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Issue
A&A
Volume 691, November 2024
Article Number A6
Number of page(s) 15
Section Extragalactic astronomy
DOI https://doi.org/10.1051/0004-6361/202451471
Published online 25 October 2024

© The Authors 2024

Licence Creative CommonsOpen Access article, published by EDP Sciences, under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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1. Introduction

Since the discovery of the first compact groups (hereafter, CGs) of galaxies (Stephan 1877; Seyfert 1948), these systems have drawn the attention of scientists as a setting that is suitable for the study of the formation and evolution of galaxies in extreme environments. Its apparent physical nature, described by a few galaxies that are confined in a small space, generated the idea that the interactions between the galaxies should be common, and that therefore, their physical properties should reflect this past. Hence, the need for large samples of these systems to allow a further study of these hypotheses became evident.

The subsequent efforts made during the 1970s through the study of the CGs of compact galaxies originally discovered by Shakhbazyan in 1957 (e.g. Robinson & Wampler 1973; Shakhbazyan 1973; Shakhbazyan & Petrosyan 1974; Baier et al. 1974), triggered a systematic search of CGs that led to the first attempt to construct a CG catalogue, which was made by Rose (1977). All those pioneering studies laid the foundations for what would be the construction of the most famous catalogue of compact groups in extragalactic literature, the catalogue developed by Hickson (1982). He visually selected 100 CGs in the plane of the sky from the Palomar Sky Survey prints based on galaxy associations with four or more bright galaxies with a high mean surface brightness and without nearby galaxy companions. These selection requirements are known as population, compactness, and isolation, respectively. Hickson et al. (1992) added redshift information, which allowed them to introduce a new criterion in the line of sight (velocity concordance criterion) that reduced the sample to 92 confirmed CGs in redshift space with three or more galaxy members. The overall procedure that involves these four criteria is called Hickson’s criteria.

During the past 30 years, several attempts have been made to construct new CG samples. Two main procedures were employed to identify CGs in galaxy surveys: the first procedure used a Hickson-like algorithm (e.g. Prandoni et al. 1994; Iovino 2002; Iovino et al. 2003; Lee et al. 2004; de Carvalho et al. 2005; Pompei et al. 2006; McConnachie et al. 2009; Pompei & Iovino 2012; Díaz-Giménez et al. 2012, 2018; Sohn et al. 2015; Zheng & Shen 2020; Soto et al. 2022; Zandivarez et al. 2022), and the second option used a friends-of-friends (FoF) algorithm (i.e. a percolation algorithm that links galaxies in the transversal and radial directions in the sky) that was especially tuned to identify very dense galaxy systems with low membership (e.g. Barton et al. 1996; Focardi & Kelm 2002; Deng et al. 2008; Hernández-Fernández & Mendes de Oliveira 2015; Sohn et al. 2016). As special cases, a few studies attempted to classify loose groups according to their compactness (e.g. Zandivarez et al. 2003; Zheng et al. 2022). Many of these previous works were used to study several physical and dynamic properties of CGs. Among the different observational works on CGs, we mention as an example of the variety of topics those related to the CG environment (Mendel et al. 2011; Díaz-Giménez & Zandivarez 2015; Lee et al. 2017; Zheng & Shen 2021; Taverna et al. 2023), gravitational lensing around CGs (Chalela et al. 2017, 2018), the gas depletion in galaxy members (Martínez et al. 2010; Bitsakis et al. 2015; Lenkić et al. 2016; Jones et al. 2019), and their path to the suppression of star formation (Walker et al. 2012; Alatalo et al. 2015; Bitsakis et al. 2016; Lisenfeld et al. 2017), physical galaxy properties such as luminosity, age, and metallicity, and their comparison with other systems (Proctor et al. 2004; Coenda et al. 2012; Martínez et al. 2013; Zheng & Shen 2021; Zandivarez et al. 2022), different types of galaxy populations, morphology, and stellar content (Kelm & Focardi 2004; Plauchu-Frayn et al. 2012; Zucker et al. 2016; Moura et al. 2020), and their faint galaxy companions (Campos et al. 2004; Krusch et al. 2006; Da Rocha et al. 2011; Konstantopoulos et al. 2013; Zandivarez et al. 2014).

All these works were carried out either with Hickson’s original sample or with subsequent catalogues, which have in common that they are mainly confined to the local Universe. All catalogues created with the FoF algorithm and most of those that used a Hickson-like algorithm have constructed samples in which the CGs are located at z < 0.1 on average. In contrast, only a few samples have reached a z ∼ 0.13 as median (Lee et al. 2004; de Carvalho et al. 2005; Pompei et al. 2006; Pompei & Iovino 2012), while others that have the potential to go beyond the local Universe (McConnachie et al. 2009) have very few redshift measurements. Only a few works ventured into confirming CGs at redshifts close to 0.3 (three CGs by Gutiérrez 2011), 1 (one CG by Gordon et al. 2023) or beyond 2 (one CG each by Shih & Stockton 2015 and Nielsen et al. 2022). Therefore, to deepen our understanding of the evolution of CGs, catalogues with a considerably larger number of these systems beyond the local Universe are required to perform reliable statistical studies of the main physical characteristics of CGs.

To achieve this goal, it is essential to have galaxy surveys whose apparent magnitude limit is large enough while the redshift completeness is high. One of the redshift surveys that accomplishes these characteristics is the Galaxy and Mass Assembly (hereafter, GAMA) spectroscopic survey (Driver et al. 2009). This catalogue is fainter by almost two apparent magnitudes than the Sloan Digital Sky Survey (SDSS) with a very high redshift completeness. Hence, we used the GAMA survey to build statistically fair CG samples that mainly probe the range of intermediate redshift (0.1 ≲ z ≲ 0.4). This regime has been poorly explored in the literature.

The layout of this work is as follows. In Sect. 2 we present the parent galaxy catalogue and the adopted sample loose groups that we used as control samples, while in Sect. 3, we describe the identification of the CGs that we performed on the parent survey. In Sect. 4 we analyse several CG properties and compare them with the sample of loose groups. We summarise and discuss our results in Sect. 5.

2. Sample of galaxies and loose groups

2.1. Parent galaxy catalogue

The main galaxy survey adopted in this work is the GAMA1 spectroscopic survey (Driver et al. 2009, 2022). The GAMA survey is an optical spectroscopic redshift survey designed to provide the best possible dataset for low- to intermediate-redshift galaxies with very high spectroscopic completeness over a volume suitable for performing fair statistical studies. The GAMA survey builds upon the two-degree field galaxy redshift survey (Colless et al. 2001) and the SDSS (York et al. 2000). The survey extends over five discontiguous fields in the sky, three of which lie in the equatorial part of the SDSS main area.

We adopted only those GAMA galaxies belonging to the equatorial region cones (Baldry et al. 2010). The cones we selected are called in the GAMA literature G09 (RA:[129°;141°] – Dec:[−2°;3°]), G12 (RA:[174°;186°] – Dec:[−3°;2°]), and G15 (RA:[211.5°;223.5°] – Dec:[−2°;3°]). Each equatorial survey field covers a region of 5 × 12 sq degrees, producing a total angular coverage of 180 deg2. The redshifts for these fields were mainly obtained by the GAMA team using the AAOmega facility at the Anglo-Australian Telescope. We selected the galaxies that fulfiled the redshift quality parameter NQ ≥ 3 (i.e. a probability higher than 90% to be correct; Liske et al. 2015), an observed colour g − r ≤ 3, and a heliocentric redshift > 0.003, to avoid stars. We used the extinction-corrected apparent r SDSS model mags in the AB system and transformed the redshift into the CMB rest frame. We adopted an apparent magnitude limit of 19.7 that ensured a redshift completeness higher than 98%. We also limited our sample to galaxies with redshifts lower than 0.5. We set this limit because very few galaxies lie beyond this distance, and it is also the maximum redshift for applying the k-correction algorithm (Chilingarian & Zolotukhin 2012) we used in this work.

We complemented the GAMA equatorial cones with galaxies with apparent magnitudes r ≤ 17.77 using the galaxy redshift survey extracted from the SDSS Data Release 16 (Ahumada et al. 2020) in the main contiguous area of the Legacy Survey as well as the compilation of Tempel et al. (2017)2 made for the SDSS Data Release 12 (Eisenstein et al. 2011; Alam et al. 2015) and some corrections3 made by Díaz-Giménez et al. (2018). This galaxy sample comprises 565 224 galaxies with observer-frame model magnitudes r ≤ 17.77 and observer-frame colour g − r ≤ 3 to avoid stars. After performing a cross-match between GAMA and SDSS, we obtained a sample of 12 409 galaxies with r ≤ 17.77 to be added to the GAMA data. Therefore, the final sample in the GAMA equatorial cones comprises 157 634 galaxies.

To estimate the galaxy rest-frame absolute magnitudes, k-corrections were computed using the code developed by Chilingarian & Zolotukhin (2012). The cosmology used here was obtained by Planck Collaboration XVI (2014): Ωm = 0.31 (matter density parameter), h = 0.67 (dimensionless z = 0 Hubble constant), and σ8 = 0.83 (standard deviation of the power spectrum on the scale of 8 h−1 Mpc).

Finally, for the GAMA galaxies (∼92% of the final sample), we used the stellar masses estimated by Taylor et al. (2011). The model of synthesis of stellar population (SPP) was developed by Bruzual & Charlot (2003) with an initial mass function (IMF) of Chabrier (2003) and a dust curve by Calzetti et al. (2000). On the other hand, the specific star formation rates (sSFR) were derived by Davies et al. (2016) using the MAGPHYS code (da Cunha et al. 2008). This code provides an estimate of the galaxy sSFR using the SSP of Bruzual & Charlot (2003), with a Chabrier (2003) IMF and the angle-averaged attenuation model of Charlot & Fall (2000). For the remaining sample of SDSS galaxies, the stellar masses and sSFRs were extracted from the MPA-JHU public catalogue4 (Kauffmann et al. 2003; Brinchmann et al. 2004; Tremonti et al. 2004; Salim et al. 2007).

Some of the properties of the final galaxy sample are shown in Fig. 1. The top left panel displays the galaxy number counts for the galaxy sample (apparent magnitude cut of 19.7 in the SDSS r band). The top right panel shows the redshift distribution of the galaxy sample, which reaches a maximum of 0.5 with a median of 0.21. The r-band absolute magnitude as a function of redshift is shown in the bottom left panel. In this panel, the lower envelope dashed black line indicates the magnitude-redshift relation for the adopted apparent magnitude limit of the catalogue, the other dashed lines show one (red), two (orange), or three (purple) magnitudes brighter than the magnitude limit of the survey. These three limits are relevant for identifying CGs in Sect. 3. Finally, the bottom right panel shows the galaxy stellar mass distribution with a median value of 1.66 × 10 10 M ⊙ h − 2 $ 1.66 \times 10^{10} \ {\cal M}_{\odot} \ h^{-2} $.

We also classified galaxies into distinct galaxy populations. Firstly, we considered the bimodal behaviour of the rest-frame galaxy colour g − r to differentiate between red and blue galaxies. Since the g − r distribution is a function of the absolute magnitude of the galaxy, we followed a procedure similar to that performed by Zandivarez & Martínez (2011) and recently applied to SDSS DR16 galaxies in Taverna et al. (2023). Briefly, we divided the whole range of r-band absolute magnitudes into several bins, and we fitted two Gaussian functions to the g − r colour distribution of galaxies within each absolute magnitude bin5. Using the colour value of the intersection of these Gaussian functions for each bin, we then fitted a second-degree polynomial empirical law as a function of the absolute magnitude. The best fit is P(x) =  − 0.00274x2 − 0.14121x − 1.02936 with x = Mr − 5log(h). Slight variations of these coefficients do not change the final statistical results of this work. Figure 2 shows the scatter plot of g − r versus r-band absolute magnitude for galaxies in the main sample. The dashed line in this figure is the second-degree polynomial function P(x) used to differentiate between red (above the curve: ∼35% of the sample) and blue (below the curve: ∼65%) galaxies. The g − r distributions for the resulting red and blue galaxy populations are shown in the marginal distribution in the right panel of Fig. 2.

thumbnail Fig. 1.

Distribution of properties for GAMA galaxies in the main galaxy sample. In the different panels, we display the galaxy number counts (top left), redshift distribution (top right), absolute magnitude in the r band vs. redshift (bottom left), and the galaxy stellar mass distribution (bottom right). The dashed lines in the bottom left panel show different apparent magnitude limits: 19.7 (black, main catalogue limit) and 18.7, 17.7, and 16.7 (red, orange, and violet). The vertical lines (top right and bottom left panels) represent the redshift limits to define the volume-limited samples used in Sects. 4.4 and 4.5.

thumbnail Fig. 2.

Galaxy colour-magnitude diagram for GAMA galaxies. The logarithm of the specific star formation rate for each galaxy is displayed according to the upper colour bar. The dashed black line in the main panel indicates the empirical law for separating red and blue galaxies (see Section 2). The right marginal plot shows the colour distributions for galaxies classified as red (above the dashed line) and blue (below the dashed line) galaxies. The upper distributions show the specific star formation rate for galaxies classified as red and blue following the empirical law. The vertical dotted line indicates the specific star formation limit (−10.7) we adopted to split the galaxy sample into quenched (to the left) and star-forming galaxies (to the right).

In the literature, the usage of galaxy colours and sSFR is common as clear indicators of the different evolution in terms of their ability to form stars (see e.g. Weinmann et al. 2006, who analysed the impact of the environment on galaxies classified according to galaxy colours and sSFR). It is typically expected that red galaxies are characterised by a low sSFR, while blue galaxies typically display a high sSFR. However, it is likely that due to very strong extinction, some star-forming edge-on disc galaxies may appear to be red in the colour-magnitude diagram. Therefore, the sSFR is more suitable for isolating galaxies with suppressed star formation (quenched) from those that apparently currently form stars. Hence, we also used the sSFR to separate our main galaxy sample into two galaxy populations. In the colour-magnitude diagram of Fig. 2, we coloured galaxies according to their logarithmic sSFR value. At the top of the diagram, we display the total bimodal distribution for the sSFR of galaxies in the sample (black line) and the distributions obtained for the populations of red and blue galaxies classified above. Several limits have been proposed in the literature over the years to split the sSFR distribution, which extends from –11 (Wetzel et al. 2012; Henriques et al. 2017; Ayromlou et al. 2021) to −10.5 (Lacerna et al. 2022). We selected the intersecting value between the red and blue sSFR distributions, −10.7, to separate quenched (lower values) and star-forming (higher values) galaxies. This limit agrees with those of Brown et al. (2017) and Cora et al. (2018), who also selected this value to distinguish two galaxy populations for galaxies in the SDSS and the Multidark simulation (Klypin et al. 2016), respectively.

2.2. Loose galaxy groups

We extracted loose galaxy groups from the above sample using the procedure described in Rodriguez & Merchán (2020). This method initially applies an FoF algorithm to identify gravitationally bound galaxy systems with at least one bright galaxy with an r-band absolute magnitude brighter than −19.5. Subsequently, a halo-based algorithm was applied (Yang et al. 2005, 2007). Using potential members of FoF galaxies in redshift space, the algorithm calculates a three-dimensional density contrast by determining a characteristic luminosity. The estimation procedure considers the incompleteness caused by the limiting magnitude of the observational catalogue. By abundance matching in the luminosity, we assigned the mass of each group. We assumed that the galaxies populate the DM haloes according to a Navarro et al. (1997) profile. We calculated the three-dimensional density contrast using the assigned mass to associate the galaxies with the groups. With this final membership assignment, the procedure recalculated the characteristic luminosity and iterated until it converges. This method reliably identifies galaxy systems with low and high membership. For a detailed description of this algorithm and how it works, we refer to Rodriguez & Merchán (2020). In this specific context, this sample of normal groups is employed as control sample groups, but in other studies, this procedure was used to identify galaxy systems for different goals such as investigating scale relations (Rodriguez et al. 2021), estimating mass through weak lensing (Gonzalez et al. 2021), and examining the influence of the environment on member galaxies (Alfaro et al. 2022; Rodriguez et al. 2022; Rodríguez-Medrano et al. 2023).

The sample of loose groups in GAMA comprises 94 423 groups with one galaxy member or more and is made publicly available in this work. We include a brief description of the catalogue in Appendix A. Here, we select loose groups with three or more galaxy members. The final number of loose groups with three or more members is 6844.

3. Compact groups in GAMA

3.1. Identification criteria for compact groups

The CG samples were identified using the CG finding algorithm of Díaz-Giménez et al. (2018). Following the definition devised by the original criteria of Hickson (1982) and Hickson et al. (1992), the algorithm searches for CGs that simultaneously satisfy constraints on membership, compactness of the group, relative isolation, and velocity concordance, as well as flux limit of the brightest group galaxy (BGG) to ensure completeness (e.g. Prandoni et al. 1994; Díaz-Giménez & Mamon 2010). The overall criteria establish the following requirements:

  • Population: There are three or more bright galaxy members (in a Δm-magnitude range from the brightest).

  • Flux limit of the BGG: The BGG of the system has to be at least Δm magnitudes brighter than the catalogue magnitude limit, rlim.

  • Velocity concordance: Galaxy members are within Vlim from the median velocity of the system.

  • Compactness: The surface brightness of the system in the r band is lower than μlim.

  • Relative isolation: There are no other bright galaxies (in a Δm-mag range from the brightest, nor brighter) within three times the radius of the minimum circle that encloses the galaxy members,

where Δm = 3 magnitudes, Vlim = 1000 km s−1, and μ lim = 26.33 mag arcsec − 2 $ \mu_{\mathrm{lim}}= 26.33 \, \rm mag \, arcsec^{-2} $ (this is the 26 limit defined by Hickson 1982 in the R band, but it was modified for the SDSS r band).

These Hickson-like criteria described above are identical to those used by Zandivarez et al. (2022) in the SDSS DR16. By applying the same algorithm to the GAMA catalogue, we can nearly double the depth of the CG sample identified in the SDSS.

However, to exploit the redshift depth of the GAMA survey, we performed a set of identifications for which we changed some of the parameters of the Hickson-like algorithm, and we analysed the effect of these variations on the resulting samples.

3.2. Choosing identification parameters

The parameters of the algorithm that can be tuned are the limiting surface brightness (μlim), the maximum velocity separation of galaxy members from the group centre (Vlim), and the magnitude gap (Δm) in which the galaxy members are selected.

  • The compactness of the system is mostly defined by the setting of μlim. The original value selected by Hickson (1982) was 26 mag arcsec − 2 $ 26 \, \rm mag \, arcsec^{-2} $ in the R band. In the literature, some works directly used the same value regardless of the photometric band (e.g. McConnachie et al. 2009; Zheng & Shen 2020). In contrast, others changed the Hickson value to match the photometric band of the parent galaxy catalogue (e.g. Prandoni et al. 1994; Iovino 2002; Díaz-Giménez et al. 2012; Taverna et al. 2016; Díaz-Giménez et al. 2018), while still others directly adopted lower limits to reduce contamination regardless of the band (e.g. Iovino et al. 2003; Lee et al. 2004; de Carvalho et al. 2005).

  • Hickson et al. (1992) analysed the difference in the radial velocity of the galaxy members of their CGs with the median velocity of the CG centre (ΔV) and observed that the great majority of galaxy members lie within ±1000 km s−1 from the centre. Therefore, he decided that this value should be set as the limit (Vlim) to select galaxies that are physically associated with the group. Several works have adopted this limit to identify CGs regardless of whether a Hickson or FoF type algorithm was used (e.g. Barton et al. 1996; Focardi & Kelm 2002; Díaz-Giménez et al. 2012; Sohn et al. 2015, 2016; Díaz-Giménez et al. 2018; Zheng & Shen 2020). However, it must be considered that 1000 km s−1 in the line of sight is a fairly large distance (∼10 Mpc h−1), and therefore, the identification is susceptible to contamination. It is worth noting that roughly 77% of galaxies in Hickson CGs differ by less than 500 km/s from the median of the systems (Hickson 1997), while this percentage is nearly ∼95% in the automatic catalogues produced by Díaz-Giménez et al. (2018), Zheng & Shen (2020), Zandivarez et al. (2022). Therefore, setting Vlim to a lower value could help to avoid interlopers in the line of sight.

  • Finally, regarding the Δm, while the original criterion is Δm = 3, different authors in the literature have adopted different gaps. For instance, some authors adopted Δm = 2 to reduce potential contamination at the expense of decreasing completeness (de Carvalho et al. 2005; Iovino et al. 2003). Others stated there is no point in using a magnitude gap when identifying CGs in redshift surveys. They argued that Hickson included this criterion to maximise the probability of having similar redshifts when selecting galaxies with similar magnitudes (Barton et al. 1996; Zheng & Shen 2020). The Δm also has an impact on the isolation criterion. As described above, the Δm determines the galaxies inside the isolation region that can be considered intruders. Nevertheless, some authors have preferred to break this dependence because they considered the criterion to be too strict, which is detrimental for detecting systems in projection whose fainter members are near the selection limit. Most of those studies searched for intruders in the isolation region within the magnitude range defined by the magnitude of the faintest members plus 0.35 or 0.5 magnitudes (Iovino 2002; Iovino et al. 2003; de Carvalho et al. 2005). The selection of Δm also affects the flux limit criterion. To ensure the completeness of the galaxy member selection within a Δm, the brightest galaxy must be brighter than mlim − Δm. This criterion produces a CG sample that is homogeneous with redshift and suitable for unbiased statistical studies (Zheng & Shen 2020).

As mentioned above, different choices of parameters have been used in the literature. It is not the aim of this work to claim a set of parameters as the most adequate for identifying CGs, but instead, we aim to produce several samples of CGs in GAMA that satisfy different restrictions that can be useful for different objectives.

Therefore, we implemented different combinations of free parameters. We kept μlim = 26.33, which is the compactness proposed by Hickson, but adapted it to the SDSS r band, and we varied the velocity limit (1000 and 500 km/s) and the magnitude gap (3, 2, 1, and none). In Fig. 3 we show the combinations used to create different CG samples: m3V10, m3V5, m2V5, m1V5, and m0V5. The different magnitude gaps allowed us to obtain CG samples with different redshift distributions, which increased the depth of the catalogue as Δm decreases. We also included one catalogue without introducing any Δm. This is an inhomogeneous sample with redshift, but comparable with previous catalogues available in the literature.

thumbnail Fig. 3.

Flowchart displaying the identification parameters we adopted to build five different CG samples from the GAMA survey.

The summary of the catalogues built in this work is outlined in Table 1, as are two previous catalogues that we used for comparisons. As an example, Fig. 4 shows the spatial distribution of one of the CG samples presented in this work (m2V5) over-plotted on the spatial distribution of galaxies in GAMA (20% of these galaxies).

Table 1.

Compact group identification criteria and number of compact groups identified in each sample.

thumbnail Fig. 4.

Spatial distribution of CGs identified in GAMA equatorial fields (large filled dots). In this case, the sample of CGs is the CG-m2V5, i.e. with members within a two-magnitude gap and a maximum velocity separation from the CG centre of 500 km s−1. Each cone displays the CG positions using the right ascension and redshift in each field. The colour distribution shows the r-band apparent magnitude of the brightest galaxy in the CG. The black points show a random sample of galaxies (∼20%) in the main survey.

4. Analysis of compact group properties

4.1. General distributions

In Fig. 5 we show the boxplot diagrams of the distributions of properties. The properties are listed below.

thumbnail Fig. 5.

Boxplot diagrams of the properties of CGs. From top left to bottom right: Median redshift of the galaxy members, radial velocity dispersion, surface brightness, absolute magnitude of the brightest galaxy, apparent magnitude difference between the brightest and faintest galaxy, absolute magnitude difference between the two brightest galaxies, median of the projected inter-galaxy distance, and dimensionless crossing time. CGs identified in the GAMA survey are shown in green and are labelled m#V#, where the first index denotes the number of magnitudes used for the magnitude gap, and the second index corresponds to a hundredth of the limit for the velocity concordance criterion. We include the boxplot diagrams of two previous SDSS CG samples in purple for comparison (see Table 1). The dots with bars represent the median properties ± quartile ranges of control groups.

  • zcm: Biweighted median of redshifts of the galaxy members (eq. 5 from Beers et al. 1990).

  • μr: Surface brightness of the group in the r band computed inside the minimum circle that encloses the galaxy members.

  • mf − mb: Difference in observer-frame apparent magnitude between the brightest and faintest galaxy members.

  • ⟨Rij⟩: Median of the projected separations between galaxy members.

  • σv: Radial velocity dispersion of the group computed with the gapper (n < 10) or the biweight (n > 10) estimators (Beers et al. 1990).

  • M1 − 5log(h): Rest frame absolute magnitude of the brightest galaxy of the group.

  • M2 − M1: Difference in absolute magnitudes between the brightest and second brightest galaxy members.

  • H0 tcr: Dimensionless crossing time of the groups computed as 100 h π ⟨ R ij ⟩ / ( 2 3 σ v ) $ 100 \, {h} \pi\langle R_{ij} \rangle /(2 \sqrt{3}\sigma_v) $.

Some of these properties are directly linked to the parameters used to perform the identification, and their variations reflect the change in the selection criteria (μr, mf − mb and M2 − M1). All these properties and the angular coordinates of the CG centres presented in this work are made publicly available. Appendix A includes the description of the released tables.

For comparisons, we used the sample of loose groups described in Sect. 2.2 as a control sample. Dots with error bars in Fig. 5 display the behaviour of control samples (loose groups). Since CG properties are computed using galaxies in a given range of magnitudes, we also imposed the magnitude range to select the galaxies in loose groups to measure the group properties and the flux limit on the brightest galaxy. We only worked with those loose groups with three or more members within a given magnitude range from the brightest galaxy. In this way, we had a control sample of loose groups for each CG sample. The control sample for m3V10 comprises 705 systems, and those with Vlim = 500 km s−1 have 1899, 1752, 721 and 5523 systems for magnitude ranges of 1, 2 and 3 magnitudes and no gap, respectively.

In addition, we compared our CG catalogues with previous CG catalogues available in the literature. Zheng & Shen (2020)6 presented a CG catalogue in the SDSS DR12 (rlim = 17.77). In this work, the authors identified CGs with a Hickson-like algorithm with μ lim = 26 mag arcsec 2 $ \mu_{\mathrm{lim}}= 26 \, \rm mag \, arcsec^2 $, Vlim = 1000 km s and did not impose a magnitude gap. Zandivarez et al. (2022)7 produced a CG catalogue in the SDSS DR16 (rlim = 17.77) using a Hickson-like algorithm with μ lim = 26.33 mag arcsec 2 $ \mu_{\mathrm{lim}}= 26.33 \, \rm mag \, arcsec^2 $, Vlim = 1000 km/s and a three-magnitude gap.

Comparing Z+22 with m3V10, the only difference in the CG finder is the flux limit of the brightest galaxy since the apparent magnitude limits of the source catalogues are different (rlim = 17.77 in SDSS DR16 vs. rlim = 19.70 in GAMA). m3V10 comprises CGs with its brightest galaxy almost two magnitudes fainter than Z+22, and therefore, this sample reaches deeper in redshift with a median of 0.09 compared to the median redshift of 0.03 for Z+22. All the other CG properties shown in Fig. 5 are similar between these two catalogues (the notches of the medians overlap).

The effect of changing the velocity limit from 1000 km/s to 500 km/s can be evaluated in the comparison between m3V10 and m3V5. As mentioned above for other automatic catalogues, most of the galaxies in m3V10 are within 500 km/s from the group centre, and therefore, the number of groups does not change significantly (from 467 to 438; see Table 1). There is a slight decrease in the velocity dispersion in the m3V5 as a reflection of a smaller Vlim that has eliminated potential interlopers in the CGs, while all the other independent properties remain indistinguishable.

As the magnitude gap changes, the flux limit of the brightest galaxy varies. Then, moving from m3V5 to m2V5 to m1V5, we included groups in which the brightest galaxies were fainter by one and two magnitudes. Therefore, the depth of the samples increased as the magnitude gap decreased. A smaller magnitude gap also means that we selected groups with more similar galaxies. The number of m2V5 almost doubles the m3V5 (Table 1), and it decreases again in m1V5. Moving from Δm = 3 to 1, there is a small increase in the projected separation between galaxies and the velocity dispersion, while a small decrease is observed in the magnitude of the brightest galaxies. We deepen the analyses of the possible dependence of the properties with redshift in the following sections.

Sample m0V5 is the most numerous but also inhomogeneous sample. There is no limit in the flux of the brightest galaxy, nor a fixed magnitude gap to search for its neighbours. The latter means that this sample is the most restrictive regarding isolation because it does not allow any galaxy to contaminate the isolation disk. Still, its properties do not differ significantly from those of the other samples, especially with the m2V5. This sample is useful to compare with the sample of ZS+20, which was identified similarly, but in the SDSS DR12 catalogue. The fainter magnitude limit of the GAMA catalogue allowed us to go farther in redshift space, while the smaller Vlim used in this work produces CGs with smaller velocity dispersions, as noted above. There are also small differences in the absolute magnitude of the brightest galaxy, and the magnitude gap between the two brightest galaxies. The larger gap arises because GAMA reaches two magnitudes beyond the SDSS (allowing the possibility of measuring larger gaps), while the brighter absolute magnitude of the first-ranked galaxy in GAMA is due to a deeper sample (higher redshifts).

4.2. Galaxy quenching in compact groups

Early studies of Hickson et al. (1992), Ribeiro et al. (1998), and Pompei & Iovino (2012) showed that there is a positive correlation in CGs between the fraction of spiral galaxies and the crossing time. CGs with the shortest crossing times display the lowest fraction of spiral galaxies, while those with the longest crossing times frequently contain a larger fraction of spiral galaxies.

These results can be interpreted in terms of star formation as a scenario in which very dense environments, such as those present in very small CGs (with the shortest crossing times), are expected to have the lowest fraction of star-forming galaxies, or, in other words, they are more efficient in suppressing the star formation in galaxies than less dense environments. Therefore, we analysed the fraction of low star formation galaxies in CGs in GAMA using two galaxy properties: the g-r galaxy colour, and the specific star formation rate. The upper panels of Fig. 6 show the median behaviour of galaxy colours and sSFR as a function of galaxy stellar mass for the samples of CGs in GAMA selected using Vlim = 500 km s−1 (green dots) as well as polynomial fittings to the scatter plots with the locally estimated scatterplot smoothing (LOESS, Cleveland et al. 1992) method (green curves) while the grey region is the 95% confidence interval. We also included the corresponding measurements for their control samples (red dots and curves). As expected, low-mass galaxies exhibit bluer colours and higher sSFR than high-mass galaxies. The curves (and their confidence intervals) show that there is a small difference (except for the m0V5 sample) between CGs and control samples for galaxies with stellar masses lower than 10 10 h − 2 M ⊙ $ 10^{10} \ h^{-2} \ \mathcal{M}_{\odot} $ (the confidence intervals do not overlap). Low-mass galaxies in CGs appear to be redder and have a lower sSFR than their counterparts in control groups. These results are consistent with previous findings of Montaguth et al. (2023), who observed a lower median sSFR in CGs than in control systems.

thumbnail Fig. 6.

Upper plots: Distribution of the specific star formation rate and colour as a function of galaxy stellar masses for galaxy members in systems. Lower plots: Distribution of the fraction of red and quenched galaxies in systems as a function of their dimensionless crossing time. In all panels, dots represent the median values per bin, and the error bars are the 95% confidence interval for the median. The solid lines show the polynomial fitting to the scatter plot with the LOESS method, and the grey region shows the 95% confidence interval. In each column, the comparison is made between CGs (green) and their control sample (red). Each sample has Vlim = 500 km s−1.

Using the procedure described in Sect. 2.1, we selected red galaxies from the g-r galaxy colour and quenched galaxies using the sSFR and computed the fraction of red (fred) and quenched (fq) galaxies in CGs. In the lower panels of Fig. 6 we show these fractions as a function of the group dimensionless crossing times. The fred of control groups show a peak near a crossing time of 0.05 and decrease towards the extremes for the m3V5, m2V5 and m0V5 subsamples. On the other hand, CGs exhibit a relatively constant behaviour. At shorter crossing times, CGs have a larger fraction of red galaxies than control groups.

On the other hand, fq for the control samples resembles the behaviour observed previously (for the fred), but CGs show a decreasing trend of the fq with crossing times for m3V5, m2V5, m1V5, and m0V5. Hence, in this case, a clearer quenching of galaxies is observed for galaxies in CGs with shorter crossing times.

Our results are consistent with those obtained by other authors who studied the fraction of spiral galaxies as a function of crossing time in CGs (Hickson et al. 1992; Ribeiro et al. 1998; Pompei & Iovino 2012). More recently, Moura et al. (2020) found the same correlation of a low fraction of spiral galaxies in CGs with shorter crossing times. They also found based on an analysis of early-type galaxies that some mechanisms, such as tidal interactions, should be present in CGs that favour gas loss in their galaxy members. It has been suggested that the HI deficiency observed in CGs can be used as an indication for the evolutionary stage of these systems. Tidal interactions are probably the main mechanisms to explain this HI deficiency observed in CGs, separating large amounts of gas in filamentary structures, or tails, as well as in a diffuse component (Verdes-Montenegro et al. 2001; Borthakur et al. 2010; Hess et al. 2017). Hence, more evolved CGs are more likely to display a clear loss of gas content. Based also on the analysis of the spiral fraction with the crossing time for a sample of Hickson CGs, Liu & Zhu (2022) found similar results. They obtained a relatively constant trend of the fraction of quiescent galaxies (defined from mid-infrared colours; Zucker et al. 2016) with crossing times. In this work, we also obtain an almost constant behaviour of the fraction of red galaxies in CGs with crossing times, while the fraction of quenched galaxies shows a decreasing trend with crossing times. Our results agree with those for the spiral fraction, suggesting that similar mechanisms likely account for both the decrease in the proportion of spiral galaxies and the increase in quenched galaxies in CGs with very short crossing times.

4.3. Non-embedded and embedded compact groups

As stated in several works (e.g. Rood & Struble 1994; Mendel et al. 2011; Díaz-Giménez & Zandivarez 2015), despite the isolation criterion for identifying CGs, these systems are only locally isolated since the criterion is performed relative to the size of the CGs themselves, which is quite small. Moreover, recent studies using synthetic catalogues have demonstrated that although approximately 60% of the CGs identified using Hickson-like criteria are dense structures in 3D space, most of them are found to inhabit larger groups. This is because Hickson’s isolation criterion does not effectively ensure global isolation in 3D real space (Taverna et al. 2022). Therefore, CGs identified using a Hickson-like algorithm could be embedded in the different galactic structures in the Universe. Recently, Taverna et al. (2023) statistically analysed the location of Hickson-like CGs identified in the SDSS DR16 (Ahumada et al. 2020) in the large-scale structure of the Universe and found that roughly 45% of them can be considered to be embedded in loose groups, filaments, and voids. Later, Taverna et al. (2024) used several mock catalogues constructed from numerical simulations and semi-analytical models of galaxy formation and confirmed that almost half of CGs are embedded, and this percentage increases to roughly 60% when they considered CGs that assembled early (more than 7 Gyr ago).

In this work, we followed Taverna et al. (2023) and classified CGs as embedded or non-embedded in loose groups to analyse the main properties of CGs as a function of their location in other galactic structures. Briefly, using the sample of loose groups described in Sect. 2.2, we first removed the loose groups that were also identified as CGs8. The percentage of loose groups considered equal to CGs is about 3–5% when we used the samples m3V10, m3V5, m2V5, and m1V5 as a reference, and 19% of the loose groups were selected as equal to CG when compared with m0V5 sample. Then, using the remaining loose groups and the sample of CGs, we performed a member-to-member comparison and considered a CG to be embedded in a loose group when they share at least two members. These embedded CGs did not necessarily have all their members in common with the loose group. They might have additional members, but we still called them embedded CGs.

The percentages of embedded CGs in GAMA are 42% of the m3V10 and m3V5, 49% of the m2V5, 45% of the m1V5, and 38% of the m0V5. In general, these percentages agree with the previously reported for CGs in SDSS (Zheng & Shen 2020; Taverna et al. 2023).

We recall that varying Δm in the CG search also implies a variation in the isolation criterion. Therefore, the m3V5, m2V5, and m1V5 samples allow galaxies inside their isolation ring fainter than three, two or one magnitudes, respectively, from the brightest galaxy of the CG. Hence, m1V5 is the most permissive of the three samples, which could mean that this sample might have more chances of being embedded in galaxy systems than the other two samples (m3V5 and m2V5). However, according to the results, this is not the case. On the other hand, the m0V5 sample imposes the strongest restriction inside the isolation since no galaxy is allowed there. This could indicate that this sample might be less likely to be embedded. Our results might confirm this hypothesis since the m0V5 sample has between 4 to 11% fewer embedded CGs than the other samples. In general, our results for CGs in GAMA agree with previous findings (Mendel et al. 2011; Zheng & Shen 2021; Taverna et al. 2023, 2024).

Figure 7 shows the violin diagrams of the distributions of properties of CGs split into non-embedded (left half) and embedded (right half) systems for compact groups identified with Vlim = 500 km/s and different magnitude gaps. This figure shows a clear distinction between embedded and non-embedded CGs. CGs considered to be embedded show the highest compactness with the smallest galaxy separations, the largest radial velocity dispersions, and the shortest crossing times, while also showing the brightest first-ranked galaxies and the largest fraction of quenched galaxies. These differences are more pronounced in the m3V5 sample, which is the most nearby redshift sample. The results are consistent with previous findings for these properties in Taverna et al. (2023) and Taverna et al. (2024). Regarding the velocity dispersion, Pompei & Iovino (2012) also found that most CGs relatively close to larger structures display large velocity dispersions, which could imply some influence of the larger potential of the surrounding structures. More recently, Montaguth et al. (2024) observed that the major structures that host CGs seem to accelerate the morphological transformation of CG galaxies. Therefore, they argued that the quenching of the CG members is due to interactions between galaxy members and also to interactions of the CG with its surroundings. Our results confirm and extend the previous findings to broader redshift ranges.

thumbnail Fig. 7.

Compact group properties when the sample is split according to whether they can be considered embedded in a loose group.

4.4. Properties of compact groups as a function of redshift

The depth of the GAMA catalogue enabled us to build samples that span broader redshift ranges. In this section, we analyse the dependence of the CG properties on redshifts. To perform this analysis, we built volume-limited samples of CGs (and control groups) for each sample identified with Vlim = 500 km s−1. This restriction facilitated the comparison of the properties of different redshift bins since we avoided the usual biases inherent in flux-limited samples. Therefore, we selected CGs in which the first-ranked galaxy had an absolute magnitude brighter than −20 in the r band, and the group biweighted median redshift was lower than 0.069, 0.1075, 0.164, and 0.248 for m3, m2, m1, and m0, respectively (see the vertical lines in the bottom left panel of Fig. 1).

Figure 8 shows the evolution of several CG properties as a function of redshift. Based on the analysis of these behaviours, most properties do not display a clear evolution with redshift. The samples m3V5, m2V5, and m1V5 display an almost constant trend with redshift for the surface brightness, luminosity of the first-ranked galaxy, median galaxy separation, and magnitude gap. There is a weak tendency for the m3V5 sample to show an increasing function for the crossing time of CGs with redshift, but the presence of large confidence intervals means that this claim is statistically not supported. Finally, we also observed an apparent trend (but not statistically reliable; Pearson coefficients r = −0.19 and p = 0.2) of the m3V5 CGs to have a larger fraction of red and quenched galaxies at lower redshift than that observed at larger distances. The m2V5 and m1V5 samples do not display a trend for the fraction of quenched galaxies with redshift either. In the m0V5 sample, some properties display some evolutionary trends with redshifts, such as the brightness of the first-ranked galaxy, the magnitude gap, and the fraction of red and quenched galaxies. However, as we stated before, this sample is non-homogeneous because there is no magnitude restriction for the first-ranked galaxies (flux limit of the BGG criterion). This characteristic of the identification process can account for the redshift trends observed in these properties.

thumbnail Fig. 8.

Group properties as a function of redshift for volume-limited samples. The left column shows CGs, and the right column shows the control groups, both with Vlim = 500 km s−1. The curves are polynomial fittings to the scatter plots displaying roughly median values per redshift bin.

Based on the comparison with the control groups, we observe that in addition to the general differences already mentioned previously (CGs are more compact, with a smaller galaxy separation, radial velocity dispersion, and crossing times, and larger magnitude gaps), the trends with redshift are similar to those observed for CGs.

Wilman et al. (2005) reported a decreasing trend for the fraction of red and quenched galaxies with redshift in loose groups and field galaxies to at least up to z ∼ 0.45, as well as a stronger effect on groups than in the field. They stated that the most likely scenario would be a transforming mechanism that accounts for the suppression of the star formation of the galaxies. In this work, we only find an apparent tendency (not statistical) for the m3V5 sample, the smallest and shallowest (zcm < 0.069) of our volume-limited samples. A larger sample is needed to confirm these results.

4.5. Tremaine-Richstone statistics for CGs

Tremaine & Richstone (1977) proposed two powerful statistics to test whether the first-ranked galaxies in galaxy systems are consistent with a statistical sampling of a luminosity function. These statistics rely on the brightness of the first-ranked galaxy (M1) and the magnitude gap between the two brightest galaxies in the system (M2 − M1). They are defined as follows:

T 1 = σ ( M 1 ) ⟨ M 2 − M 1 ⟩ and T 2 = σ ( M 2 − M 1 ) 0.677 ⟨ M 2 − M 1 ⟩ , $$ \begin{aligned} T_1 = \frac{\sigma (M_1)}{\langle M_2-M_1 \rangle } \ \ \ \ \ \mathrm{and} \ \ \ \ \ T_2 = \frac{\sigma (M_2-M_1)}{\sqrt{0.677} \ \langle M_2-M_1 \rangle }, \end{aligned} $$

where σ are the standard deviations, and angle brackets are the mean values. When T1 and T2 exceed unity, we can infer that the first-ranked galaxies are consistent with a random sampling of any given luminosity function. On the other hand, values of T1 and T2 lower than unity suggest that the first-ranked galaxies are abnormally bright compared to their closest companion in luminosity. Galaxy interactions such as galaxy mergers within systems might cause the abnormal brightness of the main galaxy that reduces the T values below 0.7 (Mamon 1987a,b).

We computed the Tremaine-Richstone statistics for the flux- and volume-limited samples of CGs and control groups with Vlim = 500 km s−1. The results are shown in Fig. 9 with error bars computed from a bootstrap resampling technique. We show the results for the flux-limited samples as it is commonly used in the literature, but a fairer comparison among the different samples (m3V5, m2V5, and m1V5 and m0V5) can be made for volume-limited samples. From the volume-limited samples, we observe that m3V5 and m2V5 CGs samples show T1 values between 0.5–0.7 and T2 around 0.75. These values are consistent with previous results, showing that first-ranked galaxies in CGs exceed the random sampling expectation. On the other hand, the value of T1 that is larger than unity that we obtained for the m1V5 CGs sample indicates that despite their compactness, these systems are not as evolved as the m3V5 and m2V5 CGs samples. This result is expected because by construction, the m1V5 CG sample comprises systems whose galaxy members have very similar luminosities. In addition, despite their inhomogeneities, m0V5 CG sample displays a very low value for T1 (∼0.5), but the T2 value is barely below unity. Finally, in general, the control samples display higher values for T1 and T2 than the observed for CGs, with the most notorious difference observed in T2 when a gap of three magnitudes is considered, obtaining a value consistent with unity. Therefore, when we consider the luminosity of their two brightest galaxies, the differences observed in T1 and T2 suggest that CGs may exhibit signs of more evolved systems compared to loose groups.

thumbnail Fig. 9.

Tremaine-Richstone statistics T1 and T2 (Tremaine & Richstone 1977) computed for the different samples of CGs (green circles) and controls (red triangles). The error bars are computed using the bootstrap technique. The left column displays the statistics computed for the flux-limited group samples, and the right column displays the results for the volume-limited sample of groups.

5. Summary and conclusions

We presented a series of CG catalogues extracted from the GAMA redshift survey. We identified CGs following the guidelines of the Hickson criteria, but modified a set of parameters to produce different CG samples.

  • We adopted a limiting galaxy velocity difference in the line of sight of 500 km s−1. This choice reduced the usual limit of 1000 km s−1 that was adopted in several previous works by a factor of two. Our value was selected to reduce misidentification by spuriously linking galaxies in the radial direction.

  • We varied the allowed magnitude range between the brightest and faintest galaxy members. We produced different CG samples by setting this magnitude range in three, two, and one magnitudes, plus a sample without a magnitude range (i.e. every galaxy was allowed to be classified as a group member). These choices affected three of the five criteria that define the Hickson-like procedure (population, flux limit of the BGG, and isolation).

These choices in the searching algorithm parameters allowed us to build CG samples with varying depths, and we probed the intermediate-redshift range in this way. The samples with a given magnitude range (3, 2, or 1) comprise between 500 and 700 systems with median redshifts between 0.1 and 0.18, and maximum redshifts of 0.2 to 0.32. The sample without a restriction in the magnitude range of its galaxy members comprises more than 2000 systems with a median redshift of 0.16 and a maximum redshift of 0.43. We performed several tests on these samples to analyse the CG properties and their galaxy members. We also built control samples for each CG sample from the catalogue of loose groups in the parent galaxy survey identified following the procedure of Rodriguez & Merchán (2020). As a summary, we highlight our results below.

  • Analysing the properties of each CG sample, we observed that most of the properties in general behave similarly for samples with different sets of parameters (except for the properties that directly depend on the identification parameters). In particular, we observed a small brightening of the first-ranked galaxy and a decreasing projected separation between galaxies when moving from one to three in the magnitude range. These results are consistent with previous CG samples (Zandivarez et al. 2022). We also observed that the sample of CGs without a magnitude restriction displays similar properties to those of CGs obtained by Zheng & Shen (2020) that were identified with similar restrictions.

  • Control samples of loose groups tailored to reproduce the membership magnitude range and the velocity restrictions of CGs show lower compactness, larger galaxy separations, and higher velocity dispersions than those observed in the corresponding CG counterparts.

  • The fraction of quenched galaxies in CGs is higher than in their control samples. These differences are mainly observed for galaxies with low stellar masses ( 10 10 h − 2 M ⊙ $ 10^{10} \ h^{-2} \mathcal{M}_{\odot} $) that inhabit CGs with the shortest crossing times. It has been shown that low-mass galaxies in compact groups have practically lost all their hot-gas content while keeping a small reservoir of cold gas at present, which strongly influences the suppression of the star formation rate (Zandivarez et al. 2023). This result resembles the previous findings about the fraction of spiral galaxies with the crossing times in CGs (Hickson et al. 1992; Ribeiro et al. 1998; Pompei & Iovino 2012; Moura et al. 2020).

  • We also analysed the influence of the near surroundings of CGs by splitting the samples according to whether they were embedded in a loose galaxy system. We obtained that 44% of the CGs are embedded in loose groups on average. A similar percentage of embedded CGs was previously reported by Taverna et al. (2023) for CGs in the SDSS and by Taverna et al. (2024) in CGs identified in several mock catalogues built from different semi-analytical models of galaxy formation. Our study also confirms previous findings that showed that embedded CGs display the highest compactness and velocity dispersions, shorter crossing times, and brightest first-ranked galaxies (Taverna et al. 2023). In addition, the distribution of the fraction of quenched galaxies is shifted toward higher values for embedded compact groups, in agreement with the findings of Montaguth et al. (2024). These behaviours are consistent with CGs characterised by an early assembly9 (Zandivarez et al. 2023), which dominate the sample of embedded CGs (Taverna et al. 2024).

  • Given the depths reached for our CG samples, we investigated the possible variation in the CG properties as a function of redshift. To perform a fair comparison, we built volume-limited samples with an absolute magnitude limit of −20 in the r band. We found no evolution of the CG properties with redshift for the CG samples selected with magnitude ranges of one, two, and three. However, several properties display some evolution with redshift for the sample of CGs that was built without a magnitude restriction. Nevertheless, these behaviours are likely to be caused by the intrinsic inhomogeneities inherent in this sample.

  • Finally, we complemented our analysis by computing the Tremaine-Richstone statistics of the CG samples (Tremaine & Richstone 1977). Our estimate of the T1 and T2 values shows signs that the first-ranked galaxies of the CGs are anomalously bright. This result is clear for the CG samples with a membership magnitude range of two and three magnitudes. As expected, the CG sample with members within a one-magnitude range shows different values than the other CG samples (mainly for T1) because the galaxy members in these systems have very similar luminosities by construction. These results that support the abnormally bright first-ranked galaxy in CGs in GAMA agree with previous estimates obtained for other observational (Díaz-Giménez et al. 2012) and synthetic CGs catalogues (Taverna et al. 2016). It has previously been suggested that the abnormal brightness of the main galaxy may be attributed to the dynamic interactions, such as the merging of galaxies within its system (Ostriker & Hausman 1977; Mamon 1987a,b; Ostriker et al. 2019).

Our findings for compact groups in GAMA support the scenario in which these highly dense systems tend to harbour exceptionally luminous first-ranked galaxies, likely due to interactions and/or mergers with their companions. This scenario makes compact groups more favourable places for the suppression of the star formation rate than loose systems. This phenomenon is enhanced in compact groups that have formed earlier and are most likely embedded in larger systems. Ultimately, the extreme proximity of galaxies and their nearby environment conspire to accelerate the quenching of their galaxies.

As a corollary, our work presents a new set of statistically reliable compact group catalogues with high-redshift completeness that can sample the intermediate-redshift zone and will be available for the astronomical community. These catalogues might represent the beginning of reliable statistical studies of these extreme systems beyond the local Universe, and will serve as a benchmark for CG studies in upcoming deep spectroscopic surveys such as DESI and others.

Data availability

Full Tables A.1, A.2, A.3, and A.4 are available at the CDS via anonymous ftp to cdsarc.cds.unistra.fr (130.79.128.5) or via https://cdsarc.cds.unistra.fr/viz-bin/cat/J/A+A/691/A6

1

Available in http://gama-survey.org/

3

They added already existing redshift estimates from other catalogues and discarded objects that were misclassified as galaxies.

5

This procedure was performed using Gaussian mixture models provided by the Mclust package of the R software (Scrucca et al. 2016).

8

We considered a CG to be the same group as a loose group when they shared at least 75% of their members, and the number of bright loose group members that are beyond three times the CG radius was smaller than half of the CG members.

9

See Díaz-Giménez et al. (2021) for a detailed description of assembly channels in Hikcson-like CGs.

10

CDS, Strasbourg Astronomical Observatory, France, DOI: 10.26093/cds/vizier

Acknowledgments

We thank the anonymous referee for their suggestions that improved the final version of the manuscript. This publication uses as a parent catalogue the GAMA survey. GAMA is a joint European-Australasian project based around a spectroscopic campaign using the Anglo-Australian Telescope. The GAMA input catalogue is based on data taken from the Sloan Digital Sky Survey and the UKIRT Infrared Deep Sky Survey. Complementary imaging of the GAMA regions is being obtained by a number of independent survey programmes including GALEX MIS, VST KiDS, VISTA VIKING, WISE, Herschel-ATLAS, GMRT and ASKAP providing UV to radio coverage. GAMA is funded by the STFC (UK), the ARC (Australia), the AAO, and the participating institutions. The GAMA website is http://www.gama-survey.org/. This publication also uses the SDSS Data Release 16 (DR16) which is one of the latest data releases of the SDSS-IV. Funding for the Sloan Digital Sky Survey IV has been provided by the Alfred P. Sloan Foundation, the U.S. Department of Energy Office of Science, and the Participating Institutions. SDSS-IV acknowledges support and resources from the Center for High Performance Computing at the University of Utah. The SDSS website is http://www.sdss.org. SDSS-IV is managed by the Astrophysical Research Consortium for the Participating Institutions of the SDSS Collaboration including the Brazilian Participation Group, the Carnegie Institution for Science, Carnegie Mellon University, Center for Astrophysics|Harvard & Smithsonian, the Chilean Participation Group, the French Participation Group, Instituto de Astrofísica de Canarias, The Johns Hopkins University, Kavli Institute for the Physics and Mathematics of the Universe (IPMU)/University of Tokyo, the Korean Participation Group, Lawrence Berkeley National Laboratory, Leibniz Institut für Astrophysik Potsdam (AIP), Max-Planck-Institut für Astronomie (MPIA Heidelberg), Max-Planck-Institut für Astrophysik (MPA Garching), Max-Planck-Institut für Extraterrestrische Physik (MPE), National Astronomical Observatories of China, New Mexico State University, New York University, University of Notre Dame, Observatário Nacional/MCTI, The Ohio State University, Pennsylvania State University, Shanghai Astronomical Observatory, United Kingdom Participation Group, Universidad Nacional Autónoma de México, University of Arizona, University of Colorado Boulder, University of Oxford, University of Portsmouth, University of Utah, University of Virginia, University of Washington, University of Wisconsin, Vanderbilt University, and Yale University. This work has been partially supported by Consejo Nacional de Investigaciones Científicas y Técnicas de la República Argentina (CONICET) and the Secretaría de Ciencia y Tecnología de la Universidad de Córdoba (SeCyT). FR would like to acknowledge support from the ICTP through the Junior Associates Programme 2023-2028

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Appendix A: Catalogues of compact groups and loose groups in the GAMA survey

In this appendix, we displayed sample tables of the compact and loose groups catalogues derived from the Galaxy and Mass Assembly spectroscopic survey (Driver et al. 2009, 2022).

We presented the first catalogues of compact groups derived from the GAMA survey. These catalogues encompass five samples of CGs: m3V10, m3V5, m2V5, m1V5, and m0V5 (see Table 1). Table A.1 details the properties of the CGs included in each catalogue, and Table A.2 shows the properties of the compact group members. Given the similarity in the format of the tables for each sample, we only show an example from the m3V10 catalogue.

We also included the header of the catalogue of loose groups identified in the GAMA survey. Table A.3 shows the properties of the groups and Table A.4 lists the properties of the galaxy members.

The full version of the whole set of catalogues used in this work will be available using the VizieR catalogue access tool10 (Ochsenbein et al. 2000).

Table A.1.

Compact groups identified in GAMA (example from sample m3V10).

Table A.2.

Galaxy members of compact groups identified in GAMA (example from sample m3V10).

Table A.3.

Loose groups identified in GAMA.

Table A.4.

Galaxy members of loose groups identified in GAMA.

All Tables

Table 1.

Compact group identification criteria and number of compact groups identified in each sample.

In the text
Table A.1.

Compact groups identified in GAMA (example from sample m3V10).

In the text
Table A.2.

Galaxy members of compact groups identified in GAMA (example from sample m3V10).

In the text
Table A.3.

Loose groups identified in GAMA.

In the text
Table A.4.

Galaxy members of loose groups identified in GAMA.

In the text

All Figures

thumbnail Fig. 1.

Distribution of properties for GAMA galaxies in the main galaxy sample. In the different panels, we display the galaxy number counts (top left), redshift distribution (top right), absolute magnitude in the r band vs. redshift (bottom left), and the galaxy stellar mass distribution (bottom right). The dashed lines in the bottom left panel show different apparent magnitude limits: 19.7 (black, main catalogue limit) and 18.7, 17.7, and 16.7 (red, orange, and violet). The vertical lines (top right and bottom left panels) represent the redshift limits to define the volume-limited samples used in Sects. 4.4 and 4.5.
In the text
thumbnail Fig. 2.

Galaxy colour-magnitude diagram for GAMA galaxies. The logarithm of the specific star formation rate for each galaxy is displayed according to the upper colour bar. The dashed black line in the main panel indicates the empirical law for separating red and blue galaxies (see Section 2). The right marginal plot shows the colour distributions for galaxies classified as red (above the dashed line) and blue (below the dashed line) galaxies. The upper distributions show the specific star formation rate for galaxies classified as red and blue following the empirical law. The vertical dotted line indicates the specific star formation limit (−10.7) we adopted to split the galaxy sample into quenched (to the left) and star-forming galaxies (to the right).
In the text
thumbnail Fig. 3.

Flowchart displaying the identification parameters we adopted to build five different CG samples from the GAMA survey.
In the text
thumbnail Fig. 4.

Spatial distribution of CGs identified in GAMA equatorial fields (large filled dots). In this case, the sample of CGs is the CG-m2V5, i.e. with members within a two-magnitude gap and a maximum velocity separation from the CG centre of 500 km s−1. Each cone displays the CG positions using the right ascension and redshift in each field. The colour distribution shows the r-band apparent magnitude of the brightest galaxy in the CG. The black points show a random sample of galaxies (∼20%) in the main survey.
In the text
thumbnail Fig. 5.

Boxplot diagrams of the properties of CGs. From top left to bottom right: Median redshift of the galaxy members, radial velocity dispersion, surface brightness, absolute magnitude of the brightest galaxy, apparent magnitude difference between the brightest and faintest galaxy, absolute magnitude difference between the two brightest galaxies, median of the projected inter-galaxy distance, and dimensionless crossing time. CGs identified in the GAMA survey are shown in green and are labelled m#V#, where the first index denotes the number of magnitudes used for the magnitude gap, and the second index corresponds to a hundredth of the limit for the velocity concordance criterion. We include the boxplot diagrams of two previous SDSS CG samples in purple for comparison (see Table 1). The dots with bars represent the median properties ± quartile ranges of control groups.
In the text
thumbnail Fig. 6.

Upper plots: Distribution of the specific star formation rate and colour as a function of galaxy stellar masses for galaxy members in systems. Lower plots: Distribution of the fraction of red and quenched galaxies in systems as a function of their dimensionless crossing time. In all panels, dots represent the median values per bin, and the error bars are the 95% confidence interval for the median. The solid lines show the polynomial fitting to the scatter plot with the LOESS method, and the grey region shows the 95% confidence interval. In each column, the comparison is made between CGs (green) and their control sample (red). Each sample has Vlim = 500 km s−1.
In the text
thumbnail Fig. 7.

Compact group properties when the sample is split according to whether they can be considered embedded in a loose group.
In the text
thumbnail Fig. 8.

Group properties as a function of redshift for volume-limited samples. The left column shows CGs, and the right column shows the control groups, both with Vlim = 500 km s−1. The curves are polynomial fittings to the scatter plots displaying roughly median values per redshift bin.
In the text
thumbnail Fig. 9.

Tremaine-Richstone statistics T1 and T2 (Tremaine & Richstone 1977) computed for the different samples of CGs (green circles) and controls (red triangles). The error bars are computed using the bootstrap technique. The left column displays the statistics computed for the flux-limited group samples, and the right column displays the results for the volume-limited sample of groups.
In the text

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