z$ galaxies. (e.g. Bian et
al. 2016, Sebastian & Bait 2019). These analogs to high-$z$ galaxies, are
usually local low metallicity, star forming dwarf galaxies. They share many
properties of high-redshift galaxies in terms of morphology, physical size,
metallicity and star formation (e.g. Kaaret et al. 2011, Adamo et al. 2011;
Östlin et al. 2014). This provides an attractive route to study the properties
of high-$z$ galaxies in better detail.

Several previous studies have defined analogs to high-$z$ galaxies using a
variety of approaches. For instance, Heckman et al. (2005) and Hoopes et al.
(2007) found that ultra-violet luminous galaxies (UVLGs) with high surface
brightness have characteristics that are remarkably similar to Lyman break
galaxies (LBGs) at high-$z$. UVLGs are rare in the local universe, but a
significant sample can be defined when extending the search to $z$$\sim$0.2.
These Lyman Break Analogs have $L_{\rm{UV}}\gtrsim 2\times 10^{10}$ L⊙, star
formation rates (SFR) of 3-30 M⊙ yr-1, sizes of a few kpc, and sub-Solar
metallicities. In a similar manner, Overzier et al. (2011) defines analogs of
high-$z$ LBGs based on their UV luminosity. Östlin et al. (2014) uses
H$\alpha$ equivalent widths as a requirement to identify local galaxies that
have Ly$\alpha$ luminosities comparable to high redshift Ly${\alpha}$ Emitters
(LAEs) and LBGs (the Ly$\alpha$ Reference Sample, or LARS). The galaxies
comprising LARS are small, metal-poor, $\sim$0.2-0.6 Z⊙, gas-rich, with an
average gas mass fraction $\sim$0.4, and SFRs ranging from 0.6 to $\sim$20 M⊙
yr-1. They are morphologically identified as dwarf irregular galaxies (Pardy
et al. (2014)). Interestingly, most of the LAE and LBG analogs defined by
Östlin et al. (2014) are also far-infrared bright, showing the presence of
significant amounts of dust in their interstellar medium, including one galaxy
with a Ly$\alpha$ escape fraction of $\sim$12%.

Another proposed analog for high redshift LAEs is the class of Green Peas
(Cardamone et al. 2009). These are compact, low-mass galaxies, with sub-Solar
metallicities. They have SFRs of $\sim$4 M⊙ yr-1, but very high specific star
formation rates (Izotov et al. 2011). Their properties overlap with Blue
Compact Dwarf galaxies (BCDGs) at the high luminosity end of BCDGs, but are
otherwise distinct. Green Peas exhibit starburst driven outflows (Yang et al.
2017) and many have a high Ly$\alpha$ escape fraction (Kim et al. 2021). The
typical distance of Green Peas is $z\sim$0.2-0.3.

Although these analogs to high-$z$ galaxies are ‘local’ in a cosmological
sense, they are still quite distant as far as detailed studies are concerned.
The UVLGs and Green Peas are at $z\sim 0.2-0.3$, or $D_{\rm{A}}\gtrsim 700$
Mpc, while the average distance to galaxies in the LARS sample is $\sim$300
Mpc. In linear scale, this correspond to $\gtrsim$3.3 and 1.5 kpc/arcsecond,
respectively. The relatively large distances are necessary because of the low
number densities of galaxies fulfilling the criteria for being selected as an
LBG and/or LAE analog.

In this paper we present an alternative approach to define local analogs, that
allows us to select nearby galaxies at distances $\sim$10-20 Mpc. This means
that scales of 50 pc can be resolved even with ground-based instruments.
Instead of matching properties like luminosity and/or equivalent widths of
emission lines, this approach relies on how well the entire UV to near-
infrared spectral energy distribution (SED) of local galaxies match those of
high-$z$ galaxies. Using templates of local galaxies spanning a wide variety
of morphology, star formation activity, gas-phase metallicity, as well as AGN
activity and environment, we identify those templates that are most successful
in fitting high redshift galaxies as potential local analogs to high-$z$
galaxies (LAHz). The sample of LAHz presented in this work, is selected from
129 local galaxy spectral templates compiled by Brown et al. (2014). These
templates are fit to $\sim$180,000 high redshift galaxies in the CANDELS
(Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey) fields. It
turns out that only a small number of the local templates provide the best-fit
SED to galaxies at $z>2$. All the local templates in this category are
designated as blue compact dwarf galaxies. The LAHz are thus scaled-down
versions of star forming high redshift galaxies.

All of the LAHz, as well as most of the galaxies in the Ly$\alpha$ Reference
Sample (Östlin et al. 2014) are detected at far-infrared (FIR) wavelengths,
suggesting thermal dust emission. The connection between star formation
activity and dust content in galaxies is well-established, and provides
information about the evolutionary stage of galaxies. In a study involving
$\sim$1600 low-redshift galaxies, da Cunha et al. (2010) found a strong
correlation between the dust-to-stellar mass ratio and the specific star
formation rate, as well as a strong correlation between the dust-to-SFR ratio
and sSFR. These correlations suggests an evolutionary sequence and can be used
as diagnostic for inferring the evolutionary stage of galaxies in combination
with other parameters, like the star formation history (SFH).

Observations with the Atacama Large Millimeter Array (ALMA) over the last few
years have vastly increased our understanding of the cold and dense
interstellar medium of galaxies in the early universe. Star-forming galaxies
on the Main Sequence (MS) at $z$$\sim$3 appear to have dust attenuation
properties similar to large and massive star-forming galaxies in our local
universe (Fudamoto et al. 2017). Recent results from the ALPINE survey (Le
Fèvre et al. 2020), however, suggest that the dust attenuation decreases
drastically for galaxies on the MS at $z$$\gtrsim$4 (Fudamoto et al. 2020),
approaching that of metal-poor dwarf galaxies in the local universe.

The shape of the far-infrared dust SED of high-$z$ galaxies has largely
remained unconstrained, as most rest-frame FIR observations at high-$z$
consists of a single photometric data point. This is also changing thanks to
data from ALMA. The FIR SED can now be constructed for galaxies at $z$$\sim$2,
where Pantoni et al. (2021) find luminosity-weighted dust temperatures
$\sim$50 K for 11 galaxies on the MS. Faisst et al. (2020) measured the dust
SED for a handful a MS galaxies at $z$$\gtrsim$4, showing that the peak
temperatures characterizing the dust SEDs are warmer than for star-forming
galaxies in the local universe.

In this paper we provide a detailed characterization of the dust in our sample
of local analogs to high redshift (LAHz) galaxies. We present new SOFIA HAWC+
observations which, together with ancillary data, allow us to define the dust
SED in detail. This is used to evaluate whether they share properties with
galaxies at high redshift, or whether they are similar to local massive star-
forming galaxies.

This paper is organized as follows: In Section. 2 we describe the sample
definition and selection method. In Section. 3 we describe our SOFIA
observations and ancillary data used. In Section. 4 we describe the two
modeling procedures used to characterize the dust continuum and the FUV to FIR
SED. In Section. 5 we present the dust properties and SFHs and in Section. 6
discuss the implications of this results. In Section. 7 we summarize our
results and conclusions about the properties of our sample of LAHz.

##  2 Sample Definition

Figure 1: The morphology fraction of the Brown et al. (2014) templates,
divided into four groups based on morphological T-class. The fraction for each
redshift interval represents the best-fit template to CANDELS H-band selected
galaxies from all five CANDELS fields. The left panel represents the fractions
where we allow additional dust extinction on top of that already implicit in
the templates. The right panel we only use the templates, without any
additional extinction. The dotted blue line represents the fraction of the
eleven templates selected to be LAHz. The dotted black line is the sum of the
two earliest morphological classes, representing quiescent spheroidal
galaxies. Figure 2: Similar to Figure 1. Here the dashed lines correspond to
the best-fit SED to CANDELS galaxies when selecting those with
$\Delta\chi^{2}_{\nu}=3$ relative the best-fit SEDs shown in Figure 1 (shown
as full drawn lines). The fraction of irregular galaxies decreases slightly,
while the fraction of spiral disk galaxies increases. The overall dominance of
irregular galaxies in the best-fit SEDs is, however, retained.  Table 1:
Properties of the local analogs

No. | Galaxy | RA | Dec | D | $\log{M_{*}}^{(a)}$ | $\log{M_{HI}}^{(b)}$ | f${}_{\mathrm{gas}}^{(c)}$ | Metallicity(d) | Alternative |  |
---|---|---|---|---|---|---|---|---|---|---|---
| Name | J2000.0 | Mpc | M⊙ | M⊙ | M⊙ | $12+\log{(O/H)}$ | Name |  |
1 | NGC 2537 | 08:13:14.4 | +45:59:13 | 8.6 | 9.02 | 8.43 | 0.17 | 8.19 | Arp 6; ‘Bear Paw’ |  |
2 | Mrk 140 | 10:16:28.3 | +45:19:18 | 27.6 | 8.60 | 8.90 | 0.56 | 8.30 | Mrk 140 |  |
3 | Haro 02 | 10:32:31.9 | +54:24:02 | 23.7 | 9.21 | 8.03 | 0.02 | 8.45 | Mrk 033 |  |
4 | Mrk 1450 | 11:38:35.6 | +57:52:27 | 14.7 | 7.27 | 7.35 | 0.54 | 7.96 |  |  |
5 | UM 461 | 11:51:33.1 | -02:22:22 | 20.7 | 7.37 | 8.47 | 0.96 | 7.78 |  |  |
6 | Mrk 1307 | 11:52:37.4 | -02:28:09 | 21.0 | 8.07 | 8.73 | 0.81 | 7.96 | UM 462 |  |
7 | Haro 06 | 12:15:18.4 | +05:45:39 | 35.1 | 8.58 | 8.83 | 0.69 | 8.18 |  |  |
8 | NGC 4670 | 12:45:17.1 | +27:07:31 | 20.0 | 9.38 | 9.02 | 0.37 | 8.30 | Arp 163 |  |
9 | Mrk 475 | 14:39:05.5 | +36:48:21 | 10.9 | 6.95 | 6.62 | 0.56 | 7.93 |  |  |
10 | UGCA 410 | 15:37:04.2 | +55:15:48 | 10.5 | 7.26 | 7.58 | 0.57 | 8.10 | Mrk 487 |  |
11 | Mrk 930 | 23:31:58.6 | +28:56:50 | 77.5 | 8.89 | 9.51 | 0.74 | 8.08 |  |  |

(a) Stellar mass ${M_{*}}$ This work. (b) HI data from Paturel et al. (2003),
except for Mrk 1450 (van Driel et al. (2016)). (c) Gas fraction is defined as
$f_{g}=M_{g}/(M_{g}+M_{*})$. (d) Metal Abundances UM 461 and UM 462 (Campos-
Aguilar1993), Haro 02 (Davidge1989).

Figure 3: SDSS $i$-band images of our sample. All these galaxies are
classified as blue compact dwarf galaxies. The bar corresponds to 1 kpc.

Our sample of local analogs to high-$z$ (LAHz) is defined by fitting spectral
templates of local galaxies to H-band selected galaxies in the CANDELS survey
(Grogin et al. 2011; Koekemoer et al. 2011), and selecting those templates
that provide the best fit to $z\gtrsim 2$ galaxies as candidates of being
local analogs.

We use the galaxy templates presented in Brown et al. (2014). This set of
templates contains SEDs covering a wavelength range from UV to the mid-
infrared for 129 nearby galaxies. The templates span a wide range of
morphologies and galaxy luminosities; they include ellipticals, lenticulars,
spirals, as well as irregular and dwarf galaxies. The sample also contains
gravitationally interacting galaxies in various stages of mergers, galaxies
with an AGN, and luminous infrared galaxies. The templates combine ground-
based optical spectroscopy with near and mid-infrared spectroscopy from
Spitzer and AKARI. The wavelength coverage is not contiguous and gaps are
filled in using MAGPHYS (Multi-wavelength Analysis of Galaxy Physical
Properties code) modeling (da Cunha et al. 2008). The spectral energy
distribution from different spectral inputs is normalized and verified with 26
bands of matched-aperture photometry. This allows the mitigation of systematic
errors, such as scattered light in IRAC images and errors in the pre-launch
WISE W4 filter curve (see Brown et al. 2014 for details).

For the purpose of establishing which type of templates provide the best-fit
SEDs to the high redshift galaxies, we divide the 129 templates into four
T-class groups (de Vaucouleurs et al. 1991) : $-6\leq T<-2$, $-2\leq T<2$,
$2\leq T<8$, $8\leq T\leq 11$. These four groups correspond to spheroidal,
bulge dominated, star forming disks, and star forming irregular galaxies.
Galaxies without a designated morphology class, are put in T-class 12,
peculiar galaxies. The number of templates in the four main groups are 22, 24,
44, and 20, respectively. In addition, the $T=12$ peculiar group contains 19
templates.

###  2.1 High redshift data

The Brown et al. (2014) templates are used to fit to the photometry of high
redshift galaxies from the Cosmic Assembly Near-infrared Deep Extragalactic
Legacy Survey (CANDELS). CANDELS is a treasury program on Hubble Space
Telescope (HST), and provide deep multi-wavelength imaging data in five legacy
deep fields: GOODS-South, GOODS-North, UDS, COSMOS and EGS (Grogin et al.
2011; Koekemoer et al. 2011). In this study we use H-band selected catalogs
from all five fields. Details on the data and photometry at different
wavelength bands are given for; GOODS-S (Guo et al. 2013; Santini et al.
2015), GOODS-N (Barro et al. 2019), UDS (Galametz et al. 2013 ; Santini et al.
2015), COSMOS (Nayyeri et al. 2017), and EGS (Stefanon et al. 2017).

The CANDELS catalogs contain photometric data from the UV to near- and mid-
infrared wavelengths, observed in broad, medium, and in some cases, narrow
band filters. The catalogs also contain derived physical parameters, such as
photometric redshift, stellar mass and star formation rates, compiled using
several different teams applying different SED methods, star formation
histories and different templates (e.g. Dahlen et al. 2013; Mobasher et al.
2015; Santini et al. 2015). The optical (HST/ACS) and near-IR (HST/WFC3) data
are consistently combined with the mid-IR data (Spitzer/IRAC) and ground-based
observations (UV and K-band). All of the photometric catalogs were selected in
the HST/WFC3 F160W band using SExtractor (Bertin & Arnouts 1996). The catalogs
for the five different CANDELS fields contain a total of 185,956 galaxies. The
number of photometric bands range from 18 (GOODS-S) to 42 (CANDELS COSMOS).
The latter catalog includes extensive narrow- and medium-band photometric
bands. The mid-infrared data are from Spitzer/IRAC (Ashby et al. 2013).

###  2.2 SED fitting

All 129 of the Brown et al. (2014) templates are used in the SED fitting of
the 185,956 CANDELS galaxies. The fits are done in two different modes:
keeping the extinction equal to the intrinsic dust extinction of the
templates, and allowing additional extinction on top of the intrinsic. The
additional extinction is parametrized through the $E(B-V)$ color index. When
treated as a free parameter, we allowed additional dust extinction up to
$E(B-V)=0.6$. We use the Calzetti et al. (2000) attenuation law for the
additional extinction. An initial test was done keeping the photometric
redshift as a free parameter. The redshifts from the SED fits were similar to
those presented in the CANDELS catalogs. For the reminder of the analysis, we
keep the redshift fixed at the CANDELS values, which includes spectroscopic
redshifts whenever available.

The SED fitting procedure involved redshifting the template to the redshift of
each galaxy, then each galaxy template is convolved with the filter response
functions appropriate for a given CANDELS data set. The template is then
fitted to the observed flux densities using $\chi^{2}$ minimization. If
additional extinction is included, we repeat this process for $E(B-V)$ values
ranging from 0.0 to 0.6, in steps of 0.01.

We divide the CANDELS galaxies into 13 redshift bins between $0.3\leq z\leq 5$
($\Delta z=0.36$). The total number of galaxies in each redshift bin range
from $\sim$2,000 at z$\sim 0.3$ to $\sim$10,000 at $z\sim 2$. For each
redshift bin we derive the number of best-fit SEDs for each morphological
group as defined above. In Fig. 1 we plot the fraction of best-fit SEDs for
the four morphological classes as a function of redshift. From the figure it
is clear that the Irregular class ($6\leq T\leq 11$), provides about 35% of
the best-fit templates at $z\sim$0.3, and increase to dominate at
$z\gtrsim$1\. At $z\gtrsim$2, these templates account for 85-90% of all best-
fit templates. Of the 20 templates in the $6\leq T\leq 11$ morphological
class, 11 of them provide the most frequent best-fit SED (dotted line in Fig.
1). These 11 galaxies are selected as our sample of potential Local Analog
candidates.

In order to test the robustness of the morphological fractions shown in Fig.
1, we selected the template galaxy with $\Delta\chi_{\nu}^{2}=3$ relative to
the best-fit SED. The resulting morphological fraction as a function of
redshift is shown in Fig. 2. At $z\gtrsim 1.5$, the fraction of the $6\leq
T\leq 11$ morphological class decreases slightly, while the fraction of star
forming spiral disks increases. However, the overall dominance of irregular
galaxies as the best templates for high redshift galaxies remains. This shows
that the result is robust and local templates with T-class morphologies
corresponding to irregular star forming galaxies are best at fitting the SED
of high redshift galaxies at $z\gtrsim 1$.

This result is not surprising as galaxies in the $6\leq T\leq 11$
morphological class share many properties with star forming galaxies at high
redshift (e.g. Elmegreen et al. 2009; Elmegreen et al. 2012). The general
properties of the 11 candidates are summarized in Table 1; they have small
physical sizes ($1-2$ kpc), they are actively forming stars, and they
typically have a high gas mass fraction. Their gas-phase metallicity is low,
with an average metallicity of $12+\log{O/H}=8.11\pm 0.20$, or $\sim$0.26 Z⊙.
In Fig. 3 we show the Sloan Digital Sky Survey (SDSS) i-band images of the 11
candidates. From these images it is clear that they have irregular morphology
with prominent star forming clumps.

All 11 galaxies fall under the classification of Blue Compact Dwarf Galaxies
(BCDGs; e.g. Sargent et al. 1970; Kunth et al. 1988; Thuan & Izotov 2005).
BCDGs have previously been proposed to be analogs of high redshift galaxies
(e.g. Hoopes et al. 2007). As a group they share many properties, such as
small sizes, clumpy and intense star formation. They show strong optical
emission lines, often exhibiting high excitation (Thuan & Izotov 2005). There
are, however, differences among the BCDGs; some show the star forming regions
superposed on faint extended stellar emission, suggesting star formation
extended over longer time periods (e.g. Noeske et al. 2003), while others
seems to be truly young systems (e.g. Hunt et al. 2014, Papaderos et al.
2008). The gas-phase metallicities can range from extremely low, like SBS
0335-052E and IZw18, with $Z\approx 0.03$ Izotov et al. (2005), to
$Z\approx$0.3 Z⊙. One feature that most BCDGs share is the presence of dust.
Even the low-metallicity systems SBS 0335-052E and IZw18 show thermal dust
emission, even when detectable CO emission is absent (Hunt et al. 2014). In
the reminder of this paper we will characterize the dust emission and the star
formation history of the 11 Local Analog candidates defined above, to see
whether they can be viewed as scaled down versions of high redshift galaxies.

Figure 4: Optical to far-infrared images of Mrk 1450. The red bar corresponds to $20^{\prime\prime}$, and the blue bar indicates 1 kpc. Table 2: Multi-wavelength data set11W stands for WISE, IRS is for Infrared Spectrograph of Spitzer, M for MIPS, S for SOFIA, H for Herschel and I for IRAS, A for AKARI. |  Galaxy | FIR Photometric Data $(\mu m)$
---|---|---
No | Name | 12-22 | 24-25 | 55 | 60 | 65 | 70 | 89-90 | 100 | 140-155 | 160 | 214 | 250 | 350 | 500
1 | NGC 2537 | W | M | S | I | - | M | S | I | S | M | - | - | - | -
2 | Mrk 140 | W | M | - | I | - | H | - | H | - | H | - | H | H | H
3 | Haro 02 | W | M-I | - | I | - | H | - | H | - | H | - | H | H | H
4 | Mrk 1450 | W | M | S | - | - | H | S | H | S | H | S | H | H | H
5 | UM 461 | IRS16 - W | M | - | I | - | H | - | H | - | H | - | H | H | H
6 | Mrk 1307 | W | M | S | I | - | M | S | I | S | M | - | - | - | -
7 | Haro 06 | W | M | S | I | - | H | S | H | S | H | S | H | H | H
8 | NGC 4670 | W | M | S | I | - | M | S | I | S | M | S | - | - | -
9 | Mrk 475 | IRS16 - W | M | - | - | - | M | - | - | - | M | - | - | - | -
10 | UGCA 410 | IRS16 | M-I | - | I | A | - | A | - | - | A | - | - | - | -
11 | Mrk 930 | W | M | - | - | - | H | - | H | - | H | - | H | H | H

##  3 Data

In this section we describe the data reduction process we used to produce maps
of our target galaxies, and the methods to obtain photometric estimates.

In order to characterize the 11 nearby galaxies that make up our sample of
potential local analogs, we compile broad-band photometric data, covering UV
to FIR. We supplement the existing UV-NIR photometric data from Brown et al.
(2014) with FIR observations of five galaxies in our sample using the
Stratospheric Observatory for Infrared Astronomy (SOFIA). We combined these
with archived infrared data from Herschel, Spitzer, AKARI, and WISE. A listing
of the infrared data sets used in our analysis, indicating the different
telescope and instrument combinations, is given in Table 2. In Figure 4 we
show an example of imaging of one of our galaxies, Mrk 1450. The images shown
consist of our SOFIA-HAWC+ observations, along with archival data from Spitzer
and SDSS band-g (Sloan Digital Sky Survey Gunn et al. 2006) and Herschel PACS
and SPIRE instruments. Mrk 1450 is well-detected in all bands, but starts to
fade in the SPIRE $350\,\mu$m band, and is undetected in the SPIRE $500\,\,u$m
band.

###  3.1 SOFIA Observations

The Stratospheric Observatory for Infrared Astronomy (SOFIA: Young et al.
2012, Temi et al. 2014 ) is a partnership between NASA and the German
Aerospace Center (DLR), consisting of an extensively modified Boeing 747SP
aircraft carrying a 2.7-meter (106 inch) reflecting telescope (with an
effective diameter of 2.5 meters or 100 inches). We were granted $\sim$8 hours
of observing time with SOFIA; on Cycle 6 under the program 06-0222 (PI: T.
Wiklind). We observed 5 galaxies of our sample, using the High-resolution
Airborne Wideband Camera-plus instrument (HAWC+; Vaillancourt et al. 2007;
Dowell et al. 2010; Harper et al. 2018), covering all available filters to
derive the far infrared spectral energy distribution.

The HAWC+ instrument has 5 bands identified as A, B, C, D and E. We observed
in bands A, C, D, and E at $53$, $89$, $154$ and $214\,\mu m$, which have beam
sizes (full width at half maximum, FWHM) of $4.85\mbox{${}^{\prime\prime}$}$,
$7.80\mbox{${}^{\prime\prime}$}$, $13.6\mbox{${}^{\prime\prime}$}$ and
$18.2\mbox{${}^{\prime\prime}$}$ respectively. Band B, was not available
during Cycle 6. The galaxies Mrk 1450, NGC 4670, and Haro 06 were observed in
all 4 bands available. Due to time constraints, Mrk 1307 and NGC 2537 were
only observed at $53$, $89$, and $155\,\mu m$ bands. The data was reduced to
obtain the total intensity maps using the HAWC+ Data Reduction Pipeline v1.3.0
as detailed in Harper et al. (2018).

Table 3: UV - FIR Photometric Data11This is an abbreviated version of the complete table of flux values used for the analysis. The complete table is available in machine readable form in the digital version of this paper. The full table contains 49 columns, indicating the telescope and instrument/band for each measurement and the uncertainty. Fluxes are given in mJy. Galaxy | HAWC+A | MIPS | HAWC+C | PACS | HAWC+D | HAWC+E | SPIRE
---|---|---|---|---|---|---|---
Name | $55\,\mu m$ | $60\,\mu m$ | $89\,\mu m$ | $100\,\mu m$ | $155\,\mu m$ | $214\,\mu m$ | $250\,\mu m$
NGC 2537 | $1302.4\pm 255.0$ | $4040.0\pm 286.4$ | $4893.3\pm 914.1$ | - | $6301.9\pm 1085.0$ | - | -
MRK 140 | - | - | - | $563.2\pm 44.8$ | - | - | $172.0\pm 26.3$
Haro 02 | - | - | - | $5051.3\pm 254.6$ | - | - | $911.9\pm 137.2$
MRK 1450 | $274.8\pm 41.6$ | - | $317.0\pm 47.9$ | $272.0\pm 21.8$ | $168.2\pm 27.4$ | $52.5\pm 17.3$ | $48.7\pm 8.4$
UM 461 | - | - | - | $132.0\pm 12.7$ | - | - | $23.1\pm 7.1$
MRK 1307 | $724.7\pm 119.9$ | $1000.0\pm 71.6$ | $1277.6\pm 198.6$ | - | $616.5\pm 115.4$ | - | -
Haro 06 | $646.3\pm 97.0$ | - | $849.4\pm 127.4$ | $742.3\pm 39.2$ | $754.3\pm 115.9$ | $309.8\pm 48.7$ | $197.3\pm 30.0$
NGC 4670 | $1428.2\pm 246.1$ | $3470.0\pm 245.7$ | $3505.9\pm 530.7$ | - | $3531.9\pm 713.4$ | $2486.7\pm 428.9$ | -
MRK 475 | - | $110.0\pm 16.0$ | - | - | - | - | -
UGCA 410 | - | - | - | - | - | - | -
MRK 930 | - | - | - | $1140.0\pm 91.9$ | - | - | $210.4\pm 31.9$

####  3.1.1 SOFIA/HAWC+Photometry

The HAWC+ data is presented in 5 levels: Level 0 is raw data, Level 1 is the
demodulated (chop not subtracted) data, after instrument corrections (flat-
fielded, dark-subtracted, bad pixel removed) becomes Level 2, level 3 is
obtained after flux calibration and telluric correction, and the combined
observations from level 3 produces the level 4 data. More details on the
pipeline process can be found in the data handbooks (Gordon et al. 2018).

We started our analysis with level 4 maps in units of Jy/pix, and performed
annular photometry using the HIPE environment. A circular aperture, centered
on the source emission, gives the flux + background emission; an annular
region, free of emission and centered on the inner aperture, provides an
estimate of the background. To determine the photometric errors we placed six
circular apertures of the same radius as the inner source aperture, at
equidistant, flux-free, areas around the source. The average flux of the six
apertures ($Bg_{ave}$) is then subtracted from the source flux:

$F_{source}=(f_{tot})-Bg_{ave}.$ (1)

As a further test, we estimated the integrated flux using
Casa111https://casa.nrao.edu/, and the built-in two-dimensional gaussian
procedure. This produced integrated fluxes very similar to the ones obtained
using aperture photometry.

To determine the photometry errors we used 6 photometry apertures around the
source, taking care to avoid any other source in the background, then then
uncertainty of the measurement was defined as the standard deviation of the
background apertures. The total error is computed as the addition on
quadrature of the uncertainty of the measurement $Bg_{std}$ plus the absolute
calibration error, the absolute calibration percentages are listed in Table 4.

$F_{unc}=\sqrt{(F_{source}*Abs_{cal})^{2}+{Bg_{std}}^{2}}.$ (2)

The SOFIA/HAWC+ and photometry measure and uncertainties is shown in Table 3
(the full table is available in machine readable form in the digital version
of this paper).

Table 4: Fractional calibration error. Telescope | Instrument | Abs. Calibration
---|---|---
SOFIA | HAWC+ | $15\%$
Herschel | PACS | $10\%$
Herschel | SPIRE | $15\%$
WISE | W1-W4 | $6\%$
AKARI | N:60,160,WIDE:S,L | $10\%$
Spitzer | IRS | $5\%$
Spitzer | IRAC | $5\%$
Spitzer | MIPS | $5\%$

###  3.2 Ancillary UV to MIR data

The Brown et al. (2014) templates contain SEDs covering a wavelength range
from UV to the mid-infrared for 129 nearby galaxies. As described in section
2, the SEDs of these templates have been normalized and verified with matched-
aperture photometry. They provide accurate photometric data, and since our
LAHz sample is drawn from these templates, we use the photometric data as
presented in Brown et al. (2014).

###  3.3 Ancillary FIR Data

Some galaxies in our sample were observed with the Herschel Space Observatory,
using PACS at $70$, $100$, and $160\,\mu m$, with a FOV of
$1.75\mbox{${}^{\prime}$}\times 3.5\mbox{${}^{\prime}$}$, and SPIRE at $250$,
$350$, $500\,\mu m$ with a FOV $4\mbox{${}^{\prime}$}\times
8\mbox{${}^{\prime}$}$.

The Herschel Dwarf Galaxy survey (Madden et al. 2013; Rémy-Ruyer et al. 2013)
provide photometric and spectroscopic FIR data on 50 dwarf galaxies. This
survey includes data for some of the galaxies in our survey: Mrk 930, Mrk 1450
and UM 461, which were observed with all bands of both PACS and SPIRE
instruments. A limited set of Herschel photometric data is available for UGCA
410 (also knowns as Mrk 140) and Haro 02 (Herschel Science. 2013). See the
full Table 3 for a complete listing of the FIR photometric data for our
sample. For consistency in our flux estimates, we reduced the available
Herschel PACS and SPIRE data using the Herschel pipeline for total intensity
photometric measurements. We used the Herschel Interactive Processing
Environment HIPE (Ott, S. 2010) with version 7 of the photometric calibration.

####  3.3.1 Herschel Photometry

We use level 2.5 or 3.0 maps for our photometry estimates. These maps are
created from a combination of different observations of the same target and
have improved S/N levels.

For more detail about Herschel products see PACS Data Reduction
Guide222http://herschel.esac.esa.int/hcss/pacsDRG .

To determine the total intensity in Herschel PACS $70$, $100$, and $160\,\mu$m
maps, we use the PACS - Photometry Script, which is part of Herschel common
science system (HCSS) provided in the HIPE environment. We started with the
maps: Photometry Color High Pass Filter Map (HPPHPFMAPR) level 2.5 or 3.0,
depending of availability. We modified parameters in the algorithm in orden to
use the optimal aperture radius. The final outputs of the algorithm, are the
peak flux in Jy/pix, the flux measured by the aperture photometry in Jy, and
the aperture corrected flux in Jy. SPIRE photometry maps can be in two
different observation mode: point source (psrc) in units of Jy/beam or
extended source (extsrc) in units of MJy/sr, and are identified as PSW, PMW,
PLW that stands for Photometry-Short/Medium/Long-Wavelength, corresponding to
250, 350 and $500\,\mu$m respectively. Here we used the psrcPSW, psrcPMW and
psrcPLW level 2.5 maps.

Similarly to PACS, for SPIRE photometry maps, we use the SPIRE Photometry
Point Source Script provided within HIPE. This, uses the
sourceExtractorSussextractor task to identify the source, and applies the
Color Correction for point sources. The Herschel pipeline assumes
$\alpha=\,-1$, we assumed $\alpha\,=\,2.0$.

##  4 Methodology

In this section we describe our modeling approach to characterize the dust
continuum and the stellar emission. We use two different methods; modified
black-body, and the Lightning fitting package (Eufrasio et al. 2017). Both
approaches allow for a determination of the far-infrared luminosity and dust
mass for each galaxy. The modified black body models are used to also derive
luminosity-weighted, dust temperatures, while the Lightning method allows a
determination of the star formation history (Section 4.2).

###  4.1 Black-body Modeling

The simplest characterization of dust emission is to fit it with a black-body
$F_{\nu}\propto B_{\nu}(T)$. In a more realistic situation, this is modified
by taking the transfer of radiation into account. This can be done by using a
so-called modified black-body curve,
$F_{\nu}\propto(1-\exp(-\tau))B_{\nu}(T)$. We will use the expression:

$mBB(T)=\Omega
B_{\nu}(T)\left(1-\exp{\left[-\left(\frac{\lambda_{0}}{\lambda}\right)^{\beta}\right]}\right),$
(3)

where $B_{\nu}(T)$ is the Planck function, and $\Omega$ the solid angle
extended by the emission. The opacity is parametrized as
$\tau=(\lambda_{0}/\lambda)^{\beta}$, where $\lambda_{0}$ is the wavelength
where the optical depth equals unity (e.g. Draine 2006), and the coefficient
$\beta$ represents the opacity spectral index (Kovács et al. 2010).
Observationally, $\beta$ has been derived to be between 1 and 2 (cf.
Hildebrand 1983; Casey 2012). Data from the Planck mission provides a robust
derivation of $\beta$ in the Milky Way, leading to $\beta=1.8\pm 0.1$ (Planck
Collaboration et al. 2011). For extragalactic objects it is common to use
$\beta$ in the range $1.3-2.0$. Here we will adopt the commonly used value
$\beta=1.5$, and keep it constant in our fits. We did try fitting with
different $\beta$ values, but did not find that it changed our results in any
significant way. We also kept the wavelength where the optical depth is unity
constant at $\lambda_{0}=100\mu$m. The two parameters $\beta$ and
$\lambda_{0}$ affects the flux on the long wavelength Rayleigh-Jeans part of
the dust SED relative to the short wavelength part; for a fixed dust
temperature, an increasing $\lambda_{0}$ leads to a relative increase in the
Rayleigh-Jeans part, while an increase in $\beta$ leads to a decrease of the
Rayleigh-Jeans part relative the Wien part of the SED.

When fitting a modified black body curve to flux measurements at
$\lambda\gtrsim 60\mu$m, most galaxies exhibit a mid-infrared excess at
wavelengths shorter than 60$\mu$m (e.g. Casey 2012). This excess can be
attributed to the presence of a warmer dust component than the one
characterizing the longer wavelengths, or an additional heating source, such
as an AGN (cf. Scoville & Kwan 1976). The observed mid-IR emission can be
modeled as a power law, with a cut-off wavelength $\lambda_{c}$ (cf. Casey
2012), or as second, dust component characterized by a warmer dust
temperature. Since we do not expect a significant AGN activity in the dwarf
galaxies making up our sample, we will adopt the latter case, and use two
modified black body curves, with the same $\beta$ and $\lambda_{0}$, but with
different dust temperatures. Our dust SED to be fitted to the observed data is
then:

$\begin{split}mBB=\Omega_{1}B_{\nu}(T_{1})\left(1-\exp{\left[-\left(\frac{\lambda_{0}}{\lambda}\right)^{\beta}\right]}\right)\\\
+\Omega_{2}B_{\nu}(T_{2})\left(1-\exp{\left[-\left(\frac{\lambda_{0}}{\lambda}\right)^{\beta}\right]}\right).\end{split}$
(4)

With the opacity spectral index kept fixed at $\beta=1.5$, and the critical
wavelength at $\lambda_{0}=100\mu$m, the modified black body curve contains
four free parameters: two dust temperatures $T_{1}$ and $T_{2}$, and two
normalization constants containing the solid angle of the source $\Omega$. We
use the Levenberg-Marquardt fitting algorithm. We explored the fitting using
values for the dust opacity coefficient $\beta$, ranging from 1.2 $-$ 2.0, and
for $\lambda_{0}$ in the range $50-200\mu$m. We found no significant variation
in the derived dust temperatures, and we conclude that a fixed $\beta=1.5$
with $\lambda_{0}=100\mu$m is sufficient and the models will be characterized
by the temperatures and normalization coefficients.

We employ Bayesian statistics inference in multi-parameter spaces to determine
the most probable set of model parameters $\phi$, of a model $mBB_{i}$ in
comparison to the data $D_{i}$. Bayesian inference is based the Bayes’
Theorem, which establish the conditional probability of the parameters $\phi$
given the data $D_{i}$ as:

$p(\phi|D_{i})=\frac{p(D_{i}|\phi)*p(\phi)}{p(D_{i})},$ (5)

where the likelihood of the model is $p(D_{i}|\phi)$. Then $p(\phi|D_{i})$ is
the posterior probability, $p(\phi)$ is the priors which is the knowledge we
have about the parameters or problem. $p(D_{i})$ is used to normalize the
posterior probability. The likelihood is assumed to be:

$p\propto\exp{\left(-\chi^{2}/2\right)}.$

By maximizing the posterior probability we will determine each $\phi$
parameters best values for the modified black-body models $mBB_{i}$. Since we
are assuming a fixed $\beta=1.5$ and $\lambda_{0}=100\mu$m for both
components, the $\phi$ parameters are the temperatures $T_{1}$, $T_{2}$, and
normalization constants $\Omega_{1}$ and $\Omega_{2}$. The priors are defined
by allowing the $\phi$ parameters to vary through a very wide range initially,
to get a posterior probability and then bayesian analysis allows us to update
the knowledge of the probability distribution of the parameters and apply new
priors multiplying by flat priors that reduce the range of the parameters
space. This allows us to reduce the region covered by the parameter and obtain
a better resolution of the parameter grid. Inspection of the marginalized
probability distribution of each parameter-space, allows us to determine the
appropriate range in which the best set of solutions are located, as well as
the most likely value. We can also define the confidence interval for each
parameter.

####  4.1.1 Dust masses

The dust mass can be derived from the flux density of optically thin emission.

$M_{\rm dust}=\frac{S_{\nu}\,D^{2}}{\kappa_{\nu}\,B_{\nu}(T_{d})},$ (6)

where, $S_{\nu}$ is the observed flux density, $B_{\nu}(T_{\rm d})$ the black-
body emission at a given temperature, $D$ is the distance, and $\kappa_{\nu}$
is the dust absorption coefficient for dust grains at frequency $\nu$. The
dust absorption coefficient is a parameter encompassing both the dust grain
composition and the grain size distribution, and is difficult to derive
observationally (e.g. Gordon et al. 2014). Weingartner & Draine (2001) derived
$\kappa_{\nu}$ for different $R_{V}$ values for Milky Way dust. We will use
the average $\kappa_{\nu}$ for $R_{V}=3.1,4.0$ and $5.5$ at $\lambda=250\mu$m.
Using updated values, we have $\kappa_{250}=0.40$ m2 kg-1. We do not have
observed flux values at $\lambda=250\mu$m for all of our galaxies. Instead we
use the flux density provided by our mBB fits to the overall dust SED.

Hence, the dust mass, using the flux density at 250$\mu$m, can be expressed
as:

$\begin{split}\frac{M_{\rm
dust}}{M_{\odot}}=188.4\,\left(\frac{S_{\nu}}{\rm{Jy}}\right)\,\left(\frac{D}{\rm{Mpc}}\right)^{2}\,\frac{\left(\exp{\left(57.55/T\right)}-1\right)}{\kappa_{250}}.\end{split}$
(7)

The dust masses derived in this way are listed in Table 5 and discussed in
Section 5.3.

###  4.2  Lightning SED Fits

We performed SED fitting using Lightning (Eufrasio et al. 2017,
ASCL:1711.009333https://github.com/rafaeleufrasio/lightning), which fits non-
parametric star formation histories (SFHs) in discrete, fixed-width, stellar
age bins. We made use of the most recent update to Lightning, which uses an
adaptive Markov Chain Monte Carlo (MCMC) algorithm (Doore et al. 2021, in
prep). For this work we have used SFH bins of 0$-$10 Myr, 10$-$100 Myr, 100
Myr$-$1 Gyr, 1$-$5 Gyr, 5$-$13.3 Gyr, assumed a Kroupa IMF (Kroupa 2001) and a
metallicity of 0.2$Z_{\odot}$. We used PEGASE (Fioc & Rocca-Volmerange 1997;
Fioc & Rocca-Volmerange 1999) stellar populations. For intrinsic attenuation,
we adopted a Calzetti et al. (2000) extinction law, modified as in Noll et al.
(2009) to include a UV bump at 2175 Å and a parameter $\delta$ to control the
UV slope. Draine & Li. (2007) Milky Way dust emission was used. For a more
thorough description of the prescriptions adopted by Lightning, we refer the
reader to Eufrasio et al. (2017).

The IR photometry previously described described was combined with literature
SEDs from Brown et al. (2014), producing SED with 45 data-points from the FUV
to the FIR (including GALEX, Swift UVOT, SDSS, 2MASS, WISE, Spitzer, SOFIA,
IRAS, AKARI, and Herschel). Our fits explore a 13-dimensional parameter space
with 5 parameters regarding the SFH intensities, $\psi_{i}\geq 0$, 3
parameters ($0\leq\tau_{V}^{\rm DIFF}\leq 3$, $-2.3\leq\delta\leq 0.4$, and
$0\leq\tau_{V}^{\rm BC}\leq 4$) controlling the dust extinction, and 5 dust
emission parameters ($0\leq\alpha\leq 4$, $0.1\leq U_{\rm min}\leq 25$,
$10^{3}\leq U_{\rm max}\leq 3\times 10^{5}$, $0\leq\gamma\leq 1$, and
$0.0047\leq q_{\rm PAH}\leq 0.0458$. We run the adaptive MCMC algorithm for
$3\times 10^{5}$ steps and derive statistics from the last 5,000 steps of the
chain. All the derived quantities quoted here (SFHs, SFRs, M⋆, Mdust) are
derived from these last 5,000 steps.

##  5 Results

We derive properties of the dust in our LAHz galaxies using two different
methods. Some of the derived parameters are duplicated in the two methods,
such as the FIR luminosity and dust mass. Others are unique for each method.
For instance, the modified black body models provide luminosity-weighted dust
temperatures, which the Lightning models do not. The Lightning models provide
an estimate of the SFR integrated over the last 100 Myr, and a parametrized
star formation history (SFH). The derived parameters from the modified back
body models are presented in Tabel 5, and those from the Lightning models in
Table 6.

Figure 5: Corner plots for the modified black-body models of the SED of Haro
06 and Mrk 1450. In each panel, in the bottom: The 2D scatter plots (blue
area) of the values recovered from the four parameters grid are shown for the
two-by-two combination of parameters (two temperatures and two normalization
constants). On top are the histograms for all 4 parameters we display Bayesian
1D marginalized likelihoods. The best-fit values to the observed data in the
histograms are shown in red and the light-red area encloses 68 $\%$ of the
likelihood. Top right: Best fit model in black, shows the two dust components,
cold component in yellow and warm in light-red. The red are the SOFIA data
points, and blue data points are the ancillary data available. The green solid
line is the Brown et al. (2014) spectral data, which is not part of the fits,
however is in good agreement with the modified black-body model.

###  5.1 Modified Black-Body models

The black-body modeling described in section 4.1 show that a reasonable
description of the dust SED can be achieved by using a two-component modified
black-body fit. The warm component is less well constrained than the cold
component due to a smaller number of photometric data points at wavelengths
$\lambda\lesssim 60\mu$m. Nevertheless, the model SED joins smoothly with the
near- and mid-IR fluxes of the Brown et al. (2014) templates.

In Figure 5 we show multi-parameter-space diagnostic plots for two galaxies in
our sample: Haro 06 and Mrk 1450. We can see the marginalized distribution for
each $\phi$ parameter in the upper frame of each column, the lower frames show
the marginalized probability in blue and the correlation values between each
two parameters. The value that maximizes the $\chi^{2}$ distribution
(likelihood) for each parameter is indicated with a red star; this is also
shown as a red dashed line in the upper frames for each parameter. The
confidence interval is shown in light red and indicates the 16 and 84
percentiles in the probability distribution, which imply that the $68\%$ of
the probability is inside that region.

This visualization of the probability distributions allow us to understand how
the different parameter relate. For instance we can see how the temperature
$T_{1}$ and the constant $\Omega_{1}$ are anti-correlated, while the two
constants $\Omega_{1}$ and $\Omega_{2}$ are positively correlated for both
galaxies. In the upper right box we see the best fit model indicating the two
black-body components representing a warm and a cold dust component. The
corresponding Brown et al. (2014) template is shown as a reference, but it is
not part of the fit. The resulting best-fit model agrees smoothly with the
Brown templates.

Figure 6:  Black-body best fits: LAHz dust emission. The best modified black-body fit shown in black solid line, the light-green and light-red dash lines are the warm and cold components. SOFIA (53, 89, 155, $216\,\mu$m) data points in red, ancillary data points in blue Herschel (60, 100, 160, 250, 350, $500\,\mu$m) data points and Spitzer-MIPS (24, 60, 100, 160 $\mu$m) in blue where used when SOFIA or Herschel data was not available in the corresponding band. The green solid line is the Brown et al. (2014) spectra templates, plotted as a reference, which are not part of the black-body fits. Table 5: Modified Black-Body Derived Quantities No | Galaxy | $T_{d\,Warm}$ | $T_{d\,Cold}$ |  $S_{24\mu m}/S_{160\mu m}$11The flux ratio $S_{24\,\mu m}/S_{160\,\mu m}$ is derived from the modified black-body best fit model. | $M_{d}$ | $L_{FIR}$
---|---|---|---|---|---|---
| Name | K | K |  | $M_{\odot}$ | $10^{9}\,L_{\odot}$
1 | NGC 2537 | $139.0_{-16}^{+70}$ | $34.0_{-1.5}^{+0.9}$ | $0.06$ | $3.83_{-0.8}^{+0.7}\times 10^{5}$ | $0.638\pm{0.028}$
2 | Mrk 140 | $79.0_{-7}^{+5}$ | $31.0_{-3.6}^{+1.0}$ | $0.10$ | $4.05_{-0.5}^{+1.1}\times 10^{5}$ | $0.784\pm{0.043}$
3 | Haro 02 | $153.0_{-29}^{+8}$ | $42.5_{-1.6}^{+0.8}$ | $0.29$ | $9.53_{-1.0}^{+0.7}\times 10^{5}$ | $6.222\pm{0.172}$
4 | Mrk 1450 | $100.0_{-9}^{+37}$ | $44.0_{-4.5}^{+5.3}$ | $0.41$ | $1.36_{-0.3}^{+0.2}\times 10^{4}$ | $0.150\pm{0.008}$
5 | UM 461 | $148.0_{-15}^{+8}$ | $46.0_{-3.8}^{+2.5}$ | $0.42$ | $1.55_{-0.3}^{+0.4}\times 10^{4}$ | $0.161\pm{0.009}$
6 | Mrk 1307 | $149.0_{-40}^{+1}$ | $46.0_{-4.2}^{+0.9}$ | $0.23$ | $1.12_{-0.2}^{+0.4}\times 10^{5}$ | $0.913\pm{0.038}$
7 | Haro 06 | $79.0_{-2}^{+10}$ | $32.0_{-1.6}^{+3.1}$ | $0.14$ | $6.67_{-1.4}^{+0.6}\times 10^{5}$ | $1.809\pm{0.092}$
8 | NGC 4670 | $162.0_{-51}^{+13}$ | $36.0_{-2.5}^{+0.4}$ | $0.08$ | $1.19_{-0.2}^{+0.3}\times 10^{6}$ | $2.665\pm{0.109}$
9 | Mrk 475 | $119.0_{-11}^{+8}$ | $51.0_{-8.6}^{+4.3}$ | $0.24$ | $1.96_{-0.7}^{+1.6}\times 10^{3}$ | $0.024\pm{0.003}$
10 | UGCA 410 | $146.0_{-10}^{+29}$ | $58.0_{-4.7}^{+5.8}$ | $0.71$ | $3.71_{-1.0}^{+0.9}\times 10^{3}$ | $0.103\pm{0.006}$
11 | Mrk 930 | $94.0_{-14}^{+6}$ | $40.0_{-8.3}^{+1.4}$ | $0.29$ | $2.18_{-0.3}^{+0.7}\times 10^{6}$ | $16.225\pm{1.313}$

Figure 7: FUV-to-FIR SEDs produced with the Lightning fitting package. Intrinsic stellar spectra are shown in blue. Attenuated stellar spectra are shown in red. Dust emission spectra are shown in orange. The observed data are shown as black diamonds. Red diamonds are the model SEDs, i.e., the attenuated stellar spectra plus the dust emission spectra convolved with the filter functions. When no observations are available in a band, models are still computed. Table 6: Lightning-Derived Quantities No. | Galaxy | $M_{dust}$ | $L_{FIR}$ | $M_{*}$ | SFR11The star formation rate averaged over the last 100 Myrs. | sSFR
---|---|---|---|---|---|---
| Name | $10^{4}\,\rm M_{\odot}$ | $10^{9}\,\rm L_{\odot}$ | $10^{8}\,\rm M_{\odot}$ | $\rm M_{\odot}\,yr^{-1}$ | $\rm Gyr^{-1}$
1 | NGC 2537 | $9.5\pm\,3.0$ | $0.77_{-0.02}^{+0.02}$ | $10.5_{-1.4}^{+1.3}$ | $0.20_{-0.04}^{+0.04}$ | $0.19\pm\,0.04$
2 | Mrk 140 | $25.7\pm\,7.2$ | $0.94_{-0.03}^{+0.03}$ | $3.9_{-0.5}^{+0.5}$ | $0.18_{-0.05}^{+0.05}$ | $0.46\pm\,0.14$
3 | Haro 02 | $21.5\pm\,4.8$ | $8.03_{-0.20}^{+0.21}$ | $16.3_{-2.2}^{+2.2}$ | $1.3_{-0.3}^{+0.4}$ | $0.80\pm\,0.23$
4 | Mrk 1450 | $1.9\pm\,0.2$ | $0.19_{-0.01}^{+0.01}$ | $0.19_{-0.02}^{+0.03}$ | $0.03_{-0.01}^{+0.01}$ | $1.60\pm\,0.44$
5 | UM 461 | $1.3\pm\,0.3$ | $0.21_{-0.01}^{+0.01}$ | $0.21_{-0.02}^{+0.03}$ | $0.03_{-0.01}^{+0.01}$ | $1.17\pm\,0.50$
6 | Mrk 1307 | $11.0\pm\,2.9$ | $1.10_{-0.04}^{+0.04}$ | $1.2_{-0.2}^{+0.2}$ | $0.29_{-0.04}^{+0.05}$ | $2.51\pm\,0.55$
7 | Haro 06 | $81.8\pm\,5.8$ | $2.18_{-0.06}^{+0.06}$ | $3.8_{-0.7}^{+0.8}$ | $0.70_{-0.06}^{+0.06}$ | $1.83\pm\,0.40$
8 | NGC 4670 | $207.0\pm\,66.0$ | $3.30_{-0.1}^{+0.1}$ | $24.2_{-2.1}^{+1.9}$ | $0.19_{-0.06}^{+0.07}$ | $0.08\pm\,0.03$
9 | Mrk 475 | $0.17\pm\,0.04$ | $0.028_{-0.002}^{+0.002}$ | $0.09_{-0.01}^{+0.01}$ | $0.003_{-0.001}^{+0.002}$ | $0.39\pm\,0.20$
10 | UGCA 410 | $2.6\pm\,1.2$ | $0.15_{-0.01}^{+0.01}$ | $0.18_{-0.03}^{+0.04}$ | $0.04_{-0.01}^{+0.01}$ | $2.21\pm\,0.64$
11 | Mrk 930 | $68.7\pm\,19.5$ | $19.1_{-0.7}^{+0.7}$ | $7.7_{-1.1}^{+1.3}$ | $3.6_{-0.4}^{+0.4}$ | $4.71\pm\,0.87$

In Figure 6 we present the SEDs, produced by the two-component black-body
models for all of our LAHz. In a few cases the warm and the cold components
show a double-peaked SED often found for star-forming spiral galaxies.
However, in several cases, the warm and cold components blend together and
form a broad single-peaked SED. In the figures we also include the
corresponding NIR and MIR spectra from Brown et al. (2014). These spectra were
not part of the fit, but they merge smoothly with the dust SED. In three cases
(NGC 2537, Haro 02 and NGC 4670), the MIR spectra suggest that there might be
additional flux at wavelengths between $24\,\mu$m and $70\,\mu$m, than what is
indicated in the two-component black-body fits.

####  5.1.1 Dust temperatures

We obtained the dust temperatures characterizing the warm and cold components
from the modified black-body fits. For comparison we use the Dwarf Galaxy
survey presented by Rémy-Ruyer et al. (2013) which includes local dwarf
galaxies, and, in order to provide a comparison with more evolved galaxies, we
use a subset of 58 galaxies from the Key Insights on Nearby Galaxies: a Far-
Infrared Survey with Herschel (KINGFISH Kennicutt et al. 2011), where we
exclude the irregular dwarf galaxies and early type galaxies from the original
set of 109 galaxies.

In order to compare our results with the galaxies in the DGS and KINGFISH
samples, we re-analyzed the FIR photometric data from these surveys using the
same modified black body method as for our LAHz galaxies. For each galaxy we
derive the temperatures characterizing the warm and cold components, as well
as the temperature corresponding to the peak of the combined SED, defined as:

$T_{\rm peak}=\frac{b}{\lambda_{\rm peak}}\,,$ (8)

where $b$ is the Wien’s displacement constant. All the galaxies were
characterized using two dust components. The average dust temperatures for the
sample of LAHz are $T_{\rm d,\,warm}\sim 124\,$K, and $T_{\rm d,\,cold}\sim
42\,$K. This is significantly higher than for star-forming disk galaxies in
the KINGFISH sample, with $T_{\rm d,\,warm}\sim 68$ K and $T_{\rm
d,\,cold}\sim 23$, and higher than for the DGS sample with $T_{\rm
d,\,warm}\sim 74$ K and $T_{\rm d,\,cold}\sim 24$. The range of temperatures
for the DGS survey for the cold component is, however, quite large, ranging
from $18-41$ K (Rémy-Ruyer et al. 2013). The results are shown in Figure 9.

###  5.2 Lightning models

As described in Sect.4.2, the Lightning models fit the whole UV-FIR SED, while
self-consistently applying an extinction correction. The FIR is modeled using
Draine & Li. (2007) templates. The resulting SEDs for our LAHz galaxies are
shown in Figure 7. The black data points represent the observed SED; the blue
line is the spectra without dust attenuation; the red line is the SED with
attenuation; the orange line is the best-fit dust emission. In Table 6 we show
luminosities, dust masses, SFR averaged over the last 100 Myrs and specific
star formation rates (sSFRs) derived from the Lightning models.

###  5.3 Far-infrared luminosity and dust mass

We derive dust masses and $L_{\rm IR}$ using the SEDs produced with the two
different modeling methods described in Sect. 4. The dust masses and $L_{\rm
IR}$ from the mBB models and the Lightning method are presented in Table 5 and
Table 6 respectively. The $L_{\rm IR}$ is derived by integrating the SED from
$8\,\mu$m to $1000\,\mu$m. The LAHz galaxies luminosities cover a wide range
of $\log(L_{\rm IR}/L_{\odot})=\,[7.4,\,10.2]$ for the black-body models,
similarly, the Lightning method luminosities range between $\log(L_{\rm
IR}/L_{\odot})$=$\,[7.5,\,10.3]$.

The derived dust masses using the black-body models are in the range of:
$\log{(M_{\rm dust}/M_{\odot})}=\,[3.1,\,6.3]$. As expected the cold dust
component dominates the total dust mass. We also derive the dust mass from the
Draine & Li best fits in the Lightning models: $\log{(M_{\rm
dust}/M_{\odot})}=\,[3.2,\,6.3]$. Some of the galaxies in our sample have
published dust mass values, derived using a subset of the photometric data
used here (e.g. Engelbracht et al. 2008; Rémy-Ruyer et al. 2013), which agree
very well with the results presented here. For the discussion presented in the
rest of this paper, we use the dust masses derived with the mBB method, since
this allows the comparison with dust masses derived for surveys samples like
the KINGFISH and DGS using the same methodology.

We compare the dust masses and FIR luminosities for our LAHz galaxies, derived
with the modified black body and Lightning methods. The latter method uses the
Draine & Li. (2007) templates to model the FIR SED. The dust masses derived
from the mBB and the Draine & Li models, show dispersion of $\sim$0.3dex, but
shows no systematic difference. The FIR luminosities, however, do show a
systematic offset, where the Draine & Li. (2007) based models give $\sim$20%
higher LFIR than those derived by integrating over the modified black body
models. This is expected since the Draine & Li. (2007) models include PAH
emission in the near-infrared, and better account for the near- to mid-IR
emission than the black body models. In the rest of the paper we will use the
mBB-derived dust masses for comparison with the DGS and KINGFISH surveys. And
the Lightning FIR luminosities for discussions and conclusions.

###  5.4 Star formation histories

Figure 8: Star formation histories for our sample. The SFHs are divided into
five time bins: 0 to 10 Myr, 10 to 100 Myr, 100 Myr to 1 Gyr, 1 to 5 Gyr and 5
to 13.3 Gyr. The solid lines denote the median values from the MCMC chain and
dotted lines show the 16th and 84th percentiles, enclosing the hashed areas.
For comparison the last frame includes the SFH of M51, derived by Eufrasio et
al. (2017), following similar approach.

In Fig. 8 we show the SFH for the LAHz galaxies in our sample. For comparison
we also show the SFH of M51 from Eufrasio et al. (2017). The SFH was derived
using the Lightning fitting package, and it is presented in 5 look-back time
bins: $0-10$ Myrs, $10-100$ Myr, $0.1-1$ Gyr, $1-5$ Gyr and $5-13.3$ Gyr. The
Lightning modeling treats the SFR as constant over each time bin. The solid
red line denote the median SFR for each time bin from the simulations and the
yellow region denotes the 16th and 84th percentiles, enclosing the 68%
confidence intervals. The SFHs of the LAHz are for the most part very
different from those of more massive galaxies. For instance, the SFH of M51, a
large spiral galaxy gravitationally interacting with a neighbor galaxy, shows
a more complex SFH, with a significant star formation contribution in bins
corresponding to ages older than 5 Gyr.

For the LAHz we recover SFHs consistent with null SFR contribution at ages
older than 1 Gyr for 6 galaxies: Mrk 1450, UM 461, Mrk 1307, Mrk 475, UGCA 410
and Mrk 930. This suggests that they are the youngest galaxies in our sample.
Two of the galaxies, NGC 2537 and NGC 4670, show significant star formation
activity in all five time bins, indicating the presence of an old stellar
population. Hence, despite being designated as Blue Compact Dwarf Galaxies,
the stellar mass of these two galaxies is dominated by old stars. The
situation is less clear for three of the galaxies in our sample, Mrk 140, Haro
02 and Haro 06, where the Lightning results indicate that it is probable that
they had a low level of star formation activity at times $\gtrsim$1 Gyr (see
Figure 8).

We consider the six LAHz galaxies that have SFHs consistent with being younger
than $\sim$1 Gyr as our prime candidates for being local analogs. NGC 2537 and
NGC 4670 are clearly not young galaxies. Mrk 140, Haro 02 and Haro 06 are
likely to have an old stellar population, although Haro 06 is a borderline
candidate for being a local analog.

##  6 Discussion

###  6.1 Characteristic dust temperatures

When fitting a two-component modified black body curve the our LAHz galaxies,
the Dwarf Galaxy Survey and spiral galaxies in the KINGFISH sample, we find
that the DGS and LAHz galaxies show a higher $T_{\rm peak}$ than the more
massive KINGFISH galaxies (Figure 9). While the average $T_{\rm peak}$ is 33 K
and 35 K for the LAHz and DGS samples, respectively, the KINGFISH galaxies
have an average of $T_{\rm peak}=23$ K. When we compare the warm/cold dust
component separately, we see that the LAHz are characterized by higher dust
temperatures than both the KINGFISH and the DGS galaxies. These results are
consistent with a general trend of warmer dust in metal-poor galaxies compared
with the more metal-rich and more massive counterparts (e.g. Rémy-Ruyer et al.
2013) as well as for high-$z$ galaxies (e.g. Faisst et al. 2017 and Sommovigo
et al. 2020).

Figure 9: Comparison of peak, warm, and cold temperatures characterizing the
dust. Solid lines indicate average temperatures; the dots are the individual
values for each galaxy. KINGFISH galaxies are shown in blue, DGS in green, and
the LAHz candidates in red. All temperatures were determined using modified
black-body procedure (see section 4.1).

###  6.2 Dust-to-stellar mass ratio

In Figure 10, (top-left frame), we show the dust-to-stellar mass ratio as a
function of stellar mass for our sample, together with those of the DGS, and
KINGFISH spiral galaxies. While the KINGFISH galaxies seem to have a weak
dependence on stellar mass, no such correlation is apparent for our LAHz
galaxies or for galaxies in the DGS sample. In fact, the dust-to-stellar mass
ratio for the LAHz seems to be approximately constant, with a median ratio
$\log(M_{\rm dust}$/$M_{\star})=(8.5\pm 5)\times 10^{-4}$. A similar argument
can be made for the DGS sample, but with a slightly lower average dust to
stellar mass ratio. The dashed line shown in Figure 10 shows the anti-
correlation between dust-to-stellar mass ratio and stellar mass found by
Clemens et al. (2013). This anti-correlation was obtained for galaxies from
the Planck Survey and the fit is dominated by galaxies in the stellar mass
range $\log{M_{*}/M_{\odot}}\sim 10-11$. The anti-correlation is surprising in
view of the fact that the gas-phase metallicity scales with stellar mass (e.g.
Tremonti et al. 2004) and one would expect a lower $M_{\rm dust}/M_{\star}$
ratio for low-mass galaxies. The Clemens et al. (2013) relation has been
interpreted as being due to a rapidly increasing gas fraction for lower-mass
galaxies, more than offsetting the metallicity effect. However, the results
shown here, both for our sample and that of DGS, show that this relation
cannot be extended to low-mass galaxies.

The mass-metallicity relation is established down to
$\log{M_{*}/M_{\odot}}\sim 8.5$. Below this stellar mass, depletion of metals
through galactic winds starts to become important (e.g. Tremonti et al. 2004).
Our LAHz galaxies have $\log{M_{*}/M_{\odot}}$ in the range $7.0-9.4$,
straddling the $\log{M_{*}/M_{\odot}}=8.5$ value. In Figure 10, top-right
panel, we show the relation between the dust-to-stellar mass ratio as function
of metallicity. In the figure we also include the DGS and KINGFISH spiral
samples. Based on the latter two samples, Rémy-Ruyer et al. (2013) defined a
correlation between the dust-to-stellar mass ratio and gas-phase metallicity,
shown as a dashed line in Figure 10. We find no obvious correlation between
the dust-to-stellar mass ratio and metallicity for our LAHz sample, and the
overall dispersion of dust-to-stellar mass ratios is large. As discussed in
Rémy-Ruyer et al. (2013), this could at least in part be due to observational
uncertainties. However, for the metal-poor, and low-mass galaxies, this could
also be due to the impact of individual star formation histories, where the
interstellar gas can be promptly enriched through recent star formation, or
depleted due to galactic winds. Evidence for both effects have been seen in
this type of galaxies; Lagos et al. (2018) found spatial variations in the
gas-phase metallicity in UM 461, attributed to recent infall of low-
metallicity gas, and Haro 02 shows clear evidence of a galactic gas outflow
(e.g. Meier et al. 2001; Beck et al. 2020).

Figure 10: Top left: The dust to stellar mass ratio as a function of stellar
mass. The magenta dashed line denotes the relation from Clemens et al. (2013),
based on more massive galaxies. Top right: Dust to stellar mass ratio as a
function of metallicity. The power-law defined by Rémy-Ruyer et al. (2013) is
shown as a dashed line. For comparison we include the extremely low-
metallicity galaxies SBS-$0335-052$ and IZw18 (purple circles; e.g. Hunt et
al. (2014)). Bottom right: $L_{\rm IR}$/$M_{\rm dust}$ as a function of
metallicity. The dashed line corresponds to the relation found by Rémy-Ruyer
et al. (2013). We include SBS-$0335-052$ and IZw18 as purple circles., The
histograms on the right show the distribution of each group. In all three
panels: red squares are the LAHz candidates; green circles are the DGS sample;
blue circles represent the KINGFISH spiral sample.

For comparison we also include two of the lowest metallicity galaxies known,
SBS 0335-052E and IZw18 in Figure 10. Both of these galaxies have very low
gas-phase metallicity, $12+\log{O/H}\sim 7.2$, but even at these extremely low
metallicities they still show dust continuum emission (SBS 0335-052E: Hunt et
al. (2014); IZw18: Wu et al. (2007). These two particularly low-metallicity
galaxies were studied by Hunt et al. (2014), and found to have dust-to-stellar
mass ratios within the range of values found for the galaxies presented in
Figure 10. The dust-to-stellar mass ratio of IZw18 is $\log({M_{\rm
dust}/M_{\star}})=-3.60$, which is consistent with the LAHz average of
$\log({M_{\rm dust}/M_{\star}})=-3.17$. SBS 0335-052E, however, has
$\log({M_{\rm dust}/M_{\odot}})\,\approx-2.15$, which is significantly higher
than the LAHz and DGS galaxies, comparable with metal-rich spiral galaxies.
While this wide disparity among the most metal-poor galaxies could be due to
observational uncertainties, it could also be an effect of the above mentioned
prompt enrichment and galactic outflow/inflow processes affecting low-mass
galaxies.

The bottom-right panel of Figure 10 shows the $L_{\rm IR}/M_{\rm dust}$ ratio
as a function of metallicity for the LAHz galaxies (red squares). For
comparison we also show the DGS and KINGFISH galaxies. The distribution of
$L_{\rm IR}/M_{\rm dust}$ is indicated on the side for each group. The $L_{\rm
IR}/M_{\rm dust}$ of the LAHz galaxies is distributed in the same range as the
DGS (Rémy-Ruyer et al. 2013). This ratio tends to increase for lower
metallicities, and when we examine the left region in the range of
metallicities, the LAHz exhibit higher $L_{\rm IR}/M_{\rm dust}$ ratio that
the DGS or KINGFISH galaxies for the corresponding metallicity. This means
that the dust grains in the LAHz sample are more efficient, on average, at
radiating far-infrared photons. It is not possible with the present data to
determine whether this is due to different grain properties, or is simply an
effect of the higher dust temperatures characterizing the dust emission in
these galaxies. As shown in Figure 10, this trend extends to the extremely
metal-poor galaxies SBS 0335-052E and IZw18, covering more than an order of
magnitude in both metallicity and $L_{\rm IR}/M_{\rm dust}$. These two
galaxies are dominated by warm dust; their single-component dust SEDs are
characterized by $T_{\rm dust}=59$ and $67$ K, respectively (Hunt et al.
2014). While these temperatures are high, they are within the range of the
temperatures characterizing our LAHz galaxies.

Figure 11: BPT diagram (Baldwin et al. 1981) for nearby galaxies in the
Moustakas et al. (2006) sample. This sample contains all eleven of our Local
Analog candidates, marked as red circles. The blue line depict the ”maximum
starburst line” (Kewley et al. 2001) and is an upper limit for excitation
caused by star formation. The red line is an alternate definition based on
Sloan Digital Sky Survey galaxies (Kauffmann et al. 2003). The green line
divides Seyferts and LINERS (Cid Fernandes et al. 2010).

###  6.3 Star Formation vs. AGN

The high temperatures characterizing the warm dust component of the LAHz
galaxies, as well as the high MIR fluxes relative to the peak far-infrared
flux of several of the LAHz galaxies, could potentially be due to the presence
of a non-stellar heating source, such as an active galactic nuclei (AGN). AGNs
in low-mass galaxies are more difficult to detect than their counterparts in
massive galaxies. None of our LAHz galaxies are, however, believed to harbor
an active AGN. Reines et al. (2020) used the VLA to show that previously
identified radio sources that could potentially be AGNs in Haro 02 and Mrk
1307 are most likely associated with star formation activity.

We explore the possibility of AGN, LINER or Seyfert activity in the LAHz
galaxies by plotting them in a BPT diagram (Baldwin et al. 1981). This
diagnostic uses the H$\alpha/N[II]$ ratio together with the
[OIII]5007/H$\beta$ ratio to distinguish between star formation and AGN
activity. The spectral line observations presented in Moustakas et al. (2006)
includes all of our LAHz galaxies, and the BPT diagram of the galaxies in
Moustakas et al. (2006) is shown in Figure 11, with our LAHz galaxies marked
as red circles. All LAHz galaxies, except Mrk 140 falls in the star formation
part of the diagram. Mrk 140 falls close to the line depicting maximum star
formation activity as defined by Kewley et al. (2001). Since there are no
other indication suggesting that Mrk 140 harbors an AGN, we will treat it as a
pure star-forming system.

###  6.4 Star Formation Rates

Figure 12: Left: The ratio of dust mass and star formation rate vs specific
star formation rate. Right: The ratio of dust and stellar mass vs specific
star formation rate. In both frames, the LAHz candidates are shown as squares.
For reference we include the KINGFISH galaxies as circles (Kennicutt et al.
2011), and the DGS galaxies as triangles (Rémy-Ruyer et al. 2013). The two
extreme low metallicity galaxies IZw18 and SBS-$0335-052$ (Hunt et al. 2014)
are shown as diamonds. The color indicates stellar mass as shown in the right
bar. More massive galaxies are red, less massive are blue.

In Figure 12 we show the correlations between $M_{\rm dust}/SFR$ and $M_{\rm
dust}/M_{\star}$ as a function of the specific star formation rate (sSFR
$=\,SFR/M_{\star}$). As shown in da Cunha et al. (2010), the dust-to-stellar
mass ratio strongly correlates with the sSFR, confirming that dust mass and
star formation are tightly related. In Figure. 12 we color code the stellar
mass of each galaxy. More massive galaxies exhibit a lower sSFR, and higher
$M_{dust}/SFR$ ratio, contrary the lower-mass galaxies, which show higher
specific SFRs, tracing a clear decreasing relation between
$\log{(M_{dust}/SFR)}$ and sSFR. In the right panel we plot $M_{\rm
dust}/M_{\star}$ ratio vs. sSFR. Here we see an increasing trend, where
$M_{\rm dust}/M_{\star}$ ratio increases as sSFR increases. Interestingly, the
less massive galaxies seem to follow a parallel path to the more massive ones.
In both cases, the LAHz candidates fall in the right side, showing that they
have higher sSFR than more massive and dusty galaxies.

##  7 Summary and Conclusions

Figure 13: Comparison star formation rate (SFR) vs stellar mass ($M_{\star}$),
the main sequence (MS) is show in the tick solid line in light-purple for a
z=0, purple for z=1.5, and in dark purple for $z=4$. As a comparison, we show
the CANDELS galaxies in gray, the KINGFISH galaxies in orange, the DGS in
cyan, the GOALS in green and the Jarrett et al. (2019) Galaxies in light-
green. The sample of LAHz (this study), are shown as black squares filled in
green whenever they follow the MS for any $z\gtrsim 1.5$, in yellow the MS for
any $z\gtrsim 0$ and filled in red when they follow below the MS for $z=0$.

The eleven galaxies designated as potential local analogs of high redshift
galaxies (LAHz) were initially selected based on the frequency with which they
provide the best fits to the SED of high-$z$ galaxies. They were drawn from
the Brown et al. (2014) sample of local galaxy templates, which, although
extensive in terms of number of galaxies, only provide a limited set of dwarf
irregular galaxies. It is highly likely that there are other local galaxies
with similar properties that could fit into the role as LAHz candidates; they
just were not part of the Brown sample.

As a group, these eleven LAHz galaxies are similar to galaxies in the more
diverse Dwarf Galaxy Survey (Madden et al. 2013). The LAHz galaxies tend to
have higher sSFRs, larger $M_{\rm dust}/M_{\star}$ ratios, and characterized
by warmer dust temperatures, than the average for the DGS sample. Despite the
small sample size of LAHz candidates, we found that they have different
properties. The SFHs for six of the eleven LAHz galaxies are consistent with
zero star formation activity at times $\gtrsim$1 Gyr, making them truly young
galaxies. This sub-set of LAHz consists of Mrk 1450, UM 461, Mrk 1307, Mrk
475, UGCA 410, and Mrk 930, and we will refer to it as the ‘gold’ sample. Two
of the LAHz galaxies, NGC 2537 and NGC 4670, have SFHs showing significant
star formation activity over a Hubble time. These two galaxies, despite their
appearance as Blue Compact Dwarf Galaxies, are therefore dominated by an old
stellar population, and hence, cannot be representative for high-$z$ galaxies.
For the benefit of this discussion, we will refer to these two galaxies as the
‘bronze’ sample. The remaining three LAHz galaxies; Haro 02, Haro 06 and Mrk
140, have SFHs that cannot rule out star formation activity on longer time
scales $\gtrsim$5 Gyr. Hence, they fall in between the ‘gold’ and ’bronze’
samples, and we will designate them as the ‘silver’ sample.

While we are dealing with small number statistics, there are several notable
differences between the ‘gold’ and ‘bronze’ galaxies; the average sSFR of the
‘gold’ sample is $\sim$15 times higher than the average of the two ‘bronze’
galaxies. The average temperature of the cold dust component of the ‘gold’
sample is 48 K, while both the ‘silver’ and ‘bronze’ galaxies have an average
$T_{\rm cold}$ of 35 K. These differences could be due to the stochastic
nature of star formation activity in these small galaxies. However, it is also
accompanied by a systematic difference in gas-phase metallicity; the ‘gold’
sample contains all the most metal-poor systems, 0.19 Z⊙, while the ‘silver’
and ‘bronze’ samples have an average metallicity of 0.40 Z⊙. The interstellar
gas fractions are 0.68, 0.46 and 0.25 for the ‘gold’, ‘silver’ and ‘bronze’
samples, respectively. Another parameter setting the ‘gold’ galaxies apart is
the flux ratio $S_{24}/S_{160}$, describing the strength of the mid-IR
relative to the FIR. These ratios are listed in Table 5. The ‘gold’ sample
have an average ratio $\sim$0.4, compared with 0.07 for NGC 2537 and NGC 4670.

In Figure 13 we plot the SFR vs stellar mass for our LAHz galaxies, and
several other surveys; including the DGS and KINGFISH samples; the GOALS
sample of ultra-luminous infrared galaxies (Armus et al. 2009), and the
Jarrett et al. (2019) sample of the 100 largest nearby galaxies. In addition
plot high redshift galaxies with $1<z<4.5$ from the CANDELS survey (Santini et
al. 2015). Diagonal dashed lines show different sSFR values, and the thick
diagonal lines show the main sequence for redshifts $z$=0, 1.5 and 4
(Schreiber et al. 2015). Our LAHz galaxies are shown as squares, with colors
corresponding to whether they belong in the ‘’gold’ (green), ‘silver’ (red),
or ‘bronze’ (orange) samples. Most of the DGS, KINGFISH, and Jarrett et al.
(2019) galaxies are centered around the main sequence (MS) for $z$=0,
including the turn-off at high stellar masses. The GOALS galaxies have, not
surprisingly, a higher sSFR. The ‘gold’ LAHz galaxies have high sSFR, and are
located above the $z$=0 MS. The two ‘bronze’ LAHz (NGC 2537 and NGC 4670), are
located below the $z$=0 MS, and the three ‘silver’ LAHz are on the $z$=0 MS.
Hence, the six LAHz in the ‘gold’ sample can be viewed either as $z$=0
starburst systems, or as MS galaxies at $z\sim$1.5-4.

Based on these findings, we propose that the six galaxies in the ‘gold’ sample
are good candidates for follow-up studies, especially determining their gas
dynamics. If they truly are young systems, their age puts their formation
epoch at $z$$\sim$0.08, or a luminosity distance of only $\sim$350Ṁpc. This
short time scale means that gravitational interaction and merging are not
likely to be driving the star formation activity.

Based on observations made with the NASA/DLR Stratospheric Observatory for
Infrared Astronomy (SOFIA). SOFIA is jointly operated by the Universities
Space Research Association, Inc. (USRA), under NASA contract NNA17BF53C, and
the Deutsches SOFIA Institut (DSI) under DLR contract 50 OK 0901 to the
University of Stuttgart. Financial support for this work was provided by NASA
through award #06-0222 issued by USRA. We extend our thanks to the SOFIA
science support team, for all the help in the data acquisition and the data
analysis process.

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