# Investigating the Effect of Galaxy Interactions on Star Formation at
$0.5<z<3.0$

Ekta A. Shah Laboratory for Multiwavelength Astrophysics, School of Physics
and Astronomy, Rochester Institute of Technology, 84 Lomb Memorial Drive,
Rochester, NY 14623, USA  Department of Physics and Astronomy, University of
California, Davis, One Shields Ave, Davis, CA 95616, USA  LSSTC DSFP Fellow
Jeyhan S. Kartaltepe Laboratory for Multiwavelength Astrophysics, School of
Physics and Astronomy, Rochester Institute of Technology, 84 Lomb Memorial
Drive, Rochester, NY 14623, USA  Christina T. Magagnoli Laboratory for
Multiwavelength Astrophysics, School of Physics and Astronomy, Rochester
Institute of Technology, 84 Lomb Memorial Drive, Rochester, NY 14623, USA
Isabella G. Cox Laboratory for Multiwavelength Astrophysics, School of Physics
and Astronomy, Rochester Institute of Technology, 84 Lomb Memorial Drive,
Rochester, NY 14623, USA  Caleb T. Wetherell  Laboratory for Multiwavelength
Astrophysics, School of Physics and Astronomy, Rochester Institute of
Technology, 84 Lomb Memorial Drive, Rochester, NY 14623, USA  Brittany N.
Vanderhoof Laboratory for Multiwavelength Astrophysics, School of Physics and
Astronomy, Rochester Institute of Technology, 84 Lomb Memorial Drive,
Rochester, NY 14623, USA  Kevin C. Cooke Laboratory for Multiwavelength
Astrophysics, School of Physics and Astronomy, Rochester Institute of
Technology, 84 Lomb Memorial Drive, Rochester, NY 14623, USA  AAAS S&T Policy
Fellow hosted at the National Science Foundation,1200 New York Ave, NW,
Washington, DC, US 20005  Department of Physics & Astronomy, University of
Kansas, Lawrence, KS 66045, USA  Antonello Calabro INAF OAR, via Frascati 33,
Monte Porzio Catone 00078, Italy  Nima Chartab Department of Physics and
Astronomy, University of California, Riverside, 900 University Ave, Riverside,
CA 92521, USA  Christopher J. Conselice Centre for Particle Theory and
Astronomy, University of Nottingham, Nottingham NG7 2RD, UK  Darren J. Croton
Centre for Astrophysics & Supercomputing, Swinburne University of Technology,
P.O. Box 218, Hawthorn, Victoria 3122, Australia  ARC Centre of Excellence for
All Sky Astrophysics in 3 Dimensions (ASTRO 3D)  Alexander de la Vega
Department of Physics and Astronomy, Johns Hopkins University, Baltimore, MD,
21218  Nimish P. Hathi Space Telescope Science Institute, 3700 San Martin Dr.,
Baltimore, MD 21218, USA  Olivier Ilbert Aix Marseille Université, CNRS, LAM
(Laboratoire d’Astrophysique de Marseille) UMR 7326, 13388, Marseille, France
Hanae Inami Hiroshima Astrophysical Science Center, Hiroshima University,
1-3-1 Kagamiyama, Higashi-Hiroshima, Hiroshima 739-8526, Japan  Dale D.
Kocevski Department of Physics and Astronomy, Colby College, Waterville, ME
04961, USA  Anton M. Koekemoer Space Telescope Science Institute, 3700 San
Martin Dr., Baltimore, MD 21218, USA  Brian C. Lemaux Department of Physics &
Astronomy, University of California, Davis, One Shields Ave., Davis, CA 95616,
USA  Gemini Observatory, 670 N. A’ohoku Place Hilo, Hawaii, 96720, USA  Lori
Lubin  Department of Physics & Astronomy, University of California, Davis, One
Shields Ave., Davis, CA 95616, USA  Kameswara Bharadwaj Mantha  Department of
Physics and Astronomy, University of Missouri-Kansas City, Kansas City, MO
64110, USA  Stefano Marchesi INAF - Osservatorio di Astrofisica e Scienza
dello Spazio di Bologna, Via Piero Gobetti, 93/3, 40129, Bologna, Italy
Department of Physics and Astronomy, Clemson University, Kinard Lab of
Physics, Clemson, SC 29634, USA  Marie Martig Astrophysics Research Institute,
Liverpool John Moores University, 146 Brownlow Hill, Liverpool L3 5RF, UK
Jorge Moreno Department of Physics and Astronomy, Pomona College, 333 N.
College Way, Claremont, CA 91711, USA  Belen Alcalde Pampliega Departamento de
Física de la Tierra y Astrofísica, Facultad de CC Físicas, Universidad
Complutense de Madrid E-2840 Madrid, Spain  David R. Patton Department of
Physics and Astronomy, Trent University, 1600 West Bank Drive, Peterborough ON
K9L 0G2, Canada  Mara Salvato Max-Planck-Institut fu ̵̈r extraterrestrische
Physik (MPE), Giessenbachstrasse 1, D-85748 Garching bei Mu ̵̈nchen, Germany
Ezequiel Treister Instituto de Astrofisica, Facultad de Fisica, Pontificia
Universidad Catolica de Chile, Casilla 306, Santiago 22, Chile

(Received July 19, 2022; Revised September 28, 2022; Accepted September 29,
2022)

###### Abstract

Observations and simulations of interacting galaxies and mergers in the local
universe have shown that interactions can significantly enhance the star
formation rates (SFR) and fueling of Active Galactic Nuclei (AGN). However, at
higher redshift, some simulations suggest that the level of star formation
enhancement induced by interactions is lower due to the higher gas fractions
and already increased SFRs in these galaxies. To test this, we measure the SFR
enhancement in a total of 2351 (1327) massive ($M_{*}>10^{10}M_{\odot}$) major
($1<M_{1}/M_{2}<4$) spectroscopic galaxy pairs at $0.5<z<3.0$ with $\Delta
V<5000$ km s-1 ($1000$ km s-1) and projected separation $<150$ kpc selected
from the extensive spectroscopic coverage in the COSMOS and CANDELS fields. We
find that the highest level of SFR enhancement is a factor of
1.23${}^{+0.08}_{-0.09}$ in the closest projected separation bin ($<25$ kpc)
relative to a stellar mass-, redshift-, and environment-matched control sample
of isolated galaxies. We find that the level of SFR enhancement is a factor of
$\sim 1.5$ higher at $0.5<z<1$ than at $1<z<3$ in the closest projected
separation bin. Among a sample of visually identified mergers, we find an
enhancement of a factor of 1.86${}^{+0.29}_{-0.18}$ for coalesced systems. For
this visually identified sample, we see a clear trend of increased SFR
enhancement with decreasing projected separation (2.40${}^{+0.62}_{-0.37}$ vs.
1.58${}^{+0.29}_{-0.20}$ for $0.5<z<1.6$ and $1.6<z<3.0$, respectively). The
SFR enhancement seen in our interactions and mergers are all lower than the
level seen in local samples at the same separation, suggesting that the level
of interaction-induced star formation evolves significantly over this time
period.

Galaxies: active, distances and redshifts, evolution, high-redshift,
interactions, irregular

††journal: ApJ

##  1 Introduction

Galaxy interactions and mergers can have a substantial impact on the evolution
of galaxies. Simulations (e.g., Mihos & Hernquist, 1996; Hopkins et al., 2008;
Di Matteo et al., 2008; Scudder et al., 2012; Moreno et al., 2015, 2019) of
galaxies in the nearby universe show that interactions and mergers cause
strong gravitational torques resulting in gas inflows towards the central
regions of the galaxies, possibly resulting in nuclear starbursts and the
triggering of AGN. This scenario is also supported by observations in the
nearby universe. For example, most of the nearby Ultraluminous Infrared
Galaxies (ULIRGs) and quasars ($>80\%$) show signatures of either an ongoing
or recent galaxy merger (Sanders et al., 1988a, b; Urrutia et al., 2008).
Simulations also show an increased star formation rate (SFR) during an
interaction throughout the galaxy, including in tidal tails caused by tidal
interaction-induced accretion, redistribution, and compression of gas (Renaud
et al., 2009, 2015; Moreno et al., 2021). Local galaxies with substantial
tidal tails, such as the galaxy pair known as “The Mice,” are clear examples
of this process (Barnes, 2004).

While galaxies that show strong visually identified morphological signatures
of an interaction or merger can be used to study the late stages of galaxy
interactions, these systems do not represent the complete merger sequence.
This is because the resulting morphological signatures depend on many
intrinsic factors of the interacting galaxies (e.g., their orbit, mass ratio,
gas fraction, and initial morphology), and the observability of these
signatures also depends on the redshift, viewing angle, depth, and wavelength
of the observations (Lotz et al., 2011; Blumenthal et al., 2020). Hence, a
spectroscopically confirmed kinematic galaxy pair sample identified based on
close physical proximity of two galaxies (which may or may not show
morphological merger signatures) is essential to develop a complete
understanding of the merger process and its effect on galaxy properties.

Numerous studies in the local universe compare the SFR of interacting galaxies
and mergers with isolated (control) galaxies (e.g., Larson & Tinsley, 1978;
Donzelli & Pastoriza, 1997; Bergvall et al., 2003; Lambas et al., 2003; Alonso
et al., 2004; Woods & Geller, 2007; Ellison et al., 2008; Knapen & James,
2009; Robaina et al., 2009; Darg et al., 2010; Xu et al., 2010; Ellison et
al., 2013b; Barrera-Ballesteros et al., 2015). Most of these studies find the
largest increase in the SFRs of galaxies in pairs relative to their controls
galaxies at projected separations of $<$30 kpc. They also find a trend of
increasing relative SFRs with decreasing projected separations of pairs.
Notably, Patton et al. (2013) observe a significant level of SFR enhancement
in local galaxy pairs with projected separations out to 150 kpc.

There is some evidence from simulations that galaxy interactions and mergers
may not enhance SFRs to the same degree at higher redshift (e.g., Fensch et
al., 2017; Patton et al., 2020). For example, based on an idealized binary
simulation of galaxy mergers, Fensch et al. (2017) find that the excess of
merger-induced star formation and its duration are both about ten times lower
at high redshift ($z\sim 2$: gas fraction $\sim 60\%$, where gas fraction =
$\frac{M_{gas}}{M_{gas}+M_{*}}$) compared to low redshift galaxies ($z\sim 0$:
gas fraction $\sim 10\%$). Other studies based on idealized binary simulations
of mergers of galaxies with high gas fractions also suggest a lower peak and
duration in SFR enhancement (Bournaud et al., 2011; Hopkins et al., 2013;
Scudder et al., 2015). Perret et al. (2014) do not find any SFR enhancement in
high redshift ($1<z<2$) galaxy mergers in the MIRAGE simulations. While Patton
et al. (2020) and Martin et al. (2017) find some evidence for a decrease in
SFR enhancement with increasing redshift ($0<z<1$) in interacting galaxies in
the IllustrisTNG and Horizon-AGN cosmological simulations, respectively, Hani
et al. (2020) find no redshift dependence of SFR enhancement in post-merger
galaxies ($0<z<1$) in IllustrisTNG. While many of these studies based on
simulations find significant differences between interaction-induced SFR
enhancement for high- and low-redshift interactions, these predictions have
yet to be confirmed using through observations of high redshift galaxy pairs.

To date, there have been a few observational studies on star formation
enhancement in high redshift mergers. Kaviraj et al. (2013) find a sSFR
enhancement of a factor of $\sim 2.2$ in major mergers in comparison with non-
interacting galaxies at $z\sim 2$. Lackner et al. (2014) also estimate an
enhancement factor of $\sim 2$ in merging galaxies (projected separation
between 2.2 and 8 kpc) compared to non-merging galaxies at $0.25<z<1.0$.
However, using the same method, Silva et al. (2018) identify merging galaxies
(projected separation between 3 and 15 kpc) at $0.3<z<2.5$ and find no
significant differences in the star formation of merging galaxies and non-
merging galaxies. Using convolutional neural networks, Pearson et al. (2019)
identify more than 200,000 galaxies in the Sloan Digital Sky Survey (SDSS),
the Kilo-Degree Survey (KiDS), and the CANDELS survey images as merging or
non-merging and see a slight SFR enhancement factor of $\sim 1.2$ in the
merging galaxies over $0<z<4$.

The change in the level of interaction-induced SFR enhancement with redshift
becomes highly relevant when studying the role of galaxy mergers in galaxy
evolution at cosmic noon ($1.5<z<3.0$), i.e., around the peak epoch of the
cosmic SFR density (Madau & Dickinson, 2014). It is now well-established that
most star-forming galaxies follow a SFR-M∗ relation often called the star-
forming main sequence (SFMS) (Brinchmann et al., 2004; Elbaz et al., 2007;
Noeske et al., 2007; Whitaker et al., 2014). At low redshifts, the regime
above the SFMS, i.e., the starburst galaxy population, is dominated by late-
stage galaxy mergers (Sanders et al., 1988a; Urrutia et al., 2008). Though
mergers are more frequent at cosmic noon (Duncan et al., 2019), some studies
based on deep observations of high redshift galaxies suggest that mergers may
not be the dominant cause of their increased star formation ($z\sim 2$; e.g.,
Rodighiero et al., 2011; Lackner et al., 2014). Kaviraj et al. (2013) estimate
that major mergers only contribute $\sim 15\%$ to the overall SF budget, while
Osborne et al. (2020) and Lofthouse et al. (2017) estimate even smaller values
of SF contribution ($3-5\%$) from mergers. This lower contribution from
mergers could be due to the fact that galaxies at high redshifts already have
high SFRs, which could make it difficult to increase the SFR even further
through mergers. The precise impact that galaxy interactions and mergers have
on star formation at high redshift is still under debate.

The star formation process is directly tied to the properties of the gas in
galaxies. The average gas fraction in galaxies changes substantially with
redshift (0.2-10% at $z\sim 0$, 40-60% at $z$ $\sim 2$ in spiral galaxies;
Daddi et al., 2010; Tacconi et al., 2010; Scoville et al., 2014). High
redshift galaxies also typically have a clumpier gas distribution with a
higher average velocity dispersion ($\sim 4\times$) than low redshift galaxies
(Stott et al., 2016). While having a much larger gas supply at high redshift
could be useful for forming new stars and generating strong gas inflows, the
high turbulence could make further compression of the gas and generation and
propagation of inflows weaker than in low redshift interactions (e.g., Daddi
et al., 2010; Fensch et al., 2017). Hence, there are multiple redshift-
dependent factors that can affect the level of interaction-induced star
formation enhancement, and studies of large samples of galaxies at high
redshift are needed to determine the combined effect of these redshift-
dependent factors on the interaction-induced SFR enhancement level of
galaxies.

Here, we study the SFR enhancement in the largest sample of massive high-
redshift spectroscopically confirmed major galaxy pairs (2351 pairs; Shah et
al. 2020) to date. We generated this sample using deep multiwavelength
photometric and dedicated spectroscopic observations in the Cosmic Assembly
Near-infrared Deep Extragalactic Legacy Survey (CANDELS; Grogin et al., 2011;
Koekemoer et al., 2011) and the Cosmic Evolution Survey (COSMOS; Scoville et
al., 2007) fields. We use a corresponding control sample generated by matching
the stellar mass, redshift, and environment of isolated galaxies to individual
paired galaxies as described in Shah et al. (2020). We compare the SFRs of the
two samples to estimate the interaction-induced SFR enhancement in paired
galaxies. We also use the same method to study the star formation enhancement
in visually identified samples of late-stage galaxy interactions and mergers
selected from Kartaltepe et al. (2015a).

The layout of this paper is as follows. We describe the observations and data
products used for our analysis in Section 2. In Section 3, we describe the
criteria for generating the pair sample, visually identified interaction and
merger samples, and their corresponding control samples. We estimate the SFRs
of galaxies in our sample in Section 4 and compute the SFR enhancement and
present our results in Section 5. We discuss our results in Section 6 and
summarize this study in Section 7. Throughout this work, we use the standard
$\Lambda$CDM cosmology with $H_{0}=70$ km s-1 Mpc-1, $\Omega_{\Lambda}=0.7$,
and $\Omega_{M}=0.3$. All magnitudes are given in the observed AB system and
mass values of the galaxies correspond to their stellar masses unless stated
otherwise.

##  2 Data

We use spectroscopic and multi-wavelength photometric observations from the
CANDELS (PIs: S. Faber and H. Ferguson; Grogin et al., 2011; Koekemoer et al.,
2011) and COSMOS (Scoville et al., 2007) surveys for our analysis. The deep
and extensive datasets available in the fields observed by these surveys
provide useful observations of a complete sample of massive galaxies with a
stellar mass greater than 1010 M⊙ out to $z\sim 3$. COSMOS is the largest
contiguous area HST survey, covering a $\sim 2$ deg2 area on the sky and
observing more than two million galaxies. Ancillary observations have now been
obtained across the entire electromagnetic spectrum, covering over 40
photometric bands (described in more detail below).

The CANDELS survey consists of HST imaging of five different well-studied
fields, including: (i) a portion of the COSMOS field, (ii) a portion of the
UKIDSS Ultra-Deep Survey (UDS; Lawrence et al., 2007), (iii) the Great
Observatories Origins Deep Survey (GOODS; Giavalisco et al., 2004) -North
(GOODS-N), (iv) GOODS-South (GOODS-S), and (v) the Extended Groth Strip (EGS;
Davis et al., 2007). CANDELS observations provide F160W and F125W imaging
taken with the HST/Wide Field Camera 3 (WFC3) and the F606W and F814W imaging
with the HST/Advanced Camera for Surveys (ACS) as well as other ancillary
observations in each of the five fields as described below.

###  2.1 Photometric Observations

We use CANDELS and COSMOS team-compiled photometric catalogs containing the
positions, stellar masses, photometric redshifts, and fluxes of galaxies at
different wavelengths (see details in Shah et al., 2020). The sources in both
surveys were identified using the source detection algorithm Source Extractor
(Bertin & Arnouts, 1996). The photometric catalogs for the full COSMOS field,
the COSMOS-CANDELS region, UDS, GOODS-N, GOODS-S, and EGS are published in
Laigle et al. (2016), Nayyeri et al. (2017), Galametz et al. (2013), Guo et
al. (2013), Barro et al. (2019), and Stefanon et al. (2017), respectively. We
use the near-UV–Far-Infrared (FIR) observations to compute the SFR and stellar
mass for galaxies in the large COSMOS (Laigle et al., 2016), CANDELS-COSMOS
(Sanders et al., 2007; Ashby et al., 2013; Nayyeri et al., 2017), GOODS (N-S)
(Dickinson et al., 2003; Giavalisco et al., 2004; Ashby et al., 2013), EGS
(Barmby et al., 2008; Ashby et al., 2015) and UDS (Ashby et al., 2013, 2015)
fields using spectral energy distribution (SED) fitting as described in
Section 4.1.

####  2.1.1 Photometry and Physical Properties of Observed Galaxies

We used the COSMOS2015 photometric catalog for the $\sim 2$ deg2 COSMOS field,
which includes photometry of more than half a million galaxies in over 30
bands as well as their estimated stellar mass and photometric redshift values
(Laigle et al., 2016). The photometric redshifts and stellar masses were
estimated using the SED fitting code
LePhare111http://www.cfht.hawaii.edu/~arnouts/LEPHARE/lephare.html (Arnouts et
al., 2002; Ilbert et al., 2006) to fit multiwavelength observations of
galaxies. The fitting process is based on the Chabrier (2003) IMF, two
metallicities (solar and half-solar), emission lines templates from Ilbert et
al. (2009), attenuation curves from (Calzetti et al., 2000; Arnouts et al.,
2013), an exponentially declining and delayed star formation history, and
Stellar Population Synthesis models from Bruzual & Charlot (2003).

The CANDELS team combined the multiwavelength observations with different
spatial resolutions and performed uniform photometry across different filters
using TFIT (Laidler et al., 2007; Lee et al., 2012) to generate the final
photometric catalogs for each of the fields (Guo et al., 2013; Nayyeri et al.,
2017; Galametz et al., 2013; Stefanon et al., 2017; Barro et al., 2019). The
team estimated the photometric redshifts of galaxies using the method of
Dahlen et al. (2013), which combines the posterior probability distribution of
photometric redshifts from various SED fitting codes and templates. The method
chooses the best-estimated value of photometric redshift by selecting the
median value of the peak redshifts of these different Probability Distribution
Functions (PDFs). Similarly, ten different teams estimated the stellar mass of
galaxies using different SED templates based on different galaxy populations
(Santini et al., 2015; Mobasher et al., 2015). The median value of the average
of all these PDFs was then selected as the best stellar mass estimate for a
given galaxy.

The stellar mass measurements in the above photometric catalogs were based on
the photometric redshifts of the galaxies. We require spectroscopic redshifts
for our sample spectroscopic galaxy pairs and controls, which could be
different from the photometric redshift of a given galaxy. Therefore, we re-
measure the stellar masses of our pairs and controls using the SED fitting
code (MAGPHYS; da Cunha et al., 2008) to fit the above-mentioned photometric
observations of galaxies and their spectroscopic redshifts as described in
detail in Section 4.1. For the galaxies that have similar photometric and
spectroscopic redshifts, our newly estimated stellar masses are consistent
with the original stellar masses from the photometric catalogs. We use these
MAGPHYS estimated stellar masses to generate the galaxy pair sample and its
corresponding control sample using the selection criteria described in Section
3. Since we use photometric redshifts to select the visually identified
interactions, mergers, and their control samples, we use the original stellar
mass measurements from the photometric catalogs rather than re-computing them.

###  2.2 Spectroscopic Observations

We used all the available existing spectroscopic observations (published, as
shown in Table 1, and unpublished) compiled by the COSMOS and CANDELS teams to
generate our spectroscopic galaxy pair sample and the corresponding control
sample. We also used spectroscopic observations obtained using Keck II/DEIMOS
(Shah et al., 2020), Gemini/GMOS (I. Cox et al., in preparation), and Keck
I/MOSFIRE (B. Vanderhoof et al., in preparation) for the UDS, COSMOS, and
GOODS-S fields. We only used reliable spectroscopic redshifts, i.e., those
with a quality flag of two or greater (Shah et al., 2020), to generate our
pair and control samples.

Table 1: Spectroscopic Observations

Telescope/Instrument | Reference
---|---
COSMOS |
VLT/VIMOS | Lilly et al. (2007); Tasca et al. (2015); Le Fèvre et al. (2015)
| van der Wel et al. (2016); Straatman et al. (2018)
VLT/FORS2 | Comparat et al. (2015); Pentericci et al. (2018)
Keck I/MOSFIRE and LRIS | Kriek et al. (2015); Masters et al. (2019)
Keck II/DEIMOS | Capak et al. (2004); Kartaltepe et al. (2010)
| Hasinger et al. (2018); Masters et al. (2019)
MMT/Hectospec spectrograph | Damjanov et al. (2018)
Subaru/MOIRCS | Onodera et al. (2012)
Subaru/FMOS | Silverman et al. (2015); Kartaltepe et al. (2015b)
HST/WFC3-IR grism | Krogager et al. (2014); Momcheva et al. (2016)
Magellan (Baade) telescope/IMACS | Trump et al. (2009); Coil et al. (2011)
UDS |
HST/WFC3-IR grism | Morris et al. (2015); Momcheva et al. (2016)
VLT/VIMOS and FORS2 | Bradshaw et al. (2013); Pentericci et al. (2018)
Keck I/MOSFIRE and LRIS | Kriek et al. (2015); Masters et al. (2019)
Keck II/DEIMOS | Masters et al. (2019)
VLT/VIMOS | McLure et al. (2018); Scodeggio et al. (2018)
GOODS-N |
HST/WFC3-IR grism | Ferreras et al. (2009); Momcheva et al. (2016)
Keck I/MOSFIRE and LRIS | Cowie et al. (2004); Reddy et al. (2006)
| Barger et al. (2008); Kriek et al. (2015); Wirth et al. (2015)
Keck II/DEIMOS | Wirth et al. (2004); Cowie et al. (2004); Barger et al. (2008); Cooper et al. (2011)
Subaru Telescope/MOIRCS | Yoshikawa et al. (2010)
GOODS-S |
VLT/VIMOS | Le Fèvre et al. (2004); Ravikumar et al. (2007); Balestra et al. (2010)
| Le Fèvre et al. (2013); McLure et al. (2018)
VLT/FORS1 and FORS2 | Daddi et al. (2004); Szokoly et al. (2004); van der Wel et al. (2004)
| Mignoli et al. (2005); Vanzella et al. (2008); Popesso et al. (2009)
| Vanzella et al. (2008, 2009); Balestra et al. (2010)
| Kurk et al. (2013); Pentericci et al. (2018)
VLT/MUSE | Inami et al. (2017); Urrutia et al. (2019)
HST/WFC3-IR grism | Ferreras et al. (2009); Morris et al. (2015); Momcheva et al. (2016)
Gemini/GMOS | Roche et al. (2006)
Keck I/MOSFIRE | Kriek et al. (2015)
Keck II/DEIMOS | Silverman et al. (2010); Cooper et al. (2012a)
AAT/LDSS++ spectrograph | Croom et al. (2001)
EGS |
Keck I/MOSFIRE and LRIS | Coil et al. (2004); Masters et al. (2019); Kriek et al. (2015)
Keck II/DEIMOS | Masters et al. (2019); Cooper et al. (2012b); Newman et al. (2013)
HST/WFC3-IR grism | Momcheva et al. (2016)

Note. — VLT: Very Large Telescope, VIMOS: Visible Multi-Object Spectrograph,
FORS: the visual and near UV FOcal Reducer and low dispersion Spectrograph,
IMACS: Inamori Magellan Areal Camera and Spectrograph, FMOS: Fiber multi-
Object Spectrograph, LRIS: Low Resolution Imaging Spectrometer, MOIRCS: Multi-
Object Infrared Camera and Spectrograph, MUSE: Multi Unit Spectroscopic
Explorer, GMOS: Gemini Multi-Object Spectrographs, MOSFIRE: Multi-Object
Spectrometer For Infra-Red Exploration, AAT: Anglo-Australian Telescope

##  3 Sample Selection

In this section we describe the selection criteria we use to identify (i) the
spectroscopic galaxy pair sample, (ii) the visually identified-interacting
galaxy and merger samples, and (iii) the corresponding mass-, redshift-, and
environment-matched isolated (control) galaxy samples. Further details can be
found in Shah et al. (2020).

###  3.1 Pair Selection

We use both photometric and spectroscopic catalogs to obtain information about
the positions, stellar masses, and best available spectroscopic redshifts of
galaxies in the CANDELS and COSMOS fields. We use the following criteria to
select our sample of massive spectroscopic galaxy pairs going through major
galaxy interactions:

  1. 1.

Redshift limit: We require that both galaxies in a pair have reliable (quality
flag greater than one) spectroscopic redshifts ($z_{spec}$) and that they span
$0.5<z_{spec}<3$, enabling the inclusion of high-redshift interactions.

  2. 2.

Mass limit: The stellar mass of both of the galaxies in the pair has to be
greater than 1010 M⊙ as this study focuses on massive galaxies within the
completeness limits of the surveys.

  3. 3.

Stellar mass ratio: The stellar mass ratio of the primary (more massive) to
the secondary galaxy has to be less than four as this study focuses on major
galaxy interactions.

  4. 4.

Projected separation: We require the projected separation between the two
companion galaxies to be less than 150 kpc. Ideally, the three dimensional
separation between the paired galaxies should be used to identify galaxy
pairs. However, with observations we only have information about the projected
separation between galaxies. We estimate the physical projected separation
between two galaxies using their angular separation and average spectroscopic
redshift. To constrain the line of sight separation, we use the relative
radial velocities obtained using the spectroscopic redshifts of the galaxies.

  5. 5.

Relative line of sight velocity ($\Delta V$): Companions are required to have
their relative line of sight velocity (obtained using their spectroscopic
redshifts) within 5000 km s-1. We also apply a stricter velocity cut of
$\Delta V<1000$ km s-1 to study how the results vary with the relative line of
sight velocity. We emphasize here that the likelihood of a pair being a true
interaction increases as the relative velocity decreases. We include a wide
range of $\Delta$V values in order to investigate the effect of different
cuts. See Shah et al. (2020) for a more detailed description and the $\Delta$V
distribution of the full sample.

We select the closest companion satisfying the criteria described above as the
secondary galaxy corresponding to a given primary galaxy. Our full
spectroscopic galaxy pair sample contains a total of 2351 pairs, while our
sample with the stricter relative line of sight velocity cut of $\Delta
V<1000$ km s-1, contains 1327 spectroscopic galaxy pairs.

###  3.2 Visually Identified Interactions and Mergers

To study different stages of interactions and mergers, we also select sub-
samples of visually identified interacting galaxies and mergers from the
CANDELS fields using the classification scheme and catalog from Kartaltepe et
al. (2015a), with the constraints described in detail in Shah et al. (2020).
Each galaxy was classified by at least three independent researchers.

We use three types of visually identified sub-samples: Merger, Blended
Interaction, and Non-blended Interaction. A merger is a single coalesced
system and an interaction is a system with at least two visually
distinguishable galaxies. An interaction is considered blended if the
photometric measurements correspond to both galaxies combined light. Galaxies
in each of these samples are required to show signs of morphological
disturbance such as tidal tails, loops, asymmetries, and off-center or highly
irregular outer isophotes. Additionally, mergers can also show double nuclei
and interactions can also show tidal bridges. In brief, the constraints for
the different visually identified samples in the CANDELS fields are:

  1. 1.

H band magnitude of the galaxy has to be less than 24.5. This is a constraint
for the Kartaltepe et al. (2015a) visual classification.

  2. 2.

$\geq 2/3$ of all classifiers agree on the classification of the system as a
Merger, Blended interaction, or Non-blended interaction.

  3. 3.

The photometric redshift of each galaxy has to be between 0.5 and 3.0.

  4. 4.

The stellar mass of galaxies classified as a merger has to be greater than
$1.25\times 10^{10}$ M⊙ (i.e., mass of the merger if two galaxies both with
M${}_{*}>10^{10}$ M⊙ and a minimum stellar mass ratio of 0.25 merge together).
Similarly, the stellar mass of the blended interacting galaxy system and non-
blended interacting galaxies has to be greater than $1.25\times 10^{10}$ M⊙
and 1010 M⊙, respectively.

In total, we compiled samples of 66 high-confidence mergers, 100 blended
interactions, and 122 non-blended interactions in the CANDELS fields.

###  3.3 Control Samples

The goal of this study is to estimate the effect of galaxy interactions on the
SFRs of galaxies. However, the SFR of a galaxy can also vary with other galaxy
properties, such as stellar mass, redshift, and environment. Hence, we
generate a control sample of isolated galaxies with similar stellar mass,
redshift, and environment as the paired galaxies and visually identified
samples, and then compare the SFR of the pairs and control samples to estimate
the effect of galaxy interaction on the SFRs of pairs.

The spectroscopic completeness varies significantly for each of different
fields used in our analysis. The availability of spectroscopic observations
for a given galaxy can depend on properties such as its stellar mass,
photometric redshift, SFR, AGN presence, etc. Furthermore, the spectroscopic
redshift completeness also varies from field-to-field. Therefore, we choose
pairs and controls from the same fields in order to avoid any bias based on
the variation in spectroscopic redshift completeness in different fields. Like
our paired galaxies, our control galaxies also must have a spectroscopic
redshift with a quality flag greater than one. This requirement is to ensure
that whatever biases are inherent in the spectroscopic redshift selection are
present in both the pair and control samples. However, as we do not require a
spectroscopic redshift for the visually identified interaction and merger
samples, we also do not require spectroscopic redshifts for their controls.

We define the environmental overdensity as the ratio of the density at the
position (RA, Dec, and redshift) of the galaxy to that of the median density
in that redshift bin. For galaxies in the CANDELS fields, the overdensity
measurement is based on the Monte Carlo Voronoi Tessellation method (Lemaux et
al., 2017; Tomczak et al., 2017). For the COSMOS field, the overdensity was
derived based on redshift-dependent ‘weighted’ adaptive kernel density maps
generated by Darvish et al. (2015). Darvish et al. (2015) also show that in
spite of these two methods being slightly different from each other, their
results are consistent. We use the methods mentioned above to estimate the
density of the paired galaxies and control candidate galaxies and then
consistently calculate the overdensity.

Figure 1: Normalized (at peak) distributions of environmental overdensity
(left panel), spectroscopic redshift (middle panel), and stellar mass (right
panel) of spectroscopic galaxy pairs (solid lines) (with projected separation
$<150$ kpc, mass ratio $<4$, and spectroscopic redshift between 0.5 and 3 for
the $\Delta V<5000$ km s-1, top, and $\Delta V<1000$ km s-1, bottom, samples,
respectively) and their corresponding mass-, redshift-, and environment-
matched control galaxies (dashed black lines).

We generate a control candidate sample of isolated galaxies that have no major
or minor companion (within a mass ratio of 10) within a $\Delta z$
corresponding to a relative velocity of less than 5000 km s-1, out to a
projected separation of 150 kpc. We also update the control candidate sample
by removing visually identified interacting or merging galaxies (Section 3.2)
from this control candidate sample.

We construct the final control sample by using this updated control candidate
sample. As we plan to control for the effects of redshift, stellar mass, and
environment on the SFRs of galaxies, the distribution of these properties for
the control sample should be similar to the distribution of these properties
for the paired galaxy sample. Therefore, we find the control galaxies for a
given paired galaxy by minimizing the difference between these properties of
the paired galaxy and control candidate galaxies. Hence, for each paired
galaxy, we select three control candidate galaxies with the smallest
$(\Delta\log{M_{*}})^{2}+(\Delta z)^{2}+(1/40)(\Delta overdensity)^{2}$ from
the updated control candidate sample. Here, 1/40 is a weighting factor for the
environment overdensity. As the stellar mass, redshift, and overdensity spans
quite different ranges of values and have a different distribution, to best
match in all three dimensions and avoid overdensity-matching dominating, we
used this weighting factor (1/40). For the final control sample, the controls
match within a stellar mass of 0.15 dex, spectroscopic redshift within 0.15,
and overdensity within a factor of one for more than 90% of the paired
galaxies.

We show normalized distributions of the environmental overdensity, redshift,
and stellar mass distribution of the final galaxy pair sample and
corresponding control sample in Figure 1. The plots show that the
environmental overdensity, redshift, and stellar mass distributions for the
pair sample and its control sample are very similar to each other, i.e., the
controls are well-matched to the pair sample, which is crucial for our
analysis (see Shah et al., 2020). Our samples span a wide range of
environment, redshift, and stellar mass. The distributions in Figure 1 show
that the number of galaxy pairs in our sample decreases rapidly at high
overdensities and high redshift.

##  4 Star formation Analysis

To estimate the star formation enhancement in merging and interacting
galaxies, we first estimate the SFR of galaxies in the spectroscopic paired
galaxy sample, visually identified interaction and merger galaxy samples, and
the corresponding control galaxy samples as described in this section. We then
compute the star formation enhancement and investigate how it varies with the
galaxy pairs’ projected separation.

###  4.1 Measurement of SFR

We use the SED fitting tool Multi-wavelength Analysis of Galaxy Physical
Properties (MAGPHYS; da Cunha et al., 2008) for fitting the model SEDs to the
photometric data points (FUV-FIR band flux and flux-error values) to measure
the SFR and stellar mass of galaxies. We choose MAGPHYS for the SED fitting
process as it self-consistently fits observations from UV to FIR based on an
energy balance argument. It considers a combination of hot and cold dust
grains as well as PAHs to estimate attenuation. MAGPHYS uses the Bruzual &
Charlot (2003) stellar population libraries. For estimating the SFR of the
spectroscopic pairs and their corresponding control galaxies, we use their
spectroscopic redshifts. For the visually identified interactions and merger
galaxies and their corresponding control galaxies, we use their photometric
redshifts.

The default version of MAGPHYS does not contain models with emission from an
AGN component. However, it has been shown (e.g., Kirkpatrick et al., 2012)
that a strong AGN can significantly impact the measured SFR of a galaxy,
therefore, it is important to take such AGN activity into account. One way to
identify galaxies that are strongly impacted by an AGN is to select galaxies
that are poorly fit in the mid-infrared without the presence of an AGN
component. Through substantial testing, it has been shown that the fitting
residual at 8 $\mu$m can be indicative of strong AGN emission that dominates
over star formation in the mid-infrared (Cooke et al., 2019). Therefore, for
galaxies for which the $8\mu m$ residual percentile from the MAGPHYS fit
($100\times\frac{flux_{obs}-flux_{MAGPHYS}}{flux_{obs}}\%$) is more than
$40\%$, we use a modified version of MAGPHYS called SED3FIT (Berta et al.,
2013) that includes an AGN emission component to calculate their SFRs. We note
that we elect not to fit the full sample using SED3FIT due to the
computational time required and the fact that adding an additional component
to the fit will often overestimate the importance of an AGN when there is not
one present.

Figure 2: Comparison of the SFR (left) and stellar mass (right) measured using
SED3FIT and MAGPHYS for galaxies in our sample with a large 8 $\mu$m residual
suggesting the presence of an AGN component in the mid-IR. The color bar shows
the value of the AGN fraction estimated using SED3FIT for a given galaxy.
Figure 3: Distribution of paired galaxies (red scatter points) and controls
(contours) on the SFR-M∗ plane for (top to bottom) the $0.5<z<3.0$,
$0.5<z<1.0$, $1.0<z<1.5$, $1.5<z<2.0$, and $2.0<z<3.0$ bins. The black solid
line in each sub-plot corresponds to the star formation main sequence
(Whitaker et al., 2014) in the given redshift range. The gray lines correspond
to the SFR value above and below the SFMS by a factor of three in each
redshift bin. Figure 4: The decision tree for computing the combined SFR,
SFRP, of a galaxy pair, where galaxy1 and galaxy2 correspond to the primary
and secondary galaxies in a given galaxy pair, respectively.

Figure 2 shows the comparison of the SFRs measured using SED3FIT and MAGPHYS.
For most galaxies, the SFRs measured using the two SED fitting routines are
similar to each other. However, for many galaxies the SFR measured with
SED3FIT is lower than the value from MAGPHYS. This is mainly due to the fact
that MAGPHYS can overestimate the total SFR when a strong AGN is present.
Similarly, the right panel of Figure 2 shows a comparison between the stellar
mass measured using SED3FIT and MAGPHYS for the same subset of galaxies with a
large 8 $\mu$m residual. Again, for most galaxies, the stellar masses measured
using SED3FIT are similar or lower than that from MAGPHYS. For these objects,
we adopt the SED3FIT masses and SFRs in order to avoid overestimates due to
the presence of a strong AGN. We note that these strong AGN are rare, and
therefore, have minimal impact on our final results. For consistency, we
remove any pairs from our sample that no longer satisfy the selection criteria
as described in § 3 with these new stellar masses. This results in the removal
of 17 pairs for the $\Delta V<1000$ km s-1 sample and 22 pairs for the $\Delta
V<5000$ km s-1 sample. These removed pairs are reflected in the final pair
sample presented in § 3 and the figures throughout the paper.

Using the results of our SED fits, we show the distribution of paired and
control galaxies on the SFR-M∗ plane in different redshift bins in Figure 3.
We also show the corresponding star-forming main sequence (Whitaker et al.,
2014) at each redshift. The two peaks in the contours in each plot correspond
to the main sequence and the quiescent galaxy populations. Overall the control
contours seem to trace the paired galaxy population well. The number of
objects decreases rapidly with increasing redshift due to spectroscopic
redshift incompleteness (especially at high redshifts). Note that the galaxy
pair population spans a wide range of properties, including starbursts and
quiescent galaxies, above and below the main sequence, respectively. The
paired galaxies are required to have a minimum stellar mass of $10^{10}$ M⊙,
however the controls can have a slightly lower stellar mass than $10^{10}$ M⊙
and so the contours extend slightly to the left of the paired galaxies in some
of the sub-plots.

###  4.2 Blending of paired galaxies at IR wavelengths

The angular resolution of the images taken at 24 $\mu$m and longer wavelengths
is poor relative to the optical/NIR filters and therefore, some galaxies in
close pair systems are blended at MIR-FIR wavelengths. This means that for
such pairs that are blended only at longer wavelength, the corresponding flux
values will represent the pair and not the individual galaxies, which will
affect their SFR measurement. Therefore, the flux values of the whole system,
i.e., both galaxies combined, have to be used across all wavelengths when
fitting an SED of the blended pair system in order to obtain an accurate
measurement.

We compute the SFR of the whole galaxy pair system (both galaxies combined,
SFRP) for all galaxy pairs for a fair comparison with systems blended in the
IR. Figure 4 illustrates the decision tree for measuring SFRP for a given
galaxy pair. We start with both galaxies (primary: galaxy1 and secondary:
galaxy2) in the pair and determine if either of them are detected in any IR
band. If neither of the two galaxies in a given galaxy pair are detected in
any IR bands (black arrows), then the combined SFRP is the summation of the
SFR of the individual galaxies. If at least one of the galaxies in the pair is
detected in at least one of the IR bands (say $\lambda$), then we check if the
galaxies are blended at that wavelength $\lambda$. We repeat this process of
checking blending for all wavelengths ($\lambda$1, $\lambda$2,…,$\lambda$n) in
which at least one of the two galaxies are detected. If both galaxies are not
blended at all wavelengths, we compute the SFRP by adding the SFR of the two
individual galaxies. If the two galaxies are blended in at least one of the
wavelengths ($\lambda$ in $\lambda$1, $\lambda$2,…,$\lambda$n) then we combine
the fluxes of the two galaxies at each wavelength to get the combined flux of
the system in each band. We then fit the SED of this combined system using
MAGPHYS to obtain SFRP. If the $8\,\mu m$ percentile residual of the MAGPHYS
fit for the combined system is more than 40 $\%$, then we refit the SED using
SED3FIT to obtain the SFRP. Only 115 galaxy pairs, i.e, $\sim 8.5\%$ of our
total galaxy pair sample, are blended in the IR.

To facilitate a fair comparison between the pair system and their control
counterparts, we compute the mean SFR of all controls of one of the two
galaxies in a given pair ($\overline{SFR_{C1}}$) and repeat the process for
the second galaxy ($\overline{SFR_{C2}}$). We then add these mean SFRs to
obtain the final control SFR for a given galaxy pair
($SFR_{C}=\overline{SFR_{C1}}+\overline{SFR_{C2}}$). We propagate the errors
in SFRs throughout this process to get the upper and lower error estimates for
the SFR of combined control SFR ($SFR_{C}$) for a given galaxy pair. We repeat
the process for all galaxy pairs.

##  5 SFR Enhancement

We define the SFR enhancement as the ratio of the weighted mean of the SFR of
all galaxy pairs and the weighted mean SFR of all the corresponding combined
controls, i.e.,

$SFR\>Enhancement=\frac{\overline{SFR_{P}}}{\overline{SFR_{C}}}.$ (1)

Figure 5: Distribution of weights (wi) with respect to the SFR of galaxy pairs
($SFR_{P}$). The distribution shows that there is no significant trend between
the weights and $SFR_{P}$.

For some galaxies, there are large errors on the SFR measured from our SED
fits, which could be due to various reasons such as a poor fit, limitation of
number of models in a given parameter space, large errors on the observed
fluxes, etc. We consider this fact while estimating the average SFR of the
pair sample ($SFR_{P}$) and the average SFR of the combined control sample
(SFRC). To decrease the dependence of the results on systems with large errors
in the SFR measurement, we multiply the SFRp and SFRC values by a weighting
function such that a lower weight value is assigned for a larger SFR
percentile error and a higher weight value is assigned for a lower SFR
percentile error. For a given galaxy pair, we use a weighting factor (w) based
on the combined error (x) for SFRP and $SFR_{C}$ using the formulae

$x_{low}=\sqrt{\left(\frac{(\Delta
SFR_{P})_{low}}{SFR_{P}}\right)^{2}+\left(\frac{(\Delta
SFR_{C})_{low}}{SFR_{C}}\right)^{2}},$ (2)

$x_{up}=\sqrt{\left(\frac{(\Delta
SFR_{P})_{up}}{SFR_{P}}\right)^{2}+\left(\frac{(\Delta
SFR_{C})_{up}}{SFR_{C}}\right)^{2}},$ (3)

$x=\sqrt{\left(\frac{x_{low}^{2}+x_{up}^{2}}{2}\right)},$ (4)

and

$w=\frac{1}{1+x}.$ (5)

Here, $(\Delta SFR_{P})_{low}$ and $(\Delta SFR_{P})_{up}$ are the lower and
upper errors on the SFR of the given pair ($SFR_{P}$). Similary, $(\Delta
SFR_{C})_{low}$ and $(\Delta SFR_{C})_{up}$ are the lower and upper errors on
the SFR of the combined controls for a given galaxy pair ($SFR_{C}$). We also
checked that our weights are not biased towards galaxy properties such as
their M∗ and SFR. Our enhancement values do not vary significantly if we use
different weighting functions. For a given galaxy pair (for example, i), we
compute the value of the combined error (xi) using Eq. 2, 3, and 4. We then
use the value of xi to calculate the value of the weight (wi) for the given
galaxy pair using Eq. 5. The weighting function values (wi) decrease with
increasing average percentage error (xi) on the SFR. We show the distribution
of these weights (wi) with respect to the SFR of galaxy pairs ($SFR_{P}$) in
Figure 5. The figure shows that there is no significant trend between the SFR
and the weights. Hence, by using the weights, our analysis is not biased
towards higher or lower SFRs.

Figure 6: SFR enhancement as a function of projected separation of pairs. SFR
enhancement is defined as the ratio of the weighted average of the SFR of the
pair sample to that of the control sample (Equation 1). The filled blue
circles and filled red diamonds correspond to the SFR enhancement for the
$\Delta V<1000$ km s-1 and $\Delta V<5000$ km s-1 pair samples, respectively.
The dotted line corresponds to a value of one, which represents no
interaction-induced SFR enhancement. We see the highest level of SFR
enhancement at a factor of 1.23${}^{+0.08}_{-0.09}$ ($\sim 2.6\sigma$) and
1.15${}^{+0.05}_{-0.04}$ ($\sim 3.0\sigma$) in the closest separation bin with
projected pair separation $<$25 kpc for galaxy pair sample with $\Delta
V<1000$ km s-1 and $\Delta V<5000$ km s-1, respectively. We note that there is
a significant amount of scatter around the SFR enhancement value of one.

Finally, we compute the weighted average of the SFR for the galaxy pair sample
and its corresponding control samples using

$\overline{SFR_{P}}=\frac{\sum_{i}^{n_{p}}w_{i}(SFR_{P})_{i}}{\sum w_{i}}$ (6)

and

$\overline{SFR_{C}}=\frac{\sum_{i}^{n_{p}}w_{i}(SFR_{C})_{i}}{\sum w_{i}}.$
(7)

Figure 7: SFR enhancement as a function of projected separation for the galaxy
pairs with $\Delta V<1000$ km s-1 at $z<1$ (left) and $z>1$ (right). The
dotted line corresponds to a value of one, which represents no interaction-
induced SFR enhancement. Figure 8: SFR enhancement as a function of projected
separation for the galaxy pairs with $\Delta V<1000$ km s-1 and with the
stellar mass of the primary (more massive) galaxy $M_{p}$ less than 10.88 M⊙
(left) and more than 10.88 M⊙ (right) for the pair sample with $\Delta V<1000$
km s-1. The dotted line corresponds to a value of one, which represents no
interaction-induced SFR enhancement. Figure 9: SFR enhancement as a function
of projected separation for the pair sample with $\Delta V<1000$ km s-1. The
left and right hand side plots correspond to the results for star-forming
blended pairs (and non-blended paired galaxies) and non-star forming blended
pairs (and non-blended paired galaxies). The dotted line corresponds to a
value of one, which represents no interaction-induced SFR enhancement.

We use the weighted average $\overline{SFR_{P}}$ and $\overline{SFR_{C}}$
values to estimate the SFR enhancement using Eq. 1. We divide the projected
separation range ($0-150\thinspace$ kpc) into six different bins of width 25
kpc. We estimate the SFR enhancement in each bin. We show the SFR enhancement
results for the spectroscopic galaxy pair samples with relative line of sight
velocity $\Delta V<1000$ km s-1 and $\Delta V<5000$ km s-1 in Figure 6. We see
the highest level of SFR enhancement of a factor of 1.23${}^{+0.08}_{-0.09}$
($\sim 2.6\sigma$) and 1.15${}^{+0.05}_{-0.04}$ ($\sim 3.0\sigma$) in the
closest separation bin of projected pair separation $<$25 kpc for the galaxy
pair sample with $\Delta V<1000$ km s-1 and $\Delta V<5000$ km s-1,
respectively. We note that there is significant scatter around the SFR
enhancement value of one (a value indicating no enhancement). The results also
do not show a clear trend of increasing SFR enhancement with decreasing
projected separation. While the error bars on the SFR enhancements obtained
using the weighted averages as described above are smaller than the ones
calculated without weighting, the overall value of the SFR enhancement
calculated with and without weights are very similar. We also note that we do
not see a large difference between the $\Delta V<1000$ km s-1 and $\Delta
V<5000$ km s-1 samples. Since there are more galaxies with $\Delta V<5000$ km
s-1, the error bars and resulting amount of scatter among the points are
smaller. However, since pairs with $\Delta V<1000$ km s-1 are more likely to
be physically associated, we adopt this subsample for the remaining plots and
discussion.

We then divide the $\Delta V<1000$ km s-1 galaxy pair sample ($z_{median}\sim
1$) into two redshift bins ($z<1$ and $z>1$), with equal numbers of pairs in
each bin, and calculate the SFR enhancement (Figure 7). We do not see a trend
of increasing SFR enhancement with decreasing projected separations in either
redshift bin. We see the highest enhancement of 1.48${}^{+0.16}_{-0.09}$ in
the lowest projected separation bin ($<25$ kpc) at $z<1$. We see a large
amount of scatter around the SFR enhancement value of one for both redshift
bins. This scatter is most likely due to the sample size and does not
represent a real trend in the SFR enhancement levels with separation.

Similarly, we also divide our $\Delta V<1000$ km s-1 galaxy pair sample into
two stellar mass bins separated at the median stellar mass ($10^{10.88}$ M⊙)
of the primary galaxy (more massive of the two galaxies) in a galaxy pair and
represent the results in Figure 8. In the smallest projected separation bin,
we see a SFR enhancement of 1.51${}^{+0.11}_{-0.10}$ ($\sim 4.6\sigma$) in the
lower mass sample, which is $\sim 1.63\times$ higher (at $\sim 5.4\sigma$
level) than the SFR enhancement of 0.96${}^{+0.10}_{-0.14}$ in the same
projected separation bin for the higher mass sample (log
$M_{prim}/M_{\odot}>10.88$). Again, with this subdivided sample, we see a
significant amount of scatter around the SFR enhancement value of one.

Furthermore, we divide the $\Delta V<1000$ km s-1 galaxy pair sample into
star-forming (SF) paired galaxies and non-star forming (non-SF/quiescent)
paired galaxies identified based on whether SFR of the galaxy is higher or
lower than the specific SFR of $sSFR=(1+z)^{2.5}\times 10^{-11}$ (Speagle et
al., 2014), respectively. In the case of blended pairs, we apply this
criterion to the sSFR of the whole blended system. We also select new sets of
SF (non-SF) controls for this SF (non-SF) blended pairs and non-blended paired
galaxies using the same method described previously. We show the SFR
enhancement results in Figure 9. In the closest separation bin, we see a SFR
enhancement of 1.32${}^{+0.09}_{-0.11}$ in the SF sample, which is $\sim
1.57\times$ larger than for the non-SF sample (0.84${}^{+0.12}_{-0.10}$). Here
also, the results show a significant amount of scatter around the SFR
enhancement value of one. We also compare the SFR enhancements in our primary
galaxy sample to the secondary galaxy sample of the non-blended galaxy pairs.
We do not see a significant difference in SFR enhancement of these two samples
(not shown here).

Figure 10: SFR enhancement for our visually identified mergers (filled green
diamonds), blended interactions (filled purple squares), and non-blended
interactions (filled orange triangles) as a function of their median projected
separations. The merged/coalesced systems are plotted at a separation of zero,
since they are no longer in a pair but are in a single coalesced galaxy. The
dotted line corresponds to a value of one, which represents no interaction-
induced SFR enhancement. Note that for this sample, we see a clear trend of
increasing SFR enhancement with decreasing projected separation, with the
largest level of enhancement seen in the coalesced systems.

We present the SFR enhancement for our visually identified interactions and
merger samples in Figure 10. We see enhancements of 1.86${}^{+0.29}_{-0.18}$,
1.31${}^{+0.16}_{-0.08}$, and 0.90${}^{+0.10}_{-0.06}$ for our visually
identified merger sample, blended-interaction sample, and non-blended
interaction sample, respectively. For this sample, we see a clear trend of
increasing SFR enhancement for decreasing median projected separation, with
the largest enhancement seen for coalesced merger systems.

Figure 11: SFR enhancement for our visually identified mergers (filled green
diamonds), blended interactions (filled purple squares), and non-blended
interactions (filled orange triangles) at $z<1.6$ (left) and $z<1.6$ (right)
compared to their star-forming control samples. The dotted line corresponds to
a value of one, which represents no interaction-induced SFR enhancement. While
we see evidence of increasing enhancement with decreasing projected separation
in both redshift bins, we see a decreased level of enhancement for the $z>1.6$
bin.

Since we use photometric redshift for the visually identified sample, rather
than spectroscopic redshifts as for the galaxy pair sample, the median
redshift of these galaxies is higher than that of the pair sample. We divide
our visually identified interaction and merger samples into two redshift bins
separated at the median redshift $z_{median}\sim 1.6$ of the samples. We show
these results in Figure 11. At low $z$ ($0.5<z<1.6$), we find that the SFR
enhancement levels are a factor of 2.40${}^{+0.62}_{-0.37}$,
1.44${}^{+0.35}_{-0.12}$, and 0.93${}^{+0.20}_{-0.11}$ for our merger,
blended, and non-blended interaction samples, respectively. Similarly, at high
$z$ ($z>1.6$), we see enhancement values of 1.58${}^{+0.29}_{-0.20}$,
1.27${}^{+0.19}_{-0.10}$, and 0.88${}^{+0.11}_{-0.08}$ for our merger,
blended, and non-blended interaction samples, respectively. In both redshift
bins, we find that there is a clear trend of increasing SFR enhancement with
decreasing projected separation with the largest enhancement see for the
coalesced merger systems. We also find that the level of enhancement at
$z<1.6$ is a factor of $\sim 1.5$ higher than the enhancement at $z>1.6$,
suggesting that there is an evolution in the interaction-induced SFR
enhancement between these two bins.

Figure 12: SFR enhancement for only star-forming visually identified mergers
(filled green diamonds), blended interactions (filled purple squares), and
non-blended interactions (filled orange triangles) at $z<1.6$ (left) and
$z<1.6$ (right) compared to their star-forming control samples.

Finally, we select the subset of galaxies in the visually identified sample
that are star-forming using the same criteria as for the pair sample
($sSFR>(1+z)^{2.5}\times 10^{-11}$; Speagle et al. 2014) and selected their
corresponding star-forming control samples. As shown in Figure 12, we
calculate a SFR enhancement of 2.08${}^{+0.42}_{-0.15}$,
1.09${}^{+0.18}_{-0.09}$, and 1.23${}^{+0.18}_{-0.21}$ at low $z$ for our
merger, blended, and non-blended interaction samples, respectively. At high z,
the corresponding SFR enhancement values are 1.23${}^{+0.18}_{-0.12}$,
1.06${}^{+0.15}_{-0.10}$, and 0.86${}^{+0.11}_{-0.07}$. We find that for the
coalesced systems, the SFR enhancement if a factor of $\sim 1.7$ times higher
at $z<1.6$ than at $z>1.6$.

##  6 Discussion

We investigate the level of SFR enhancement in major spectroscopic galaxy
pairs relative to their stellar mass-, redshift-, and environment-matched
control sample of isolated galaxies at $0.5<z<3.0$. We find that there is a
slight SFR enhancement of a factor of 1.23${}^{+0.08}_{-0.09}$ ($\sim
2.6\sigma$) and 1.15${}^{+0.05}_{-0.04}$ ($\sim 3.0\sigma$) in the lowest
projected separation bin ($<25$ kpc) for our $\Delta V<1000$ km s-1 and
$\Delta V<5000$ km s-1 spectroscopic galaxy pair samples, respectively. We
find a stronger level of enhancement of a factor of 1.86${}^{+0.29}_{-0.18}$
in the coalesced visually identified mergers. We compare these result with
similar studies in the literature.

###  6.1 Comparison with Studies in the Local Universe

Figure 13: Comparison of the SFR enhancement as a function of the projected
separation of interactions and mergers ($0.5<z<3.0$) to the pair sample in the
local universe ($0.02<z<0.20$) from Patton et al. (2013). SFR enhancement is
defined as the ratio of the average SFR of the pair sample to that of the
control sample (Equation 1.) The dotted line corresponds to a value of one,
which represents no interaction-induced SFR enhancement. The blue filled
circles show the SFR enhancement for our complete galaxy pair sample
($log(M_{pair}/M_{\odot})>10.3$ and $0.5<z<3.0$). The filled green diamond,
filled purple square, and filled orange triangle correspond to the SFR
enhancements for our visually identified merger sample, blended interaction
sample, and non-blended interaction sample, respectively. The gray region
corresponds to the merger (or post merger) stage. The filled black stars
correspond to the Patton et al. (2013) SFR enhancement results for a pair
sample in the local ($0.02<z<0.20$) universe selected from SDSS observations.
While we see significant enhancement for galaxy mergers in our high redshift
sample, the overall level of enhancement is less than that seen in the local
pair sample, indicative of a change in role of interactions and mergers in
inducing star formation over cosmic time.

In contrast to our results at $0.5<z<3.0$, there are several studies in the
local universe ($z\sim 0$) that find considerable SFR enhancement in galaxy
interactions by comparing the star formation of interacting and isolated
(control) galaxies (e.g., Alonso et al. (2004); Woods & Geller (2007); Ellison
et al. (2008); Xu et al. (2010); Ellison et al. (2013b); Patton et al.
(2013)). Almost all of these studies find the strongest enhancement for pairs
with projected separation less than 30 kpc.

The closest study on SFR enhancement in the local universe to our high
redshift study is Patton et al. (2013) (P13 from here on). Therefore, we
compare our SFR enhancement results with P13 results to understand how the
level of SFR enhancement differs at high redshift compared to the local
universe. P13 measure the SFR enhancement for local pairs ($0.02<z<0.2$ and
stellar mass ratio $<10$) identified using SDSS observations. We compare their
SFR enhancement results with our results in Figure 13. The highest level of
SFR enhancement that they find is a factor of $\sim 2.9$ for pairs with
projected separation less than 10 kpc. Their estimated SFR enhancement value
for pairs with projected separation between 10 kpc and 20 kpc is $\sim 1.95$,
which is a factor of 1.6 times ($\sim 3.2\sigma$) higher than our SFR
enhancement result of 1.23${}^{+0.08}_{-0.09}$ for pairs with projected
separation between 0 kpc and 25 kpc (median $\sim 14$ kpc). P13 see a clear
trend of increasing SFR enhancement with decreasing projected separation. We
find that this trend is absent in our for pairs with projected separations of
$>$25 kpc based on Figure 13, at separations $<$25 kpc, we see an overall
trend of increasing SFR enhancement with decreasing projected separation. This
is particulary notable for our visually identified merger and interaction
sample. For the SFR enhancement our blended interactions sample, we see a
value slightly less than one, which could be due to low number statistics.
Unlike our results, P13 observe an enhancement in SFR for pairs with projected
separations of up to 150 kpc. Many factors can impact the measurement of SFR
enhancement (such as redshift evolution, sample selection, SFR measurement
method, etc.) that would be cumulatively responsible for the differences
between our results and P13 results.

Apart from the clear differences in the redshift range of our pair sample
($0.5<z<3.0$) and their pair sample ($0.02<z<0.2$), the P13 pair sample also
consists of both major and minor pairs (mass ratio $<10$), unlike our sample
of only major galaxy pairs. They also apply a stricter line of sight velocity
difference constraint to identify galaxy pairs ($\Delta V<300$ km s-1)
compared to our relatively liberal constraint ($\Delta V<1000$ km s-1).
Furthermore, similar to many local studies, P13 select only star forming
galaxies identified using emission lines, which means that their sample
consists of only star-forming galaxies compared to our sample which also
contains non-star forming galaxies. If we only consider the subset of our
galaxies that are star-forming, our SFR enhancement result (Figure 9) of
1.32${}^{+0.09}_{-0.11}$ at 0-25 kpc is 1.5$\times$ (compared to 1.6$\times$
for the complete sample), lower than P13 SFR enhancement at 10-20 kpc. Hence,
even for the star-forming sample, our pair sample ($0.5<z<3.0$) shows less SFR
enhancement than a local ($0.02<z<2.0$) pair sample P13 at similar projected
separations.

Similarly, Ellison et al. (2013a) find a SFR enhancement of $\sim 3.5$ in a
post-merger sample compared to the controls generated using SDSS observations.
Their SFR enhancement is $\sim 1.9\times$ higher than our visually identified
merger sample ($0.5<z<3.0$). For our sample divided into two redshift bins,
the local enhancement for coalesced mergers is $1.4\times$ and $2.2\times$
higher than our $0.5<z<1.6$ and $1.6<z<3.0$ samples, respectively. This
suggest evolution in the merger-induced star formation enhancement between
these three redshift bins.

###  6.2 Comparison with Simulations

A relative decrease in the SFR enhancement level and its duration in merging
galaxies with a significantly higher gas fraction has been suggested by some
studies conducted using idealized binary merger simulations (Bournaud et al.,
2011; Hopkins et al., 2013; Scudder et al., 2015; Fensch et al., 2017). For
example, Fensch et al. (2017) use idealized binary merger simulations to study
the effect of a galaxy merger on the SFR of galaxies and show that the amount
and the duration of the merger-induced star formation excess is about ten
times lower for a high redshift galaxy (gas fraction $\sim$ 60%, $z\sim 2$)
mergers compared to their low redshift counterparts (gas fraction $\sim 10\%$,
$z\sim 0$). Fensch et al. (2017) suggest that the high turbulence in these
systems makes further compression of gas and generation and propagation of
inflows weaker at high-$z$ than in low-$z$ interactions, causing a
considerable decrease in the ability of mergers to increase the SFR of
galaxies at high-$z$.

Similarly, using a suite of nine binary merger simulations with the same
orbital parameters but with varying gas fractions ($0.04\leq M_{*}/M_{gas}\leq
1.78$), Scudder et al. (2015) find an anti-correlation between the SFR
enhancement at coalescence and the pre-merger gas fraction of galaxies.
However, such studies based on binary merger simulations usually do not
contain cosmic gas inflows and have a very limited sample size. Therefore, we
also compare our results with SFR enhancement studies based on hydrodynamical
cosmological simulations. Many of these studies also show the weakening of
enhancement levels with increasing redshift. For example, Patton et al. (2020)
present a study (similar to their observational study P13) of interacting
galaxy pairs identified in the IllustrisTNG cosmological simulations. They
select massive ($M_{*}>10^{10}M_{\odot}$) galaxy pairs at $0\leq z<1$ and
their corresponding stellar mass-, redshift-, and environment-matched controls
and see a gradual decrease in the sSFR enhancement levels with increasing
redshift in the same projected separation bin.

Martin et al. (2017) also find a decrease in merger-induced SFR enhancement
level with increasing redshift in the Horizon-AGN cosmological simulations.
This reduction in SFR enhancement is also suggested by our higher SFR
enhancement result (2.40${}^{+0.62}_{-0.37}$) for the visually identified
merger sample at low $z$ ($0.5<z<1.6$) compared to a lower SFR enhancement
(1.58${}^{+0.29}_{-0.20}$) at high $z$ ($1.6<z<3.0$) shown Figure 11.
Comparisons of our results with the local results, as discussed earlier, also
show this reduction in SFR enhancement with redshift.

Hani et al. (2020) also use IllustrisTNG (TNG300-1) to identify post-mergers
at $0\leq z\leq 1$ and a well-matched control sample. They see a SFR
enhancement of a factor of $\sim 2$ in their merger sample compared to the
control sample, which is within error bars of our SFR enhancement result
(2.40${}^{+0.62}_{-0.37}$) for the visually identified merger sample at
$z<1.6$ (left panel of Figure 11). They match SF mergers with SF controls,
which is analogous to our results for SF mergers (Figure 12), where our SFR
enhancement (2.08${}^{+0.42}_{-0.15}$) for the merger sample at low $z$
($0.5<z<1.6$) is almost same as their SFR enhancement ($\sim 2$).

While Hani et al. (2020) see no redshift evolution in their SF mergers
compared to SF controls over $0\leq z<1$, we see a $1.75\times$ higher SFR
enhancement at low-$z$ ($z<1.6$) compared to high-$z$ ($z>1.6$). Hence, the
comparison suggests that there might be a mild evolution of SFR enhancement
with redshift, which becomes detectable when enhancement is compared over a
larger redshift range ($0.5<z<3.0$ in our study), and which could be missed
when enhancement is compared over a smaller redshift range ($0\leq z\leq 1$ in
Hani et al., 2020). For a larger redshift range, our SFR enhancement value
(1.86${}^{+0.29}_{-0.18}$) for the visually identified merger sample at
$0.5<z<3.0$ is within the error bars of the sSFR enhancement in a merger
sample compared to a mass-matched control sample at $0<z<2.5$ in the Simba
Simulation (Rodríguez Montero et al., 2019).

###  6.3 Comparison with Previous High Redshift Studies

There are also observational studies exploring the effects of interactions and
mergers on the SFR of galaxies at high redshift. For example, Lackner et al.
(2014) identify merging galaxies in the COSMOS field at $0.25<z<1.00$ with log
$M_{*}/M_{\odot}>10.6$ by applying an automated method of median-filtering the
high-resolution COSMOS HST images to distinguish two concentrated galaxy
nuclei at small separations (2.2-8 kpc). This method is sensitive to very
close pairs and advanced stage mergers with double nuclei and hence are all
roughly at a similar merger stage. We note that because of the differences in
the selection criteria, some objects in their sample could be included in our
visually identified merger and blended interaction samples. They find a SFR
enhancement of value of 2.1$\pm$0.6 in their merging sample compared to the
non-merging sample. Their result is consistent with our result for the
visually identified merger sample (2.40${}^{+0.62}_{-0.37}$) and blended-
interaction sample (1.44${}^{+0.35}_{-0.12}$) at $0.5<z<1.6$ (left panel of
Figure 11). This comparison also shows the importance of comparing the
definition or identification criteria (e.g., merging galaxies in this case)
when comparing the results of two studies. Our SFR enhancement result for the
visually identified merger sample at $z>1.6$ is also consistent with the sSFR
enhancement ($\sim 2.2$) in mergers compared to non-interacting galaxies at
($z\sim 2$) found in Kaviraj et al. (2013).

In contrast to these results, Wilson et al. (2019) do not see a significant
SFR enhancement in merging galaxy pairs compared to a control sample of
isolated galaxies at $1.5\lesssim z\lesssim 3.5$. The differences in our
results could be due to differences in the pair selection and control matching
criteria used. They identify pairs as two objects whose spectra were obtained
on the same Keck/MOSFIRE slit. Their 30 galaxy pairs have projected
separations less than 60 kpc, relative velocities less than 500 km s-1, and
stellar mass ratios ranging from 1.1 to 550 ($<3$ for 40 $\%$ of pairs). These
criteria mean that their sample consists of major interactions (mass ratio
$<4$), minor interactions (mass ratio between 4 and 10), and systems with even
larger mass ratios; hence, a much larger difference between the stellar mass
of the primary and secondary galaxies, which can significantly dilute the
estimated interaction effects.

Similarly, Pearson et al. (2019) train and use convolutional neural networks
to identify over 200,000 galaxies at $0.0<z<4.0$ in the SDSS, KiDS, and
CANDELS surveys as merging or non-merging galaxies. They then compare the SFR
of merging and non-merging galaxies and find a slight enhancement of $\sim
1.2$ in merging galaxies compared to their non-merging sample. Our SFR
enhancement results for pairs in the closest projected separation bin as well
the blended-interactions in strong agreement with their SFR enhancement
results. However, we also note a large difference in the redshift range and
other selection criteria, making a direct comparison difficult.

###  6.4 Variation in SFR Enhancement with Stellar Mass

Some studies also find a decrease in the SFR enhancement or sSFR enhancement
with the stellar mass of galaxies. For example, Hani et al. (2020) find a
change in sSFR enhancement with stellar mass. For $M_{*}<10^{11}M_{\odot}$
they see an sSFR enhancement of 1.5-2.5 (depending on the TNG simulation
used), which reduces to $\sim 1$ at higher masses. The same trend is also seen
for mergers in the Simba Simulation (Rodríguez Montero et al., 2019). A
similar trend of decreasing level of SFR enhancement with increasing stellar
mass is also seen in the merger sample of Silva et al. (2018). Our SFR
enhancement results for galaxy pairs with $M_{*}<10^{10.88}M_{\odot}$
(1.51${}^{+0.11}_{-0.10}$) and $M_{*}>10^{10.88}M_{\odot}$
(0.96${}^{+0.10}_{-0.14}$) shown in Figure 8 suggest a similar trend as seen
in the above-mentioned studies.

As discussed in Shah et al. (2020), selecting a well-matched control sample is
crucial for this type of analysis. While we have only selected isolated
galaxies that do not show obvious visual signs of interactions or mergers as
control candidates, our control sample can unintentionally still contain
merging galaxies that were not identified as mergers in the visually
identified sample. This is mainly due to the difficulty in identifying mergers
and interactions at high redshift due to weak or non-detectable merger
signatures. This potential contamination of mergers or interactions in the
control sample would dilute the estimated SFR enhancement in our sample.
Hence, the estimated SFR enhancement values are most likely lower limits of
the actual SFR enhancements.

##  7 Summary

The goal of this study is to understand the effect of galaxy interactions and
mergers on the star formation of galaxies at high redshift and compare the SFR
enhancement results with the local interaction results in order to understand
evolutionary effects on the role of interactions in driving star formation
activity. We used our sample of major spectroscopic galaxy pairs, visually
identified interactions and mergers, and their corresponding control samples
at $0.5<z<3.0$, as described in detail in Shah et al. (2020). The samples were
generated using deep multi-wavelength photometric and spectroscopic
observations from the CANDELS and COSMOS surveys. Our 1327 (2351)
spectroscopic galaxy pairs satisfy five criteria: a spectroscopic redshift in
the range $0<z<3$, a relative line of sight velocity less than 1000 (5000) km
s-1, stellar mass of each of the galaxies in a pair more than
10${}^{10}M_{\odot}$, stellar mass ratio of the primary (more massive) to the
secondary galaxy less than four, and projected separation less than 150 kpc.
Our controls are closely matched to individual paired galaxies in their
stellar mass, redshift, and environment density. We estimate the SFR
enhancement in the galaxy pair sample by taking the ratio of the weighted mean
of the SFR of the galaxy pair sample over that of the corresponding control
sample. Our main findings are:

  1. 1.

We see evidence for a slight enhancement of a factor 1.23${}^{+0.08}_{-0.09}$
($\sim 2.6\sigma$) in the closest projected separation bin ($d<25$ kpc) for
our full galaxy pair sample (Figure 6). However, we also see a significant
amount of scatter around the SFR enhancement value of one at all separation
bins. Therefore, in contrast to results for local pair studies, we do not see
a clear trend of increasing SFR enhancement with decreasing projected
separation in our spectroscopic galaxy pair sample.

  2. 2.

We divide our pair sample by redshift and find that at the closes separation
($<25\thinspace kpc$), the low-$z$ ($0.5<z<1$ sample has a higher SFR
enhancement than the high-$z$ ($1<z<3.0$) sample by a factor of $\sim 1.3$
(2.2$\sigma$) (Figure 7). Though this difference is marginally significant, it
is consistent with interaction-induced star formation having a decreased role
at higher redshift.

  3. 3.

We see an enhancement level of a factor of 1.86${}^{+0.29}_{-0.18}$ ($\sim
3\sigma$) in our visually identified merger sample. There is a clear trend of
increasing SFR enhancement with decreasing projected separation in our
visually identified sample of interactions and mergers as shown in Figure 10.
The sample shows this trend at both low-$z$ ($0.5<z<1.6$) and high-$z$
($1.6<z<3.0$) (2.40${}^{+0.62}_{-0.37}$ vs. 1.58${}^{+0.29}_{-0.20}$; Figure
11), with the level of enhancement decreasing with increasing redshift. This
result again suggests evolution in the level of merger-driven star formation
with time.

  4. 4.

We see an enhancement of 1.51${}^{+0.11}_{-0.10}$ ($\sim 4.6\sigma$) for our
pairs with a lower mass primary galaxy ($M_{prim}<10.88\,M_{\odot}$) compared
to the enhancement (0.96${}^{+0.10}_{-0.14}$) in pairs with a higher mass
primary galaxy ($M_{prim}>10.88\,M_{\odot}$) in the same closest projected
separation bin. This factor of 1.6 difference in the enhancement level hints
at stronger effects of interactions in enhancing SF of lower mass galaxy
pairs. This is consistent with the results of both observational (e.g., Silva
et al., 2018) and simulation (e.g., Rodríguez Montero et al., 2019) studies.

  5. 5.

We also see a slightly higher level of SFR enhancement
(1.32${}^{+0.09}_{-0.11}$ vs. 0.84${}^{+0.12}_{-0.10}$) in the star-forming
paired galaxy sample compared to the non-star forming paired galaxy sample in
the closest projected separation bin. For blended pairs, we used the combined
SFR of the system to check if it is star-forming or non-star forming.

Overall, our results show a slight SFR enhancement in close pairs and a
significant enhancement in advanced stage mergers at $0.5<z<3.0$, and an
absence of SFR enhancement at larger pair separations ($>25$ kpc). Comparison
of our results with local studies (Figure 13) suggests that the effect of
interactions and mergers on SFR weakens at high $z$, which is consistent with
the predictions of some simulations. Our study on SFR enhancement in a large
sample of spectroscopic galaxy pairs and mergers provides a deeper
understanding of the role of galaxy mergers and interactions in galaxy
evolution in the high redshift universe. Future spectroscopic surveys of close
pairs at high redshift are required, especially at $z>2$, to increase the
sample size and improve statistics to enable a more comprehensive analysis of
the evolving role of galaxy interactions over cosmic time.

## Acknowledgments

Support for this work was provided by NASA through grants HST-GO-13657.010-A
and HST-AR-14298.004-A awarded by the Space Telescope Science Institute, which
is operated by the Association of Universities for Research in Astronomy,
Inc., under NASA contract NAS 5-26555. Support was also provided by NASA
through grant NNX16AB36G as part of the Astrophysics Data Analysis Program.
This work was also supported by start-up funds and the Dean’s Research
Initiation Grant fund from the Rochester Institute of Technology’s College of
Science. Spectral energy distribution fitting was performed using the
computational resources and support from Research Computing Services at the
Rochester Institute of Technology (Rochester Institute of Technology, 2019).
ES thanks the LSSTC Data Science Fellowship Program, which is funded by LSSTC,
NSF Cybertraining Grant #1829740, the Brinson Foundation, and the Moore
Foundation; The participation of ES in the program has benefited this work.
DRP acknowledges financial support from NSERC of Canada. ET acknowledges
support from CATA-Basal AFB170002 and FB210003, FONDECYT Regular grant
1190818, and Millennium Nucleus NCN19_058 (TITANs). BL acknowledges support
from the National Aeronautics and Space Administration under NASA grant no.
80NSSC21K0986. This paper does not reflect the views or opinions of the
National Science Foundation or the American Association for the Advancement of
Science (AAAS).

This work was supported by a NASA Keck PI Data Award, administered by the NASA
Exoplanet Science Institute. Some of the data presented herein were obtained
at the W. M. Keck Observatory, which is operated as a scientific partnership
among the California Institute of Technology, the University of California and
the National Aeronautics and Space Administration. The Observatory was made
possible by the generous financial support of the W. M. Keck Foundation.

Based in part on observations obtained at the international Gemini Observatory
and processed using the Gemini IRAF package, a program of NOIRLab, which is
managed by the Association of Universities for Research in Astronomy (AURA)
under a cooperative agreement with the National Science Foundation. on behalf
of the Gemini Observatory partnership: the National Science Foundation (United
States), National Research Council (Canada), Agencia Nacional de Investigación
y Desarrollo (Chile), Ministerio de Ciencia, Tecnología e Innovación
(Argentina), Ministério da Ciência, Tecnologia, Inovações e Comunicações
(Brazil), and Korea Astronomy and Space Science Institute (Republic of Korea).
The authors wish to recognize and acknowledge the very significant cultural
role and reverence that the summit of Maunakea has always had within the
indigenous Hawaiian community. We are most fortunate to have the opportunity
to conduct observations from this mountain.

Based in part on observations made with the NASA/ESA Hubble Space Telescope,
obtained from the Data Archive at the Space Telescope Science Institute, which
is operated by the Association of Universities for Research in Astronomy,
Inc., under NASA contract NAS 5-26555. This work is based in part on
observations made with the Spitzer Space Telescope, which is operated by the
Jet Propulsion Laboratory, California Institute of Technology under a contract
with NASA.

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