# Multiwavelength exploration of Extreme Emission Line Galaxies detected in
miniJPAS survey

Iris Breda,1,2 Stergios Amarantidis,3 José M. Vilchez,2 Enrique Pérez-
Montero,2 Carolina Kehrig,2 Jorge Iglesias-Páramo,2 Antonio Arroyo-Polonio,2
Juan A. Fernández-Ontiveros,4 Rosa M. González Delgado,2 Luis A. Díaz-García,2
Raul Abramo,5 Jailson Alcaniz,6 Narciso Benítez,2 Silvia Bonoli,4,7,8 Javier
A. Cenarro,9 David Cristóbal-Hornillos,4 Renato Dupke,6 Alessandro
Ederoclite,4 Antonio Hernán-Caballero,9 Carlos López-Sanjuan,7 Antonio Marín-
Franch,9 Claudia Mendes de Oliveira,10 Mariano Moles,4 Laerte Sodré,10 Keith
Taylor,11 Jesús Varela,9 Héctor Vázquez-Ramió9
1Instituto de Astrofísica de Andalucía - Glorieta de la Astronomía, s/n, 18008
Granada, Spain
2Department of Astrophysics, University of Vienna, Türkenschanzstraße 17, 1180
Vienna, Austria
3Instituto de Radioastronomía Milimétrica, Av. Divina Pastora 7, Núcleo
Central E-18012, Granada, Spain
4Centro de Estudios de Física del Cosmos de Aragón, Plaza San Juan, 44001
Teruel, Spain
5Instituto de Física, Universidade de São Paulo, Rua do Matão 1371, CEP,
05508-090 São Paulo, Brazil
6Observatório Nacional – MCTI (ON), Rua Gal. José Cristino 77, São Cristóvão
20921-400, Rio de Janeiro, Brazil
7Donostia International Physics Centre (DIPC), Paseo Manuel de Lardizabal 4,
20018 Donostia-San Sebastián, Spain
8IKERBASQUE, Basque Foundation for Science, 48013 Bilbao, Spain
9Centro de Estudios de Física del Cosmos de Aragón (CEFCA), Unidad Asociada al
CSIC, Plaza San Juan 1, 44001 Teruel, Spain
10Instituto de Astronomia, Geofísica e Ciências Atmosféricas, Universidade de
São Paulo, 05508-090 São Paulo, Brazil
11Instruments4, 4121 Pembury Place, La Canada Flintridge, CA 91011, USA
E-mail<EMAIL_ADDRESS>
###### Abstract

Extreme Emission Line Galaxies (EELGs) stand as remarkable objects due to
their extremely metal poor environment and intense star formation. Considered
as local analogues of high-redshift galaxies in the peak of their star-forming
activity, they offer insights into conditions prevalent during the early
Universe. Assessment of their stellar and gas properties is, therefore, of
critical importance, which requires the assembly of a considerable sample,
comprehending a broad redshift range. The Javalambre-Physics of the
Accelerating Universe Astrophysical Survey (JPAS) plays a significant role in
assembling such a sample, encompassing $\sim$8000 $\rm deg^{2}$ and employing
54 narrow-band optical filters. The present work describes the development and
subsequent application of the tools that will be employed in the forthcoming
JPAS spectrophotometric data, allowing for the massive and automated
characterization of EELGs that are expected to be identified. This fully
automated pipeline (requiring only the object coordinates from users)
constructs Spectral Energy Distributions (SEDs) by retrieving virtually all
the available multi-wavelength photometric data archives, employs SED fitting
tools and identifies optical emission lines. It was applied to the sample of
extreme line emitters identified in the miniJPAS Survey, and its derived
physical properties such as stellar mass and age, coupled with fundamental
relations, mirror results obtained through spectral modeling of SDSS spectra.
Thorough testing using galaxies with documented photometric measurements
across different wavelengths confirmed the pipeline’s accuracy, demonstrating
its capability for automated analysis of sources with varying characteristics,
spanning brightness, morphology, and redshifts. The modular nature of this
pipeline facilitates any addition from the user.

###### keywords:

galaxies: star-formation – galaxies: starbust – galaxies: evolution

††pubyear: 2024††pagerange: Multiwavelength exploration of Extreme Emission
Line Galaxies detected in miniJPAS survey–Multiwavelength exploration of
Extreme Emission Line Galaxies detected in miniJPAS survey

##  1 Introduction

Extreme emission line galaxies (EELGs) are a sub-sample of extra-galactic
objects which thorough understanding remains elusive. These exceptional
astronomical entities, which may seem irrelevant at first sight due to their
faint nature and reduced dimensions, might hold the key to our understanding
of galaxy formation.

Typically, EELGs are highly compact objects with vivid blue/green colors,
being characterized by very high (frequently reaching three orders of
magnitude) equivalent widths (EW) of relatively high-excitation emission lines
indicative of intense and ongoing star-formation (SF), such as H$\alpha$,
optical [OIII], and [OII] or Ly-$\alpha$ when observed at higher redshifts
where they are more frequently detected (e.g., Kunth & Sargent, 1986; Erb et
al.,, 2016). By presently harbouring the most violent SF events in the
Universe, these galaxies provide a window to the past, resembling epochs where
global SF was at its zenith (e.g., Amorín et al.,, 2015; Pérez-Montero et
al.,, 2021). They produce a substantial amount of photoionising radiation
originated from complexes of young, massive stars which, although
controversial (Loeb & Barkana,, 2001; Bosch-Ramon,, 2018), is considered by
various authors to significantly contribute to the re-ionization of the
Universe (e.g., Salvaterra et al.,, 2011; Dressler et al.,, 2015; Erb et al.,,
2016). In addition, this kind of galaxies are acknowledged as likely being the
building blocks of local, more massive galaxies (Dressler et al.,, 2011), and
are amidst the most metal poor objects observed in the Universe (e.g., Kunth &
Östlin, 2000; Papaderos et al.,, 2008, for a review). Wolf-Rayet features are
often identified in these galaxies (e.g., Schaerer, Contini, & Pindao, 1999;
Amorín et al.,, 2012). They might be classified as strong He II emitters
(e.g., Kehrig et al,, 2018; Fernández et al.,, 2021) and, depending upon the
adopted selection criteria and redshift at which they are observed, as blue
compact galaxies or blue compact dwarfs (BCGs/BCDs; e.g., Thuan & Martin,,
1981; Loose & Thuan,, 1986; Kunth & Sargent, 1986; Papaderos et al.,, 1996;
Cairós et al.,, 2001; Reverte et al.,, 2007), green pea galaxies (GPGs;
Cardamone et al.,, 2009; Amorín et al.,, 2015), blueberries (Yang et al.,,
2016) and/or ELdots (Bekki,, 2015).

Reviewing the vast complexity of the subject and the broad scientific
framework that these exceptional galaxies comprehend, the identification of a
statistically meaningful sample across redshift is of uttermost importance.
Such will allow to pursue a thorough study of the nature of these objects, and
ultimately portray a coherent narrative of their role in the broader context
of galaxy formation and evolution. There are several essays attempting for the
assembly of a representative sample of EELGs across redshift, such as, e.g.,
van der Wel et al., 2011 (70 galaxies in the CANDELS fields at $z\sim$ 1.7),
Amorín et al., 2010 ($\sim$ 180 galaxies from the 20k zCOSMOS bright survey
with 0.11 $\leq$ $z$ $\leq$ 0.93) or Pérez-Montero et al., 2021 ($\sim$ 2000
galaxies in the SDSS-DR7 with 0 $\leq$ $z$ $\leq$ 0.49) and Lumbreras-Calle et
al. 2021 (466 EELGs at $z$ < 0.06) using objective prisms to identify strong
emission lines. From these studies it was disclosed that EELGs display compact
morphologies (with R50 $\leq$ 2kpc), and a broad range of stellar masses (6.5
$\leq$ log M⋆/M⊙ $\leq$ 10), with higher masses being mostly found at higher
redshifts. Quite importantly, some of these galaxies exhibit pristine
environments (e.g., Izotov, Thuan, & Guseva, 2012; Griffith et al.,, 2011),
with oxygen abundances ranging from 12 + log O/H = $\sim$7.3 to 8.6, and
nitrogen-to-oxygen ratios from $\sim$ 0.01 to 0.1. Additional works have
provided further insight on this galaxy type. For instance, Maseda et al.,
(2013, 2014) has analysed 2 samples of 19 and 22 EELGs detected at 1.3 $\leq$
z $\leq$ 2.3, revealing that these have low stellar masses (108 \- 109 M⊙) and
are experiencing intense starbursts. Additional studies have found similar
results, such as the works by Tran et al., (2020); Gupta et al., (2021, 2023),
which explores data from the MOSEL survey, covering a sample of strong [OIII]
5007 $\AA$ emitting galaxies at 3 $\leq$ z $\leq$ 4 extracted from the ZFOURGE
survey. Furthermore, by analysing the physical properties of 19 EELGs
identified in the ZFOURGE survey, Cohn et al., (2018) revealed that these
galaxies show evidence of a starburst in the most recent 50 Myr, assembling
15% of their total stellar mass within this short time period, a highly
significant value when contrasted with typical star-forming galaxies which in
the same time period have formed only 4% of their total stellar mass. In
addition, Gupta et al., (2021, 2023) have identified signatures of galactic-
scale outflows, attributing the pronounced emission lines to interactions
and/or mergers. EELGs with even lower stellar masses and higher sSFRs have
been identified by Tang et al., (2019) at 1.3 $\leq$ z $\leq$ 2.4, ranging
from 107 to 108 M⊙. In addition, these authors have detected O32 values
associated with significant Lyman continuum escape.

An excellent opportunity to keep expanding the acquired knowledge on this
galaxy genus, and to enlarge the number of these galactic specimens, resides
on the exploration of the Javalambre-Physics of the Accelerating Universe
Astrophysical Survey (JPAS; Bonoli et al.,, 2021). Such survey is highly
suitable for this purpose, considering that it will observe a vast section of
the sky ($\sim$8000 deg2) by means of 54 narrow band filters in the optical
regime with an average full width at half maximum (FWHM) of 145 Å (translated
into an R of $\sim$ 60) and an average spatial resolution with a FWHM $<$
1.5”, spanning from 3780 to 9100 Å, and 2 additional broad filters extending
to the ultra-violet (UV) and near infra-red (NIR). It will yield, when
complete, one of the largest repositories of photo-spectra or low (spectral)
resolution integral field spectroscopy for all morphological galaxy types from
the local Universe up to $z\sim 1$. Moreover, past studies have demonstrated
that via this photometric survey one can efficiently identify and characterize
emission line galaxies up to $z$ < 0.35 (Martínez-Solaeche et al., 2021, 2022)
and the evolution of both red and blue galaxy populations since $z$ = 1
(González Delgado et al., 2021, 2022). Low and intermediate redshift QSOs
should be also easily detected (Queiroz et al., 2022) as well as extended
Lyman alpha QSOs (Rahna et al., 2022). However, for the correct identification
of such elusive objects as EELGs, given their low surface brightness stellar
continuum and the similarity with QSOs when observed through the filter scheme
of JPAS, our team developed an efficient technique that identifies EELGs
candidates by searching for especially strong EW emission lines (EW $\geq 200$
Å). This method (Iglesias-Páramo et al., 2022, hereafter IP22) was tested
using the data released by the miniJPAS survey (covering 1 square degree of
the AEGIS field), having identified 20 EELGs and 11 QSO candidates.

This article describes the characterization of the EELGs $\&$ QSOs identified
by the aforementioned tool, outlining the development of the employed fully
automated, modular pipeline, and including an informative discussion and
comparison of the obtained properties. The strategy here delineated serves as
a proof of concept and is expected to be massively employed in the forthcoming
JPAS narrow-band data, being well suited to analyse all galaxy types. This
article is organized as follows: Sect. 2 briefly describes how the EELG and
QSO candidates were identified in the AEGIS field through the miniJPAS data,
Sect. 3 outlines each module of the developed pipeline, Sect. 4 presents the
obtained results and Sect. 5 summarizes the main conclusions of the present
work.

##  2 Identifying the galaxy sample

The galaxy sample was identified by applying the detection algorithm developed
by IP22 to the miniJPAS data. This technique relies on the exploration of the
miniJPAS catalogues, thereby constructing and subsequently analysing the
photo-spectra of all the listed objects. These catalogues were produced
firstly by identifying the numerous detected sources in post-reduced
photometric data with SExtractor (Bertin & Arnouts, 1996) by adopting two
parallel strategies, i.e., dual and single mode (see Bonoli et al.,, 2021, for
a detailed description of imaging data reduction and post-processing). In this
fashion, several quantities of interest were collected, such as coordinates
and fluxes integrated within various apertures. Having as primary objective
the correct identification of all EELGs in the photometric data-frames from
JPAS, the IP22 detection tool’s selection criteria was tailored after thorough
examination of real EELGs and QSO spectra, and how such quantitative
measurements would translate when visualized through the JPAS filter scheme.
This approach resulted in the following procedure for the selection of EELGs
candidates in miniJPAS photometric data. Considering the dual catalogue and
each of the medium-band JPAS filters from J0400 to J0900, it were selected the
sources that fulfil the subsequent criteria:
\- For each photometric frame Fλ, only sources with a flux density $\geq$
10-17 erg$\cdot$cm-2 $\cdot$ Å-1 $\cdot$ s-1 were selected;
\- From the previously selected sources, the ones with FLAG $>$ 3 or MASK_FLAG
$>$ 0 were discarded, this way avoiding uncertainties appertaining to
instrumental artefacts and false detections;
\- To prevent the detection of artificial red objects, sources with f8500 /
f4300 > 1.2 were rejected (where f8500 and f4300 are the integrated fluxes in
the filters J0800 to J0900 and J0378 to J0480, respectively);
\- Only sources detected in both catalogues (single and dual) for each JPAS
data frame were considered;
\- Only sources with minimum contrast (fn \- fc)/ fn $\geq$ 0.674 (where fn is
the total flux of the source in filter $n$ and fc is the estimated continuum
flux density), being comparable to an EW of $\sim$ 300 Å in at least one
emission line, were accepted as EELG candidates (if the rest-frame EW $<$ 300
Å the source will be discarded);
\- To avoid spurious detections, it were rejected all objects to which the
intensity peak in filter $n$ is lower than 5$\sigma$, where $\sigma$
corresponds to the standard deviation of the sky in the given frame $n$;
\- The candidates identified by the preceding criteria were ascertained as
EELG or QSO by direct comparison of their photo-spectra with a clearly defined
sample of SDSS-DR14 spectroscopic data of both SF galaxies and QSOs, as well
as with a set of synthetic spectra, after convolution with the medium-band
JPAS filters. Additionally, this approach enabled to determine the redshift of
the source, by performing a systematic comparison of the observed photo-
spectra with red/blue-shifted SDSS spectra. The latter were shifted from $z$
\- 0.05 to $z$ \+ 0.05 in increments of 0.002, thus covering a wide and
continuous redshift range.

This strategy resulted in the successful detection of 31 extreme line
emitters, from which 20 were classified as EELGs and as 11 QSO.

##  3 Description of the modular pipeline

In pursuance of attaining complete characterization of the detected sources by
extraction of all physical properties that can be estimated from the analysis
of photometric data, a fully automated, modular pipeline was developed. This
section is dedicated to the description of such pipeline, which was scripted
in the _python_ programming language (Rossum & Drake, 2009), and besides
employing widely used _python_ packages such as _numpy_ , _scipy_ , etc., it
exploits the astronomy targeted _python_ packages _astropy_ (Astropy
Collaboration et al.,, 2018), _astroquery_ (Ginsburg et al.,, 2019),
_photutils_ (Bradley et al.,, 2020), _specutils_ (Earl et al.,, 2020) and
_sewpy_ 111https://github.com/megalut/sewpy (the _python_ implementation of
SExtractor).

As a synopsis, the pipeline herein introduced retrieves and processes imaging
data (miniJPAS and virtually all publicly available multi-wavelength data) for
the desired source, thus extracting the optical photo-spectra and the spectral
energy distribution (SED) in subsequent stages. Thereafter, it de-redshifts
and examines the photo-spectra, seeking for emission lines. If any emission
line is detected it estimates its properties, in particular central
wavelengths, fluxes and EWs. In the following stage, it incorporates all the
retrieved data in the analysis and constructs the SED, which will be fitted by
means of the SED fitting tools CIGALE (Burgarella, Buat, & Iglesias-Páramo,
2005) and Prospector (Johnson et al.,, 2021, both delayed-$\tau$ and $\alpha$
models), subsequently estimating restframe SDSS intrinsic colors and
magnitudes, fluxes and luminosities in each of the observed passbands. As a
remark, considering the modular nature of the pipeline, it is straightforward
to encompass any additional SED fitting tool that the user may desire. The
last module extracts the surface brightness profile (SBP) of the source by
utilizing a _python_ adaptation of the isophotal annuli (isan) surface
photometry technique by Papaderos et al. (2002). If desired, this module may
be used to extract the photo-spectra in each individual isan.

###  3.1 Pre-processing of the narrow-band data

Although the user may provide additional details such as observed redshift and
the presence/absence of active galactic nucleus (AGN), the pipeline merely
requires the coordinates of the desired galaxy. The processing begins by
locating the input coordinates in the miniJPAS catalogue and downloading the
photometric frames (Fλ) and respective weight maps. If the redshift is not
provided, it queries the NASA/IPAC extragalactic database222The NASA/IPAC
Extragalactic Database (NED) is operated by the Jet Propulsion Laboratory,
California Institute of Technology, under contract with the National
Aeronautics and Space Administration. (NED) for the recorded spectroscopic
redshift. In case that such information is not available it will subsequently
use the photometric redshift previously extracted from the miniJPAS
catalogues. It additionally fetches the E(B-V) value by querying the input
coordinates via the _astroquery.irsa_dust_ sub-module. The photometric frames
Fλ will be corrected from the effects of interstellar Galactic dust by
following the prescription for the extinction curve by Cardelli, Clayton &
Mathis (1989, herein designated as CCM89), and adopting RV = 3.1:

$\rm F_{\lambda}^{\>cor}=F_{\lambda}/10^{-0.4\cdot CCM89(\lambda)\cdot A_{V}}$
(1)

where $\rm A_{V}=R_{V}\cdot E(B-V)$.

Supplementary data such as zero point magnitudes and band widths are retrieved
from the miniJPAS catalogues. Effective wavelengths ($\lambda_{\rm eff}$) are
computed through the filter transmission curves (T$(\lambda)$) in such manner,
while considering the nature of the detector:

$\lambda_{\rm eff}\equiv\sqrt{\dfrac{\int\rm
T(\lambda)\cdot\lambda\>d\lambda}{\int\rm T(\lambda)/\lambda\>d\lambda}},$ (2)

for photon counter detector types (namely all JPAS data), and

$\lambda_{\rm eff}\equiv\sqrt{\dfrac{\int\rm T(\lambda)\>d\lambda}{\int\rm
T(\lambda)/\lambda^{2}\>d\lambda}},$ (3)

for electron counter detector types (such as several of the multi-wavelength
data here analysed, namely, 2MASS, WISE, Spitzer and Herschel).

The following procedure is to perform a first square-cut (length of 200”) of
all of the extinction corrected miniJPAS frames, and to produce a true-color
(RGB) image of the sub-frame. The latter is subsequently fed to _sewpy_. Such
will deliver a table listing all the detected objects (which will be
considered in later stages, assisting to determine the presence/absence of
extra sources within the aperture adopted for the photometric analysis) and
the radial extent of our source (Rgal, i.e., the galactic extent estimated by
SExtractor in the RGB image), which will be utilized for estimating the
aperture radius (Rap) in later stages.

A second square-cut of length of 5 Rgal centred in our source is executed in
each Fλ and respective root-mean-square (RMS) map (defined as the square-root
of the inverse of the observed frame), and the trimmed imaging data are
compressed in one data-cube. Supplementary information that will be useful in
later stages of the processing is annexed to the ensuing data-cube, namely the
pixel-scale, detector type (’P’/’E’ for photon/electron), name and location of
the file pertaining the filter transmission curve T($\lambda$), effective
lambda $\lambda_{\rm eff}$, band’s width, average FWHM of the instrument’s
point spread function (PSF), zero point magnitude, canonical detection limits
in the data units and in mJy, exposure time, AB conversion factor and RMS map.

###  3.2 Retrieval of multi-wavelength data

By supplying the coordinates, the following module yields nearly all the
photometric data that are publicly available for the requested source, and the
respective RMS maps (when accessible). It stands to benefit from the Barbara
A. Mikulski Archive for Space Telescopes333The Mikulski Archive for Space
Telescopes (MAST) is a NASA funded project to support and provide to the
astronomical community a variety of astronomical data archives, with the
primary focus on scientifically related data sets in the optical, ultraviolet,
and near-infrared parts of the spectrum. MAST is located at the Space
Telescope Science Institute (STScI). (MAST) as a sub-module of the
_astroquery_ , and IRSA-IPAC (Alexov et al.,, 2005) API444The IRSA-IPAC
Application Programming Interface may be accessed though
https://irsa.ipac.caltech.edu/ibe/sia.html services, to automatically retrieve
the located imaging data.

In particular, this module seeks:
\- the MAST database for the Galaxy Evolution Explorer (GALEX) _Deep Imaging
Survey_ (DIS) photometric data or, in case it is not available, for GALEX
_All-sky Imaging Survey_ (AIS) (see Martin et al.,, 2005, for a review on the
mission and main surveys that were carried out)555For further information
regarding the data reduction pipeline and thorough description of the data
provided by the MAST database the reader is addressed to the website:
http://galex.stsci.edu/gr6/?page=ddfaq. The decommissioned space telescope
GALEX has collected imaging and spectroscopic data in two ultraviolet bands,
Far UV (FUV) 1350 – 1780 Å and Near UV (NUV) 1770 – 2730 Å, providing
simultaneous co-aligned FUV and NUV images with spatial resolution (i.e., the
FHWM of the PSF) of 4.3 and 5.3”, respectively. The GALEX-DIS covered an area
of 80 deg2 with integration times spanning 10ks – 250ks, with a typical
integration time of 30ks, reaching a limiting AB magnitude of the order of 25
mag (3.92e10-4, 3.71e10-4 mJy for the two respective bands). In contrast, the
GALEX-AIS has a typical integration time of 100 s, covering over an area of
26,000 deg2, reaching a depth of mAB of 20 in both bands (0.428 and 0.337 mJy,
respectively). GALEX data units of flux are given in counts/s.
\- the IRSA-IPAC database obtaining photometric data from the Two Micron All-
Sky Survey (2MASS; see Skrutskie et al.,, 2006; Cutri et al.,, 2003, for a
description of the pipeline data-reduction and specifics of the data produts).
The 2MASS All-Sky Data Release covers approximately the entire celestial
sphere in the near-infrared, providing imaging data on three bandpasses,
namely J (1.235 $\mu$m), H (1.662 $\mu$m,) and Ks (2.159 $\mu$m), achieving
the canonical sensitivities of $\sim$ 15.8, 15.1 and 14.3 mVega (0.8, 1.0 and
1.3 mJy), respectively, and a spatial resolution of roughly 3” for the three
bands. 2MASS data units of flux are given in Data Numbers (DN).
\- the IRSA-IPAC database for ALL Wide-field Infrared Survey Explorer imaging
data (the space-based telescope WISE; see Wright et al., 2010 and Cutri et
al., 2021 for a detailed description on the pipeline, data-reduction and data
specifics). ALLWISE was assembled by combining the entire data from both WISE
and NEOWISE (Mainzer et al.,, 2011) survey phases. It delivers enhanced data
products with increased photometric sensitivity and accuracy, and improved
astrometric precision as compared to the previously available WISE All-Sky
imaging data. The data are comprised of 4 bands, W1, W2, W3 and W4, at the
respective central wavelengths of 3.4, 4.6, 12 and 22 $\mu$m, with an average
spatial resolution of $\sim$6” for the first three bandpasses, and $\sim$12”
for W4. The respective canonical sensitivity estimates within 95% of the
retrieved data is, in mVega, of 17.36, 15.97, 11.73 and 8.1 (0.037, 0.079,
0.67 and 5.1 mJy). WISE data units of flux are given in DN.
\- the IRSA-IPAC database for Spitzer (Werner et al.,, 2004) Enhanced Imaging
Products (SEIP 666The reader is address to the ”Spitzer Enhanced Imaging
Products - Explanatory Supplement”, for a detailed description of the SEIP
final products, in addition to a thorough outline of the data-reduction
procedure. The document can be found at:
https://irsa.ipac.caltech.edu/data/SPITZER/Enhanced/SEIP/docs/
seip_explanatory_supplement_v3.pdf), a repository comprising high resolution
infrared data acquired during the 5 year Spitzer Space Telescope’s cryogenic
space mission, across a sky area of over 1500 deg2. From the database it
extracts the combined images of all four bands (3.6, 4.5, 5.8, and 8 $\mu$m)
of the Infrared Array Camera (IRAC; Fazio et al.,, 2004), and the 24 micron
band of the Multiband Imaging Photometer for Spitzer (MIPS; Rieke et al.,,
2004), with each respective bandpass featuring an average spatial resolution
of 1.95, 2.02, 1.88, 1.98 and 6”. The respective sensitivity limits within the
AEGIS filed777Such and more specific information on the AEGIS field can be
accessed though https://aegis.ucolick.org/ were estimated as 9e10-4, 9e10-4,
6.3e10-3, 5.8e10-3 and 0.03 mJy. Spitzer data units of flux are given in MJy
per steradian. Both the science and uncertainty imaging data are converted to
units of mJy, by division by the conversion factor (1 steradian = 1 rad2 =
4.25e10${}^{10\;\square}$) and subsequent multiplication by the square of the
respective pixel-scale of each passband.
\- the IRSA-IPAC database for Herschel888The ”Herschel Product Definition
Document”, which comprehends a complete guide on Herschel data, including
calibration, reduction and specifics on the data products, can be accessed at
http://herschel.esac.esa.int/hcss-doc-15.0/print/pdd/pdd.pdf, in particular
the Herschel Multi-tiered Extragalactic Survey (HerMES; Oliver et al.,, 2012),
a legacy program that comprehends space acquired imaging data, totaling an
area of 380 deg2, at wavelengths between 100 and 500 $\mu$m. It comprises data
collected with the Herschel Spectral and Photometric Imaging Receiver (SPIRE;
Griffin et al.,, 2010; Bendo et al.,, 2013) at 250, 350 and 500 $\mu$m and the
Herschel Photodetector Array Camera and Spectrometer (PACS; Poglitsch et al.,,
2010; Balog et al.,, 2014) at 100 and 160 $\mu$m. The spatial resolution is of
18.2, 24.9 and 36.3” for SPIRE bandpasses, with estimated canonical
sensitivity limits of 5.8, 6.3 and 6.8 mJy, respectively, and of 6.8 and
10.7”, with a typical sensitivity of 5 and 10 mJy, respectively, for the PACS
bandpasses. HerMES-SPIRE data units of flux are given in Jy per steradian,
while HerMES-PACS are in Jy. The SPIRE science frames are converted to units
of mJy by adopting the aforementioned method and PACS data are reduced by
three orders of magnitude.

In the successive step, the herein introduced _python_ module trims the images
to a reduced area of interest with a length of 5 Rgal centered in our source
and condenses all the retrieved photometry into one data-cube, which
additionally includes all the functional information, as previously described
for the miniJPAS data. Lastly, the newly acquired data frames will be
corrected from Galactic extinction by performing an equivalent procedure as
the one described in the previous sub-sect. (Rλ values for $10<\lambda<46.2$
$\mu$m were retrieved from the IRSA-IPAC "Galactic Dust Reddening and
Extinction" service999https://irsa.ipac.caltech.edu/applications/DUST/ and for
$\lambda>$ 46.2 $\mu$m a negligible extinction is assumed).

####  3.2.1 Assessment of the pipeline’s performance on the automated
retrieval of photometric fluxes

A series of tests were developed with the goal of evaluating the accuracy of
the herein developed pipeline on estimating fluxes through the automatic
analysis of photometric frames. Initially, the estimates of the 32 EELGs under
study were contrasted with the results from the miniJPAS Public Data Release
(MINIJ-PAS-PDR201912; Bonoli et al.,, 2021). In the interest of adequately
compare the two works, both auto and Petrosian fluxes were considered, and to
ensure reliability of the data analysis and interpretation, sources with an
uncertainty higher that 50% of the measured flux (i.e., SNR < 2) were
discarded. Comparison between the retrieved catalogued fluxes and the ones
obtained by the pipeline can be appreciated from Fig. 1, where the flux ratios
are displayed. Inspection of this Fig. reveals that this pipeline comes in
excellent agreement with the results from the miniJPAS catalogue (mean values
of ratios from all filters of 1.036 and 0.915 for the auto and Petrosian
radii, respectively), with the larger differences emerging for the higher-
wavelength filters. To note that specially (but not exclusively) for the
miniJPAS filters, in the case of no visual detection (i.e., there is no
appreciable difference between the flux estimated within the aperture and the
surrounding sky), this pipeline is deriving a zero flux. Such can be
attributed to the criteria employed by this work in order to exclude possible
contaminant sources from the background, thus favouring an accurate
photometric analysis of faint galaxies. Additionally, fluxes derived within
the Petrosian radius tend to be higher than the ones obtained by using the
auto. This disparity can be explained by considering the inherent difference
between the Petrosian radius, which in the analysed sample consistently tends
to be larger than the auto.

Regarding the additional filters, a similar comparison was conducted. By
exploiting the minijpas.xmatch_allwise catalogue, 10000 randomly selected
galaxies were cross-matched (adopting 5 ″as angular separation) with the
Hermes-Herschel catalogue (Hermes Team et al.,, 2017), resulting in 1104
sources (including both extended and point-like sources). Adding the 32
galaxies under study, a number of 1135 galaxies were identified and further
cross-match with the Spitzer-SEIP catalogue (105 galaxies; Spitzer Science
Center, SSC), 2MASS (74 galaxies; Cutri et al.,, 2003) and GALEX-AIS (30
galaxies; Bianchi et al.,, 2011). All the admitted flux measurements were
required to satisfy the criterion of having an uncertainty which is lower than
50% of its flux measurement, along with being classified as an accurate
detection (e.g., photometric quality flag for WISE with a value of B or A; for
more details see Cutri et al.,, 2003). To note that these criteria and the
relatively limited depth of all-sky surveys have led to a reduction in the
number of available galaxies in the additional filters.

For all examined filters, the flux ratio between the one retrieved by the
pipeline and the reference value was estimated (in the case where solely a
magnitude was available from the archival catalogues, the ratio was converted
by following $\rm 10^{0.4*|m_{Breda+23}-m_{literature}|}$). Likewise the
miniJPAS comparison, the results from this exercise are included in Fig. 1,
with the median displayed with red colour and with a mean ratio value of
1.062. Although the herein presented is meant to exclusively analyse BCDs
observed by JPAS, this exercise further supports its robustness, demonstrating
its applicability to both extended and point-like sources, and rendering it
qualified for the automated study of both faint and bright galaxies, both in
the local and high-z Universe.

Figure 1: Comparison between the fluxes estimated by this pipeline and the
fluxes provided in the literature. The ratios are presented as box-plots
containing the 25-75% percentiles, whilst the upper and lower limits depict
the minimum and maximum ratios. The lines inside the box-plots correspond to
the median values for the auto, Petrosian and additional bands (blue, orange
and red coloured lines), and the legend indicates the mean value of these
medians. The coloured values at the bottom of the figure display the number of
galaxies that were employed to estimate the box plots for each filter.

###  3.3 Analysis of the imaging data

The present module’s first design is to execute aperture photometry on the
narrow and medium-band JPAS filters, thuswise extracting the photo-spectra
from the imaging data, whereas SED extraction will be performed by the same
module in later stages of the processing. Regarding that the construction of
both optical photo-spectra and SED share most of the processing stages we
decided to compile both proceedings in this sub-sect..

In the interest of selecting an aperture radius Rap that encompasses virtually
all of the flux emitted by our source, for miniJPAS data the adopted Rap (pix)
is equal to 1.2Rgal (”) divided by the pixel scale of each individual frame
Fλ. With respect to the multi-wavelength data, given their lower spatial
resolution as compared to JPAS, R${\rm ap}$ is set to be twice the size of the
FWHM of each individual instrument’s PSF, effectively resulting in an aperture
of 4 $\times$ FWHM${\lambda}$." Regarding the sky annulus, it is defined as
the area between a circle with radius Rin = Rap \+ inc and a second circle
with radius Rout = Rin \+ inc. The increment inc is adopted to be 10 pixels in
all cases except for Herschel data where it is 5, given its large pixel scale.
The choice of these values has an empirical foundation, resulting from several
trials adopting different values. Two masks centered in the input coordinates
are created, mapping the circular area of the aperture (maskap) and the sky
annulus (masksky), which will be subsequently used in the aperture photometry
operation. On account of the different pixel-scales of multi-wavelength data,
when constructing the SED, the two previously mentioned masks will be created
for each multi-wavelength frame.

Figure 2: Visual output resulting from the module described in sub-section 3.3
for the source located at RA: 214.32208∘, DEC: 52.538199∘ when extracting the
SED from multi-wavelength imaging data. It displays all the bandpasses that
will be considered in the SED fitting, with the adopted aperture overplotted
in white and the sky annulus in black, in addition to the shaded areas which
reflect the maskBSF$\lambda$. Moreover, the name of Fλ and the adopted
aperture is noted on top of each frame and the overlapped red "x" on some of
the panels indicates a non-detection.

####  3.3.1 Identifying bright sources

The subsequent phase is to identify and mask all additional bright sources
that may exist in each frame, this way mitigating contamination from external
sources in the aperture photometry procedure. Aiming for the construction of
an algorithm that operates soundly at all circumstances, this is achieved by
an elaborated operation which combines several actions:

  * a)

After sky modelling and subtraction, for each individual frame create a mask
comprising all bright sources (maskBSF$\lambda$), by detecting all excess
emission brighter than 3$\sigma$ as compared to the image background. This
operation is performed by means of the sigma-clipping method, specifically the
sub-module _make_source_mask_ from the _python_ package _photutils_.

  * b)

Identify and extract contours in the mask images, thus obtaining the number,
location and dimensions of all the bright sources present in Fλ.

  * c)

Obtain maskBSF$\lambda$-1 by performing grayscale erosion with a square
connectivity equal to one (Soille,, 1999) in maskBSF$\lambda$, this way
minimizing the likelihood of our source to be merged with others (i.e., the
same contour to outline our and additional sources).

  * d)

Proceed to the identification of contours in maskBSF$\lambda$-1 and compare it
with the same obtained for the previous set of masks, prior to erosion. If the
number of contours have decreased, re-apply the greyscale erosion
morphological operation to the original set of masks while adopting a
structure of 2$\times$2, thus avoiding neglecting sources with length in one
of the spatial dimensions equal to 1 pixel.

  * e)

To avoid any risk of neglecting small-scale sources within the aperture
radius, the algorithm compares maskBSF$\lambda$ with maskBSF$\lambda$-1,
seeking for possible contours with area of 1 pixel that were extinct after the
erosion operation. If it detects any it re-places it in maskBSF$\lambda$-1.

  * f)

Detect the contour corresponding to our source by identifying the one that is
closer to the input coordinates and remove it from maskBSF$\lambda$-1.

  * g)

Perform binary dilation twice (Soille,, 1999) in maskBSF$\lambda$-1, expanding
the size of each detected source, thereby obtaining the final mask of bright
sources, maskBSF$\lambda$.

  * h)

Exclusively for multi-wavelength data, having in mind the substantially broad
PSF of several of the instruments here considered, it might be impossible to
resolve our source if there is an external bright source in its vicinity.
Towards the identification of possible nearby sources, it will estimate the
distance between our source and every detected contour. Additionally, it will
verify the distances of all objects listed in the SExtractor table. To redress
this problem, if any of the estimated distances are lower than the PSF of the
instrument, the error that will be fed to the SED fitting tools in later
stages will be significantly enhanced, assumed to be 50% of the measured flux
within the aperture.

####  3.3.2 Aperture photometry

At this stage, all requirements are fulfilled to commence the aperture
photometry operation, which is conducted in the following manner:

  * a)

Seclude and extract the flux of our source within the aperture (and respective
sky contribution) firstly by multiplying each Fλ by the logical NOT (!) of
maskBSF$\lambda$ (thuswise ensuring that all bright sources within the imaging
frame render 0) obtaining F!maskBS, followed by multiplication with maskap.
The total flux (fap) and number of pixels (nap) within the aperture are
assessed by summing the flux of the image resulting from the previous
operation, and by accounting for every non-zero, non-nan pixel within this
area, respectively. Error estimation ($\sigma_{\rm ap}$) is performed by
exploiting the _python_ sub-modules _calc_total_error_ and
_aperture_photometry_ from _photutils_ , using the individual RMS and the
previously compiled exposure times. The number of valid pixels within the
aperture (i.e., non-zero and non-nan) is estimated. If lower than 30% of the
total number of pixels within the aperture, this particular Fλ will not be
considered in later stages of the processing.

  * b)

Multiply each F!maskBS by the mask annulus masksky. The average flux within
the annulus ($\mu_{\rm sky}$) is estimated by summing the image flux resulting
from the preceding operation and subsequent division by the number of non-zero
pixels within this area. In addition, the mode (Mosky) and the standard
deviation of the sky ($\sigma_{\rm sky}$) are estimated.

  * c)

The sky flux within the aperture area (fsky) is assessed by multiplication of
$\mu_{\rm sky}$ by nap. Finally, the corrected flux radiated by our source is
given by fgal = fap \- fsky.

Once the SED is extracted, as a control point, the module offers visual aid as
illustrated by Fig. 2 for an example source (galaxy located at RA: 214.32208∘,
DEC: 52.538199∘). It is exhibited the various bandpasses that will be
considered for this source in the SED fitting procedure, with the white circle
depicting the adopted aperture whereas the black annulus indicates the area of
the sky used for sky statistics. It is additionally depicted each
maskBSF$\lambda$ overlapping external bright sources. On top of each frame it
is inscribed the name of Fλ and the adopted aperture. The red "x" indicates a
non-detection, i.e., an upper limit for the SED fitting tools.

####  3.3.3 Identifying non-detections

When considering the multi-wavelength data and the subsequent SED fitting
operation, it is crucial to devise a strategy for the non-supervised, accurate
identification of the presence/absence of emission in the current frame.
Intending to determine the criteria that are well-suited for this task, it
were drafted a set of trials adopting different conditions that were later
evaluated by visual inspection of an exhaustive number of individual frames.
This experiment resulted in the choice of the following criteria, used for the
determination of a positive detection:

  * c1)

the circle’s area, encompassing pixels brighter than Mo${\rm sky}$ \+
3$\sigma{\rm sky}$ within the aperture, should be greater than or equal to the
area of a circle equivalent to the FWHM of the instrument’s PSF. In this
manner we assure that the detection (herein defined as 3$\sigma_{\rm sky}$
above the sky level) can be resolved by the observing telescope.

  * c2)

the fraction of the number of pixels within Fap which are brighter than Mosky
\+ 3$\sigma_{\rm sky}$ must be higher than the same within fsky, assuring that
the percentage of pixels that are at least 3$\sigma_{\rm sky}$ above the sky
level is superior within the aperture, as compared to the same within the sky
annulus.

When the algorithm adjudicates a non-detection, the observed flux will be
assigned to the SED fitting tools as an upper limit.

The pipeline continues by performing aperture corrections to all multi-
wavelength data whenever deemed necessary (i.e., when Rap is lower enough not
to encompass at least 95% of the source’s luminosity, as given by SExtractor),
following the recipes provided by each instrument’s handbook (see sub-sect.
3.2 for helpful references). Error estimation for the multi-wavelength data
that do not provide RMS maps, such as 2MASS, is executed by adopting the
prescriptions described in Cutri et al., (2003). Regarding Herschel data, as
indicated by Balog et al., (2014) and Bendo et al., (2013), respectively, the
flux error is of 5% in all three PACS filters and 5.5% in all SPIRE filters.

####  3.3.4 Conversion of the data units to physical units of flux

Subsequently, for each Fλ that is not in units of mJy, the measured emission
fgal is converted to physical units of flux, namely, flux density in fλ
(erg$\cdot$cm-2 $\cdot$ Å-1 $\cdot$ s-1) and fν (erg$\cdot$cm-2 $\cdot$
Hz$\cdot$ s-1), mJy and mAB. This is achieved by following the prescription:

$\begin{split}\rm m_{AB}&=\rm-2.5\;log_{10}(f_{gal})+zpt+AB-cF\\\
\rm\sigma_{AB}&=(2.5/\rm ln(10))\cdot(\sigma_{\rm gal}/\rm f_{gal})\\\ \rm
f_{\lambda}&=\rm 10^{(m_{AB}\;+\;48.60)/-2.5}\cdot(c/\lambda_{\rm eff}^{2})\\\
\rm\sigma_{\lambda}&=\rm f_{\lambda}-\rm
10^{(m_{AB}+\;\sigma_{AB}\;+\;48.60)/-2.5}\cdot(c/\lambda_{\rm eff}^{2})\\\
\rm f_{\nu}&=\rm 10^{(m_{AB}\;+\;48.60)/-2.5}\\\ \rm\sigma_{\nu}&=\rm
f_{\nu}-10^{(m_{AB}+\;\sigma_{AB}\;+\;48.60)/-2.5}\\\ \end{split}$ (4)

where, for each Fλ, zpt is the zero-point magnitude, AB is the AB correction
factor between the Vega magnitude system, cF is the aperture correction factor
and c is the speed of light in units of Å/s.

For Fλ that are in units of mJy, such as all passbands from Spitzer and
Herschel, the reverse operations are applied, thus retrieving the flux in the
remaining units. Both the optical photo-spectra and the SED in all the
aforementioned flux units are stored in two fits files.

Figure 3: Visual output resulting from the module outlined in sub-section
3.3.5. In the left-hand-side it displays the RGB image of the source with the
adopted aperture overplotted in white, and in the right-hand-side it exhibits
the rest-frame optical photo-spectra obtained for the source located at RA:
214.32208∘, DEC: 52.538199∘ (and its respective spline-interpolation in grey),
along with the [OIII] emission line outlined by the light-blue dashed line.
The determined continuum is depicted by the purple dashed line. The shaded
light-blue area highlights the region where the emission line was identified.

####  3.3.5 Detecting and assessing emission lines

After de-redshifting the extracted optical photo-spectra, the subsequent step
consists in analysing the latter, seeking for emission lines. The procedure
begins by executing the sub-module _find_lines_threshold_ from the _python_
package, _specutils_ , which operates by identifying deviations larger than
the spectrum’s assessed uncertainty. This module was designed for the analysis
of spectral data where emission lines are substantially better resolved, with
its detection being therefore straightforward. For the case of low resolution
spectra such as the one obtained from miniJPAS imaging data, it is frequent
for the aforementioned routine to erroneously identify emission lines. To
overcome this issue, the list of detections proceeding from this _python_
routine will be examined, being contrasted with a record of all the emission
lines most commonly observed in star-forming systems, namely, Ly-$\alpha$,
CIV, [OII], MgII, H$\beta$, [OIII], H$\alpha$, [NII] & [SII]. Provided that
there are entries to which the observed wavelength differs less than 75 Å from
the central wavelength of any of the well-known emission lines, it is
considered a true detection.

Modelling of the detected emission lines is performed by means of the sub-
module _RickerWavelet1D_ from _astropy_ (after empirically determining that,
in most cases, the latter provided better results as compared to a Gaussian
fit), thus obtaining the mean (central wavelength) and standard deviation
(broadness) for each emission line that was previously detected. The continuum
in the vicinity of the emission line is estimated by fitting a second degree
polynomial to the photo-spectra, deprived of all deviant points (defined as
being lower/higher than the average flux minus/plus its estimated standard
deviation). Fluxes and EWs are determined by means of the _specutils_ sub-
modules _line_flux_ and _equivalent_width_ , respectively, applied in the
spectral region between the previously estimated central wavelength of each
detected emission line minus/plus its standard deviation. After conducting
several tests to establish the threshold of detectability, the empirical limit
of 25 Å was selected as the minimum acceptable equivalent width (EW) (note
that the narrow bands from JPAS partly overlap, hindering any exact assessment
of fluxes and EWs of emission lines). If the detected emission line is listed
in the CIGALE default filters, its integrated flux will be taken into account
in the SED fitting operation (the poor precision of the measured fluxes should
not obstruct the SED fitting procedure, considering that CIGALE does not
require exact measurements but merely their order of magnitude). An
illustrative example is given by Fig. 3, which displays the visual output
generated by this module for one of the processed galaxies. It is displayed
the RGB image in the left-hand-side with the adopted aperture overplotted in
white, and the restframe photo-spectra in the right-hand-side, where it is
highlighted the detected emission line (most probably resulting from the sum
of [OIII]4959 and [OIII]5007), with an estimated EW of 592 Å). The light-blue
dashed line depicts the fit to the emission line and the purple dashed line
the estimated continuum utilized for the determination of the line EW.

####  3.3.6 Executing CIGALE

At this stage, the present module initiates the execution of an additional
module which addresses the creation of the CIGALE input files and subsequent
execution. For the sake of completeness, we provide a brief description of the
necessary files: the CIGALE data file must contain one row with the galaxy ID,
adopted redshift and the flux values in units of mJy. The model configuration
file (_pcigale.ini_) will define CIGALE’s context, encoding the
characteristics of the data (namely the passbands to which the values in the
data file correspond) and of the models that will be generated to fit the
observational data. By default it is assumed a Salpeter IMF (Salpeter,, 1955)
and a delayed star-formation history (SFH) with optional exponential burst. To
note that originally CIGALE does not have the JPAS filter scheme installed.
This operation must be previously performed independently (see Burgarella,
Buat, & Iglesias-Páramo, 2005, for detailed information on CIGALE and how to
prepare and execute it).

As a control point, an additional illustration is produced (Fig. 4),
displaying the observed SED to be fitted by CIGALE and Prospector,
subsequently. It indicates the spectral regions of the various pass-bands,
color coded from blue to red by increasing wavelength, and the emission lines
that were detected in grey. The observational points are displayed in black if
considered detections and dark-red if deemed non-detections by the technique
elaborated in sub-sect. 3.3.3.

Figure 4: Illustration of the observed SED prior of fitting for the sample
galaxy displayed in previous figures, with the x axis in logarithmic units and
the y axis in units of mJy. The spectral regions of the different passbands
are shown, with colors ranging blue to red, and the filed points are color
coded according to a detection (black) or non-detection (dark-red). It is
additionally highlighted in grey the region where the emission line was
detected.

Once CIGALE’s operation is complete, the previously introduced module 3.3
generates a graphical output of the resulting fit, additionally displaying
some of the obtained physical properties, as apparent from inspection of the
left-hand side of Fig. 5. The latter displays the best-fit SED model retrieved
by CIGALE and the observational points and the upper limits in black and red,
respectively.

Figure 5: Visual output illustrating the SED fitting results by CIGALE (left-
hand side) and Prospector (right-hand side) for the same exemplar. Both the
flux density (in units of 1e-17 erg/cm${}^{2}\cdot$ s $\cdot$ Å) and the
restframe wavelength (Å) are displayed in a logarithmic scale. The best-
fitting SED models are represented by solid blue lines, the observational
points by the black points and the upper limits by the dark red points. The
residuals are shown in the low panel and some of the derived physical
properties are shown in the image title.

####  3.3.7 Executing Prospector

The module accountable for the execution of Prospector starts by assembling
the two models for inference, implementing a parametric (delayed-$\tau$) and a
non-parametric ($\alpha$, Leja et al.,, 2017) star-formation history (SFH).
The model referring to the parametric SFH was constructed by selecting the
default parametric SFH from Prospector template library, with a log uniform
mass prior, and adding the models for both dust and nebular emission (see
Prospector user guide for a comprehensive description of the provided models).
With regard to Prospector-$\alpha$, it includes dust attenuation and re-
radiation, a flexible attenuation curve, nebular emission, stellar
metallicity, and a non-parametric SFH with 6-components. The simple stellar
population (SSP) library is produced according to the adopted SFH. In
addition, nebular emission and dust (and AGN, if the object was previously
classified as QSO) contribution are included. The redshift is fixed and it is
adopted a Salpeter IMF. The dictionary encompassing the observational data is
assembled, which comprises the list of the utilized passbands, the evaluated
fluxes and respective uncertainties in each filter in maggies units, and the
effective wavelength for each of the filters. The subsequent step is to
optimize the parameters of the calibration functions for each generated model.
The posterior probability distribution is estimated by sampling, either
through an affine-transform-invariant version of Metropolis-Hastings MCMC
($emcee$) or nested sampling ($dynesty$). Hereupon, the SED is fitted and the
results are stored. Similarly as the CIGALE module, a graphical output is
produced, summarizing the obtained physical properties for both models (such
as present-day stellar mass (M⋆), mass-weighted mean stellar age (tM) and
metallicity (ZM), and SFRs), and displaying the resulting best-fitting models,
as exemplified by the right-hand side of Fig. 5.

Figure 6: Visual representation of the estimates of the present-day stellar
mass, SFR, mass-weighted stellar age and metallicity (from left to right) for
the three models, for the sample galaxies. Datapoints (EELGs and QSOs) are
color coded according to the logarithm of their redshift and QSOs are
encircled within larger, empty circles. The solid line represents unity and
the dashed lines the average dispersion (1$\sigma$).

####  3.3.8 Estimate restframe SDSS colors and magnitudes, fluxes and
luminosities for each bandpass

Subsequently, restframe magnitudes, fluxes and luminosities, along with
intrinsic colors and apparent and absolute magnitudes in the SDSS filters $i$,
$g$ and $r$ are estimated. Such is accomplished by convolving each of the
filter transmission curves T$(\lambda)$ with the restframe SED, as
subsequently clarified.

Primarily, the restframe best-fitting SED is interpolated to the step of each
T$(\lambda)$, followed by its normalization, so that the sum of its flux
density is equal to one, resulting in sp$(\lambda)$. Subsequently, to obtain
the flux in each bandpass f$\rm{}_{F_{\lambda}}$ in units of fλ
(erg$\cdot$cm-2 $\cdot$ Å-1 $\cdot$ s-1), it is applied the successive
equation:

$\rm f_{\rm F_{\lambda}}=\dfrac{\int\rm sp(\lambda)\cdot\rm
T(\lambda)\cdot\lambda\>d\lambda}{\int\rm sp(\lambda)\cdot\rm
T(\lambda)\>d\lambda},$ (5)

Figure 7: Absolute magnitude in the $r$ SDSS optical band versus the estimated
present-day stellar mass M⋆ as obtained by CIGALE (left-hand side column),
Prospector delayed-$\tau$ (central column) and Prospector-$\alpha$ (right-hand
side column). Analogously to Fig. 6, data-points are color coded according to
the logarithm of their redshift and QSOs are encircled within larger, empty
circles. The transparent points represent the reference EELG sample.

Conversion to fν units (erg$\cdot$cm-2 $\cdot$ Hz$\cdot$ s-1) is performed by
multiplying f${}_{\rm F_{\lambda}}$ by $\lambda_{\rm eff}^{2}$ / c $\times$
1e1023, obtaining f$\rm\nu_{F_{\lambda}}$, which in turn is converted to
apparent and absolute magnitudes101010SDSS magnitudes are obtained by
processing the unattenuated best-fitting SED (m/M) through:

$\begin{split}\rm m&=\rm-2.5\;log_{10}(f\nu_{F_{\lambda}}/3631),\\\ \rm M&=\rm
m-25+5\;log_{10}(D),\end{split}$ (6)

where D is the galaxy distance in units of Mpc. Restframe SDSS colors are
estimated through:

$\begin{split}(g-i)&=\rm m_{g}-m_{i},\\\ (g-r)&=\rm m_{g}-m_{r},\\\ (r-i)&=\rm
m_{r}-m_{i}.\end{split}$ (7)

Finally, restframe luminosities within each bandpass L${}_{\rm F_{\lambda}}$
are estimated by:

$\rm log(L_{\rm F_{\lambda}})=\rm f_{\rm F_{\lambda}}\cdot 4\pi R^{2},$ (8)

where R is equal to D in units of cm.

##  4 Exploration of the physical properties of the miniJPAS EELGs

The coordinates of the 31 extreme line emitters identified by the IP22
algorithm (20 EELGs and 11 QSOs) in the miniJPAS data were provided to the
pipeline described in the previous section . In this fashion, their multi-
wavelength photometry was retrieved and analysed by both CIGALE and Prospector
(delayed-$\tau$ and $\alpha$ SFH models), thus obtaining stellar mass, mass-
weighted stellar age estimates, SFRs, AGN fractions, etc.. In addition, by
convolving the resulting SED with the filter transmission curves of each
instrument, we retrieved absolute magnitudes and colors. The collected results
were contrasted with the physical properties of a reference sample of $\sim$
500 EELGs, as obtained by processing their SDSS optical spectra with FADO
(Gomes & Papaderos,, 2017), as provided by Breda et al. (2022).

Certain galactic properties assessed by the various SED fitting operations
here probed are displayed in Fig. 6, which illustrates, from left to right,
the estimates for the stellar mass, SFRs, mass-weighted mean stellar age and
metallicity. The solid line represents the equality line and the dashed lines
denote the mean dispersion of the differences between the estimates obtained
for each model. Inspection of this figure evidences that the stellar mass and
the SFRs are fairly consistent (the mean $\sigma$ of the differences between
models is of 0.5 dex for the stellar mass estimates and of 0.35 M⊙/yr for the
SFR), although Prospector delayed-$\tau$ tends to provide lower stellar mass
underestimates. Nevertheless, it is evident that, as expected, QSOs register
rather higher differences as compared with the EELGs. Regarding the stellar
properties, the mass-weighted mean stellar ages display a higher degree of
variation, with an average dispersion of 0.73 dex, whereas the mass-weighted
mean stellar metallicities exhibit a mild degree of inconsistency between the
three sets of estimates (with their differences displaying an average $\sigma$
of 0.16 Z⊙). Such result is also observed in previous works using state-of-
the-art spectral synthesis techniques, which are the most reliable method
available for the characterization of stellar populations in galaxies (e.g.,
Breda et al., 2022, and references within).

Figure 8: Relation between the logarithm of the stellar surface density
$\Sigma_{\star}$ and the logarithm of the estimated mass-weighted stellar age
tM as obtained for the three SED fitting operations and contrasted with the
same for the reference sample.

Figure 7 illustrates the stellar mass estimates versus the absolute magnitude
in the three SDSS bands $g$, $r$ and $i$, as obtained by both SED fitting
tools, simultaneously while contrasting with the same for the reference EELG
sample. Generally it is observed a sound agreement between the EELGs here in
study and the reference sample, with the results by CIGALE and
Prospector-$\alpha$ displaying higher coherency and lower levels of
dispersion. In contrast, stellar masses recovered by Prospector delayed-$\tau$
tend to be systematically lower for the respective intrinsic magnitudes, as
compared to the reference sample, being in average 0.42 dex lower as compared
to the stellar masses recovered by CIGALE. These findings are remarkably
consistent with previous results – in Lower et al. (2020), the authors compare
the derived galaxy properties obtained by SED fitting, by assuming a
parametric and a non-parametric model, for a set of $\sim$ 1600 mock galaxies
from the SIMBA cosmological simulation (Davé et al.,, 2019), with stellar
masses ranging from 4.4$\times$107 to 1.4$\times$1012. As illustrated by their
Fig.3, these authors report that the estimated stellar masses assuming the
Prospector delayed-$\tau$ model were, on average, approximately $\sim$0.4 dex
lower than the true stellar mass. In contrast, the Prospector-$\alpha$ model
recovers more accurately the true stellar masses within their sample.
Regarding the QSOs, as expected, these are located at the high mass vs. high
magnitude locus, yet following the same extrapolated trend for the SDSS EELG
sample, demonstrating that the stellar mass estimates obtained for the EELGs
and QSOs identified in miniJPAS are sound. An additional test was conducted by
running the sample exclusively using optical JPAS data. Such resulted in a
systematically higher SFR ($\sim$ 0.42 dex, in average), with no significant
variation in the remaining parameters. This result is not surprising,
considering that the FIR photometry which constrain the dust temperature, is
tightly correlated with the level of SFR (the FIR photometric points, or upper
limits in most cases, compel the solution to converge to lower dust
temperatures, implying lower SFRs).

An additional test was conducted by running the sample exclusively using
optical JPAS data. The obtained results remained consistent with those derived
from utilizing the complete set of available photometric data, showing
slightly higher SFRs.

The stellar surface density was computed by dividing the obtained stellar mass
estimates by the projected area (i.e., $\Sigma_{\star}$ = M⋆ / $\pi$ R${}_{\rm
gal}^{2}$). The relations between the logarithm of $\Sigma_{\star}$ and the
estimated logarithm of the mass-weighted stellar age is shown in Fig. 8 for
the three SED fitting operations (from left to right, CIGALE, Prospector
delayed-$\tau$ and Prospector-$\alpha$). Although there are severe differences
between the mass-weighted stellar age estimates as given by the three SED
fitting procedures, they all roughly follow the reference correlation.

Finally, the top panels of Fig. 9 illustrate the logarithm of the present-day
stellar mass M⋆ versus the logarithm of the specific star formation rate sSFR
(i.e., the SFR divided by the estimated stellar mass) whereas the bottom
panels display the relation between the estimated mass-weighted mean stellar
age tM and the logarithm of the sSFR, for the three SED fitting procedures.

Comparison between the three SED fitting operations indicates that the
obtained results are consistent with the reference correlations. Nevertheless,
although the stellar mass estimates and sSFRs are fairly coherent between
models, the parametric model (Prospector delayed-$\tau$) tends to
underestimate the total stellar mass as compared to the remaining
determinations, as previously noted by Leja et al., 2019a ; Leja et al., 2019c
; Leja et al., (2020). Regarding mass-weighted stellar ages and metallicities,
these display higher level of variance. As clearly shown by the middle panel
of the bottom row of Fig. 9, mean stellar ages recovered by Prospector
delayed-$\tau$ tightly correlate with the sSFR. Such result is expected
considering that stellar ages are a direct function of the SFH (i.e., the
adopted $\tau$), being analytically computed from the assumed SFH, contrasting
with the Dirichlet SFH prior (Prospector-$\alpha$) which only weakly couples
the SFR to the earlier SFH (Leja et al.,, 2017, 2018). This outcome reinforces
the notion that parametric methods are over-simplistic and incapable of
realistically recovering the complex SFHs of galaxies as the EELGs studied
herein (e.g., Lower et al., 2020). Inspection of this panel additionally
reveals an inconsistency between the theoretical prediction and observations,
strongly suggesting that an exponentially delayed SFH with a specific $\tau$
is over-simplistic, being unable to appropriately describe the SFH of EELGs,
which are expected to be considerably more complex. On the other hand, the
mean stellar ages estimated by Prospector-$\alpha$ tend to accumulate at the
high end of the parameter space. For the lower mass galaxies, the mass-
weighted stellar age given by this model appear over-estimated as compared to
the reference sample, as reflected by the third panel of Fig. 8.

Besides the registered inconsistencies, mainly in stellar ages and
metallicities, overall the obtained relations indicate that the developed
pipeline produces physically reasonable results and is an appropriate tool for
the automatic multi-wavelength analysis and assessment of the main properties
of galaxies which lack spectroscopic information.

Figure 9: The top panels illustrate the logarithm of the present-day stellar
mass M⋆ versus the logarithm of the specific star formation rate sSFR, i.e.,
the SFR over the estimated stellar mass. The bottom panels display the
logarithm of the mass-weighted stellar age tM versus the logarithm of the
specific star formation rate. The three panels display the respective results
for each of the three SED fitting operations (from left to right, CIGALE,
Prospector delayed-$\tau$ and Prospector-$\alpha$).

##  5 Summary and conclusions

The present work describes the development of a fully automated, modular
pipeline with the main purpose of the characterization of galaxies with absent
spectroscopic information, through a multi-wavelength exploration. The
pipeline, scripted in the _python_ programming language, fully open-access and
offered to the community via the repository GitHub, requires only the
coordinates of the desired object.

It commences by retrieving the available photometric information of the source
by seeking in several repositories, namely in the miniJPAS, MAST and IRSA-IPAC
databases, collecting photometric data from GALEX, JPAS, 2MASS, WISE, Spitzer
and Herschel, and the respective RMS maps, when available. Subsequently, and
after masking all bright sources apart from the one under study, it performs
aperture photometry in all available passbands, with adapted apertures
according to the optical radial extent of the object and the respective FWHM
of each instrument. It follows by identifying non-detections, which will be
interpreted as upper limits in the SED fitting operations while constructing
the observed SED. Additionally, the pipeline detects and assesses emission
lines in the optical data, whose integrated fluxes will be used as input in
the SED fitting with CIGALE. Finally, it initiates the SED fitting procedures
by means of two distinct SED fitting tools (CIGALE and Prospector), for three
individual models.

This strategy, contrary to retrieving data from the available catalogues with
fixed apertures, assures consistency and homogeneity throughout the whole set
of multi-wavelength data, which is particularly important, specially in light
of the faint nature of these objects. The obtained results are in overall
agreement with previously known relations derived from detailed spectral
synthesis of a sample of $\sim$ 500 EELGs from SDSS, reassuring reliability of
the developed method. In addition, the soundness of the retrieved photometry
was assessed, revealing solid agreement between the reference values, and
demonstrating that the herein developed pipeline is suitable for the automated
study of bright and faint sources, point-like or extended, and at local or
high-z.

While EELGs are infrequent in our nearby cosmic neighborhood, their study
holds great significance, since these serve as valuable counterparts within
our local environment mirroring the characteristics of star-forming galaxies
from the early Universe, similar to those newly uncovered by the James Webb
Space Telescope (JWST; e.g., Withers et al.,, 2023). This work serves as a
prof of concept, demonstrating the reliability of the developed pipeline,
paving the way for attaining massive and automated characterization of the
EELGs that will be soon detected within the $\sim$ 8000 deg2 of the northen
sky covered by JPAS.

## Acknowledgements

I.B., J.V.M., J.I.P., C.K., E.P.M, A.A.P. and R.G.D. acknowledge financial
support from the State Agency for Research of the Spanish MCIU through the
"Center of Excellence Severo Ochoa" award to the Instituto de Astrofísica de
Andalucía (SEV-2017-0709). J.V.M., J.I.P., C.K., and E.P.M. acknowledge
financial support from projects Estallidos7 PID2019-107408GB-C44 (Spanish
Ministerio de Ciencia e Innovacion), and grant P18-FR-2664 (Junta de
Andalucía). R.G.D. and L.A.D.G. acknowledge financial support from the State
Agency for Research of the Spanish MCIU through ’Center of Excellence Severo
Ochoa’ award to the Instituto de Astrofísica de Andalucía (SEV-2017-0709) and
CEX2021-001131-S funded by MCIN/AEI/10.13039/501100011033. L.A.D.G., R.M.G.D.,
R.G.B., G.M.S., and J.E.R.M. are also grateful for financial support from the
projects PID-2019-109067-GB100 and PID2022-141755NB-I00.

Based on observations made with the JST250 telescope and PathFinder camera for
the miniJPAS project at the Observatorio Astrofísico de Javalambre (OAJ), in
Teruel, owned, managed, and operated by the Centro de Estudios de Física del
Cosmos de Aragón (CEFCA). We acknowledge the OAJ Data Processing and Archiving
Unit (UPAD) for reducing and calibrating the OAJ data used in this work.

Funding for OAJ, UPAD, and CEFCA has been provided by the Governments of Spain
and Aragón through the Fondo de Inversiones de Teruel; the Aragonese
Government through the Research Groups E96, E103, E16_17R, and E16_20R; the
Spanish Ministry of Science, Innovation and Universities (MCIU/AEI/FEDER, UE)
with grant PGC2018-097585-B-C21; the Spanish Ministry of Economy and
Competitiveness (MINECO/FEDER, UE) under AYA2015-66211-C2-1-P,
AYA2015-66211-C2-2, AYA2012-30789, and ICTS-2009-14; and European FEDER
funding (FCDD10-4E-867, FCDD13-4E-2685).

## Data Availability

The data underlying this article are publicly available in the miniJPAS
archive, at
https://www.j-pas.org/datareleases/minijpas_public_data_release_pdr201912.

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