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A&A
Volume 671, March 2023
Article Number A118
Number of page(s) 26
Section Extragalactic astronomy
DOI https://doi.org/10.1051/0004-6361/202244738
Published online 13 March 2023

© The Authors 2023

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

The spring sky, comprised between 10 and 16 h of right ascension in the northern hemisphere, has hosted most of the near-field cosmology studies in the history of astrophysics, due to several unique characteristics. It contains: (i) the northern Galactic pole, the area less crowded by the Milky Way, which is thus less susceptible to dust extinction; (ii) the local supercluster, including (iii) the Virgo cluster, and (iv) the Coma cluster (with the ‘Great Wall’).

Starting from the 1950s, when the Palomar sky survey became available, a large number of astronomers interested in near-scale cosmology (e.g. Fritz Zwicky, Allan Sandage, Gerard de Vaucouleurs, and collaborators) focused their work in this region of the sky using photographic material taken at 1.5- to 2.5-meter-class telescopes (see, e.g. Zwicky et al. 1961; Tully 1982; Einasto & Corwin 1983; Binggeli et al. 1985). Their goal was to shed light on the physical mechanisms that govern the structure of the Universe on a local scale (within the distance of the Coma cluster, i.e. ∼100h−1 Mpc). Among the early studies, many efforts aimed to establish the scale relations that govern the evolution of galaxies, such as the ‘downsizing’ law (Gavazzi et al. 1996).

At the turn of the millennium, the digital revolution took place with the announcement of the Sloan Digital Sky Survey (SDSS, York 2000). The SDSS combined five-band (u, g, r, i, z) photometry from digital imaging with exquisite medium-resolution (R ≈ 2000) spectroscopy taken (except for a few cases) in the nuclei of external galaxies brighter than 17.7 mag in the r-band. However, instead of reforming the study of the local supercluster, the SDSS shifted the focus of near-field cosmology to a slightly higher redshift (0.1 ≲ z ≲ 0.5). This turn was owing to the difficulty of extracting precise photometric parameters from digital images in areas crowded with extended galaxies using automatic algorithms because of significant overestimates of the sky background; another consequence of this effect is the so-called shredding that affects the SDSS photometry and spectroscopy of nearby galaxies (Blanton et al. 2005a,b,c).

Because of these difficulties, the vast majority of near-field-cosmology works based on the SDSS are at 0.1 ≲ z ≲ 0.5 (see, e.g. Kauffmann et al. 2003a; Gómez et al. 2003; Blanton et al. 2017; Peng et al. 2010). Alternatively, a number of studies employed versions of the SDSS with improved background determinations in cases of very extended galaxies (e.g. the NASA-Sloan Atlas catalogue of Blanton et al. 2011), or with the limitation z > 0.01 (which excludes the local supercluster and the Virgo cluster in any case). This was, for example, the case of Yang et al. (2007, 2008, 2009), who, despite limiting their sample (in this case, the DR4) to 0.01 < z < 0.2, retained more than 360.000 galaxies in it. This number was sufficiently large to ensure a satisfactory statistical coverage of the sky, but was too large to allow for the firsthand inspection of all galaxies (images and spectra) in a search for errors (e.g. false objects caused by diffraction spikes of bright stars, duplicates, and spectroscopy not taken at the centre of galaxies). Such mistakes did not affect the sample statistically, but polluted the catalogue with unwanted individual objects.

Besides the SDSS, another outcome of the third millennium digital revolution was the availability of multi-wavelength photometry (from the ultraviolet to the far-infrared bands) based on space missions such as GALEX (Martin et al. 2005; Morrissey et al. 2007) and NASA’s Wide-field Infrared Survey Explorer (WISE, Wright et al. 2010). Multi-wavelength data have proven to be very effective in the investigations of the role of the environment in galaxy evolution (see, e.g. Gavazzi et al. 2013a,c). This is the case of the extensive work on the Virgo cluster carried out by Boselli et al. (2014; built upon the pioneering work by Binggeli et al. 1985, which, despite being a fully analogical work, represented a breakthrough for the study of the Virgo cluster). The work of Boselli et al. (2014) set a new standard for the study of galaxy evolution in the Virgo cluster by dividing galaxies into red-sequence, green-valley, and blue-cloud galaxies, according to their NUV − i colour, where both NUV and i magnitudes are corrected for extinction by the Milky Way and for dust attenuation taking advantage of 22 μm photometry from WISE (Wright et al. 2010). This colour range contains all galaxies, with red galaxies separated by approximately 5 mag from blue ones. This new standard has opened a new method for studying the influence of the environment (e.g. ram pressure) on the transition between the blue cloud and the red sequence, passing through the green valley due to the progressive ablation of H I gas necessary for the formation of new stars. With the present work, we apply the new standards of Boselli et al. (2014) to the entire spring sky, expanding from ∼1000 Virgo galaxies to ∼30 000 galaxies analysed here. We aim to provide a census of galaxies in the local Universe, including their nuclear classification.

It is possible to disentangle the variety of galactic nuclear activities in diverse ways: at optical wavelengths owing to a number of emission-line ratios, for example [NII]/Hα and [OIII]/Hβ (see Baldwin et al. 1981; Cid Fernandes et al. 2010, 2011), Kauffmann et al. (2003b), Kewley et al. (2001, 2006), Decarli et al. (2007), Gavazzi et al. (2011, 2013b, 2018), Reines et al. (2013); by the detection of nuclear radio continuum (Bregman 1990); by X-ray sources (Dewangan et al. 2008); or by a combination of the two (Ballo et al. 2012; Agostino & Salim 2019). This work also aims to provide a bridge between the empirical analysis carried out by Peng et al. (2010), who examined the statistical dependence of star-formation quenching on stellar mass and environmental density, and other local studies of selected clusters, such as Virgo, where the perturbing mechanisms are identified using multi-frequency observations, tuned models, and simulations of representative galaxies (see Boselli et al. 2014, 2022).

Despite the impressive statistics of the SDSS (∼1.5 × 106 galaxies extracted with photometric and spectroscopic data in the redshift range 0.02 < z < 0.085), which makes Peng et al. (2010) a reference for years to come, the identification of the dominant perturbing mechanisms is hampered by the lack of multi-frequency data covering the whole electromagnetic spectrum, which are necessary to quantify the effects of the perturbations on the different galaxy components (stars, gas, and dust) and the star-formation process. Furthermore, the lack of multi-frequency data (such as X-ray data) necessary to describe the properties of the intra-cluster medium in high-density regions is also a major limit in identifying the dominant perturbing mechanisms. Despite its limited statistics, the sample analysed in the present work has a broad multi-frequency coverage (from the UV to the infrared). It includes well-known structures such as the local supercluster with Virgo, the most studied cluster in the sky, but also the Coma supercluster – including the two massive clusters Coma and A1367 – and the Great Wall, the closest filamentary structure linking high-density regions in the Universe (see, e.g. Gavazzi et al. 2010; Cybulski et al. 2014).

The outline of the paper is as follows. Section 2 presents our flux- and volume-limited sample (r < 17.7 mag, cz < 10 000 km s−1) and describes the galaxy characterisation based on their photometry (Sect. 2.1), nuclear spectra (Sect. 2.2), and environment (Sect. 2.3). The nuclear spectra are analysed and classified in Sect. 3 using the Baldwin-Phillips-Terlevich diagnostic (BPT diagram Baldwin et al. 1981, Sect. 3.1), and the EWHα versus [NII]/Hα diagnostic (WHAN diagram Cid Fernandes et al. 2010, 2011, Sect. 3.2). This classification is adopted to investigate the relation between nuclear excitation properties, galaxy stellar mass, and the environment in Sect. 3.3. A critical feature of the method we adopted is that we checked all ∼30 000 images and spectra individually using the visualisation tool provided by SDSS. This enabled us to disregard targets that were, in fact, duplicates of galaxies observed in peripheral positions (spiral arms or H II regions) not corresponding to galaxy nuclei. Finally, we summarise our results and conclude in Sect. 4. Throughout the paper, we assume a ΛCDM cosmology with ΩM = 0.3, ΩΛ = 0.7, H0 = 73h73 km s−1 Mpc−1.

To mitigate the incompleteness of the SDSS nuclear spectroscopy at the bright luminosity end, in 2014–2020 we took 302 nuclear spectra with the Loiano 1.5 m telescope. Appendix A provides the spectra obtained at Loiano. Appendix B contains a one-page sample of the SPRING dataset, containing the first 30 of 30 597 galaxies. The full table is available electronically at the CDS.

2. The sample

The sample analysed in this work is based on the SDSS Portsmouth emission-line catalogue (‘emissionLinesPort’, ELP henceforth, see Thomas et al. 2013), which was queried via SQL with the SDSS Catalogue Archive Server Jobs System (CasJobs)1. Photometric data were obtained by cross-correlating our sample with the NASA-Sloan Atlas2 (NSA, Blanton et al. 2011) that, being limited to z > 0.01, excludes the local supercluster and the Virgo cluster. Across this work, our matching criteria is δθ < 5″ and cδz < 200 km s−1 unless stated otherwise. All non-unique associations were visually inspected. Our sample comprises: (i) local galaxies in the spring quarter of the northern sky (10h < RA < 16h; 0° < Dec < 65°), (ii) within a distance of cz < 10 000 km s−1, and (iii) selected to have Petrosian magnitudes r < 17.7 mag (i.e. the spectroscopic completeness limit of the SDSS).

We find 28 190 galaxies meeting criteria (i)–(iii) in the ELP table. However, adopting identical criteria to select galaxies in the NSA table of Blanton et al. (2011; who claimed to have solved some of the incompleteness problems of bright galaxies), we find 28 440 galaxies, among which 26 966 are in both catalogues, 1474 are only in the NSA table and 1224 are only in the ELP table. The differences between the NSA and the ELP may be due to a number of factors. The NSA was built on the basis of several catalogues: Sloan Digital Sky Server (DR8), NASA Extragalactic Database, Six-degree Field Galaxy Redshift Survey, Two-degree Field Galaxy Redshift Survey, ZCAT and ALFALFA, while the ELP table (DR12) was built from the SDSS and BOSS galaxies. Our analysis considers all (29 590) targets found in either of the two catalogues. We further integrate this sample with the updated Zwicky catalogue (UZC, Zwicky et al. 1961; Falco et al. 1999), the VCC catalogue (Binggeli et al. 1985), the UGC catalogue (Nilson 1973), the ALFALFA survey α.100 (Durbala et al. 2020), the optical spectroscopic survey by Ho, Filippenko, and Sargent (Ho et al. 1993, 1995, 1997), the spectra taken by us with the Loiano 1.5 m telescope (e.g. Gavazzi et al. 2013b), and the spectra taken with the MUSE IFU by Consolandi et al. (2017), Fossati et al. (2019), Pedrini et al. (2022). We dub the database obtained in this way as the ‘SPRING’ database, which amounts to 30 597 unique objects. Its sky distribution is given in Fig. 1.

thumbnail Fig. 1.

Celestial distribution of the 30 597 galaxies analysed in the SPRING catalogue, distributed over nearly one-fourth of the northern sky. They represent the totality of galaxies in the spring sky within cz < 10 000 km s−1 and r < 17.7 mag. The colours represent the overdensity parameter Δρ/⟨ρ⟩ (defined in Sect. 2.3.1) for galaxies in four bins of local galaxy overdensity (red = UH, green = H, blue = L, black = UL). In the left panel, the third dimension emphasises the overdensity parameter. The highest narrow peak represents the Coma cluster, the second narrow peak is A1367, and the broader peak is Virgo.

We claim that our SPRING catalogue is a robust and exhaustive compilation of all galaxies in the northern spring sky, including the brightest ones. For example, all 29 galaxies in the Messier catalogue projected in this region are listed in our database; also, we cross-correlated our catalogue with the UZC (Falco et al. 1999) and found that, in the window 150 < RA < 240, 0 < Dec < 65, cz< 10 000, Falco lists 5904 galaxies, among which 5878 are found in the SPRING sample (99.5%). We manually inspected the remaining 29 objects by means of the SDSS navigator tool, finding that they were constituted by fake detections such as diffraction spikes (7), faint and diffused local objects (14), and objects that were not covered by the SDSS (8). Nonetheless, this is not a suitable criteria for establishing completeness, since by construction our catalogue includes the UZC. Thus, we cross-matched our sample with the RC3 catalogue (Corwin et al. 1994), which we did not use in the construction of our sample. We found 94 objects in RC3 that are missing in SPRING. We manually inspected the 94 entries, and found that only five objects are indeed missing galaxies, while the remaining are either targets with wrong coordinates, faint (r > 17.7 mag) objects, or errors in RC3.

2.1. Photometry

Photometric optical data are available for all galaxies in the sample. We took Petrosian (u, g, r, i, z) magnitudes from the SDSS. The GALEX ultraviolet (NUV and FUV) data (Martin et al. 2005; Morrissey et al. 2007) were taken from Voyer et al. (2014) for Virgo galaxies and from the Revised Catalog of GALEX Ultraviolet Sources (Bianchi et al. 2017) otherwise; 25 526 galaxies have NUV magnitudes, 24 545 have FUV, and 24 405 have both. For six galaxies, for which SDSS Petrosian u, g, r, i, z magnitudes were not available, we took B and V magnitudes from the NASA/IPAC Extragalactic Database (NED).

Since a number of galaxies in the ELP/NSA tables have their photometry taken in off-nuclear positions, their given magnitudes are sometimes not representative of the whole galaxy. For those objects, we re-measured the u, g, r, i, z magnitudes at the central position using the SDSS navigator. For a small sub-sample of the SPRING catalogue (6764, 22%), we collected information on the precise morphological type from Gavazzi et al. (2003), which were determined after the visual inspection of GOLDMine (GM, Gavazzi et al. 2003) images.

The optical and UV data were corrected for Galactic extinction and dust attenuation. For this purpose, we cross-correlated the SPRING sample with WISE (Wright et al. 2010) and extracted 22 μm W4 band data to quantify the attenuation in the FUV and NUV bands following the methods of Boselli et al. (2014; see also Hao et al. 2011). W4 band data are available for 30 373 objects. For galaxies without WISE 22 μm data (224 objects), we estimated the internal attenuation using the mean value derived for objects of similar stellar mass using the relation:

A NUV [ mag ] = 0.111 × ( log M star ) 2 − 1.555 × log M star + 5.444 , $$ \begin{aligned} A_{\mathrm{NUV} } [\mathrm{mag} ] = 0.111 \times (\log M_{\mathrm{star} })^2 - 1.555 \times \log M_{\mathrm{star} } + 5.444, \end{aligned} $$(1)

calibrated for the star forming galaxies on the rest of the sample. This relation was applied to the blue galaxies (FUV − i < 6) without WISE data, whereas we assumed A(NUV) = 0 mag for the red (presumebly early-type) systems.

The adopted values of the stellar mass were calculated by the g and i (corrected) magnitudes, and the i-band luminosity assuming a Chabrier IMF (Chabrier 2003), and following the prescription of Zibetti et al. (2009) as:

log ( M star / M ⊙ ) = − 0.963 + 1.032 ( g − i ) + log l I , $$ \begin{aligned} \log (M_{\mathrm{star} }/{M}_{\odot }) =-0.963+1.032(g-i)+\log l_I, \end{aligned} $$(2)

where g, i are the corrected g-band and i-band magnitudes, lI is the i-band luminosity computed as log lI = (I − 4.56)/(−2.5), and I is the absolute i-band magnitude (since our catalogue is limited to very low redshifts (z ≲ 0.03, no K-correction is applied). When the g and i magnitudes were not available, we employed the B and V magnitudes instead, and calculated the stellar mass as

log ( M star / M ⊙ ) = − 1.075 + 1.837 ( B − V ) + log l V . $$ \begin{aligned} \log (M_{\mathrm{star} }/{M}_{\odot }) =-1.075+1.837(B-V)+\log l_V. \end{aligned} $$(3)

The SPRING catalogue was cross-matched with H I-data from the GOLDMine database (GM, Gavazzi et al. 2003), the Westerbork Coma Survey (WCS, Molnár et al. 2022), the ALFALFA survey (AA, Haynes et al. 2011; Durbala et al. 2020), and the HyperLeda catalogue (LEDA, Makarov et al. 2014). We cross-matched our sample by coordinates separation with the criteria cδz < 200 km s−1 (for all datasets), δθ < 5″ for GM, AA, and WSB and δθ < 10″ for LEDA. This choice was motivated by the lower accuracy of LEDA celestial coordinates; non-unique associations were visually inspected. H I data are available for 9461 objects (∼30%). If H I-data from more than one source were available, we selected data taken from GM over WCS, AA, and LEDA, respectively. This choice was motivated by: (i) the higher sensitivity of GM/WCS over AA, and (ii) the inhomogeneity in LEDA sources, which were listed across a wide time span with diverse methodology.

A widely adopted method to identify galaxies at different evolutionary stages is the colour-stellar-mass relation (Boselli et al. 2008, 2014; Hughes & Cortese 2009; Cortese & Hughes 2009; Gavazzi et al. 2010). We used the NUV − i versus Mstar relation (Kennicutt 1998) as a tracer of the mean age of the stellar population of galaxies in our sample, which we separated into three ‘chromatic’ classes: (i) a red sequence, composed of quiescent ETGs (early-type galaxies); (ii) a blue cloud of star-forming LTGs (late-type galaxies; Gil de Paz et al. 2007; iii) and a green valley separating these two sequences (Martin et al. 2007). We identified the red sequence as the region in the (NUV − i) versus Mstar plane for which NUV − i > 0.47 log Mstar + 0.1; the blue cloud is the region for which NUV − i < 0.47 log Mstar − 1.0; and the green valley is the region between the former two. This representation, chosen by Boselli et al. (2014) to study the Virgo cluster, is here extended to a much larger (∼30×) sample.

2.2. Nuclear spectra

Besides the SDSS, several other sources of nuclear spectroscopy are available in the literature; we integrated the SDSS spectroscopic data (available for 28971 targets) with two extensive spectral surveys: the one by Ho et al. (1993, 1995, 1997) undertaken with the Palomar 200-inch telescope (224 spectra added), and the one by Falco et al. (1999) using the Tillinghast 1.5 m telescope (366 spectra added).

On our side, since 2005 we have undertaken a spectroscopic project using the Cassini1.5 m telescope of the Loiano Observatory, aiming to integrate the SDSS with the nuclear spectroscopy of galaxies in the local Universe (Gavazzi et al. 2011, 2013b). In 2014–2020, we obtained 302 new nuclear spectra (see Appendix A). These spectra were generally taken with the red channel of the spectrograph, namely between 6000 and 8000 Å; only a dozen galaxies were observed also with the blue grism. In addition, five spectra were taken with the MUSE IFU by Consolandi et al. (2017), Fossati et al. (2019), Pedrini et al. (2022). Altogether, the total number of galaxies with nuclear spectra observed independently from SDSS was 884.

All entries (images and spectra) in the SPRING catalogue were individually inspected in search of errors (e.g. false objects caused by diffraction spikes of bright stars, duplicates, misclassified objects). This initial iterative examination led us to clear ∼1% of the objects in our sample; also, the direct inspection of all targets using the SDSS navigator tool (DR13 version) allowed us to eliminate 74 (less than 0.3%) targets with more than one spectrum available (i.e. galaxies with two or more nuclear spectra and galaxies with non-nuclear spectra). We left two entries for the same object only in obvious post-merger targets where two nuclei were visible.

The SPRING catalogue is limited to cz < 10 000 km s−1. Up to this redshift, corresponding to a distance of ∼137h−1 Mpc and embracing a volume of 780 000h−3 Mpc3, the sample is volume- and magnitude-limited, and thus the number counts in bins of Mstar < 109 M⊙ (or r < −17.7 mag) can be converted into mass and/or luminosity functions. We verified that the r-band luminosity function (LF) of our sample matched that derived by Blanton et al. (2001; including normalisation, Mknee, and slope). To this aim, we compared our galaxy distribution in the r-band magnitude space to a galaxy LF model using a maximum likelihood model (Fig. 2). We adopted a Schechter function (Schechter 1976) and found the best-fit parameters α = −1.08, M* = 20.64, ϕ* = 2.1 × 10−2; the normalised mean residual between the fit and our data was ∼0.28 dex.

thumbnail Fig. 2.

Luminosity function of SPRING galaxies in the r-band (blue dots and line). Our best-fitting Schechter function (red line) agrees well with the data points.

2.3. Probing the galaxy environment

Our analysis of the environmental properties of SPRING galaxies relied on differing techniques: in Sect. 2.3.1, we outline our reconstruction of the three-dimensional distribution of ∼30 000 galaxies in 5400 square degrees of the spring sky and our determination of the density around each galaxy measuring the number of neighbours within fixed cylindrical volumes; in Sect. 2.3.2, we outline our adoption of the halo mass as a tracer of the galaxy environment; in Sect. 2.3.3, we exlain how we employed the H I-deficiency parameter to investigate the H I-content distribution of galaxies.

2.3.1. Galaxy density

The local galaxy density around each object was quantified according to the method of Gavazzi et al. (2010), which we briefly summarise in the following. After reducing the fingers of God of the three major clusters by collapsing the individual galaxy velocities to the average cluster velocity ⟨cz⟩, the density ρ was determined in cylinders of 1h−1 Mpc radius and 1000 km s−1 half-length. Given an average density ⟨ρ⟩ of 0.04 gal h3 Mpc−3, the resulting overdensity parameter (ρ − ⟨ρ⟩)/⟨ρ⟩≡Δρ/⟨ρ⟩ was estimated. This procedure was applied to all galaxies with a distance greater than 15h−1 Mpc, to exclude very local galaxies, which can be subject to large distance uncertainties due to peculiar velocities; 879 local galaxies were thus excluded from our overdensity determination. Following Gavazzi et al. (2010), we defined four arbitrary overdensity thresholds that were chosen to highlight increasing levels of aggregation:

Ultra low ( UL ) : Δ ρ / ⟨ ρ ⟩ ≤ 0 Low ( L ) : 0 < Δ ρ / ⟨ ρ ⟩ ≤ 4 High ( H ) : 4 < Δ ρ / ⟨ ρ ⟩ ≤ 20 Ultra high ( UH ) : Δ ρ / ⟨ ρ ⟩ > 20 . $$ \begin{aligned} \begin{aligned}&\mathrm {Ultra \ low \ (UL)}\!: \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \Delta \rho /\langle \rho \rangle \le 0 \\&\mathrm {Low \ (L)}\!: \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ 0 < \Delta \rho /\langle \rho \rangle \le 4 \\&\mathrm {High \ (H)}\!: \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ 4 < \Delta \rho /\langle \rho \rangle \le 20 \\&\mathrm {Ultra \ high \ (UH)}\!: \ \ \ \ \ \ \ \ \ \ \ \ \ \ \Delta \rho /\langle \rho \rangle >20. \end{aligned} \end{aligned} $$

The UL density bin describes the underlying cosmic web; the L density bin is for the loose groups and the filaments in the Great Wall; the H bin includes the significant groups and cluster outskirts; the UH bin represents the cores of the clusters. Figure 1 shows the overdensity parameter distribution in the whole sample.

To check the qualitative consistency with the morphology-segregation effect observed by Dressler (1980), we calculated that the fraction of galaxies in the red sequence over the total in the four different density bins is 7% in UL, 14% in L, 29% in H, and 59% in UH. In addition, to check consistency with Gavazzi et al. (2010; see Fig. 8 therein), we used the colour-stellar-mass relation to study the variation in the chromatic distribution with an increasing overdensity parameter. Figure 3 displays four NUV – i versus stellar-mass diagrams, highlighting the chromatic distributions of galaxies in the four overdensity bins. For each panel, the fractions of galaxies in the blue cloud, green valley, and red sequence are reported. We observe that low-mass galaxies in the blue cloud dominate the population of galaxies in the UL bin (∼86%); still, there is a significant contribution of galaxies in the red sequence (and, to a lesser extent, in the green valley), which is, however, restricted to the larger (Mstar > 109.5 M⊙) masses. Conversely, in the UH bin, there is a minimal contribution of blue-cloud galaxies altogether (∼20%), and the red sequence includes a significant population of dwarf galaxies.

thumbnail Fig. 3.

Corrected NUV − i colour versus stellar-mass diagram relation of all galaxies in this sample divided into BC, GV and RS galaxies, according to the chromatic criterion of Boselli et al. (2014; see Sect. 2.1), in four bins of galaxy overdensity (from UH to UL clockwise from top left). In each panel (corresponding to the four overdensities) are reported the fractions of galaxies in the three chromatic classes with respect to the total number of galaxies in the corresponding overdensity bin.

2.3.2. Halo mass

An alternative, widely used method to evaluate a galaxy’s environment is by the halo mass of the group to which it belongs (see, e.g. Muldrew et al. 2012; Fossati et al. 2015). The halo mass is obtained from abundance matching techniques between the halo-mass function and the stellar mass (or luminosity) of the most massive galaxy identified within the group (see e.g. Yang et al. 2007, 2008, 2009). We correlated our cz < 10 000 km s−1 catalogue with the one by Yang et al. (2007), which extends much further out than our local sample, although excluding the local supercluster and the Virgo cluster (cz > 3000 km s−1). We found 21608 matches in the shared redshift range between the two catalogues.

Only 427 galaxies in the Yang catalogue have no counterpart in our database, and a visual inspection revealed them to be primarily extranuclear H II regions, not galactic nuclei. For the galaxies in common, we took the group halo-mass estimate obtained from the total stellar mass in the group. For the Virgo cluster, which is not present in Yang et al. (2007), and thus does not have an estimate of the virial mass, we used the estimate (M200 = 1014.4 M⊙) reported by Boselli & Gavazzi (2006).

In analogy with the overdensity parameter, we defined three bins of increasing halo-mass as

Low ( L ) : log ( M halo / M ⊙ ) ≤ 12.5 , Intermediate ( M ) : 12.5 < log ( M halo / M ⊙ ) ≤ 13.5 , High ( H ) : log ( M halo / M ⊙ ) > 13.5 . $$ \begin{aligned} \begin{aligned}&\mathrm {Low \ (L)}\!: \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \log (M_{\mathrm{halo} }/{M}_{\odot }) \le 12.5, \\&\mathrm {Intermediate \ (M)}\!: \ \ \ \ \ 12.5 < \log (M_{\mathrm{halo} }/{M}_{\odot }) \le 13.5, \\&\mathrm {High \ (H)}\!: \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \log (M_{\mathrm{halo} }/{M}_{\odot }) > 13.5. \end{aligned} \end{aligned} $$

2.3.3. H I deficiency

A third method for investigating environmental effects is studying the atomic hydrogen (H I) content of galaxies, because galaxies in clusters are H I deficient compared to galaxies in lower density environments. H I observations provide powerful diagnostics of the role of the environment in regulating the evolution of late-type galaxies in the local Universe (Gavazzi et al. 2012, 2013a,c, 2015b,a; Fossati et al. 2013). In the present work, we employed ALFALFA (AA, Durbala et al. 2020), GOLDMine (GM, Gavazzi et al. 2003), the Westerbork Coma Survey (WCS, Molnár et al. 2022), and the HyperLeda catalogue (LEDA, Makarov et al. 2014) H I 21 cm line data to estimate the H I-deficiency parameter H Idef, defined as (Haynes & Giovanelli 1984; Giovanelli & Haynes 1985; Gavazzi et al. 2003)

H I def = log M ( H I ) D − log M ( H I ) obs , $$ \begin{aligned} \mathrm{H} {\text{ I}}_{\mathrm{def} } = \log M(\mathrm{H} {\text{ I}})_D - \log M(\mathrm{H} {\text{ I}})_{\mathrm{obs} }, \end{aligned} $$(4)

where log M(H I)D is the H I mass computed as a function of the optical linear diameter D (Meyer et al. 2017), as

log M ( H I ) D = C 1 + C 2 log ( D 2 ) . $$ \begin{aligned} \log M(\mathrm{H} {\text{ I}})_D = C_1 + C_2 \log (D^2). \end{aligned} $$(5)

The coefficients C1 and C2 were empirically determined by studying a control sample of isolated objects (see, e.g. Haynes & Giovanelli 1984; Gavazzi et al. 2013a). The SPRING catalogue includes 9461 galaxies with H I data from GM, WCS, AA, and LEDA. When data from more than one catalogue were available, we chose GM data over WCS, AA, and LEDA. The diameters of this subset of galaxies were retrieved from NED. Since our HI data were taken from surveys with different limiting sensitivities, our HI-mass determination only includes detections and no upper limits.

As discussed in Cortese et al. (2021), there are different methods that can be employed to quantify the H I-deficiency parameter. We performed a recalibration of the optical diameter-based H I-deficiency parameter, employing a reference sample of isolated galaxies corresponding to the UL overdensity bin of our catalogue containing 11051 objects, among which 3416 have H I data. We find that the slope of the log M(H I) versus log(D2) relation is strongly dependent on the galaxy stellar mass; in contrast, no significant discrepancy in the linear fits for different morphological types is observed. To further investigate the stellar-mass dependence, we divided the galaxies in our sample into six bins of stellar mass (dubbed as M1,..., M6), defined as

M 1 : log ( M star / M ⊙ ) ≤ 8.5 ; M 2 : 8.5 < log ( M star / M ⊙ ) ≤ 9 ; M 3 : 9 < log ( M star / M ⊙ ) ≤ 9.5 ; M 4 : 9.5 < log ( M star / M ⊙ ) ≤ 10 ; M 5 : 10 < log ( M star / M ⊙ ) ≤ 10.5 ; M 6 : log ( M star / M ⊙ ) ≥ 10.5 . $$ \begin{aligned} \begin{aligned}&\mathrm M1\!: \ \ \ \ \ \ \ \ \ \ \ \log (M_{\mathrm{star} }/{M}_{\odot }) \le 8.5; \\&\mathrm M2\!: \ \ 8.5 < \log (M_{\mathrm{star} }/{M}_{\odot }) \le 9; \\&\mathrm M3\!: \ \ \ \ \ 9 < \log (M_{\mathrm{star} }/{M}_{\odot }) \le 9.5; \\&\mathrm M4\!: \ \ 9.5 < \log (M_{\mathrm{star} }/{M}_{\odot }) \le 10; \\&\mathrm M5\!: \ \ \ 10 < \log (M_{\mathrm{star} }/{M}_{\odot }) \le 10.5; \\&\mathrm M6\!: \ \ \ \ \ \ \ \ \ \ \ \log (M_{\mathrm{star} }/{M}_{\odot }) \ge 10.5. \\ \end{aligned} \end{aligned} $$

For each stellar-mass bin, we studied individually the log M(H I) versus log(D2) relation and found that the slope of M1 galaxies (log(Mstar/M⊙) ≤ 8.5) is significantly steeper than that of galaxies in larger stellar-mass bins (see Fig. 4). The values of the best-linear fit coefficients C1 and C2 in the two mass bins are reported in Table 1. The histogram in Fig. 5 displays the distribution of our parametrisation for the H I-deficiency parameter of the galaxies in our sample divided in overdensity bins. In Table 2 we give the mean values and the standard deviations of Gaussian fits to the H Idef distribution in each overdensity bin. We observe that the mean value of H Idef increases as we move from isolated galaxies (UL bin, ⟨H Idef⟩ ∼ 0) to galaxies in the centre of clusters (UH bin, ⟨H Idef⟩ ∼ 0.7).

thumbnail Fig. 4.

log M(H I) vs. log(D2) distribution of galaxies belonging to the UL overdensity bin and with log(Mstar/M⊙)≤8.5 (blue dots), and log(Mstar/M⊙) > 8.5 (red dots). The coloured straight lines denote the best-fit linear regression in each mass bin, whose coefficients are given in Table 1. The dashed black line denotes the linear fit given in Gavazzi et al. (2013a).

thumbnail Fig. 5.

Distribution of the newly calibrated H Idef parameter in four bins of galaxy overdensity. The mean values and standard deviations for each overdensity bin are given in Table 2.

Table 1.

Coefficients of the log M(H I) versus log(D2) relation for the two stellar-mass bins M1 (log(Mstar/M⊙) ≤ 8.5) and M2–M6 (log(Mstar/M⊙) > 8.5).

Table 2.

Mean value and standard deviation of the Gaussian fits for the H I-deficiency distributions in each overdensity bin.

We classify galaxies as deficient (non-deficient) if H Idef > 0.4 (H Idef ≤ 0.4). The H Idef parameter is a proxy for the influence of the environment on the neutral Hydrogen content of galaxies (in fact, it significantly correlates with our overdensity parameter Δρ/⟨ρ⟩).

Our sample contains three rich clusters (Coma, A1367 and Virgo, see Fig. 1), and thus it includes a significant number of H I-deficient objects (see Fig. 6). Since the deficiency parameter depends on the distance, AA detects galaxies with H Idef ≳ 0.3 in the local supercluster, but it only manages to detect galaxies with normal H I content at the distance of Coma. The detection of deficient galaxies in Coma and A1367 requires more sensitive observations than those by AA, such as those by GM or WCS.

thumbnail Fig. 6.

Sky distribution of galaxies in the SPRING sample in classes of the HI-deficiency parameter. The horizontal line at Dec = 36 deg traces the limiting pointing declination of the Arecibo dish. Grey circles stand for galaxies for which the H I content is unknown. Blue dots denote galaxies with H Idef ≤ 0.4, while red dots are for galaxies with H Idef > 0.4. The latter class is distributed consistently around (in) the Coma cluster and the Virgo cluster, with a small number of entries belonging to the A1367 cluster. It is noticeable that our knowledge of the H I properties of local galaxies in such an extended stretch of the sky depends on the availability of Arecibo data, which are limited to declination ≲ 36°.

Using different symbols (and colours) for the non-deficient galaxies (H Idef ≤ 0.4, blue), for the deficient galaxies (H Idef > 0.4, green) and for early-type galaxies whose detailed morphological information is available from Gavazzi et al. (2003; red), we show the dependence of the NUV − i colour on the stellar mass (see Fig. 7, right panel).

thumbnail Fig. 7.

Corrected NUV − i colour versus stellar-mass diagram relation for galaxies in the Spring catalogue. Left: Sample divided according to the chromatic criterion of Boselli et al. (2014; see Fig. 2 therein), showing whether galaxies belong to the RS, the GV, or the BC. For each class, the linear regression analysis is given. Right: Same relation for the sub-sample of ETGs with detailed morphological classifications available (red), and of deficient (H I > 0.4, green) and non-deficient galaxies (H I ≤ 0.4, blue). The slopes of the linear regressions for the ETGs and the H I-deficiency classes are consistent with those corresponding to the three chromatic classes. The NUV and i magnitudes are corrected for dust attenuation (see Boselli et al. 2014).

Along with the subsets of galaxies coded by H I content, we chose to display ETGs to highlight the correspondence between the three classes (ETGs, HIdef, non-HIdef) and the chromatic classification (red sequence, green valley, blue cloud).

We observe that our chromatic classification is closely related to the content of H I, as the slopes of the H I-deficient and H I-normal galaxies in the colour-mass diagram (Fig. 7, right) parallel those of the three colour classes representation (Fig. 7, left), which was chosen by Boselli et al. (2014) to study the Virgo cluster. The similarity between the classes of H I content and the BC and GV chromatic classes is visible also in the main sequence, i.e. star-formation rate (SFR) versus stellar mass relation. We cross-matched our catalogue with the Hα3 catalogue (Gavazzi et al. 2012, 1396 galaxies in common), from which we extracted the star formation rate, which was derived from observed, integrated Hα flux after applying correction for: (i) Galactic extinction (assuming the extinction law by Cardelli et al. 1989; ii) [NII] contamination in the narrow-band imaging filter; and (iii) internal extinction, estimated as described in Gavazzi et al. (2012, 2015b,a; see also Fossati et al. 2013). Figure 8 shows the main-sequence relation for this subset of galaxies coded according to their H I-content (right) and their chromatic classification (left). We observe that H I-deficient galaxies display a lower SFR than H I-normal objects of the same stellar mass, suggesting that the lack of H I gas leads to a reduction in the activity of star formation (Boselli et al. 2023).

thumbnail Fig. 8.

Main-sequence relation of galaxies in the Spring catalogue. Left: Sample divided according to the chromatic criterion of Boselli et al. (2014; see Fig. 2 therein), showing whether they belong to the RS, the GV, or the BC. For each class, the linear regression analysis is given. Right: Same relation for the sub-sample of ETGs with detailed morphological classifications available (red), and of deficient (H I > 0.4, green) and non-deficient galaxies (H I ≤ 0.4, blue). The slopes of the linear regressions for the ETGs and the H I-deficiency classes are consistent with the ones corresponding to the three chromatic classes.

2.4. The Spring Ring

As shown in Fig. 6, deficient objects are mostly distributed in clusters. The fraction of objects with H Idef > 0.4 in the three clusters Virgo, Coma, and A1367 is ∼62.4%. In addition, we observe that a consistent fraction of deficient galaxies is distributed in filamentary structures. To further investigate the significance of this finding, we selected galaxies at intermediate distances (velocities) between Virgo and Coma (1500 < cz/km s−1 < 6000), excluding objects belonging to the fingers of God of either Coma or Virgo (see Fig. 9, left panel). A total of 6095 objects were selected in this way. In addition, we removed from this selection 194 objects belonging to the highest (UH) overdensity bin. The celestial distribution of 5911 galaxies selected with this criterion is given in Fig. 9 (right panel), showing the existence of a definite filamentary structure that we dub the ‘Spring Ring’ (SR). We manually isolate galaxies in the ring to study their H I content. Two- and three-dimensional sky distributions of this structure are given in Figs. 10 and 11. We identified four main branches within the SR, denoted as fA, fB, fC, and fD in the figures. The fA branch displays evidence of gravitational influence from the Virgo cluster; fB is made by galaxies that show hints of gravitational attraction from A1367; fC comprises galaxies infalling towards the Coma cluster; and fD does not exhibit signs of influence from one of the three clusters considered in the present work.

thumbnail Fig. 9.

Selection criterion discussed in Sect. 2.4 used to identify the SR. Left: Distribution of the Hubble velocity cz as a function of RA. Blue dots indicate galaxies with 1500 < cz/km s−1< 6000, (with the exclusion of objects belonging to the fingers of God of either Coma or Virgo). Right: Sky distribution of galaxies in the 1500 < cz/km s−1 < 6000 range highlighting a filamentary structure that we dub the SR.

thumbnail Fig. 10.

Sky distribution of galaxies belonging to the SR in two bins of the H I-deficiency parameter: H Idef ≤ 0.4 (blue) and H Idef > 0.4 (red).

thumbnail Fig. 11.

Sky distribution of the 515 galaxies belonging to the SR and having H I data. The third dimension is given by the Hubble velocity cz. The colour map displays the H Idef parameter.

The SR is mostly composed of blue-cloud galaxies (∼80%) with stellar mass Mstar ≲ 1010 M⊙. Remarkably, when we divided galaxies with H I data belonging to the SR (515 entries) among H I-deficient and H I-normal objects, we found that 158 (∼30.7%) of them were H I-deficient (see Figs. 10 and 11). Conversely, the fraction of H I-deficient objects among field galaxies (i.e. SPRING galaxies that did not belong to the UH overdensity bin) was ∼12%. We cross-matched these 515 SR galaxies with the recent catalogue by Castignani et al. (2022a). We find that 143 galaxies in the SR are reported also in Castignani et al. (2022a). Among these, the most noticeable fractions are found among galaxies belonging to the W-M sheet (57 entries), the NGC 5353_4 filament (41), and the Coma Berenices filament (29). It is noticeable that the majority of the left branch of the SR is not identified in Castignani et al. (2022a).

To accurately compare the fraction of H I-deficient objects in the filament to the general population, we randomly selected a sample of 515 objects from the galaxies in the SPRING sample that had H I data, and counted the fraction of H I-deficient objects. We repeated this procedure 10 000 times, and found that the fraction of H Idef galaxies is (16.7 ± 1.6)%.

In addition, we investigated whether the fraction of H I-deficient objects in the SR is related to the distribution of galaxy overdensity within the filamentary structure; to this end, we randomly selected 10 000 overdensity-matched samples of 515 galaxies with H I data (i.e. the overdensity parameter distribution of each random sample matched the overdensity distribution within the SR). We find that (20.2 ± 1.6)% galaxies in the matched-samples are H I-deficient. This value is larger than the fraction found in the random samples with no restriction on the overdensity distribution (20.2% vs. 16.7%). Still, it is considerably smaller than the fraction of deficient objects found within the SR (30.7%). This result suggests that: (i) local galaxies in filamentary structures are more H I-deficient than field galaxies, and (ii) their H I-content does not depend (only) on their local galaxy overdensity.

In the literature, there is controversial evidence regarding the H I content of filament galaxies. Lee et al. (2021) claim that the H I content of galaxies in filaments is normal, whereas Castignani et al. (2022b) find a higher incidence of H I-deficient galaxies in filaments (see, e.g. Fig. 17 in Castignani et al. 2022b). Since galaxies in the SR are mostly low-mass galaxies, they have low binding energies, and thus can be easily affected by perturbations as they move along the filament. This scenario is consistent with the findings of Crone Odekon et al. (2018), who examined a sample of ∼4000 LTGs with H I data and found that galaxy H I content decreases with distance from a filament spine. Multiple mechanisms (e.g. gas stripping, tidal interaction) may affect filament galaxies before they enter higher-density regions; in Fig. 11, we see that most SR galaxies at the early stage of gas stripping (yellow dots) are found in the fD branch, while galaxies with a higher H I deficiency (purple dots) mostly appear to be falling towards the core of a cluster. In addition, a high fraction of deficient objects is found in the proximity of the intersection between the fD and fC branches.

3. Nuclear spectral classification

In this section, we aim to link the photometric and environmental properties of the galaxies in the SPRING sample with the spectral features of their nuclei. We provide nuclear-emission classification by optical spectra based on two ionisation diagnostic diagrams: (i) the BPT (Baldwin et al. 1981), and the WHAN diagrams (Cid Fernandes et al. 2010, 2011). These diagnostics aim to identify the main ionising mechanisms in galactic nuclei (e.g. young or old stars, AGN). Out of the total of 30 597 entries, we restricted our classification of nuclear activity to spectra having all lines with a signal-to-noise ratio > 3: we classified 22 564 (74%) galaxies of the SPRING catalogue using the BPT, and 24 559 galaxies (80%) using the WHAN. To investigate the impact of the inclusion of non-SDSS spectra on our sample, we defined the spectral completeness as the fraction of galaxies with nuclear spectra over the total number of galaxies. In Fig. 12, we show the completeness as a function of the r-band magnitude (top panel) and of the stellar mass log(Mstar/M⊙) (bottom panel). The blue lines represent SDSS-only spectra, and the black lines include spectra taken from the SDSS and other sources. Along with the completeness of the SPRING catalogue compared to SDSS-only data (thick straight lines, left y-axis), the actual numbers of galaxies with nuclear spectra for both samples is displayed as well (dashed lines, right y-axis).

thumbnail Fig. 12.

Completeness (defined as the fraction of galaxies with nuclear spectra over the total number of galaxies, left axis and thick straight lines) and actual number of galaxies with nuclear spectra (right axis and dashed lines) as a function of r apparent magnitude (top) and as a function of stellar mass (bottom). The blue lines represent SDSS-only spectra and the black lines include spectra taken from the SDSS and other sources.

The addition of non-SDSS spectra to our flux- and volume-limited sample dramatically increases the completeness at the high-mass end of the catalogue (or, similarly, at the bright end), which is known to suffer a residual incompleteness in the SDSS database. Only 361 targets (∼1%) in the SPRING catalogue remain without nuclear spectra3.

3.1. Baldwin-Phillips-Terlevich diagnostic

The BPT diagnostic relies on four optical emission-line intensity ratios ([OIII]λ5007/Hβ vs. [NII]λ6584/Hα) to classify galaxy nuclear spectra into four independent classes of nuclear activity: star-forming nuclei (SF) are characterised by intense star-formation activity.

Seyfert (SEY) or strong AGN contain a powerful galactic nucleus, likely triggered by a supermassive black hole; LINERs (LIN) or weak AGN display a milder nuclear activity than SEY, possibly triggered by a mixture of stellar and true AGN processes (Cid Fernandes et al. 2004); composite regions (star-formation + AGN) are transition objects with spectra intermediate between ‘pure’ SF and ‘pure’ SEY or LIN.

The prominent left wing of the BPT is traced by pure SF nuclei, lying below the empirical line included in the BPT scheme by Kauffmann et al. (2003b; K03, red straight line in Fig. 13, top panels). Active nuclei (SEY+LIN) are defined as the galaxies that lie on the right wing above the ‘maximum starburst line’ determined by Kewley et al. (2001; K01, long-dashed blue line). Composite nuclei lie in between AGN and SF nuclei. Seyfert nuclei and LINERs are divided by the empirical separation line defined by Schawinski et al. (2007; S07, dashed black line).

thumbnail Fig. 13.

Optical emission-line diagnostics for galaxies in the Spring catalogue. Top row: Baldwin-Phillips-Terlevich diagnostic diagram for galactic nuclei in the blue cloud, green valley, and red sequence (left, centre, and right respectively). The SDSS galaxies are given with blue points. Measurements from other sources are given with larger red dots. The associated percentage of galaxies is given for each spectral category. The red straight line represents the model of Kewley et al. (2001) to identify AGN. The long-dashed blue line gives the model of Kauffmann & Haehnelt (2000) above which AGN are found. Composite nuclei fall between these two models. The dashed black line (Schawinski et al. 2007) separates Seyfert galaxies from LINERs. Bottom row: EWHα versus [NII]/Hα diagram diagnostic diagram for galactic nuclei in the blue cloud, green valley, and red sequence (left, centre, and right respectively). The SDSS galaxies are given with blue points. Measurements from other sources are given with larger red dots. The associated percentage of galaxies is given for each spectral category.

In the top row of Fig. 13, we display the BPT diagnostic diagrams separately for the three chromatic classes (blue cloud, green valley, and red sequence) defined in Sect. 2.1.

In the blue cloud, most galactic nuclei are classified as star-forming (∼90%). Approximately 7% of objects are composite, and only a few percent are LINERs and Seyfert nuclei (∼2% LIN and ∼1% SEY). In the green valley, the majority of targets are still found in the SF wing of the BPT, although fewer than in the blue cloud (∼60%). A substantial amount of nuclei fall in the composite and LINER branches (∼20% and ∼16%, respectively). As in the blue cloud, only a small fraction of objects are classified as SEY (∼2%). Star-forming nuclei in the red sequence decrease to less than a fifth of the total (∼19%), whereas composite nuclei and LINERs account for more than half of the RS nuclei (∼38% each). Seyfert nuclei are ∼6%.

We point out that our nuclear classification of galaxies belonging to the SPRING sample considers only emission-line galaxies (ELG) whose nuclear spectra have all four lines with signal-to-noise ratio > 2. This selection introduces a noticeable bias in our classification as far as the RS concerns, since only ∼30% of galaxies in the RS (1572 out of 5258) satisfies our criteria, opposite to the BC and the GV, whose majority of galaxies show all four lines in emission, and therefore are classified with the BPT diagram (∼90% and ∼70%, respectively).

The mass metallicity relation (cf. Fig. 3 in Thomas et al. 2019) is partly responsible for the pattern in the star-formation arm of the BPT. From 12 + log(O/H) = 7.7 to 12 + log(O/H) = 9.2, most star-forming galaxies with stellar mass between 108.5M⊙ and 1011 M⊙ are found, due to the well-known correlation between 12 + log(O/H) and log([NII]/Hα) (see, e.g. Yates et al. 2012). However, it does not fully explain the two orders of magnitude decrease in log[OIII]/Hβ over the entire SF branch, a fact that hints at a dependence of the ionisation properties on stellar mass. With increasing stellar mass, galaxies have a decreasing fraction of OB associations in their arms. OB stars are the only stars capable of ionising hydrogen and their fraction is a steep function of mass (Kennicutt et al. 1994; Kennicutt 1998; Boselli et al. 2009). Since OB stars have the shortest lives of all stars, this implies that low-mass galaxies have a ‘younger’ stellar population than higher-mass galaxies. This is consistent with the picture of Gavazzi et al. (2002).

3.2. EWHα versus [NII]/Hα diagnostic

In addition to the BPT diagnostics, the classification of the nuclear activity based on optical emission-lines is also performed following the two-line WHAN classification scheme. It is a less demanding diagnostic than the BPT, as it only requires the intensities of two lines (Hα and [NII]λ6584), and is based on the [NII]/Hα ratio combined with the strength of the Hα line corrected4 for underlying stellar absorption. Owing to this two-line diagnostic, even the red-channel spectra such as the majority of those obtained at Loiano (Gavazzi et al. 2011, 2013b, 2018) contribute to increasing the number of known AGN, especially those associated with nearby bright galaxies, most affected by the residual incompleteness of the SDSS spectral database.

The WHAN diagnostic was introduced by Cid Fernandes et al. (2010, 2011) to disentangle ‘true’ active nuclei, believed to be triggered by supermassive black holes, from ‘fake AGN’ (retired galaxies, that is to say, galaxies with a low SFR that are ionised by the hot low-mass evolved stars (HOLMES) within them (Trinchieri & di Serego Alighieri 1991; Binette et al. 1994; Macchetto 1996; Cid Fernandes et al. 2004; Stasińska et al. 2008; Sarzi et al. 2010; Capetti & Baldi 2011). We identified five classes of nuclear activity adopting the modified WHAN classification of (Gavazzi et al. 2011):

Star − Forming nuclei ( SF ) : log [ NII ] / H α < − 0.3 and EWH α > 1.5 Å ; Seyfert ( SEY ) : log [ NII ] / H α > − 0.3 and EWH α > 6 Å ; LINERs ( LIN ) : log [ NII ] / H α > − 0.3 and EWH α < 6 Å ; Retired galaxies ( RET ) : EWHα < 1.5 Å ; Passive galaxies ( PAS ) : EWH α , EW [ NII ] < 0.5 Å . $$ \begin{aligned} \begin{aligned}&\mathrm {Star-Forming \ nuclei \ (SF)}\!: \ \log \mathrm{[NII]} /\mathrm{H} \alpha < -0.3 \ \mathrm{and} \\&\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \mathrm{EWH} \alpha >1.5 \ \AA ; \\&\mathrm {Seyfert \ (SEY)}\!: \ \log \mathrm{[NII]} /\mathrm{H} \alpha > -0.3 \ \mathrm {and \ EWH} \alpha >6 \ \AA ; \\&\mathrm {LINERs \ (LIN)}\!: \ \log \mathrm{[NII]} /\mathrm{H} \alpha > -0.3 \ \mathrm {and \ EWH} \alpha <6 \ \AA ; \\&\mathrm {Retired \ galaxies \ (RET)\!: \ EWH} \alpha < 1.5 \ \AA ; \\&\mathrm {Passive \ galaxies \ (PAS)\!: \ EWH} \alpha , \ \mathrm {EW[NII]} \ < 0.5 \ \AA . \\ \end{aligned} \end{aligned} $$

Analogously to the BPT, we display the WHAN diagnostic diagrams separately for the three chromatic classes (Fig. 13, bottom panels). Star-forming nuclei are the majority of objects in the blue cloud (90%). The fraction of AGN is larger than in the BPT-classification, as both LIN and SEY account for ∼4% of the total each. Retired objects are ∼1%. Fewer than 1% of the nuclei are passive.

Approximately 64% of the nuclei in the green valley are star-forming. The LINERs increase to ∼20%, whereas the fraction of Seyfert nuclei mildly dereases (∼3%). Retired objects are ∼10%. Again, fewer than 1% of the nuclei are passive.

In the red sequence, star-forming nuclei drop to ∼20%. The fraction of LINERs is ∼20%. The fractions of retired and passive objects both increase to ∼45% and ∼16%, respectively. Finally, SEY only account for ∼1% of nuclei.

In general, there is a satisfactory agreement between the BPT and the WHAN classification among star-forming systems. A visual comparison between the two classification schemes is given in Fig. 14, displaying the BPT (WHAN) diagram of galaxies classified according to the WHAN (BPT).

thumbnail Fig. 14.

Visual comparison between the two optical emission-line diagnostics used in this work. Left: Baldwin-Phillips-Terlevich diagram of galaxies grouped according to their WHAN classification (blue=SF, red==SEY, green=LIN, violet=RET). Right: EWHα versus [NII]/Hα diagram of galaxies grouped according to their BPT classification (blue=SF, red==SEY, green=LIN, yellow=Comp).

We observe that the two diagnostics display some noticeable deviations in the classification of non-star-forming nuclei (see Tables 3 and 4): there are significant overlaps between the classification of SEY and LIN; for example, most BPT-classified LINERs are identified as retired by the WHAN diagnostic (48.6%); similarly, most WHAN-classified LINERs are classified as composite nuclei by the BPT (55.6%); the majority of galaxies classified as SEY by the BPT are marked the same way by the WHAN (40.3%); conversely, WHAN-classified Seyfert galaxies are mostly classified as composite nuclei (65.7%) by the BPT.

Table 3.

Fractions of the nuclear excitation classes classified with the WHAN diagram (SF, LIN, SEY, RET) calculated in four samples defining the BPT-classified nuclear activity (SF, LIN, SEY, Comp).

Table 4.

Fractions of the nuclear excitation classes classified with the BPT diagram (SF, LIN, SEY, Comp) calculated in four samples defining the WHAN-classified nuclear activity (SF, LIN, SEY, RET).

The disparity between the BPT and WHAN classifications of AGN (and, especially, LINERs) is not surprising. The two schemes employ a different number of emission lines, and were proposed following different theoretical and empirical criteria (Baldwin et al. 1981; Kewley et al. 2001; Kauffmann et al. 2003b; Cid Fernandes et al. 2011). In addition, there is no clear consensus yet on the ionisation source of LINERs. Heckman (1980) proposed that gas shocks are mainly responsible for LINER-like emission. Halpern & Steiner (1983) and Ferland & Netzer (1983) suggested that the primary mechanism behind the ionisation of LINERs is the accretion of gas onto a central massive black hole. Later, Terlevich & Melnick (1985) and Shields (1992) proposed that the excitation of these nuclei is produced by photoionisation due to hot stars. Recently, Oliveira et al. (2022) argued that a sample of 43 LINERs is probably ionised by post-asymptotyc giant branch (AGB) stars. The uncertainty on the mechanisms driving LINER-like emission could be a major factor for the differences between the BPT and WHAN classifications.

3.3. Stellar mass and environment versus nuclear activity

The ionisation diagnostics considered here are sensitive to the stellar mass of the targets (see, e.g. Kauffmann et al. 2003b). To explore the relation between nuclear activity and stellar mass, we classified the galaxy nuclear properties with the BPT and WHAN diagnostics separately in each stellar-mass bin (M1, ..., M6). In Fig. 15, we show the BPT diagnostic diagram for the emission-line galaxies in our catalogue. Using different colours to represent different mass bins, we see the characteristic dependence of the nuclear excitation properties on the stellar mass: in accordance with Kauffmann et al. (2003b), the nuclei of lowest-mass galaxies are SF regions, progressively filling the left-wing of the BPT from the top left to the bottom centre, with decreasing ionisation strength (top to bottom) and increasing metallicity (left to right). For three representative regions of the BPT diagram, a typical spectrum is also reported showing the progressive decrease in Hα over [NII], and in [OIII] over Hβ. Only in the two highest stellar-mass bins (i.e. 1010 to ≳1010.5Mstar/M⊙) is a higher fraction of ionised nuclei (LINERs, and, to a lower extent, Seyfert nuclei) found.

thumbnail Fig. 15.

Baldwin-Phillips-Terlevich diagnostic diagrams for galaxies in the SPRING catalogue. Three examples of spectra are given. Colours provide the logarithm of Mstar in solar-mass units.

In Fig. 16, we show the incidence5 of the BPT- and WHAN-classified SPRING emission-line galaxies as a function of their stellar mass. We see that up to Mstar = 109−9.5 M⊙ (mass bin M3), almost the entire spectroscopic sample is constituted of galaxies with SF nuclei, whose incidence is highest in the M2 bin. The fraction of LINERs becomes non-negligible in the M4 bin (Mstar = 109.5−10 M⊙) and turns out to be dominant over the SF fraction (∼30% over ∼10%, respectively) for Mstar ≳ 1010.5 M⊙ (M6 bin; see Kauffmann et al. 2003b).

thumbnail Fig. 16.

Frequency of BPT (top) and WHAN (bottom) excitation classes as a function of log(Mstar/M⊙) (represented by the six bins M1, ..., M6). The binomial errors are computed following Wilson (1927).

To verify if the incidence of the nuclear excitation properties is a general feature of high or low-mass galaxies, or, conversely, if it depends on other environmental features, we replicated Fig. 16, dividing the sample according to: (i) the galaxy chromatic class (RS, BC, and GV, Sect. 2.1), (ii) the overdensity parameter (Sect. 2.3.1), and (iii) the halomass (Sect. 2.3.2). Our results are reported in Figs. 17–22 and summarised in the following.

thumbnail Fig. 17.

Baldwin-Phillips-Terlevich-computed frequencies of SF, SEY, LIN and Comp as a function of log(Mstar/M⊙) (represented by the six bins M1, ..., M6) in the three chromatic regimes (blue cloud, green valley, red sequence, see Sect. 2.1). The y-axis scale in the right panel (SEY) is magnified to highlight the differences between the three chromatic classes.

thumbnail Fig. 18.

Baldwin-Phillips-Terlevich-computed frequencies of SF, SEY, LIN and Comp as a function of log(Mstar/M⊙) (represented by the six bins M1, ..., M6) in the four overdensity bins defined in Sect. 2.3.1. The y-axis scale in the right panel (SEY) is magnified to highlight the differences between the four overdensity classes.

thumbnail Fig. 19.

Baldwin-Phillips-Terlevich-computed frequencies of SF, SEY, LIN and Comp as a function of log(Mstar/M⊙) (represented by the six bins M1, ..., M6) in the three halo-mass bins defined in Sect. 2.3.2. The y-axis scale in the right panel (SEY) is magnified to highlight the differences between the three halo-mass classes.

thumbnail Fig. 20.

EWHα versus [NII]/Hα-computed frequencies (criteria of Gavazzi et al. 2011) of SF, SEY, LIN and RET as a function of log(Mstar/M⊙) (represented by the six bins M1, ..., M6) in the three chromatic regimes (blue cloud, green valley, red sequence, see Sect. 2.1).

thumbnail Fig. 21.

EWHα versus [NII]/Hα-computed frequencies (criteria of Gavazzi et al. 2011) of SF, SEY, LIN and RET as a function of log(Mstar/M⊙) (represented by the six bins M1, ..., M6) in the four overdensity bins defined in Sect. 2.3.1.

thumbnail Fig. 22.

EWHα versus [NII]/Hα-computed frequencies (criteria of Gavazzi et al. 2011) of SF, SEY, LIN and RET as a function of log(Mstar/M⊙) (represented by the six bins M1, ..., M6) in the three halo-mass bins defined in Sect. 2.3.2.

Star-forming: in general, the frequency of SF nuclei is highest in the lower-mass bins M1 – M3 (Mstar ≤ 109.5 M⊙), and decreases as the stellar mass increases. For a bin of fixed stellar mass it diminishes, migrating from isolated galaxies to galaxies in clusters, indicating that star-forming systems live in relatively lower-density environments (see also Constantin & Vogeley 2006). A similar decrease is observed as we increase the halo mass or as we move from the BC to the RS. For example, in the M5 bin, we find that the BPT-computed SF nuclei are ∼45% in the BC, ∼10% in the GV, and ∼5% in the RS (see Fig. 20, left panel).

The BPT and WHAN classifications for SF nuclei are in good agreement.

LINER: the frequency of LINERs computed with the BPT diagram proves to be not very sensitive to the galaxy overdensity and halo mass (Figs. 18–19 and 21–22). It exhibits the same strong dependence on the stellar mass in each overdensity and halo-mass bin, as it is consistent with ∼0% for Mstar < 1010 M⊙, and increases to ∼30%–40% for Mstar > 1010.5 M⊙. We find a dependence of the LIN fraction on the chromatic class in the mass range Mstar > 1010 M⊙, where it is ∼20% higher in the GV than in the BC and RS (Fig. 17, third panel), whereas LIN fractions in the BC and RS are consistent with each other across the whole mass range.

Conversely, the LIN fraction computed with the WHAN scheme exhibits a mild overdensity and halo-mass dependence for Mstar > 109.5 M⊙, increasing by ∼5%–10% as galaxies move from a high to low overdensity and halo mass. Consistent with the BPT-computed fraction, we find a similar chromatic dependence of the LIN frequency on the chromatic class in the mass range Mstar > 109.5 M⊙, where galaxies in the GV show a steep increase to ∼50% in the M5 and M6 bins; the fractions of LIN galaxies in the BC and RS are consistent with each other up to Mstar ∼ 109.5 M⊙; in the M5-M6 bins, the LIN fraction in the BC increases to ∼30%, whereas the RS fraction settles to less than 10% (Fig. 20, third panel).

Seyfert: unlike the frequency of LINERs, which is found to increase with stellar mass to 40–50%, the BPT-computed fraction of Seyfert galaxies shows a much milder increase with the stellar mass and, in general, reaches its maximum (∼5%) in the M5 bin (1010 < Mstar/M⊙ < 1010.5). We find a similar dependence of the SEY fraction on the overdensity and halo-mass indicators (i.e. it decreases, moving from a high to low overdensity and halo-mass, see Figs. 18–19, right panels). Conversely, it does not show considerable dependence on the chromatic class.

The WHAN-computed SEY fraction displays significant variations with respect to the BPT counterpart. In general, it is consistent with ∼0% for Mstar < 109.5 M⊙, and increases for higher masses, reaching a peak in the M5 bin, which rises up to ∼15% as we move from high- to low-overdensity and from high- to low-halo-mass galaxies. These results are consistent with Constantin & Vogeley (2006), who observed that Seyfert galaxies reside in less massive halos than those that host LINERs (see Figs. 19 and 22). The fraction of SEY among galaxies in the BC further increases for masses Mstar > 1010.5 M⊙, reaching ∼18% and ∼25% in bins M5 and M6, respectively.

Composite: as for the LINERs frequency, the frequency of composite (Comp) galaxies reaches its maximum in the highest-stellar-mass bins, M5 or M6, and shows a mild dependence on the galaxy overdensity and the halo-mass; that is to say, the fraction of composite nuclei in the M4-M6 bins moderately increases migrating from galaxies in clusters to isolated galaxies; similarly, the Comp fraction in the higher stellar-mass range is ∼5% higher in lower halo-mass galaxies (Fig. 19, second panel). A higher fraction of Comp galaxies is found in galaxies belonging to the GV for all but one (M6) mass bins (Fig. 17, second panel). The Comp frequency in the GV reaches its maximum (∼40%) in the M5 bin.

Retired: the fractions of RET in the three overdensity and four halo-mass bins display similar trends, steadily increasing for Mstar > 109.5 M⊙ up to ∼30% in the M5 and M6 bins and showing little dependence on the overdensity and the halo-mass; for 109 < Mstar M⊙ < 109.5, it is a few percent higher in the RS than in the BC and GV. For masses Mstar > 109.5 M⊙, it follows similar trends for RS and GV galaxies, and is about ∼15% lower for galaxies in the BC.

Again, the disparities in the features of non-star-forming nuclei may be attributed to deviations among the BPT and WHAN classifications (Tables 3–4). In general, our results show that one of the most important factors (if not the most important) that affects the triggering of nuclear activity is the galaxy stellar mass (Kauffmann et al. 2003b). The dependence of the prevalence of nuclear activity on the local galaxy density and halo-mass appears to indicate that the fraction of Seyfert nuclei decreases towards the highest-density regions and highest halo-masses (see also Sabater et al. 2013, 2015; Li et al. 2019; Lopes et al. 2017), as well as the fraction of star-forming nuclei. The fraction of LINERs displays minor dependencies on the galaxy density and halo-mass, but appears to be related to the galaxy colour in the mass range Mstar > 1010 M⊙, where it is ∼20% higher in the green valley.

4. Summary

In this work we present the SPRING catalogue, a complete photometric database (r < 17.7 mag) of local galaxies in the northern spring sky (10h < RA < 16h; 0° < Dec < 65°, cz < 10 000 km s−1) by merging the Portsmouth emission-line table by Thomas et al. (2013) extracted from the SDSS and the NASA-Sloan atlas (Blanton et al. 2011). To further reduce the bright-end incompleteness, we complemented our sample with the updated Zwicky catalogue (UZC) Zwicky et al. (1961), Falco et al. (1999), and with the UGC catalogue Nilson (1973). We got the available nuclear spectroscopy from the SDSS, and integrated it with optical spectra taken by other surveys (Zwicky et al. 1961; Nilson 1973; Ho et al. 1993, 1995, 1997; Falco et al. 1999; Gavazzi et al. 2011, 2013b), and with the spectroscopy obtained by us in 2014-2020 using the Cassini 1.5 m telescope of the Loiano Observatory. The SPRING catalogue includes 891 galaxies with optical spectra observed independently from SDSS. We claim that the resulting sample of 30 597 targets constitutes the cleanest and most complete catalogue of galaxies available so far for the northern spring sky limited to r < 17.7 mag and cz < 10 000 km s−1. The construction of such a sample constitutes the first goal of the present work (Sect. 2). We have studied the extensive properties (colours, stellar mass, and H I content) of galaxies in such a sample, confirming that galaxy colour (NUV − i) is a steep function of stellar mass, with the red sequence well separated from the blue cloud by the green valley (Boselli et al. 2014, Sect. 2.1).

We performed a recalibration of the optical diameter-based H I-deficiency parameter, employing a reference sample of 3416 isolated galaxies extracted from our catalogue, and find that the slope of the log M(H I) versus log(D2) relation is strongly dependent on the galaxy stellar mass (Sect. 2.3.3). We observe that most green-valley objects are consistent with galaxies with deficient H I content, stressing the role of gas in fuelling (and quenching) star-formation, as claimed by Gavazzi et al. (2013c).

Remarkably, we find that a filamentary structure of galaxies extending in the spring sky at intermediate distances between Virgo and Coma contains ∼30.7% of H I-deficient galaxies, which significantly exceeds the average H I-deficiency fraction (∼12%) of field galaxies. To probe the relevance of the deficiency of H I in filaments, we constructed 10 000 overdensity-matched samples and investigated their H I content. We find that the average fraction of H I-deficient galaxies in the matched-samples is (20.2 ± 1.6)%. This could suggest that galaxies in filaments are more H I-deficient than field galaxies.

The nuclear classification of galaxies in our sample (Sect. 3) is based on the four-lines BPT and the two-lines WHAN diagnostic diagrams. We confirm that the fraction of AGN (especially LINERs) over SF nuclei is a steep function of stellar mass (Kauffmann et al. 2003b), that is to say, the fraction of AGN (Seyfert and LINERs) is consistent with zero up to Mstar < 109.5 M⊙, and becomes ∼40% for Mstar > 1010.5 M⊙. A general agreement between the BPT and WHAN classifications is observed. We investigated the mass dependence of the excitation properties determined with both diagnostics, and found that star-forming nuclei, Seyfert galaxies, and LINERs display similar trends (Sects. 3.1 and 3.2).

In addition, we investigated whether the nuclear-excitation fractions depend predominantly on the stellar mass or on other environmental related indicators (Sect. 3.3). To this end, we examined the connection between the mass dependence of the galaxy excitation properties and their environment, which we determined using three different indicators: (i) a chromatic criterion, (ii) the local galaxy density, and (iii) the halo mass of the group to which galaxies belong. In general, we observe that the mass dependence of the fraction of Seyfert nuclei is not very sensitive to the galaxy environment (with noticeable variations in the SEY percentage depending on the adopted diagnostic scheme), while the fraction of star-forming nuclei is a steeper function of stellar mass in lower-density environments and in blue-cloud galaxies. The fraction of LINERs exhibits a dependence on the galaxy colour and, for Mstar ≳ 109.5 − 10 M⊙, is significantly larger in galaxies belonging to the green valley.

5. Discussion

The analysis presented in this work – based on a statistically significant sample – suggests that the main parameter driving the level of nuclear activity is the galaxy stellar mass (Kauffmann et al. 2003b; Decarli et al. 2007; Juneau et al. 2011; Rodríguez del Pino et al. 2017) and shows that the fraction of AGN does not vary significantly with the galaxy density, the mass of the hosting halo, or the colour of galaxies (see Sect. 3.3). This result agrees overall with the results of Kauffmann et al. (2004) based on a larger SDSS optically selected sample of galaxies, and with Constantin & Vogeley (2006). Our result also agrees with the results gathered by other teams that analysed optically and X-ray-selected clusters (Vollmer et al. 2001, 2004, 2008; Steyrleithner et al. 2020).

It is interesting that the highest density regions in our sample are those within well-known clusters of galaxies such as Coma, A1367, and Virgo. Detailed multi-frequency studies of these clusters clearly indicate that the dominant perturbing mechanism affecting the evolution of the infalling, gas-rich spiral population is ram pressure. This mechanism has been identified without ambiguity by the presence of long cometary tails in H I (Gavazzi 1989; Dickey & Gavazzi 1991; Bravo-Alfaro et al. 2001; Scott et al. 2010, 2012; Chung et al. 2007), radio continuum (Gavazzi 1978; Gavazzi & Jaffe 1985, 1987; Gavazzi et al. 1995; Miller et al. 2009; Chen et al. 2020; Lal 2020; Vollmer et al. 2004, 2010; Crowl et al. 2005; Kantharia et al. 2008), and Hα (Yoshida et al. 2002; Boselli et al. 2018; Fossati et al. 2018; Yagi et al. 2007, 2010, 2017), by detailed multi-frequency studies of large statistical samples (e.g. Vollmer et al. 2001; Boselli et al. 2014; Gavazzi et al. 2012), and of individual objects (Boselli et al. 2016; Kenney & Koopmann 1999; Kenney et al. 2004, 2008; Vollmer 2003; Vollmer et al. 2004, 2008).

A relation between the incidence rate of galaxies hosting AGN and the density of the environment in which they reside has been looked for in several statistical studies, since it is expected in different evolutionary scenarios (e.g. Kauffmann et al. 2004). In a starvation scenario (e.g. Larson et al. 1980), in which the hot halo surrounding galaxies is removed once galaxies become satellites of larger structures, the feedback mechanism induced by a strong nuclear activity easily removes most of the cold-gas component host on the galaxy disc, as indicated by the most recent hydrodynamical simulations (Stevens & Lagos 2021; Tonnesen & Bryan 2009; Boselli et al. 2021). For this reason, the activity of star formation of the host galaxies is strongly reduced, producing quiescent systems. Recently, it has also been claimed that the loss of angular momentum that follows a ram-pressure-stripping event might induce gas infall in the galaxy centre, feeding nuclear activity (e.g. Schulz & Struck 2001; Tonnesen & Bryan 2009; Poggianti et al. 2017a; Ramos-Martínez et al. 2018).

Our analysis of the nuclear properties of galaxies in different environments and stellar-mass ranges shows that the incidence of Seyfert nuclei in galaxies within the core of a cluster (UH overdensity) is minimum (Figs. 18–21). This result agrees with a number of recent works aimed at studying the incidence of strong AGN in different environments (Sabater et al. 2013, 2015; Li et al. 2019; Lopes et al. 2017). Conversely, the incidence of nuclei with BPT-classified LINER-like emission-line ratios displays no dependence on the local galaxy overdensity (Fig. 18, third panel), but is strongly connected with the galaxy stellar mass and reaches ∼40% for Mstar > 1010.5 M⊙ (similar results are yielded for LINERs classified by the WHAN diagnostic). This evidence is at odds with the results of Poggianti et al. (2017a), who found an enhanced AGN (Seyfert) fraction in a sample of ram-pressure-stripped (jellyfish) objects (5/7). However, such results were not confirmed by the follow-up analysis of Peluso et al. (2022) on a larger selection of jellyfish galaxies. The recent analysis by Roman-Oliveira et al. (2019) of a sample of ram-pressure-stripped galaxies did not find a high incidence of AGN (5/70). The disparities between these studies may be attributed to the different nuclear-activity classification schemes that they adopted. Poggianti et al. (2017a) and Peluso et al. (2022) used the BPT diagnostic, while the classification of the sample analysed in Roman-Oliveira et al. (2019) employed the WHAN (see also Rodríguez del Pino et al. (2017)). As we point out in Sect. 3.2, the two diagrams display noticeable deviations in the classification of non-star-forming nuclei (e.g. among the galaxies classified as Seyfert by the BPT, only ∼16% are classified as SEY by the WHAN).

Another potential element of discrepancy among different analyses of the nuclear activity versus environment relation is the characterisation of AGN. Poggianti et al. (2017a) denote galaxies that are classified as Seyfert by the BPT diagram as AGN; Peluso et al. (2022) classifies a galaxy as an AGN if its nuclear region has a Seyfert or a LINER BPT-classification; similarly, Rodríguez del Pino et al. (2017) labels galaxies that are classified as Seyfert or LINERs by the WHAN as AGN. As we see in Figs. 17–19, active nuclei classified with the BPT diagnostic diagram are dominated by the LINERs, suggesting that more lenient AGN definitions are biased towards LINER-like behaviour. The LINERs differ from Seyferts in many of their characteristics; also, the ionisation source of LINER-like objects is still uncertain. Thus, caution should be exercised when investigating the interplay between the galaxy environmental properties (e.g. being subject to ram-pressure-stripping) and the triggering of nuclear activity.

As extensively discussed in Boselli et al. (2022), the results of Poggianti et al. (2017a) may be due to the fact that their sample of seven galaxies was composed of very massive objects (Mstar > 4 × 1010 M⊙), which are more likely to host an active nucleus. The sample examined by Poggianti et al. (2017a) was further expanded in Peluso et al. (2022), who considered a total of 51 galaxies observed in the context of the GASP survey Poggianti et al. (2017b) along with additional 82 galaxies retrieved from the literature, and they found that the fraction of galaxies that host AGN (Seyfert or LINER) is ∼27% at masses Mstar > 109 M⊙, and ∼51% at masses Mstar > 1010 M⊙. The majority of objects considered by Peluso et al. (2022) are high-mass galaxies (all galaxies hosting AGN in GASP have Mstar > 1010.5 M⊙). In contrast to Poggianti et al. (2017a), the AGN fractions considered by Peluso et al. (2022) include both Seyfert galaxies and LINERs. Also, the AGN fractions are calculated with respect to a sample of emission-line galaxies only, introducing possible bias. For comparison, the BPT-computed SEY+LIN fraction f(SEY + LIN)/fELG of SPRING galaxies in the M6 bin (i.e. with stellar mass Mstar > 1010.5 M⊙) calculated over all emission-line galaxies in the same stellar-mass bin is ∼58.2%, whereas the fraction f(SEY + LIN)/fTOT in the same mass range (where fTOT accounts for both emission-line and passive galaxies) is ∼37%. In general, our analysis suggests that the investigation of the occurrence of AGN in different galaxy environments requires care, as one needs to take into account possible biases that may arise due to: (i) the particular diagnostic adopted (BPT vs. WHAN vs. other diagnostics), (ii) the characterisation of AGN (whether LINERs are true AGN), and (iii) the sample that defines the underlying galaxy population.

Finally, our analysis suggests that the incidence of LINERs is higher in galaxies belonging to the green-valley (Figs. 17 and 20). In a future companion paper, we aim to further investigate the nuclear activity versus environment relation of a sample of SPRING galaxies that are subject to ram-pressure-stripping in the cluster environment.

3

We point out that all galaxies in our sample have a spectroscopic redshift, including those with no nuclear spectrum, for which the values of zspec were taken from NED.

4

Since Hα and [NII] are close to one another, and since we used fluxes normalised to the continuum under Hα, the same correction for stellar absorption was applied to the fluxes as to the EWHs.

5

We emphasise that the fractions of ELG computed throughout this paper (e.g. fSEY/fTOT) are calculated with respect to the whole galaxy sample in each stellar-mass bin; i.e.fTOT consists of both ELG and passive galaxies. This choice allows for a more accurate determination of the relative number of each class of ELG in each stellar-mass bin.

6

The long-slit optical nuclear spectra taken at Loiano are available in machine-readable format via the Strasbourg Astronomical Data Center (CDS) and NED.

Acknowledgments

The authors thank the anonymous referee for their constructive comments and suggestions. We are grateful to Paolo Franzetti and Alessandro Donati for their contribution to GoldMine, the Galaxy On Line Database (Gavazzi et al. 2003) extensively used in this work (http://goldmine.mib.infn.it). We thank Andrea Macció for useful discussion. We also thank the coordinator of the TAC Giovanna Stirpe for the generous time allocation at the 1.52 m Cassini Telescope, run by INAF-Osservatorio Astrofisico e Scienza dello Spazio di Bologna at Loiano. This research has made use of the NASA/IPAC Extragalactic Database (NED), which is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration. The present study made extensive use of SDSS. Funding for the Sloan Digital Sky Survey (SDSS) and SDSS-II has been provided by the Alfred P. Sloan Foundation, the Participating Institutions, the National Science Foundation, the U.S. Department of Energy, the National Aeronautics and Space Administration, the Japanese Monbukagakusho, and the Max Planck Society, and the Higher Education Funding Council for England. The SDSS Web site is http://www.sdss.org/. The SDSS is managed by the Astrophysical Research Consortium (ARC) for the Participating Institutions. The Participating Institutions are the American Museum of Natural History, Astrophysical Institute Potsdam, University of Basel, University of Cambridge, Case Western Reserve University, The University of Chicago, Drexel University, Fermilab, the Institute for Advanced Study, the Japan Participation Group, the Johns Hopkins University, the Joint Institute for Nuclear Astrophysics, the Kavli Institute for Particle Astrophysics and Cosmology, the Korean Scientist Group, the Chinese Academy of Sciences (LAMOST), the Los Alamos National Laboratory, the Max-Planck-Institute for Astronomy (MPIA), the Max-Planck-Institute for Astrophysics (MPA), New Mexico State University, Ohio State University, University of Pittsburgh, the University of Portsmouth, Princeton University, the United States Naval Observatory, and the University of Washington.

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Appendix A: Loiano observations

We used the Bologna Faint Object Spectrograph and Camera (BFOSC, Gualandi & Merighi 2001) attached to the 152cm F/8 Cassini Telescope locatedin Loiano, which belongs to the Observatory of Bologna, to obtain optical spectra of the nuclei of 376 galaxies6. The observations took place from 2014 to 2020. The long-slit spectra were taken through a slit of 2 or 2.5 arcsec width (depending on the seeing conditions), with a intermediate-resolution red-channel grism (R ∼ 2200) that covers the 6100 - 8200 Å portion of the spectrum, which contains Hα, [NII], and [SII] lines (three galaxies were observed also using a blue-channel grism covering Hβ and [OIII]).

The detector used by BFOSC is an EEV LN/1300-EB/1 CCD of 1300x1340 pixels, with 90% quantum efficiency near 5500 Å. Its spatial scale of 0.58 arcsec/pixel results in a field of view of 12.6′×13′. The dispersion of the red-channel grism is 8.8 nm/mm and results in spectra with 1.6 Å/pix. The instrumental broadening is typically 6 Å full-width-half-maximum (FWHM), as checked on the 6300.3 Å sky line. We obtained exposures of 3-5 minutes, repeated typically three-six times per run (to remove the cosmic rays), but several galaxies were re-observed in more than one run (see Table A.1). The seeing at Loiano is typically 1.5 - 2.5 arcsec. The slit was mostly set in the east-west direction, except when it was positioned along the galaxy major axis or along the direction connecting two adjacent objects to accommodate both in one exposure.

Table A.1.

List of 302 galaxies whose spectra were taken at Loiano between 2014 and 2020.

The wavelength was calibrated using frequent exposures of a He-Ar hollow-cathode lamp. We used several sky lines to check a posteriori the wavelength calibration. The spectrograph response was obtained by daily exposures of the star Feige-34.

Table A.1 summarises the spectra obtained at Loiano (2014-2020) as follows:

  1. Column 1: Galaxy name;

  2. Column 2, 3: Right Ascension; Declination (J2000);

  3. Column 4: redshift cz (km/s);

  4. Column 5: r-band magnitude;

  5. Column 6: duration of the individual exposures;

  6. Column 7: number of individual exposures;

  7. Column 8: observing date;

  8. Column 9: slit orientation (counterclockwise from N) 270 corresponds to east-west;

  9. Column 10: measured equivalent width (EW) of the Hα line (negative EW values represent emission);

  10. Column 11: measured EW of [NII]λ 6584 line;

  11. Column 12: measured EW of Hβ line;

  12. Column 13: measured EW of [OIII]λ 5007 line.

Appendix B: the Local Spring database

A sample of the SPRING database containing the first 30 of 30597 galaxies is given in Table B.1 (full table available at the CDS) as follows:

  • Column 1: Right Ascension (in degrees);

  • Column 2: Declination (in degrees);

  • Column 3-6: corrected magnitudes FUV, NUV, g, i;

  • Column 7: recessional velocity (in km s−1);

  • Column 8: adopted distance in h−1 Mpc;

  • Column 9: origin of the line measurement (SL=SDSS, HO=Ho et al. (1995, 1997), ZW = Falco et al. (1999), LOI = Loiano (Gavazzi et al. 2011, 2013b, this work), NO=no nuclear spectrum available);

  • Column 10-13: EW (Å) of Hα, N[II], Hβ, O[III];

  • Column 14-15: adopted nuclear classification according to the BPT and WHAN diagnostic diagrams;

  • Column 16: adopted stellar mass log(Mstar/M⊙) calculated following Zibetti et al. (2009);

  • Column 17: local galaxy over-density (ρ − ⟨ρ⟩/⟨ρ⟩;

  • Column 18: halo mass MHalo;

  • Column 19: H I-deficiency parameter.

Table B.1.

First 30 among 30597 galaxies analysed in this work.

All Tables

Table 1.

Coefficients of the log M(H I) versus log(D2) relation for the two stellar-mass bins M1 (log(Mstar/M⊙) ≤ 8.5) and M2–M6 (log(Mstar/M⊙) > 8.5).

In the text
Table 2.

Mean value and standard deviation of the Gaussian fits for the H I-deficiency distributions in each overdensity bin.

In the text
Table 3.

Fractions of the nuclear excitation classes classified with the WHAN diagram (SF, LIN, SEY, RET) calculated in four samples defining the BPT-classified nuclear activity (SF, LIN, SEY, Comp).

In the text
Table 4.

Fractions of the nuclear excitation classes classified with the BPT diagram (SF, LIN, SEY, Comp) calculated in four samples defining the WHAN-classified nuclear activity (SF, LIN, SEY, RET).

In the text
Table A.1.

List of 302 galaxies whose spectra were taken at Loiano between 2014 and 2020.

In the text
Table B.1.

First 30 among 30597 galaxies analysed in this work.

In the text

All Figures

thumbnail Fig. 1.

Celestial distribution of the 30 597 galaxies analysed in the SPRING catalogue, distributed over nearly one-fourth of the northern sky. They represent the totality of galaxies in the spring sky within cz < 10 000 km s−1 and r < 17.7 mag. The colours represent the overdensity parameter Δρ/⟨ρ⟩ (defined in Sect. 2.3.1) for galaxies in four bins of local galaxy overdensity (red = UH, green = H, blue = L, black = UL). In the left panel, the third dimension emphasises the overdensity parameter. The highest narrow peak represents the Coma cluster, the second narrow peak is A1367, and the broader peak is Virgo.
In the text
thumbnail Fig. 2.

Luminosity function of SPRING galaxies in the r-band (blue dots and line). Our best-fitting Schechter function (red line) agrees well with the data points.
In the text
thumbnail Fig. 3.

Corrected NUV − i colour versus stellar-mass diagram relation of all galaxies in this sample divided into BC, GV and RS galaxies, according to the chromatic criterion of Boselli et al. (2014; see Sect. 2.1), in four bins of galaxy overdensity (from UH to UL clockwise from top left). In each panel (corresponding to the four overdensities) are reported the fractions of galaxies in the three chromatic classes with respect to the total number of galaxies in the corresponding overdensity bin.
In the text
thumbnail Fig. 4.

log M(H I) vs. log(D2) distribution of galaxies belonging to the UL overdensity bin and with log(Mstar/M⊙)≤8.5 (blue dots), and log(Mstar/M⊙) > 8.5 (red dots). The coloured straight lines denote the best-fit linear regression in each mass bin, whose coefficients are given in Table 1. The dashed black line denotes the linear fit given in Gavazzi et al. (2013a).
In the text
thumbnail Fig. 5.

Distribution of the newly calibrated H Idef parameter in four bins of galaxy overdensity. The mean values and standard deviations for each overdensity bin are given in Table 2.
In the text
thumbnail Fig. 6.

Sky distribution of galaxies in the SPRING sample in classes of the HI-deficiency parameter. The horizontal line at Dec = 36 deg traces the limiting pointing declination of the Arecibo dish. Grey circles stand for galaxies for which the H I content is unknown. Blue dots denote galaxies with H Idef ≤ 0.4, while red dots are for galaxies with H Idef > 0.4. The latter class is distributed consistently around (in) the Coma cluster and the Virgo cluster, with a small number of entries belonging to the A1367 cluster. It is noticeable that our knowledge of the H I properties of local galaxies in such an extended stretch of the sky depends on the availability of Arecibo data, which are limited to declination ≲ 36°.
In the text
thumbnail Fig. 7.

Corrected NUV − i colour versus stellar-mass diagram relation for galaxies in the Spring catalogue. Left: Sample divided according to the chromatic criterion of Boselli et al. (2014; see Fig. 2 therein), showing whether galaxies belong to the RS, the GV, or the BC. For each class, the linear regression analysis is given. Right: Same relation for the sub-sample of ETGs with detailed morphological classifications available (red), and of deficient (H I > 0.4, green) and non-deficient galaxies (H I ≤ 0.4, blue). The slopes of the linear regressions for the ETGs and the H I-deficiency classes are consistent with those corresponding to the three chromatic classes. The NUV and i magnitudes are corrected for dust attenuation (see Boselli et al. 2014).
In the text
thumbnail Fig. 8.

Main-sequence relation of galaxies in the Spring catalogue. Left: Sample divided according to the chromatic criterion of Boselli et al. (2014; see Fig. 2 therein), showing whether they belong to the RS, the GV, or the BC. For each class, the linear regression analysis is given. Right: Same relation for the sub-sample of ETGs with detailed morphological classifications available (red), and of deficient (H I > 0.4, green) and non-deficient galaxies (H I ≤ 0.4, blue). The slopes of the linear regressions for the ETGs and the H I-deficiency classes are consistent with the ones corresponding to the three chromatic classes.
In the text
thumbnail Fig. 9.

Selection criterion discussed in Sect. 2.4 used to identify the SR. Left: Distribution of the Hubble velocity cz as a function of RA. Blue dots indicate galaxies with 1500 < cz/km s−1< 6000, (with the exclusion of objects belonging to the fingers of God of either Coma or Virgo). Right: Sky distribution of galaxies in the 1500 < cz/km s−1 < 6000 range highlighting a filamentary structure that we dub the SR.
In the text
thumbnail Fig. 10.

Sky distribution of galaxies belonging to the SR in two bins of the H I-deficiency parameter: H Idef ≤ 0.4 (blue) and H Idef > 0.4 (red).
In the text
thumbnail Fig. 11.

Sky distribution of the 515 galaxies belonging to the SR and having H I data. The third dimension is given by the Hubble velocity cz. The colour map displays the H Idef parameter.
In the text
thumbnail Fig. 12.

Completeness (defined as the fraction of galaxies with nuclear spectra over the total number of galaxies, left axis and thick straight lines) and actual number of galaxies with nuclear spectra (right axis and dashed lines) as a function of r apparent magnitude (top) and as a function of stellar mass (bottom). The blue lines represent SDSS-only spectra and the black lines include spectra taken from the SDSS and other sources.
In the text
thumbnail Fig. 13.

Optical emission-line diagnostics for galaxies in the Spring catalogue. Top row: Baldwin-Phillips-Terlevich diagnostic diagram for galactic nuclei in the blue cloud, green valley, and red sequence (left, centre, and right respectively). The SDSS galaxies are given with blue points. Measurements from other sources are given with larger red dots. The associated percentage of galaxies is given for each spectral category. The red straight line represents the model of Kewley et al. (2001) to identify AGN. The long-dashed blue line gives the model of Kauffmann & Haehnelt (2000) above which AGN are found. Composite nuclei fall between these two models. The dashed black line (Schawinski et al. 2007) separates Seyfert galaxies from LINERs. Bottom row: EWHα versus [NII]/Hα diagram diagnostic diagram for galactic nuclei in the blue cloud, green valley, and red sequence (left, centre, and right respectively). The SDSS galaxies are given with blue points. Measurements from other sources are given with larger red dots. The associated percentage of galaxies is given for each spectral category.
In the text
thumbnail Fig. 14.

Visual comparison between the two optical emission-line diagnostics used in this work. Left: Baldwin-Phillips-Terlevich diagram of galaxies grouped according to their WHAN classification (blue=SF, red==SEY, green=LIN, violet=RET). Right: EWHα versus [NII]/Hα diagram of galaxies grouped according to their BPT classification (blue=SF, red==SEY, green=LIN, yellow=Comp).
In the text
thumbnail Fig. 15.

Baldwin-Phillips-Terlevich diagnostic diagrams for galaxies in the SPRING catalogue. Three examples of spectra are given. Colours provide the logarithm of Mstar in solar-mass units.
In the text
thumbnail Fig. 16.

Frequency of BPT (top) and WHAN (bottom) excitation classes as a function of log(Mstar/M⊙) (represented by the six bins M1, ..., M6). The binomial errors are computed following Wilson (1927).
In the text
thumbnail Fig. 17.

Baldwin-Phillips-Terlevich-computed frequencies of SF, SEY, LIN and Comp as a function of log(Mstar/M⊙) (represented by the six bins M1, ..., M6) in the three chromatic regimes (blue cloud, green valley, red sequence, see Sect. 2.1). The y-axis scale in the right panel (SEY) is magnified to highlight the differences between the three chromatic classes.
In the text
thumbnail Fig. 18.

Baldwin-Phillips-Terlevich-computed frequencies of SF, SEY, LIN and Comp as a function of log(Mstar/M⊙) (represented by the six bins M1, ..., M6) in the four overdensity bins defined in Sect. 2.3.1. The y-axis scale in the right panel (SEY) is magnified to highlight the differences between the four overdensity classes.
In the text
thumbnail Fig. 19.

Baldwin-Phillips-Terlevich-computed frequencies of SF, SEY, LIN and Comp as a function of log(Mstar/M⊙) (represented by the six bins M1, ..., M6) in the three halo-mass bins defined in Sect. 2.3.2. The y-axis scale in the right panel (SEY) is magnified to highlight the differences between the three halo-mass classes.
In the text
thumbnail Fig. 20.

EWHα versus [NII]/Hα-computed frequencies (criteria of Gavazzi et al. 2011) of SF, SEY, LIN and RET as a function of log(Mstar/M⊙) (represented by the six bins M1, ..., M6) in the three chromatic regimes (blue cloud, green valley, red sequence, see Sect. 2.1).
In the text
thumbnail Fig. 21.

EWHα versus [NII]/Hα-computed frequencies (criteria of Gavazzi et al. 2011) of SF, SEY, LIN and RET as a function of log(Mstar/M⊙) (represented by the six bins M1, ..., M6) in the four overdensity bins defined in Sect. 2.3.1.
In the text
thumbnail Fig. 22.

EWHα versus [NII]/Hα-computed frequencies (criteria of Gavazzi et al. 2011) of SF, SEY, LIN and RET as a function of log(Mstar/M⊙) (represented by the six bins M1, ..., M6) in the three halo-mass bins defined in Sect. 2.3.2.
In the text

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