Open Access
Issue
A&A
Volume 665, September 2022
Article Number A144
Number of page(s) 22
Section Extragalactic astronomy
DOI https://doi.org/10.1051/0004-6361/202142215
Published online 21 September 2022

© C. R. Mulcahey et al. 2022

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.

This article is published in open access under the Subscribe-to-Open model. Subscribe to A&A to support open access publication.

1. Introduction

How supermassive black holes (SMBHs) and their host galaxies coevolve has yet to be fully understood. During growth periods, in which SMBHs actively accrete gas and are known as active galactic nuclei (AGN), they can release an enormous amount of radiation across the entire electromagnetic spectrum and can form winds and jets in their host galaxies. Current cosmological models of galaxy evolution (e.g., Bower et al. 2006; Schaye et al. 2015; Pillepich et al. 2019) require AGN to inject energy and momentum into their host galaxies’ circumambient gas and interstellar medium (ISM) to reproduce the observed stellar mass and luminosity function and prevent the formation of over-massive galaxies. Observationally, the relation between star-formation (SF) history and the growth of SMBHs at the center of galaxies has been the subject of many studies (e.g., Mullaney et al. 2012; Chen et al. 2013; Hickox et al. 2014, and references therein) that have found that SFRs and black hole accretion rates are intimately tied at all redshifts (e.g., Boyle & Terlevich 1998; Aird et al. 2015). This relationship likely indicates that SFRs and black hole accretion share a common fuel source (e.g., Silverman et al. 2009). The correlation between the mass of the black hole and the stellar velocity dispersion (MBH − σ*; e.g., Haehnelt & Kauffmann 2000) as well as the link between MBH and the mass of the stellar bulge (MBH − Mbuldge; e.g., Häring & Rix 2004) further hint at the coevolution of black holes and stellar bulges, thereby suggesting a link between black hole accretion rates and SFRs. However, studies investigating the relation between AGN activity and star-forming activity (e.g., Netzer 2009; Rosario et al. 2012; Gürkan et al. 2015; Stanley et al. 2015; Jackson et al. 2020) have so far yielded mixed results.

There are two prominent ways that AGN feedback can affect its host galaxy. Outflows from AGN can enhance SF (positive feedback) by compressing molecular clouds (e.g., Schaye et al. 2015) and/or the ISM (e.g., Ishibashi & Fabian 2012). Direct evidence of positive feedback is rare (e.g., Cresci et al. 2015; Shin et al. 2019; Nesvadba et al. 2020) and is typically observed in a companion satellite along the host galaxy’s radio axis (e.g., Klamer et al. 2004; Croft et al. 2006; Feain et al. 2007; Rodríguez Zaurín et al. 2007; Elbaz et al. 2009; Crockett et al. 2012; Gilli et al. 2019). Conversely, AGN can suppress SF (negative feedback) via mechanical energy from winds, outflows, or jets heating the surrounding ISM and preventing molecular gas from radiatively cooling or due to AGN-driven outflows expelling gas from the host galaxy (e.g., Binney & Tabor 1995; Ciotti & Ostriker 2001, 2007; Croton et al. 2006; McNamara & Nulsen 2007; Nesvadba et al. 2008, 2010; Cattaneo et al. 2009; Ciotti et al. 2010; Fabian 2012; Yuan & Narayan 2014; Heckman & Best 2014). On longer timescales, jets can heat the circumgalactic and halo gas, preventing the cooling of gas and future SF (e.g., Ciotti & Ostriker 2001, 2007; McNamara & Nulsen 2007). Furthermore, the role of AGN feedback varies depending on the type of AGN the galaxy hosts. Radio-loud AGN can either be radiatively efficient or radiatively inefficient. Radiatively efficient AGN are typically connected to the most luminous AGN and accrete gas close to the Eddington limit from an optically thick, geometrically thin accretion disk. Radio-loud quasi-stellar objects and high excitation radio galaxies (HERGs) – further classifications of radiatively efficient AGN – are capable of producing powerful, two-sided jets that produce synchrotron radiation detectable at radio wavelengths. Energy released from the accretion disk may be capable of driving massive outflows of gas and may ultimately remove it from the potential well (e.g., Cattaneo et al. 2009; Fabian 2012). Conversely, radiatively inefficient AGN – also referred to as low excitation radio galaxies (LERGs) – are linked to low-to intermediate-luminosity AGN and contain geometrically-thick, advection-dominated accretion flows, which can also produce powerful radio jets. Radiatively inefficient AGN have been shown to inject heat into their surroundings at a rate that is commensurate with the rate of cooling from the intergalactic medium, and are responsible for maintaining galaxy quiescence (e.g., Binney & Tabor 1995; Ciotti & Ostriker 2001, 2007; Bower et al. 2006; McNamara & Nulsen 2007; Cattaneo et al. 2009; Ciotti et al. 2010; Fabian 2012; Yuan & Narayan 2014; Heckman & Best 2014; Smolčić et al. 2017; Hardcastle et al. 2019).

Significant advances in our understanding of the effect of radio-mode AGN on their host galaxies have been achieved by coupling radio surveys such as the National Radio Astronomy Observatory (NRAO) Very Large Array Sky Survey (NVSS; 1.4 GHz continuum; Condon et al. 1998), the Faint Images of the Radio Sky at Twenty centimeters (FIRST; 1.4 GHz continuum; Becker et al. 1995), the Very Large Array Sky Survey (VLASS; 2−4 GHz; Hales 2013), and the Tata Institute of Fundamental Research (TIFR) Giant Metrewave Radio Telescope (GMRT) Sky Survey (TGSS; 150 MHz; Intema et al. 2017) with optical spectroscopic surveys such as the Sloan Digital Sky Survey (SDSS; York et al. 2000; Stoughton et al. 2002, and references therein) and the Two-degree-Field Galaxy Redshift Survey (2dFGRS; Colless et al. 2001). Statistical studies that have combined these surveys (e.g., Best et al. 2005a,b; Sadler et al. 2002) have improved our understanding of the physical properties and prevalence of radio-AGN activity, but the nature of AGN emitting at radio frequencies lower than 1.4 GHz is yet to be fully understood. Sabater et al. (2019) combined data from the first data release (DR1) of the Low-Frequency Array (LOFAR; 10−240 MHz; van Haarlem et al. 2013) Two-Metre Sky Survey (LoTSS; Shimwell et al. 2017) with optical spectroscopic data from SDSS DR7 (Abazajian et al. 2009) and found that the most massive AGN host galaxies (> 1011M*) always exhibit radio-AGN activity. These results suggest that radio-AGN activity is dictated by the host galaxy’s fuel supply and that radio-AGN play a significant role in maintaining quiescence.

Simultaneously, integral field spectroscopy (IFS) surveys are revolutionizing our understanding of AGN by enabling more detailed investigations than previously possible. Unlike long-slit spectroscopy, which obtains a spectrum for a single point in the galaxy, or acquiring spectra along a “slice” of the galaxy, IFS obtains resolved, two-dimensional spectra across the surface of the galaxy. Integral field spectroscopy, in combination with stellar population modeling, permits the spatially resolved study of a galaxy’s properties, such as current SFRs, metallicities, and stellar ages. Moreover, the gas and stellar kinematics over an entire galaxy can be obtained, enabling the effect of winds and dynamical disturbances to be examined. One of the largest optical IFS surveys is the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA; Bundy et al. 2015) survey, which is one of three core parts of the fourth phase of SDSS (SDSS-IV). MaNGA has acquired observations with a spatial resolution of 25 for ∼10 000 unique, low-redshift (0.01 < z < 0.15; median z = 0.03), massive (M* > 109M) galaxies (Yan et al. 2016a). Previous MaNGA AGN studies underscore the importance of spatially resolved measurements to provide unprecedented insight into the prevalence and properties of AGN and their host galaxies (e.g., Rembold et al. 2017; Sánchez et al. 2018; Wylezalek et al. 2018, 2020; Comerford et al. 2020). Moreover, multiple IFS studies (e.g., Sánchez et al. 2018; Comerford et al. 2020; Wylezalek et al. 2020; Venturi et al. 2021) have found evidence for AGN driving outflows and turbulence and suppressing SF over time.

In this study, we build and improve on these previous works by coupling IFS data from MaNGA DR16 (Bundy et al. 2015; Ahumada et al. 2020) with data from the second data release of LoTSS (Shimwell et al. 2022). By leveraging the unique capabilities of LOFAR, our sample contains fainter radio-AGN – as well as remnant emission from sources that have recently shut off their jet activity – than those that have been previously analyzed with MaNGA data. We will determine where in relation to the SFMS the AGN host galaxies and non-active galaxies lie, compare the distribution of SFRs in regions ionized by hot stars, and will investigate how the age of stellar populations in AGN galaxies and non-active galaxies change as a function of galactocentric radius. We describe the sample and data used to achieve our research goals in Sect. 2. After outlining the methods used to define the radio-detected AGN (RDAGN) and control sample in Sect. 3, we determine these galaxies’ relation to the star-forming main sequence (SFMS) in Sect. 4. In Sects. 5 and 6, we examine the spatially resolved properties of the stellar and nebular gas populations and probe their stellar light-weighted age gradients, respectively. Finally, we discuss our interpretation of these results and present a summary of our conclusions in Sects. 7 and 8. Throughout this work, we assume the cosmological parameters of H0 = 70 km s−1, ΩM = 0.3 and a Salpeter initial mass function (IMF; Salpeter 1955).

2. Sample and data

This work makes use of the second data release of LoTSS, which is an ongoing radio continuum (120−168 MHz) survey of the northern sky. The scientific objectives of LoTSS are to exploit the unique capabilities of LOFAR to shed new light on the formation and evolution of massive black holes, cluster galaxies, and the high-redshift Universe (see Shimwell et al. 2019, and references therein). LoTSS uses LOFAR’s high band antennas (HBA) and aims to reach a sensitivity < 0.1 mJy beam−1 at an angular resolution of ∼6″. LoTSS DR2 (Shimwell et al., in prep.) covers 27% of the northern sky and is composed of two discrete fields – denoted the 0 h and 13 h fields – covering 5700 deg2 in total (1480 deg2 in the 0 h field and 4240 deg2 in the 13 h field). The astrometric accuracy of the images is ∼0.2″. The flux calibration of LOFAR DR2 is uncertain to < 10%.

MaNGA uses the Baryon Oscillation Spectroscopic Survey (BOSS) spectrograph (Smee et al. 2013) on the 2.5-m telescope at Apache Point Observatory (APO; Gunn et al. 2006) to obtain high-resolution (R ∼ 2000) spectra over a large wavelength range (3600−10 300 Å). MaNGA uses integral field units (IFUs) that consist of tightly packed hexagonally bundled 2″ fibers, which have five different sizes (19, 37, 61, 91 and 127 fibers) corresponding to physical diameters of 12″, 17″, 22″, 27″, and 32″ (Drory et al. 2015). Raw fiber spectra have a calibration accuracy better than 5% (Yan et al. 2016a,b). We use MaNGA observations from the sixteenth data release of SDSS-IV, which includes observations of 4824 galaxies taken before August 2018 (Ahumada et al. 2020). Final data cubes and row stacked spectra (RSS) were produced using the MaNGA DRP (Law et al. 2016). Global emission line fluxes used in this study were obtained from the Portsmouth Group (Thomas et al. 2013). In addition, we use measured galaxy properties from several MaNGA Value Added Catalogs (VACs). We relied on the Pipe3D VAC (Sánchez et al. 2016a,b, 2018) for cumulative stellar mass (M*) measurements, SFR (obtained from stellar population modeling), and stellar, light-weighted age gradient (α; slope of the gradient of the luminosity-weighted log-age of the stellar population within a galactocentric distance of 0.5 to 2.0 Re). To determine the morphological classifications of galaxies, we use T-TYPE values from the Morphology Deep Learning DR15 Value Added Catalog (VAC; Domínguez Sánchez et al. 2018). We probe the environment in which these galaxies reside using measurements from the Galaxy Environment for MaNGA (GEMA) VAC (Argudo-Fernández et al. 2015, and in prep.). Finally, Dn4000 and M* values used in the Dn4000 versus L1.4 GHz/M* diagram (see Sect. 3.1) were taken from the MPA-JHU VAC1.

Wide-Field Infrared Survey Explorer (WISE; Wright et al. 2010) data used in this work come from unWISE forced photometry performed by Lang et al. (2016) on un-blurred co-added WISE images (Lang 2014) at over 400 million optical SDSS source positions. Therefore, the unWISE data are naturally connected to the SDSS parent sample from which MaNGA targets were drawn.

The sky position of the LoTSS and MaNGA catalogs were matched using Tool for OPerations on Catalogues And Tables (TOPCAT). We matched the two source catalogs using a 5″ matching radius in RA and Dec. We estimate the total fraction of spurious matches to be < 10% based on the average of 15 simulated MaNGA catalogs with randomized positions. Based on the cross-matching criteria, there are 1410 sources detected between the LoTSS and MaNGA survey. We use the SDSS spectroscopic ID (specObjID) to cross match the MaNGA-LoTSS catalog with the MPA-JHU catalog and with the global emission line flux catalog. For the Pipe3D, Morphology Deep Learning, and GEMA value added catalogs, we used the MANGAID identifier for cross matching.

3. Sample selection and properties

3.1. Selecting radio-detected AGN

The most reliable method for building a sample of pure radio-AGN is to select objects whose radio luminosity greatly surpasses that from their SF (Hardcastle et al. 2016, 2019; Calistro Rivera et al. 2017; Smolčić et al. 2017). In this work, we chose to separate our AGN host galaxies from SF galaxies using global optical emission-line properties, radio luminosities, and mid-infrared luminosities following the approach used by Sabater et al. (2019). We chose to take a multiwavelength approach in order to build a complete AGN sample with varying host galaxy properties. However, we highlight a subsample of classical radio-loud AGN (RLAGN), which is composed of RDAGN whose radio emission is higher than what is expected from their SFR alone based on our third diagnostic technique.

The first technique is the Dn4000 versus L1.4 GHz/M* diagram, which was developed by Best et al. (2005a), and it is shown in the upper upper left panel of Fig. 1. This method uses the strength of the 4000 Å break (Dn4000) in each galaxy’s spectrum as a function of the ratio of the radio luminosity to stellar mass. This diagnostic diagram was developed using 1.4 GHz data, so we converted the LoTSS radio luminosity from 150 MHz to 1.4 GHz by assuming the established spectral index value of α = 0.7 (Sv ∝ να; Condon et al. 2002; Smolčić et al. 2017). We use Dn4000 and M* values from the MPA-JHU VAC. Best et al. (2005a) demonstrated that because Dn4000 and L1.4 GHz/M* both depend on the SFR of galaxies, SF galaxies will populate a similar region in the Dn4000 versus L1.4 GHz/M* plane. Moreover, SF galaxies can be separated from AGN host galaxies because they will typically have a weaker Dn4000 values than AGN host galaxies of a comparable radio luminosity. The curved division line between SF/radio-quiet AGN and radio-AGN represents the 3 Gyr exponential SF track (Best et al. 2005a)2. At Dn4000 > 1.7 we replace the 3 Gyr exponential SF track with a horizontal line, as proposed by Sabater et al. (2019). The purpose of the addition is to avoid misclassifying AGN galaxies with large Dn4000 values as SF galaxies. The second diagnostic line is defined by Dn4000 = 1.45 − 0.55 × (L1.4 GHz/M* − 12.2) (Best & Heckman 2012). All sources that lie above the 3 Gyr exponential SF track and to the right of this second line are classified as radio-AGN. Conversely, galaxies that fall above the 3 Gyr exponential SF track and to the left of the second diagnostic line are intermediate, which means that both SF and AGN activity likely contribute to the radio emission. Finally, all sources that lie below the 3 Gyr exponential SF track are classified as SF/radio-quiet AGN.

thumbnail Fig. 1.

Location of the RDAGN host galaxies on the four diagnostic diagrams used to separate galaxies whose radio emission was from SF from those galaxies likely powered by AGN. Top row from left to right: Dn4000 versus L1.4 GHz/M* from Best et al. (2005b), the [NII]/Hα BPT diagram (Baldwin et al. 1981). Bottom row from left to right: LHα versus L150 MHz, WISE W1 − W2 versus W2 − W3 color-color diagram. The lines in each diagram represent division between SF/radio-quiet AGN, intermediate, and radio AGN. The final RDAGN sample, obtained following our criteria described in Sect. 2, is indicated by green “x’s”. The gray circles represent the full sample of MaNGA-LoTSS galaxies. Classical RLAGN are represented on the LHα versus L150 MHz diagram with dark gray diamonds.

The second technique that we use to separate AGN galaxies from SF galaxies is [NII] Baldwin, Phillips & Telervich (BPT; Baldwin et al. 1981) diagram, which is shown in the upper right panel of Fig. 1. For this diagnostic, we use global emission line fluxes that were obtained by the Portsmouth Group (Thomas et al. 2013). The diagram utilizes the ratio of narrow lines [OIII] λ5007 to Hβ and [NII] λ6583 to Hα to separate SF galaxies, from composite galaxies (a mix of ionizing sources likely contribute to the emission), from AGN galaxies. These line ratios can separate SF galaxies from AGN galaxies because the emission lines are affected by the hardness of the ionizing radiation field and the ionizing parameter. AGN galaxies will therefore have enhanced [NII]/Hα ratios because they have a harder ionizing radiation field than SF galaxies. The first diagnostic line on this diagram, represented by the solid, black line in Fig. 1, is the maximum starburst line from Kewley et al. (2001), which is defined by (log([OIII]/Hβ) < 0.61/(log([NII]/Hα)−0.47)+1.19). Unlike Sabater et al. (2019), we include the “composite” classification on the [NII] BPT (classification “Int” in Table 1). This second diagnostic line, represented by the dashed, black line in Fig. 1 separates pure SF galaxies from composite galaxies (Kauffmann et al. 2003) and is defined by log([OIII]/Hβ) < 0.61/(log([NII]/Hα)−0.05)+1.3. Using the Kauffman line results in a more complete AGN-host selection than the Kewley classification, but it is far from a pure AGN selection as, for example, hot low-mass evolved stars, and shock ionization can also produce composite line ratios (e.g., Sánchez 2020). For our [NII] BPT classification, we also require all galaxies to have EW(Hα) > 3 Å to avoid passive galaxies whose ionization is dominated by old stars (e.g., Stasińska et al. 2008).

Table 1.

Classification of galaxies detected in the MaNGA and LoTSS surveys.

A limitation of the global BPT diagrams is that average or integrated emission line ratios are affected by various galactic properties; galaxies are rarely only “star-forming”, “AGN”, or “quiescent”. Extinction may bias this selection, but because emission-line ratios are close together in wavelength ([NII] and Hα) and ([OIII] and Hβ), we expect similar extinction values for each line and therefore do not expect extinction to significantly bias our results. The optical narrow-line ratios of Type 1 AGN will have lower [NII]/Hα values than Type 2 AGN because the AGN are unobscured and the narrow emission lines are “blended” with broad emission lines (e.g., Zhang et al. 2008; Stern & Laor 2013). There is only one Type 1 AGN in our final RDAGN sample (plateifu 8549−12702), which we identified using the SDSS-DR7 Type 1 AGN catalog developed by Oh et al. (2015). Emission line ratios can also be enhanced by other non-AGN activity, such as Wolf-Rayet stars (e.g., Brinchmann et al. 2008), post-asymptotic giant branch stars (e.g., Binette et al. 1994; Yan & Blanton 2012; Belfiore et al. 2016), and shocks driven by galaxy mergers, jets, and stellar winds (e.g., Rich et al. 2011; Kewley et al. 2013). We explore these other mechanisms in Sect. 5.1.

Our third technique, which is shown in the bottom left panel of Fig. 1, is the relation between the luminosity of Hα (LHα) and the LoTSS radio luminosity (L150 MHz). Using the global emission line fluxes measured by the Portsmouth Group (Thomas et al. 2013), we measured the dust-corrected LHα using the average, RV-dependent extinction function from Cardelli et al. (1989) and assume RV to be 3.1 (Savage & Mathis 1979; Cardelli et al. 1989). Direct measurements of a galaxy’s SFR can be determined from LHα and, in the absence of an AGN, L150 MHz. Therefore, the locus of SF galaxies on the LHα versus L150 MHz diagram is separate from the locus of AGN host galaxies. The diagnostic lines to separate SF galaxies from AGN galaxies are adopted from Sabater et al. (2019): log10(LHα/L) = log10(L150 MHz/W Hz−1) − 16.9 and log10(LHα/L) = log10(L150 MHz/W Hz−1) − 16.1. Galaxies that lie below the bottom diagnostic line are classified as radio-AGN, intermediate if the galaxies lie between the two lines, and SF/radio-quiet AGN if the galaxies lie above the top diagnostic line. Our classical RLAGN subsample (52 galaxies in total) consists of the RDAGN host galaxies classified as a radio-AGN on this diagram (represented by the gray diamonds).

Our final method of separating AGN host galaxies from SF galaxies is the W1 − W2 versus W2 − W3 mid-infrared WISE colors diagnostic diagram, which is shown in the lower right panel of Fig. 1. We obtain the mid-infrared WISE colors from the unWISE forced photometry catalog of 400 million SDSS sources Lang et al. (2016). WISE colors are useful for detecting both obscured and unobscured AGN because hot dust surrounding AGN radiates strongly in mid-infrared emission. Following Sabater et al. (2019), we use the division from Herpich et al. (2016) where galaxies with W2 − W3 < 0.8 mag (AB) are radio-AGN.

We select our radio-detected AGN (RDAGN) sample by combining the classifications from these four selection techniques to determine an overall classification for each galaxy in the MaNGA-LoTSS catalog. In the diagnostic diagrams presented in Fig. 1, galaxies can be classified as radio-AGN, SF, intermediate, or unclassified (i.e., low signal-to-noise ratio (S/N) or no measurement), which results in 192 unique combinations of classifications. When choosing the final classification, we weighted each classification from the diagnostic diagrams equally. Galaxies classified as intermediate in the Dn4000 versus L1.4 GHz/M*, [NII] BPT diagrams, or LHα versus L150 MHz were chosen to “favor” AGN over SF in order to build the most complete sample of AGN possible. For example, if a galaxy’s classification is intermediate in the Dn4000 versus L150 MHz/M* and [NII] BPT diagrams, SF in the LHα versus L150 MHz, and AGN in the WISE color-color diagram, the overall classification of the galaxy is AGN. Any combination that consisted of half SF and half AGN is “unclassified”. Similarly, a galaxy is unclassified if it has a combination consisting of the following designations: one AGN, one SF, one intermediate, and one unclassified. In Table 1, we present the number of galaxies and their overall classification for different combinations of the four diagnostic methods of Fig. 1. We show only the combinations that classified at least five galaxies to save space. In each diagnostic diagram, galaxies whose emission is dominated by AGN activity or SF are classified as “AGN” and “SF”, respectively. “Int” indicates both AGN activity and SF contribute to galaxy’s emission, and “Unc” means that there were no measurements for those galaxies to be classified. In total, there are 380 AGN galaxies, 783 SF galaxies, and 247 unclassified galaxies. From the 380 AGN galaxies, we removed galaxies that had MANGA_DRP3QUAL flags indicating that the final cubes and RSS files did not meet quality standards. Additionally, we visually inspected the radio contours and removed galaxies that had no radio emission greater than 3× the rms noise (41 galaxies, see Table A.2). Our final RDAGN sample consists of 307 unique RDAGN-host galaxies. In Table 2, we provide the number of galaxies classified as AGN/SF/intermediate/unclassified in each diagnostic diagram for the entire MaNGA-LoTSS catalog and for the final RDAGN sample. The green, bracketed numbers on the second row for each diagnostic represent the number of galaxies with an overall classification as RDAGN. We provide radio-optical overlays of two of the RDAGN host galaxies in Fig. 2. The example in the top panel of Fig. 2 exhibits radio emission likely powered by both SF and AGN activity. Conversely, the early-type galaxy example (bottom panel of Fig. 2) has two-sided radio jets.

thumbnail Fig. 2.

Overlay of LOFAR 150 MHz radio contours on optical SDSS three color image of late-type RDAGN 8978−9101 and early-type RDAGN 8244−6103 (bottom). The magenta hexagon represents the MaNGA IFU footprint. Positive contours are defined by rms noise × [3, 6, 12, 24, 48, 96, 192, 384, 768, 1536, 3072]. Negative contours are shown by the gray, dashed line and represent the rms noise × [−3, −6, −12]. The LOFAR beam size is shown in the lower left corner of each image.

Table 2.

Classification of galaxies in the MaNGA-LoTSS cross catalog.

3.2. Control sample criteria

From the galaxies in MaNGA DR16 within the LoTSS DR2 footprint, we have selected a control sample of galaxies that closely match the properties of the RDAGN host galaxies except that their nuclei, based on the [NII] BPT and the equivalent width of Hα (W(Hα)) versus [NII]/Hα (WHAN; Cid Fernandes et al. 2010) diagrams, are inactive. The control sample was built as follows: First, we selected galaxies whose overall classification was not “AGN” and whose central ionizing source was not AGN. Therefore, we considered a galaxy as a potential control sample candidate if it was in the SF region of the BPT diagram or was classified as a Low-Ionization Emission-line Region (i.e., in the Low-Ionization Nuclear Emission-line Region (LINER; Heckman 1980) region with W(Hα) < 3 Å). From these inactive galaxies, we created a preliminary list of control sample candidates for each RDAGN host, selecting galaxies whose z and M* did not vary by more than 30% from the RDAGN host’s z and M*. Finally, we selected one control galaxy for each RDAGN host galaxy by visually inspecting the SDSS three color image of each control sample candidate and choosing the galaxy whose morphology and inclination were most similar to the SDSS three color image of the RDAGN host galaxy. Priority was given to morphological features within the MaNGA IFU hexagon footprint. We provide the plateifu identifer for the RDAGN galaxies and their assigned control galaxy in Table A.1.

In some cases, a particular MaNGA galaxy was the best control galaxy for multiple RDAGN samples. For example, although we identify 307 RDAGN galaxies, there are only 157 unique controls. Hence, we use the same best control galaxy more than once so that the total number of RDAGN and controls are equal. To ensure that using the same control galaxy multiple times and visually selecting control galaxies did not affect our results, we performed the analyses presented in Sects. 4 and 6 using the entire non-active galaxy sample (3231 galaxies in total), and found that our results did not change. We chose to use our selected control sample to better understand how RDAGN host galaxies compare to non-active galaxies with similar properties and to overcome potential biases in our SFR measurements (see Sect. 5). In Fig. 3, we show the distribution of properties of the RDAGN sample and of the control sample. By selection, the stellar mass and redshift distributions are the same. Moreover, we find no significant difference in the environment in which these RDAGN and control galaxies reside. Our RLAGN span a large range of stellar mass and radio power based on the distribution of L150 MHz provided in Fig. 4.

thumbnail Fig. 3.

Distribution of measured properties of the RDAGN (green), control sample (brown), the classical RLAGN (black), and RLAGN control galaxies (red). The median value for each sample is indicated by the dashed, vertical lines. Top row from left to right: local galaxy overdensity evaluated at the fifth nearest neighbor (δ5), M*. Bottom row from left to right: z, SFR as measured by Pipe3D.

thumbnail Fig. 4.

Distribution of the L150 MHz for the RDAGN (green) and RLAGN (gray) samples. The median value is indicated by the dashed, vertical line.

3.3. Existing MaNGA AGN catalogs

We briefly compare our sample of 307 unique RDAGN to previous studies that have identified AGN in the MaNGA survey, (Rembold et al. 2017; Wylezalek et al. 2018; Sánchez et al. 2018; Comerford et al. 2020). Rembold et al. (2017), Wylezalek et al. (2018), and Sánchez et al. (2018) select AGN using optical emission line ratios and cuts in the EW(Hα). In our sample of 307 RDAGN, 100 (33%) have EW(Hα) > 1.5 Å, and 41 (13%) have EW(Hα) > 3 Å. In the following, we report the percentage of our sample that overlaps with the other MaNGA AGN catalogs and the percentage of our high-EW(Hα) subsample that overlaps. We note, however, that our sample is distinct from these other MaNGA catalogs with RDAGN (e.g., Comerford et al. 2020) because with LOFAR, we are able to detect fainter radio emission from AGN than previously possible. In Fig. 4 we present the distribution of the radio luminosity for our RDAGN sample. The distribution peaks at ∼22.5 W Hz−1, which is lower than the average equivalent 1.4 GHz radio luminosities of radio-AGN in Best et al. (2005b). These lower luminosities are consistent with the results of Sabater et al. (2019), who found many RDAGN at the luminosity range 21 < log(L150 MHz [W Hz−1]) < 24 that are only detected with the deeper LoTSS data, and not found in NVSS/FIRST.

3.3.1. Rembold et al. (2017) catalog

Rembold et al. (2017) used SDSS integrated spectra to construct the [NII] BPT diagram (Baldwin et al. 1981) and WHAN diagram to identify “true” AGN in the galaxies observed in the fifth MaNGA Product Launch (MPL-5). Rembold et al. (2017) identify 62 “true” AGN out of the 2778 galaxies (2727 unique galaxies) observed in MPL-5. Of the 62 AGN presented in Rembold et al. (2017), 11 are in our radio-detected AGN catalog (∼4%; 27% with EW(Hα) > 3 Å).

3.3.2. Wylezalek et al. (2018) catalog

Wylezalek et al. (2018) used spatially resolved methods to identify AGN candidates in MPL-5 and determined the classification of each spaxel based on its location on the [NII] and [SII] BPT diagrams. Their sample consists of 308 “AGN candidates” that have a high spaxel fraction of AGN in both the [NII] and [SII] BPT diagrams and have cuts on the equivalent width and surface brightness of Hα. 28 AGN candidates from Wylezalek et al. (2018) are in our AGN catalog (∼9%; 32% with EW(Hα) > 3 Å).

3.3.3. Sánchez et al. (2018) catalog

Sánchez et al. (2018) chose AGN using Pipe3D’s integrated emission-line ratios within the central 3″ × 3″ of MPL-5 galaxies. They classified galaxies as AGN if their integrated emission-line ratios were above the [NII] BPT maximum starburst line from Kewley et al. (2001) and whose W(Hα) was greater than 1.5 Å. Sánchez et al. (2018) identified 98 AGN from the 2700 galaxies in MPL-5, 22 of which overlap with our RDAGN sample (∼7%; 19% with EW(Hα) > 1.5 Å).

3.3.4. Comerford et al. (2020) catalog

Comerford et al. (2020) selected AGN in galaxies observed in MPL-8 using broad Balmer emission lines from SDSS DR7 spectra, radio observations from NVSS (Condon et al. 1998) and FIRST (Becker et al. 1995), WISE mid-infrared colors, and ultra-hard X-ray observations from the Swift observatory’s Burst Alert Telescope (BAT). Comerford et al. (2020) used the SDSS DR7 AGN catalog from Best & Heckman (2012) to identify radio AGN in MaNGA MPL-8. Best & Heckman (2012) selected radio AGN using the Dn4000 versus L1.4 GHz/M*, [NII] BPT diagram, and LHα versus L150 MHz. Unlike the WISE diagnostic methods presented in this study, Comerford et al. (2020) follow Assef et al. (2018), adopting the 75% reliability criteria of W1 − W2 > 0.486e0.092(W2 − 13.07)2 and W2 > 13.07, or W1 − W2 > 0.486 and W2 ≤ 13.07. Of the 6261 galaxies observed in MPL-8, Comerford et al. (2020) identify 406 unique AGN. Comerford et al. (2020) focused their analyses on comparing 81 radio-quiet galaxies undetected in the radio with 143 radio-mode AGN. 52 AGN from Comerford et al. (2020) are in our AGN catalog (∼17% of our sample, or ∼13% of their total AGN and 38% of their RDAGN sample).

3.4. Comparison of global properties

We show SFR as a function of M* for our sample of RDAGN, as well as for those AGN in other MaNGA AGN catalogs outlined above in Fig. 5. Compared to optically selected AGN catalogs (Rembold et al. 2017; Sánchez et al. 2018; Wylezalek et al. 2018), radio-selected AGN (Comerford et al. 2020, and this study) have a strong tendency to occupy massive host galaxies. This is already a well observed trend (e.g., Gürkan et al. 2018; Sabater et al. 2019) and indicates that radio-AGN selection intrinsically selects for a different population of host galaxies (i.e., massive early-type galaxies – hereafter ETGs – for radio-AGN, and less massive late-type galaxies – hereafter LTGs – for optically selected AGN). Figure 5 illustrates that galaxies experiencing quenching (Green Valley galaxies) and AGN host galaxies share a similar location on the SFR* plane, which is also well observed (e.g., Sánchez et al. 2018; Lacerda et al. 2020).

thumbnail Fig. 5.

Relation between SFR and M* for existing MaNGA AGN catalogs. The gray-colored image represents the density of MaNGA galaxies in the plot. The RDAGN studied in this work are indicated in green. Dark yellow contours represent the density of AGN host galaxies for from the Comerford et al. (2020) AGN catalog, the dark blue contours represent the density of galaxies from the Sánchez et al. (2018) AGN catalog, the Wylezalek et al. (2018) AGN catalog are represented by orange contours, and the red contours exhibit the density of Rembold et al. (2017) AGN catalog. The SFR–M* space is divided into 50 × 50 bins, and the contours are drawn at 25, 50, 75, and 100% of the maximum number density. The dotted line represents the SFMS derived for SDSS-IV MaNGA galaxies derived by Cano-Díaz et al. (2019), the dark gray shading represents the errors on slope, and the light gray shading represents the standard deviation.

4. Relation to the star-forming main sequence

Star-forming galaxies fall on a tight correlation (∼0.2 intrinsic scatter; Speagle et al. 2014) between SFR and M*, known as the main sequence (Brinchmann et al. 2004; Daddi et al. 2007; Elbaz et al. 2007; Noeske et al. 2007). Previous studies have found that AGN add additional complexity to the regulatory processes of SF when compared to non-active galaxies of comparable mass. For example, Mullaney et al. (2015) and Shimizu et al. (2015) have found that the SFRs in X-ray-selected AGN host galaxies have more suppressed SFRs than non-active galaxies of similar mass, whereas Young et al. (2014) and Pitchford et al. (2016) have found that quasar host galaxies have higher SFRs than comparably massive, non-active galaxies. Moreover, the type of AGN – such as radio-quiet or radio-loud, LERGs or HERGs – that a galaxy hosts appears to influence the SFRs (e.g., Hardcastle et al. 2013; Heckman & Best 2014; Ellison et al. 2016; Magliocchetti et al. 2016, 2018; Roy et al. 2018; Comerford et al. 2020).

In this section, we show the relation of RDAGN host galaxies and control galaxies to the SFMS using the integrated stellar mass and SFR from the Pipe3D VAC. In Fig. 6, we show the correlation between SFR and M* for the full sample and for AGN subsamples based on the diagnostic diagrams used in Sect. 3.1 and based on morphology. We include the SFMS relation derived for SDSS-IV MaNGA galaxies from Cano-Díaz et al. (2019), which is defined by log(SFR/M yr−1) = −8.06±0.04+(0.78±0.01) × log(M/M) and has a standard deviation of 0.23. Additionally, we show the best-fit relation for radio-quiet and radio-mode MaNGA AGN derived by Comerford et al. (2020). These lines are defined as log(SFR/M yr−1) = α + β log(M/M), where α = −88.1 ± 8.1 and β = 7.7 ± 0.7 for radio-mode AGN and α = −21.5 ± 0.7 and β = 2.01 ± 0.06 for radio-quiet AGN.

thumbnail Fig. 6.

Relation between SFR and M* for RDAGN host galaxies (green), the full control sample (brown), RLAGN subsample (black x’s), RLAGN control galaxies (red x’s), and entire Pipe3D catalog (gray). For reference, the SFMS derived for SDSS-IV MaNGA galaxies is indicated by the dotted line (Cano-Díaz et al. 2019), the dark gray shading represents the errors on slope, and the light gray shading represents the standard deviation. The best-fit relations for radio-quiet AGN (dashed line) and radio-mode AGN (solid line) are from Comerford et al. (2020).

We present the M*–SFR function for our RDAGN and control samples in Fig. 6 and find that both the RDAGN sample and the control sample typically lie below the main sequence. To confirm the observed similarity between the two samples, we calculated the distance from the SFMS (Δ log10(SFR)) by subtracting the (logarithmic) SFR of the SFMS from the SFR of the sample (Δ log10(SFR) = log10(SFRsample) − log10(SFRSFMS)). We present these values in the left-most column of Table 3. All error values presented in Table 3 represent the standard deviation. Although the median Δ log10(SFR) of the RDAGN sample (−1.51 dex ± 3.20) lies closer to the SFMS than the median of the control sample (Δ log10(SFR) = −2.29 dex ± 3.03), the standard deviation errors on the median overlap. Therefore, the difference between the median Δ log10(SFR) at a fixed stellar mass for the RDAGN sample and the control sample is not statistically significant (see Table 3).

Table 3.

Median distance from the SFMS (left), and the average light-weighted age gradient (right).

RDAGN classified as “AGN” in the [NII] BPT diagram and those residing in LTGs tend to agree with the best-fit relation for radio-quiet AGN of Comerford et al. (2020) (median Δ log10(SFR) ∼ −0.359 and −0.465, respectively). This is expected as the BPT diagram tends to select radiatively efficient AGN, which are typically radio quiet. Furthermore, radio quiet AGN are often hosted by LTGs.

Conversely, we find that early-type RDAGN and RDAGN classified as “AGN” on the LHα versus L150 MHz typically agree with the best fit relation for radio-mode AGN (Comerford et al. 2020, median Δ log10(SFR) ∼ −1.74 and −2.35, respectively). This is again expected as the selection criterion LHα versus L150 MHz selects radio loud objects, and radio-loud AGN typically reside in ETGs.

We have found that the majority of the RDAGN lies below the SFMS, which is consistent with what is expected for the position of radio AGN relative to the main sequence (e.g., Gürkan et al. 2018). Unlike previous studies (e.g., Young et al. 2014; Mullaney et al. 2015; Shimizu et al. 2015; Leslie et al. 2016; Pitchford et al. 2016), which found that AGN host galaxies have different SFRs than non-active galaxies of similar mass, we find no statistically significant difference between the SFR of the RDAGN sample and the control sample selected by mass and morphology. This result compliments the findings presented in previous explorations of AGN feedback with MaNGA (e.g., Sánchez et al. 2018) and with the CALIFA survey (Calar Alto Legacy Integral Field Area; e.g., Lacerda et al. 2020), specifically that there is no significant difference between the properties of galaxies in the Green Valley hosting an AGN and those without an AGN. Our results indicate that the RDAGN, selected based on their current activity, are not responsible for any quenching that has taken place in their host galaxies. The mechanism or mechanisms responsible for suppressing SF must be related to the host galaxy’s properties (i.e., the fact that these are preferentially ETGs, with lower SF than star forming galaxies), which is in agreement with the burgeoning literature that the growth of galactic bulges, AGN activity, and the halting of SF appear to occur concomitantly (e.g., Lacerda et al. 2020, and references therein).

Toward understanding how the SFRs between each RDAGN and its assigned control galaxy directly compare, we look at the fractional difference of Pipe3D’s SFR measurement, which is the difference between the SFR of the RDAGN and its control divided by the SFR of the RDAGN ((SFRAGN–SFRControl)/SFRAGN). Dividing the difference by the SFR of the RDAGN helps scale the range of measured SFRs. When the fractional difference is positive, it means that the RDAGN host galaxy has a higher SFR than its assigned control galaxy. Conversely, when the difference is negative, the control galaxy has a higher SFR. We present the distribution of the fractional difference in Fig. 7.

thumbnail Fig. 7.

Distribution for the fractional difference in the SFR as measured by Pipe3D of the RDAGN and its control galaxy for the entire sample, LTGs, and ETGs (green shading). The same values are also shown for the classical RLAGN subsample and its controls (black shading). The vertical, dashed lines represent the median of the distribution. A one-to-one line at zero is represented by the red, dotted line.

In Fig. 7, the fractional difference of the SFR between the RDAGN sample and control sample is represented by the distribution shaded in green. We find that ∼44% of the RDAGN-control pairs exhibit a positive fractional difference. The percentage increases when late-type AGN host galaxies are considered; ∼51% of the RDAGN LTGs have higher SFRs than the corresponding controls. Finally, for the ETGs, only ∼43% of the RDAGNs have higher SFRs. For classical RLAGN and their corresponding control galaxies, we discover a higher percentage of positive fractional differences. The full RLAGN sample and the early-type RLAGN subsample express a similar percentage (∼54%) of positive fractional differences. We find that ∼80% of late-type RLAGN express a positive fractional difference.

Our fractional difference of SFR results are both agree and disagree with those of do Nascimento et al. (2019; a MaNGA AGN study that uses the Rembold et al. 2017 catalog). We note that the SFR measurements that do Nascimento et al. (2019) use in their fractional difference analysis were taken using similar methods outlined in Sect. 5.2. We chose to use Pipe3D’s values instead of the ones we calculate in Sect. 5.2 in order to have a SFR measurement for each RDAGN and control galaxy (discussed further at the beginning of Sect. 5). Nevertheless, both do Nascimento et al. (2019) and Pipe3D measure SFR using the extinction-corrected LHα equation from Kennicutt (1998; see Eq. (1)) facilitating comparison.

Whereas do Nascimento et al. (2019) find that 76% of ETG AGN have higher SFRs than their assigned control galaxies, only ∼43% of our RDAGN ETG host galaxies have higher total SFRs than their controls. Our values agree more when comparing the percentage of positive fractional differences in the early-type RLAGN sample (∼54%). We believe that the difference in our percentages and those reported by do Nascimento et al. (2019) is due the differences in our AGN samples.

Interestingly, we discover that ∼51% of our late-type RDAGN host galaxies have higher total SFRs than their controls, which is the same percentage reported by do Nascimento et al. (2019). This might be a sign either of positive feedback playing a role at earlier stages of a galaxy’s evolution or that LTGs simply have more availability of fuel. To distinguish between these two scenarios, we would need to prove that radio jet activity is physically reaching regions where SF is occurring.

5. Spatially resolved stellar and nebular gas properties

5.1. Ionization classification maps

A galaxy’s spectrum contains a wealth of information that is used to infer the physical processes taking place within the galaxy. Historically, the dominant excitation mechanism of a galaxy was inferred using single-aperture spectroscopy (e.g., Kauffmann et al. 2003; Kewley et al. 2006, and references therein). However, with IFS data, multiple ionizing sources can be determined and spatially mapped because a spectrum of light is measured at every spatial pixel observed with the IFU. Here, we optically classify the spaxels of the RDAGN and control galaxies to separate multiple ionizing sources and to gauge the frequency of these mechanisms at three different galactocentric radii. Knowing where the gas is being excited by these mechanisms is important for obtaining accurate SFR from the luminosity of Hα, which is the approach used in Sect. 5.2.

Emission-line fluxes across the surface for each galaxy were obtained using the Maps galaxy tool from SDSS Marvin (Cherinka et al. 2019). We determined the S/N of each 2D map using the get_snr() function. We also masked spaxels at six wavelengths (Hβλ4862, [OIII] λ5008, [NII] λ6585, Hαλ6564, [SII] λ6718, 6732) that contained negative flux values as well as those that had a S/N less than 3 using the get_masked() function. In addition to the emission-line fluxes, we obtained the equivalent width of Hα line (W(Hα)) to construct the W(Hα) versus [NII]/Hα (WHAN) diagram and measurements of the elliptical radius in order to determine the excitation mechanisms within the nuclear region of each galaxy.

To determine the excitation mechanism of each spaxel we combined information obtained from three diagrams: the [NII] BPT diagram, [SII] BPT diagram, and the WHAN diagram. Figure 8 shows an example of the classification methods combined to create the ionization classification map (bottom right) for RDAGN galaxy, 8978−9101. Using the emission-line fluxes at each spaxel, we measured the ratio of [NII] to Hα, [SII] to Hα, and [OIII] to Hβ. To determine whether the excitation mechanism was from starburst activity/young hot stars (HII) or “composite”, meaning that the gas is likely being excited by a blend of AGN activity and SF, we used the [NII] BPT diagram and its diagnostic lines. In Fig. 8, the [NII] BPT diagram is shown in the upper left panel. The points colored brown and beige represent the spaxels of RDAGN 8978−9101 whose excitation mechanism is SF and composite, respectively. The points colored gray, represent spaxels whose emission is likely powered by AGN activity.

thumbnail Fig. 8.

Example of the diagrams used to determine the gas excitation mechanisms across the surface of each galaxy. Each point on the diagrams represents a spaxel. Top row from left to right: [NII] BPT diagram and [SII] BPT diagram of a late-type RDAGN galaxy example. The solid line represents the maximum starburst line from Kewley et al. (2001). The dashed line on the [NII] BPT diagram represents the Kauffmann et al. (2003) line, which separates pure SF galaxies from composite galaxies. The dotted line on the [SII] BPT diagram separates Seyfert-like excitation from LINER-like excitation (Kewley et al. 2006). Bottom row from left to right: WHAN diagram and final ionization classification map of RDAGN galaxy 8978−9101. The [NII] BPT was used to separate HII (brown) and composite (beige) excitation, the [SII] diagram was used to distinguish Seyfert-like excitation (green) from LINER-like excitation (light green), and the WHAN diagram was used to differentiate LIER-like excitation (dark green) from LINER-like excitation. The red ellipses represent, from the inside outward, 0.2 R/Re, 0.6 R/Re, 1 R/Re.

The [SII] BPT diagram was used to distinguish emission line regions dominated by Seyfert-like and LINER-like excitation. We chose the [SII] BPT (upper right corner of Fig. 8) to separate these ionizing mechanisms because the low ionization potential of the [SII]/Hα reveals the low ionization emission lines of LINER spectra better than [NII]/Hα. Consequently, the Seyfert-LINER demarcation is more robust on the [SII] BPT than on the [NII] BPT (Kewley et al. 2006). The solid line on the [SII] BPT represents the demarcation between HII excitation from AGN excitation and it is defined by log([OIII]/Hβ) = 0.72/(log([SII]/Hα) − 0.32) + 1.30 (Kewley et al. 2001), where every spaxel above the line is dominated by AGN activity and SF below the line. Seyfert-like excitation is separated from LINER-like excitation by the line log([OIII]/Hβ) = 1.89 × log([SII]/Hα) + 0.76 (Kewley et al. 2006), which is shown by the dotted line on the [SII] BPT diagram in Fig. 8. All spaxels that fall above this line are classified as Seyfert and spaxels are classified as LINER if they are below the line. In the [SII] BPT, spaxels whose excitation mechanism is Seyfert-like are colored green and those spaxels with LINER-like excitation are colored light green.

There are multiple ionizing mechanisms that are connected to LINER-like emission in galaxies. Those mechanisms include shock ionization, a weak AGN, or photo-ionization from hot, evolved stars (e.g., post-asymptotic giant branch stars (pAGB); Binette et al. 1994; Stasińska et al. 2006; Sarzi et al. 2010; Cid Fernandes et al. 2011; Yan & Blanton 2012; Belfiore et al. 2016, and references therein). IFU surveys such as CALIFA and MaNGA have revealed that LINER-like emission-line ratios can be seen throughout galaxies (e.g., Singh et al. 2013; Belfiore et al. 2016), which is attributed the extended LINER-like emission from pAGB stars, which is known as Low-Ionization Emission line Region-like (LIER) excitation (see Gomes et al. 2016; Lacerda et al. 2018; Espinosa-Ponce et al. 2020, for a more detailed exploration of the pAGB origin of diffuse ionization in galaxies). To separate LIER-like excitation from LINER-like excitation, we constructed the WHAN diagram (lower left panel of Fig. 8). Several lines of demarcation appear on the WHAN diagram: the solid, vertical line at log10([NII]/Hα) = −0.4 separates SF (left) from AGN/non-SF activity (right), the dotted, horizontal line separates Seyfert-like excitation from LINER-like excitation, and the dashed, horizontal line separates LIER-like excitation from LINER-like excitation. Points colored dark green on the WHAN diagram in Fig. 8 represent the spaxels in RDAGN 8978−9101 with LIER-like excitation.

After the dominant ionizing mechanism was determined for each spaxel, we spatially mapped (see lower right panel in Fig. 8) the excitation mechanisms. The ionization classification maps for RDAGN 8978−9101 and RDAGN 8244−6103, are compared to those of their controls in Fig. 9. In the LTG example (RDAGN 8978−9101, top panel of Fig. 9), both the RDAGN host galaxy and the control are dominated by spaxels consistent with HII excitation (brown) and by composite emission (beige). In the central 5″, there is LIER (dark green) and LINER-like (light green) excitation, likely from pAGB stars and from a weak AGN, respectively. Conversely, the spaxels in the ETG example (RDAGN 8244−6103, see Fig. 9) are mostly classified as LIER (dark green), which likely correspond to their old stellar populations.

thumbnail Fig. 9.

Surface distribution of gas excitation mechanisms (middle panel) and ∑SFR (bottom panel) for late-type RDAGN 8978−9101 and its control galaxy 9881−12705 (top) and early-type RDAGN 8244−6103 and its control galaxy 8483-6104 (bottom). For the early-type RDAGN and its control, the ∑SFR maps are blank because those galaxies do not contain SF or composite spaxels. The optical SDSS image overlaid with the MaNGA IFU footprint (magenta hexagon) is shown on the top panel of the figure.

It is important to emphasize that although we have separated “HII” and “Composite” spaxels, in IFS data, gas with both HII and composite emission line ratios is most likely excited by SF. This is why, in Sect. 5.2, we calculate SFRs from the Balmer lines in both HII and composite spaxels. We should also keep in mind that shocks can reproduce line ratios that are typical for the HII, Composite, to the Seyfert and LINER regions of the diagnostic diagrams (e.g., Allen et al. 2008). Future work to identify shocks from mergers or outflows driven by toward or AGN activity in our sample will require a combination of emission line analysis with spatial and velocity information (e.g., López-Cobá et al. 2019, 2020).

In Fig. 10 we provide line graphs, which display the percentage of galaxies that have HII, Composite, LINER, Seyfert, and LIER at 0.2, 0.6, and 1.0 effective radius (Re) as the dominant excitation mechanism and provide the numerical values in Table 4. When the percentage equals 0, it indicates that the specific ionizing mechanism is not the dominant type at the given Re. Before elaborating further on these results, some samples appear to not have certain spaxel-types (i.e., 0%). To be clear, that does not mean that the specific excitation mechanism does not occur in that given galaxy. Instead, it means that the excitation type was not the dominant ionizing mechanism (i.e., by number of spaxels) within the radial bin of 0.2, 0.6, or 1.0 Re.

thumbnail Fig. 10.

Percentage of galaxies that have emission typical of SF, Composite, LINER, Seyfert, and LIER activity within 0.2, 0.6, and 1.0 effective radius (Re) from the nucleus of the galaxy. We use a broken y-axis for the top and bottom rows.

Table 4.

Dominant ionizing mechanism at 0.2, 0.6 and 1.0 Re.

We find that within 0.2 Re of each galaxy (i.e., the nuclear region), LIER-like excitation (represented by the dark green line in Fig. 10) is the most common ionizing mechanism in all samples. ∼85% of RDAGN galaxies and ∼93% of control galaxies exhibit LIER spaxels near the nuclear region. Approximately 69% and 86% of LTGs and ETGs galaxies are dominated by as LIER spaxels in the nuclear region, respectively. At larger effective radii, LIER spaxels become less common (varies between ∼83−85% for the entire RDAGN sample and ∼87−92% for the control sample), but still remain the dominant excitation mechanism. In the RDAGN sample and the subsample of early-type AGN host galaxies, the percentage of galaxies dominated by LIER spaxels peaks at 0.6 R/Re (∼87%). The presence of LIER-like emission throughout the entire galaxy, regardless of activity or morphology, is consistent with previous studies (e.g., Singh et al. 2013; Gomes et al. 2016; Belfiore et al. 2016; Wylezalek et al. 2018). Although pAGB stars are likely responsible for the photoionziation of gas in these spaxels, another possible interpretation of the LIER emission is that it is a relic ionization signature from an AGN that has recently stopped accreting material and has “turned off” (e.g., Papaderos et al. 2013; Gomes et al. 2016; Schirmer et al. 2016; Keel et al. 2017; Ichikawa et al. 2019).

Galaxies dominated by composite spaxels (represented by the beige line in Fig. 10) are the next most common type. In the RDAGN sample and the late-type RDAGN host galaxy subsample, the percentage of composite spaxel-dominated galaxies increases with increasing distance from the center of the galaxy. We find that the fraction of LTGs dominated by composite spaxels exhibits the largest increases in frequency with radius (∼31% from 0.2 to 1.0 Re). In the control sample, the percentage of galaxies dominated by composite-like excitation peaks at 0.6 R/Re (5.21%).

Compared to the entire control galaxies, we find that there are fewer RDAGN dominated by HII excitation (illustrated by the brown line in Fig. 10). This could indicate that these RDAGN galaxies are more quenched than the control galaxies.

We find that only RDAGN exhibit LINER spaxels (light green colored line in Fig. 10) and that the percentage of galaxies dominated by this excitation mechanism decreases with increasing R/Re (∼6.5% to ∼1.6% from 0.2 to 1.0 R/Re).

Similar trends are observed for RDAGN galaxies dominated by Seyfert spaxels (mid-green line in Fig. 10, although at smaller percentages than LINER spaxels (remains < 1%)). It is not surprising that we do not find any Seyfert or LINER dominated control galaxies because our selection excluded galaxies dominated by LINER and Seyfert excitation in the central 3″ of the SDSS fiber (see Sect. 3.2).

5.2. SFR surface density (∑SFR)

In order to obtain the SFR surface density ∑SFR), we calculated the SFR in each spaxel using the extinction-corrected LHα equation from Kennicutt (1998):

(1)

where L(Hα) is in units of erg s−1. We correct Hα emission for extinction (λ = 6563 Å) in magnitudes calculated by Cardelli et al. (1989):

(2)

where AV is derived by comparing the ratio of extinction for the observed fluxes of Hα (F(Hα)) and Hβ (F(Hβ)) to theoretical intrinsic value from case B recombination of Osterbrock & Ferland (2006):

(3)

From there, we calculated the extinction-corrected F(Hα) (F(Hα)0 in units 10−17 erg s−1 cm−2 spaxel−1)

(4)

and finally the extinction-corrected L(Hα)

(5)

where dcm is the luminosity distance in centimeters at the redshift of each galaxy, calculated using Astropy’s3 function cosmo.luminosity_distance(). To convert the angular size of each spaxel to physical size, we calculated the following scale-factor using the small angle approximation and the galaxy’s luminosity distance in kpc, dkpc. The area of the spaxel was then determined by multiplying the scaling relation by the angular size of the spaxel (0.5″ for MaNGA IFU) squared. Finally, after calculating the SFR in HII and composite spaxels, we divided each spaxel by its physical size to obtain the ∑SFR.

In the bottom panels of Fig. 9, we present the surface distribution of the ∑SFR. Unlike the spatial maps for the late-type RDAGN example and its assigned control galaxy, the maps for the early-type RDAGN host and control are blank. This is expected because neither the early-type RDAGN host galaxy nor its control contained HII or composite spaxels.

In Fig. 11, we present histograms for the median ∑SFR and total SFR (sum of SFR across every spaxel) for the RDAGN sample and control sample and for samples subdivided according to morphology. To the left of the black vertical line, these quantities were derived using all spaxels with a S/N > 0, and to the right of the line, only HII and composite spaxels with a S/N > 3. We show the results from these different scenarios to gauge whether or not our choice to measure the SFR spaxels with HII and composite spaxels affected our final result. We report a statistically significant difference between the median ∑SFR of the RDAGN and the control sample when all spaxels are considered, but no difference in the total SFRs. The RDAGN show higher median ∑SFR than the control sample (∑SFR = 10−2.56 compared to 10−4.35M yr−1 kpc−2), which could indicate either that there are regions with enhanced SFR within our RDAGN (signs of positive feedback) or that calculating SFRs from Hα in these cases is not reliable. We interpret these results as confirmation of our choice to measure the SFR in HII and composite spaxels. We chose not to show the RLAGN subsample on these panels because there are too few galaxies in the sample for any differences in the ∑SFR and total SFR between the AGN host galaxies and the control galaxies to be called statistically significant.

thumbnail Fig. 11.

Left of the black vertical line: distribution of the median ∑SFR (top panel) and for the total SFR (bottom panel) for RDAGN galaxies (green) and their controls (brown). We calculate these quantities using all spaxels with a S/N > 0 in all emission-lines used for classification. Right of the black vertical line from left to right: distribution of the median ∑SFR (top panel) and for the total SFR (bottom panel) for RDAGN galaxies (green) and their controls (brown) for the entire sample, LTGs, and ETGs. These quantities are derived from spaxels with a S/N > 3. When no spaxels in the galaxy meet the relevant criteria, the median or total SFR is set to “NaN”.

When considering HII and Composite spaxels with S/N > 3, we find that the average ∑SFR for RDAGN galaxies is −2.36 in logarithmic units of M yr−1 kpc−2, which is higher than the controls’ value of −2.41. We find that the total SFR for RDAGN ranges between ∼10−4.23M yr−1 and 101.09M yr−1. The total SFR of the controls range from ∼10−5.25 to 101.21M yr−1.

Toward assessing the probability that the RDAGN sample and the control sample were drawn from the same parent population, we performed a two-sample Anderson-Darling (A−D) test. When the A−D statistic is less than the critical value at the specified significance level, the null hypothesis – that the ∑SFR RDAGN sample and the control sample were drawn from the same distribution – cannot be rejected in favor of the alternative hypothesis, which is that the distributions of the two samples are different. Before performing the test, we set the reference significance level to 0.05. For the ∑SFR of entire RDAGN and control samples, which is presented in the top panel of Fig. 11, the A−D statistic is ∼0.08, which is less than the critical value at p = 0.05 (∼4.59). Therefore, the null hypothesis is not rejected and we concluded that the distributions of the ∑SFR for the RDAGN and the control galaxies are statistically similar. We found the same conclusions for the late-type RDAGN subsample and their control galaxies. Conversely, we found that the distribution of ∑SFR are statistically different for the early-type subsample of RDAGN and their controls (the null hypothesis can be rejected at the > 5% level). The early-type RDAGN galaxies tend to have higher ∑SFR values (median value of −2.57 in logarithmic units of M yr−1 kpc−2) than the ∑SFR of their assigned control galaxies, which averages at −3.06 in logarithmic units of M yr−1 kpc−2.

For the distribution of total SFRs, which are shown in the bottom panels of Fig. 11, only the late-type subsample of RDAGN and their control galaxies exhibit a statistically similar distribution based on the A−D test (p ∼ 0.12). While the distributions for the entire RDAGN, the early-type RDAGN subsample, and their assigned control galaxies most likely reveal physical differences, our analyses would benefit from more accurate SFR measurements, which would require decomposing each spectrum into SF, AGN, and shock components.

Our results are both consistent and at variance with the findings of do Nascimento et al. (2019), who use the MaNGA AGN and control sample selected by Rembold et al. (2017). By interpreting the p-values of A−D tests, both this study and do Nascimento et al. (2019) find that the ∑SFR are statistically similar for the AGN and controls. We report, however, a wider range of total SFRs; do Nascimento et al. (2019) find both the AGN and control sample to range in SFR from 10−3 to 101M yr−1.

Neither our study nor that of do Nascimento et al. (2019) accounted for disk inclination when calculating ∑SFRs, which could cause SFRs to be underestimated by a factor of ∼0.2−0.4 dex due to not completely correcting dust attenuation (e.g., Morselli et al. 2016). However, given that the inclination of the RDAGN and of their assigned control sample were visually matched, our comparison does not suffer from a large inclination bias. Furthermore, both this study and do Nascimento et al. (2019) only consider HII and composite spaxels when calculating ∑SFR. The composite spaxels could be contaminated by shocks. Following Davies et al. (2017), future work could include calculating a more accurate SFR by decomposing the nuclear spectra into SF, AGN, and shock components.

6. Stellar age gradient

To find evidence for suppressed SF in RDAGN host galaxies and potentially in the control galaxies, we examined how the age of the stellar populations changes as a function of galactocentric distance. For this analysis, we use the gradient of the light-weighted log-age of the stellar population within a galactocentric distance of 0.5−2.0 Re (hereafter α) from the Pipe3D VAC. When α is negative, the stellar populations become younger with distance from the center of the galaxy. Conversely, a positive age gradient indicates the stellar populations become older with increasing distance away from the galaxy’s center. We compare α in stellar mass bins of 0.2 dex because previous studies have demonstrated that a correlation exists between a galaxy’s M* and stellar age gradient (e.g., González Delgado et al. 2014; Zheng et al. 2017; Goddard et al. 2017) and for comparison purposes with the Pipe3D stellar, light-weighted age gradients binned in M* for radio-quiet and radio-mode AGN host galaxies from Comerford et al. (2020). In Fig. 12, we present the Pipe3D stellar, light-weighted age gradient in M* bins of 0.2 dex for the entire RDAGN and control samples as well as for RDAGN subsamples and their controls.

thumbnail Fig. 12.

Stellar, light-weighted age gradient (α) in M* bins of 0.2 dex for RDAGN host galaxies (green), the full control sample (brown), RLAGN galaxies (black), RLAGN control galaxies (red), and the entire Pipe3D VAC (gray). A horizontal line is plotted at α = 0 for reference. The x’s represent the median value in each bin and the error bars represent the standard deviation of the sample. We provide the median α values for radio-quiet (α ∼ 0) and radio-mode (α ∼ −0.15) AGN from Comerford et al. (2020; C+20) in the solid and dotted blue lines, respectively.

We find that the average stellar age gradients for the RDAGN sample and control sample as measured by Pipe3D are negative. We provide all the average values in the right-most column of Table 3. Their average values (α ∼ −0.101 for RDAGN and α ∼ −0.097 for the controls) are nearly identical indicating that the stellar populations within the RDAGN sample and the control sample become younger with distance from the center. These results may point to the inside-out suppression SF in these galaxies. Moreover, the consistency between the age gradient values between the AGN and controls indicates that there is no clear correlation between the current AGN activity and their host galaxies’ SF history.

The average α value for late-type AGN host galaxies (α ∼ −0.294) is significantly steeper than early-type AGN host galaxies (α ∼ −0.070), which agrees with the results from previous MaNGA investigations (Goddard et al. 2017; Parikh et al. 2021). The negative radial stellar age gradients in LTGs are consistent with inside-out growth of the disk (González Delgado et al. 2015). On the other hand, strong AGN feedback can stop SF in the galaxy’s center, and this inside-out quenching may also result in a negative age gradient (Comerford et al. 2020).

The number of fibers in an IFU bundle affects the accuracy of the estimate of α (Ibarra-Medel et al. 2019). So, IFUs with a larger fiber bundle will have a more accurate measurement of α. Comerford et al. (2020) have investigated the magnitude of this effect on their sample of 406 MaNGA-AGN by looking at the stellar age gradients of galaxies that were observed with the largest MaNGA fiber bundle size (127 fibers, commensurate with a diameter of 325). Comerford et al. (2020) found that α decreased by ∼0.05, but that did not change their result that radio-mode AGN host galaxies have more negative stellar age gradients when compared to radio-quiet AGN host galaxies. We find that the age gradients of RDAGN and control galaxies observed with the largest MaNGA fiber bundle decrease by ∼0.10. These RDAGN and control galaxies have an identical average age gradient of α ∼ −0.20 ± 0.30. By checking the magnitude of the effect of IFU fiber bundle size, we have reconfirmed the striking similarity between the RDAGN sample and control sample.

Residual AGN contamination can bias the stellar population fits (e.g., Cardoso et al. 2017). However, quantifying and rectifying this bias is beyond the scope of this work.

7. Discussion

In establishing whether or not AGN are responsible for quenching massive galaxies, we compare the SF properties of radio-detected AGN with non-active galaxies of similar stellar mass, redshift, visual morphology, and inclination. AGN remain a key ingredient in cosmological models of galaxy evolution to reproduce the observed stellar mass and luminosity function and to prevent the formation of over-massive galaxies. However, the observational perspective has yielded mixed results, and therefore, the consensus on the effect of an AGN on their host galaxies’ SFR has yet to be agreed upon. One of the most interesting results of our paper is that both radio-detected AGN and control galaxies typically lie below the main sequence, have broad SFR distributions, and exhibit negative stellar, light-weighted age gradients.

One possible explanation for the statistical similarity between the quenching patterns of our AGN-host galaxies and the control sample of non-active galaxies is the visibility timescales of AGN feedback. Much remains unclear about the timescales of the duty cycle of AGN, the duration of visible AGN episodes, the spatial scale at which these interactions occur and AGN variability (e.g., Alexander & Hickox 2012; Hickox et al. 2014; Sartori et al. 2018). Studies (e.g., Sánchez et al. 2018; Lacerda et al. 2020, and references therein) suggest that the timescales required to quench SF and the triggering of AGN activity could be completely different. Moreover, the fact that RLAGN appear to preferentially reside in ETGs, and that they are considerably more quenched than just RDAGN might suggest that radio activity is supported for a longer period, and quenching has occurred earlier in their host galaxies’ lifetime. Additionally, how long it takes for AGN to have an observed effect on SF is still an unanswered question. Hence, the timescale of the suppression of SF from an AGN episode – or multiple AGN episodes – might be longer than the timescale of observable AGN activity (Harrison 2017). Furthermore, the flickering on and off of AGN may also play a role in maintaining galaxy quiescence, which could explain why we see little differences in the AGN and control galaxies.

An abundance of physical mechanisms have been evoked to explain galaxy quiescence. In our study, we do not expect that environmental effects play a significant role in quenching our RDAGN and control galaxies given their average stellar masses and redshifts (∼1011M* and z ∼ 0, respectively; Peng et al. 2010). Furthermore, results from SDSS-IV MaNGA-DR15 and the GASP survey suggest that for environmental quenching, quenching is expected to occur from the outside-in (e.g., Bluck et al. 2020a; Vulcani et al. 2020). Recent studies (e.g., Bluck et al. 2018, 2020a,b) have demonstrated that there is indeed a connection between quenching and the presence of central SMBH, which is consistent with expected models of quenching via AGN feedback. Our study reveals a similarity in the star-forming properties of radio-detected AGN host galaxies and non-active control galaxies, which may indicate that AGN feedback is likely not the only origin of inside-out quenching. Additionally, our results suggest that the effect of mass quenching from negative AGN feedback is indistinguishable from the effect of other mass quenching mechanisms such as virial shock heating in massive dark matter haloes, which prevents the accretion of cold gas onto galaxies (e.g., Birnboim & Dekel 2003; Kereš et al. 2005, 2009; Dekel & Birnboim 2006, 2008; Birnboim et al. 2007). Alternatively, SF may be quenched in galaxies without the expulsion and/or heating of gas. Instead, SF can be halted as a galaxy transitions to being dominated by a stellar spheroid, which stabilizes the gas disk and prevents it from fragmenting into star-forming clumps (i.e., morphological quenching; Martig et al. 2009).

Finding direct evidence for AGN feedback quenching SF in local radio galaxies would naturally be difficult because they predominately reside in massive galaxies where SF has already been quenched. Additionally, the bulk of the energetic impact of a radio AGN is injected into the hot phase of their host galaxies’ halo, where it only has a long-term effect on the SF history of the host galaxy.

Throughout this work, we compare our sample of RDAGN host galaxies to existing MaNGA AGN Catalogs (see Sect. 3.3). Several of these MaNGA AGN catalogs (Rembold et al. 2017; Wylezalek et al. 2018; Sánchez et al. 2018) select AGN with optical emission line ratios and cuts in the EW(Hα), and Comerford et al. (2020) take a multiwavelength approach. The main differences we see among these studies and our own is that the selection method determines the number of sources that are considered AGN host galaxies, and the intrinsic global properties they select for.

Our results are both consistent and in disagreement with those presented in do Nascimento et al. (2019), who compare the optically selected AGN sample from Rembold et al. (2017) with a control sample of non-active galaxies with similar global properties as each AGN host galaxy. Similar to our results, do Nascimento et al. (2019) find no differences in SFR between optically selected, late-type AGN host galaxies and their controls. However, do Nascimento et al. (2019) report that early-type AGN host galaxies typically exhibit higher SFRs and larger ionized gas masses than their assigned control galaxies. They attribute this result to AGN and SF activity being fueled by the same reservoir of gas. Hence, do Nascimento et al. (2019) suggest that it is unlikely that negative AGN feedback is occurring in the Rembold et al. (2017) MaNGA AGN sample. While our results do not indicate that AGN selected based on their current activity are responsible for suppressing their host galaxies’ SF, they support the maintenance mode role that RDAGN are expected to play in the local Universe. We believe the difference in our findings for early-type AGN host galaxies is a result of sample selection methods.

We find that RDAGN, and classical RLAGN preferentially reside in ETGs, lie below the SFMS, and exhibit younger stellar populations with increasing distance from the host galaxies’ centers. Our work compliments the findings presented in Comerford et al. (2020), who compare the SF properties of radio-mode and radio-quiet AGN host galaxies. They find that radio-quiet and radio-mode AGN preferentially reside in LTGs and ETGs, respectively, both populations fall below the SFMS, although radio-mode AGN host galaxies lie further below the SFMS, and that radio-mode AGN exhibit older stellar populations and have more negative stellar age gradients than the radio-quiet sample. From these results, Comerford et al. (2020) suggest that radio-mode AGN played a role in quenching SF in their host galaxies’ pasts. Despite showing similar, albeit less obvious signs of past quenching, Comerford et al. (2020) do not provide a suggestion for the role radio-quiet AGN played in their host galaxies’ past. Our study is different in that we compared these radio-selected AGN to non-active galaxies that match the stellar mass, redshift, visual morphology, and inclination of their RDAGN counterpart. Furthermore, our comparison to non-active galaxies, and our finding that there is no statistically significant difference between these two populations, is a more robust evaluation of the role RDAGN played in the SF quenching in the past.

Sánchez et al. (2018), and other IFS investigation of the role of AGN feedback in quenching SF (e.g., Lacerda et al. 2020, and references therein) have found that we cannot yet establish a causal connection between the presence of an AGN and the quenching of their host galaxies’ SF. Instead, AGN activity and SF processes present an apparent coevolution, which could be affected by the growth of galactic bulges. Similarly, the results presented here do not establish a casual connection between AGN activity and the halting of SF. Ours points to a scenario where there could be multiple quenching mechanisms occurring simultaneously, and where AGN play a role maintaining quiescence.

8. Conclusions

In this work, we have investigated whether negative AGN feedback is responsible for quenching massive galaxies. We combined the LoTSS DR2 and MaNGA DR16 data to form a sample of 1250 galaxies from which 307 RDAGN host galaxies were identified by combining selection techniques using global emission-line properties, radio luminosities, and WISE mid-infrared luminosities. Our investigation is the largest, IFS multiwavelength study of AGN that has a control sample of non-active galaxies. Furthermore, thanks to the low frequencies and sensitivities reached by LOFAR, this study detects fainter radio emission from lower-powered jets – as well as remnant emission from sources that have recently shut-off their jet activity – than what was previously possible for radio surveys (e.g., NVSS, FIRST, etc.). Therefore, this work has resulted in significant progress toward understanding the effect of AGN feedback in a representative sample of low-luminosity AGN host galaxies.

We spatially mapped the dominant excitation mechanism of emission-line gas in RDAGN and control galaxies by combining the [NII] BPT, [SII] BPT, and the WHAN diagram. In regions ionized by toward, we calculated the SFR surface density (∑SFR) using the dust corrected luminosity of Hα. We also used cumulative and gradient properties taken from the Pipe3D value added catalog to determine the relation of these galaxies to the SFMS and how the age of their stellar populations changes as a function of galactocentric radius. Our main results are summarized below:

  1. RDAGN and control galaxies display a statistically similar distribution for the median toward rate surface density (∑SFR). The fractional difference in ∑SFR of the RDAGN and its assigned control galaxy reveal that RDAGN host galaxies typically have higher SFRs.

  2. RDAGN host galaxies lie below the SFMS, which suggests that RDAGN occupy galaxies with suppressed toward. RDAGN host galaxies have an average Δ log10(SFR) ∼ −1.5, while control galaxies fall further below the SFMS at an average Δ log10(SFR) ∼ −2.3.

  3. The average SFR for RDAGN, as measured by Pipe3D, is higher (∼10−1M yr−1) than the average SFR for the control sample of non-active galaxies (∼10−1.8M yr−1). Taken together with the preceding points, we find no direct evidence that SF is quenched in RDAGN host galaxies. In fact, when compared to the control galaxies, our results may indicate that the effect of negative AGN feedback has not yet fully halted SF or that positive AGN feedback might be occurring in some late-type systems.

  4. The average stellar, light-weighted age gradient for the RDAGN and control galaxies are identical at α ∼ −0.10. The negative age gradient implies that the stellar populations in the centers of galaxies are older than the populations on the outskirts. These results may point to inside-out quenching of SF in both samples. We find that early-type RDAGN host galaxies have a relatively flat average age gradient (α ∼ −0.08) whereas LTGs exhibit a steeper gradient (α ∼ −0.26).

This work demonstrates that the physical mechanisms behind the origin of the quenching of SF are yet to be fully understood. To further our understanding of how these RDAGN and their host galaxies are co-evolving, a detailed kinematic analysis could help determine the prevalence and velocity of outflows. Furthermore, the RDAGN sample in this work includes galaxies that have both AGN activity and some SF activity. Additional work is needed to decompose the radio emission into that coming from SF and that from jets. This will involve using LOFAR’s international baselines to obtain high (subarcsecond) resolution images, which will allow us to identify genuine AGN emission and its effect on its host galaxy. We have already begun additional investigations on the molecular gas content of a subsample of these RDAGN host galaxies (Leslie et al., in prep.). We intend to use these observations to determine whether there is a deficiency of molecular gas in the central regions of RDAGN galaxies, which would quench central SF. Additionally, we could establish whether radio-mode AGN suppress SF either through their jet’s mechanical energy heating the surrounding ISM and preventing molecular gas from radiatively cooling or if AGN-driven outflows expel the molecular gas out of the galaxy by correlating radio source size with stellar age and determining the SF efficiency.


1

VAC created by the Max Planck for Astrophysics (MPA) and Johns Hopkins University (JHU) groups.

2

The tracks used in this study were provided by Philip Best.

3

Publicly available software package for the Python programming language: https://www.astropy.org/

Acknowledgments

We thank the referee for valuable comments on the paper. C.R.M. thanks Dr. Dominika Wylezalek for providing the IDs for the Wylezalek et al. (2018) sample. I.P. acknowledges support from INAF under the SKA/CTA PRIN “FORECaST” and the PRIN MAIN STREAM “SAuROS” projects. M.J.H. acknowledges support from STFC [ST/V000624/1]. K.M. has been supported by the National Science Centre (UMO-2018/30/E/ST9/00082). M.B. acknowledges support from INAF under the SKA/CTA PRIN “FORECaST” and the PRIN MAIN STREAM “SAuROS” projects and from the Ministero degli Affari Esteri e della Cooperazione Internazionale – Direzione Generale per la Promozione del Sistema Paese Progetto di Grande Rilevanza ZA18GR02. LOFAR (van Haarlem et al. 2013) is the Low Frequency Array designed and constructed by ASTRON. It has observing, data processing, and data storage facilities in several countries, which are owned by various parties (each with their own funding sources), and that are collectively operated by the ILT foundation under a joint scientific policy. The ILT resources have benefited from the following recent major funding sources: CNRS-INSU, Observatoire de Paris and Université d’Orléans, France; BMBF, MIWF-NRW, MPG, Germany; Science Foundation Ireland (SFI), Department of Business, Enterprise and Innovation (DBEI), Ireland; NWO, The Netherlands; The Science and Technology Facilities Council, UK; Ministry of Science and Higher Education, Poland; The Istituto Nazionale di Astrofisica (INAF), Italy. This research made use of the Dutch national e-infrastructure with support of the SURF Cooperative (e-infra 180169) and the LOFAR e-infra group. The Jülich LOFAR Long Term Archive and the German LOFAR network are both coordinated and operated by the Jülich Supercomputing Centre (JSC), and computing resources on the supercomputer JUWELS at JSC were provided by the Gauss Centre for Supercomputing e.V. (grant CHTB00) through the John von Neumann Institute for Computing (NIC). This research made use of the University of Hertfordshire high-performance computing facility and the LOFAR-UK computing facility located at the University of Hertfordshire and supported by STFC [ST/P000096/1], and of the Italian LOFAR IT computing infrastructure supported and operated by INAF, and by the Physics Department of Turin university (under an agreement with Consorzio Interuniversitario per la Fisica Spaziale) at the C3S Supercomputing Centre, Italy. Funding for the Sloan Digital Sky Survey IV has been provided by the Alfred P. Sloan Foundation, the US Department of Energy Office of Science, and the Participating Institutions. SDSS-IV acknowledges support and resources from the Center for High Performance Computing at the University of Utah. The SDSS website is www.sdss.org. SDSS-IV is managed by the Astrophysical Research Consortium for the Participating Institutions of the SDSS Collaboration including the Brazilian Participation Group, the Carnegie Institution for Science, Carnegie Mellon University, Center for Astrophysics | Harvard & Smithsonian, the Chilean Participation Group, the French Participation Group, Instituto de Astrofísica de Canarias, The Johns Hopkins University, Kavli Institute for the Physics and Mathematics of the Universe (IPMU)/University of Tokyo, the Korean Participation Group, Lawrence Berkeley National Laboratory, Leibniz Institut für Astrophysik Potsdam (AIP), Max-Planck-Institut für Astronomie (MPIA Heidelberg), Max-Planck-Institut für Astrophysik (MPA Garching), Max-Planck-Institut für Extraterrestrische Physik (MPE), National Astronomical Observatories of China, New Mexico State University, New York University, University of Notre Dame, Observatário Nacional/MCTI, The Ohio State University, Pennsylvania State University, Shanghai Astronomical Observatory, United Kingdom Participation Group, Universidad Nacional Autónoma de México, University of Arizona, University of Colorado Boulder, University of Oxford, University of Portsmouth, University of Utah, University of Virginia, University of Washington, University of Wisconsin, Vanderbilt University, and Yale University. This project makes use of the MaNGA-Pipe3D dataproducts. We thank the IA-UNAM MaNGA team for creating this catalogue, and the ConaCyt-180125 project for supporting them.

References

  1. Abazajian, K. N., Adelman-McCarthy, J. K., Agüeros, M. A., et al. 2009, ApJS, 182, 543 [Google Scholar]
  2. Ahumada, R., Prieto, C. A., Almeida, A., et al. 2020, ApJS, 249, 3 [Google Scholar]
  3. Aird, J., Coil, A. L., Georgakakis, A., et al. 2015, MNRAS, 451, 1892 [Google Scholar]
  4. Alexander, D. M., & Hickox, R. C. 2012, New Astron. Rev., 56, 93 [Google Scholar]
  5. Allen, M. G., Groves, B. A., Dopita, M. A., Sutherland, R. S., & Kewley, L. J. 2008, ApJS, 178, 20 [Google Scholar]
  6. Argudo-Fernández, M., Verley, S., Bergond, G., et al. 2015, A&A, 578, A110 [Google Scholar]
  7. Assef, R. J., Stern, D., Noirot, G., et al. 2018, ApJS, 234, 23 [Google Scholar]
  8. Baldwin, J. A., Phillips, M. M., & Terlevich, R. 1981, PASP, 93, 5 [Google Scholar]
  9. Becker, R. H., White, R. L., & Helfand, D. J. 1995, ApJ, 450, 559 [Google Scholar]
  10. Belfiore, F., Maiolino, R., Maraston, C., et al. 2016, MNRAS, 461, 3111 [Google Scholar]
  11. Best, P. N., & Heckman, T. M. 2012, MNRAS, 421, 1569 [Google Scholar]
  12. Best, P. N., Kauffmann, G., Heckman, T. M., et al. 2005a, MNRAS, 362, 25 [Google Scholar]
  13. Best, P. N., Kauffmann, G., Heckman, T. M., & Ivezić, Ž. 2005b, MNRAS, 362, 9 [Google Scholar]
  14. Binette, L., Magris, C. G., Stasińska, G., & Bruzual, A. G. 1994, A&A, 292, 13 [NASA ADS] [Google Scholar]
  15. Binney, J., & Tabor, G. 1995, MNRAS, 276, 663 [NASA ADS] [Google Scholar]
  16. Birnboim, Y., & Dekel, A. 2003, MNRAS, 345, 349 [Google Scholar]
  17. Birnboim, Y., Dekel, A., & Neistein, E. 2007, MNRAS, 380, 339 [NASA ADS] [CrossRef] [Google Scholar]
  18. Bluck, A., Teimoorinia, H., Ellison, S. L., & Mendel, T. 2018, Am. Astron. Soc. Meet. Abstr., 231, 227.01 [NASA ADS] [Google Scholar]
  19. Bluck, A. F. L., Maiolino, R., Piotrowska, J. M., et al. 2020a, MNRAS, 499, 230 [Google Scholar]
  20. Bluck, A. F. L., Maiolino, R., Sánchez, S. F., et al. 2020b, MNRAS, 492, 96 [Google Scholar]
  21. Bower, R. G., Benson, A. J., Malbon, R., et al. 2006, MNRAS, 370, 645 [Google Scholar]
  22. Boyle, B. J., & Terlevich, R. J. 1998, MNRAS, 293, L49 [Google Scholar]
  23. Brinchmann, J., Charlot, S., White, S. D. M., et al. 2004, MNRAS, 351, 1151 [Google Scholar]
  24. Brinchmann, J., Kunth, D., & Durret, F. 2008, A&A, 485, 657 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  25. Bundy, K., Bershady, M. A., Law, D. R., et al. 2015, ApJ, 798, 7 [Google Scholar]
  26. Calistro Rivera, G., Williams, W. L., Hardcastle, M. J., et al. 2017, MNRAS, 469, 3468 [Google Scholar]
  27. Cano-Díaz, M., Ávila-Reese, V., Sánchez, S. F., et al. 2019, MNRAS, 488, 3929 [Google Scholar]
  28. Cardelli, J. A., Clayton, G. C., & Mathis, J. S. 1989, ApJ, 345, 245 [Google Scholar]
  29. Cardoso, L. S. M., Gomes, J. M., & Papaderos, P. 2017, A&A, 604, A99 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  30. Cattaneo, A., Faber, S. M., Binney, J., et al. 2009, Nature, 460, 213 [Google Scholar]
  31. Chen, C.-T. J., Hickox, R. C., Alberts, S., et al. 2013, ApJ, 773, 3 [Google Scholar]
  32. Cherinka, B., Andrews, B. H., Sánchez-Gallego, J., et al. 2019, AJ, 158, 74 [CrossRef] [Google Scholar]
  33. Cid Fernandes, R., Stasińska, G., Schlickmann, M. S., et al. 2010, MNRAS, 403, 1036 [Google Scholar]
  34. Cid Fernandes, R., Stasińska, G., Mateus, A., & Vale Asari, N. 2011, MNRAS, 413, 1687 [Google Scholar]
  35. Ciotti, L., & Ostriker, J. P. 2001, ApJ, 551, 131 [NASA ADS] [CrossRef] [Google Scholar]
  36. Ciotti, L., & Ostriker, J. P. 2007, ApJ, 665, 1038 [NASA ADS] [CrossRef] [Google Scholar]
  37. Ciotti, L., Ostriker, J. P., & Proga, D. 2010, ApJ, 717, 708 [Google Scholar]
  38. Colless, M., Dalton, G., Maddox, S., et al. 2001, MNRAS, 328, 1039 [Google Scholar]
  39. Comerford, J. M., Negus, J., Müller-Sánchez, F., et al. 2020, ApJ, 901, 159 [NASA ADS] [CrossRef] [Google Scholar]
  40. Condon, J. J., Cotton, W. D., Greisen, E. W., et al. 1998, AJ, 115, 1693 [Google Scholar]
  41. Condon, J. J., Cotton, W. D., & Broderick, J. J. 2002, AJ, 124, 675 [Google Scholar]
  42. Cresci, G., Marconi, A., Zibetti, S., et al. 2015, A&A, 582, A63 [Google Scholar]
  43. Crockett, R. M., Shabala, S. S., Kaviraj, S., et al. 2012, MNRAS, 421, 1603 [NASA ADS] [CrossRef] [Google Scholar]
  44. Croft, S., van Breugel, W., de Vries, W., et al. 2006, ApJ, 647, 1040 [Google Scholar]
  45. Croton, D. J., Springel, V., White, S. D. M., et al. 2006, MNRAS, 365, 11 [Google Scholar]
  46. Daddi, E., Dickinson, M., Morrison, G., et al. 2007, ApJ, 670, 156 [Google Scholar]
  47. Davies, R. L., Groves, B., Kewley, L. J., et al. 2017, MNRAS, 470, 4974 [Google Scholar]
  48. Dekel, A., & Birnboim, Y. 2006, MNRAS, 368, 2 [Google Scholar]
  49. Dekel, A., & Birnboim, Y. 2008, MNRAS, 383, 119 [Google Scholar]
  50. Domínguez Sánchez, H., Huertas-Company, M., Bernardi, M., Tuccillo, D., & Fischer, J. L. 2018, MNRAS, 476, 3661 [Google Scholar]
  51. do Nascimento, J. C., Storchi-Bergmann, T., Mallmann, N. D., et al. 2019, MNRAS, 486, 5075 [NASA ADS] [CrossRef] [Google Scholar]
  52. Drory, N., MacDonald, N., Bershady, M. A., et al. 2015, AJ, 149, 77 [Google Scholar]
  53. Elbaz, D., Daddi, E., Le Borgne, D., et al. 2007, A&A, 468, 33 [Google Scholar]
  54. Elbaz, D., Jahnke, K., Pantin, E., Le Borgne, D., & Letawe, G. 2009, A&A, 507, 1359 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  55. Ellison, S. L., Teimoorinia, H., Rosario, D. J., & Mendel, J. T. 2016, MNRAS, 458, L34 [Google Scholar]
  56. Espinosa-Ponce, C., Sánchez, S. F., Morisset, C., et al. 2020, MNRAS, 494, 1622 [Google Scholar]
  57. Fabian, A. C. 2012, ARA&A, 50, 455 [Google Scholar]
  58. Feain, I. J., Papadopoulos, P. P., Ekers, R. D., & Middelberg, E. 2007, ApJ, 662, 872 [NASA ADS] [CrossRef] [Google Scholar]
  59. Gilli, R., Mignoli, M., Peca, A., et al. 2019, A&A, 632, A26 [Google Scholar]
  60. Goddard, D., Thomas, D., Maraston, C., et al. 2017, MNRAS, 466, 4731 [NASA ADS] [Google Scholar]
  61. Gomes, J. M., Papaderos, P., Kehrig, C., et al. 2016, A&A, 588, A68 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  62. González Delgado, R. M., Pérez, E., Cid Fernandes, R., et al. 2014, A&A, 562, A47 [CrossRef] [EDP Sciences] [Google Scholar]
  63. González Delgado, R. M., García-Benito, R., Pérez, E., et al. 2015, A&A, 581, A103 [Google Scholar]
  64. Gunn, J. E., Siegmund, W. A., Mannery, E. J., et al. 2006, AJ, 131, 2332 [Google Scholar]
  65. Gürkan, G., Hardcastle, M. J., Jarvis, M. J., et al. 2015, MNRAS, 452, 3776 [CrossRef] [Google Scholar]
  66. Gürkan, G., Hardcastle, M. J., Smith, D. J. B., et al. 2018, MNRAS, 475, 3010 [Google Scholar]
  67. Haehnelt, M. G., & Kauffmann, G. 2000, MNRAS, 318, L35 [NASA ADS] [CrossRef] [Google Scholar]
  68. Hales, C. A. 2013, ArXiv e-prints [arXiv:1312.4602] [Google Scholar]
  69. Hardcastle, M. J., Ching, J. H. Y., Virdee, J. S., et al. 2013, MNRAS, 429, 2407 [NASA ADS] [CrossRef] [Google Scholar]
  70. Hardcastle, M. J., Gürkan, G., van Weeren, R. J., et al. 2016, MNRAS, 462, 1910 [Google Scholar]
  71. Hardcastle, M. J., Williams, W. L., Best, P. N., et al. 2019, A&A, 622, A12 [Google Scholar]
  72. Häring, N., & Rix, H.-W. 2004, ApJ, 604, L89 [Google Scholar]
  73. Harrison, C. M. 2017, Nat. Astron., 1, 0165 [Google Scholar]
  74. Heckman, T. M. 1980, A&A, 87, 152 [Google Scholar]
  75. Heckman, T. M., & Best, P. N. 2014, ARA&A, 52, 589 [Google Scholar]
  76. Herpich, F., Mateus, A., Stasińska, G., Cid Fernandes, R., & Vale Asari, N. 2016, MNRAS, 462, 1826 [NASA ADS] [CrossRef] [Google Scholar]
  77. Hickox, R. C., Mullaney, J. R., Alexander, D. M., et al. 2014, ApJ, 782, 9 [Google Scholar]
  78. Ibarra-Medel, H. J., Avila-Reese, V., Sánchez, S. F., González-Samaniego, A., & Rodríguez-Puebla, A. 2019, MNRAS, 483, 4525 [NASA ADS] [CrossRef] [Google Scholar]
  79. Ichikawa, K., Ueda, J., Bae, H.-J., et al. 2019, ApJ, 870, 65 [NASA ADS] [CrossRef] [Google Scholar]
  80. Intema, H. T., Jagannathan, P., Mooley, K. P., & Frail, D. A. 2017, A&A, 598, A78 [Google Scholar]
  81. Ishibashi, W., & Fabian, A. C. 2012, MNRAS, 427, 2998 [NASA ADS] [CrossRef] [Google Scholar]
  82. Jackson, T. M., Rosario, D. J., Alexander, D. M., et al. 2020, MNRAS, 498, 2323 [NASA ADS] [CrossRef] [Google Scholar]
  83. Kauffmann, G., Heckman, T. M., Tremonti, C., et al. 2003, MNRAS, 346, 1055 [Google Scholar]
  84. Keel, W. C., Lintott, C. J., Maksym, W. P., et al. 2017, ApJ, 835, 256 [NASA ADS] [CrossRef] [Google Scholar]
  85. Kennicutt, R. C., Jr. 1998, ARA&A, 36, 189 [Google Scholar]
  86. Kereš, D., Katz, N., Weinberg, D. H., & Davé, R. 2005, MNRAS, 363, 2 [Google Scholar]
  87. Kereš, D., Katz, N., Davé, R., Fardal, M., & Weinberg, D. H. 2009, MNRAS, 396, 2332 [CrossRef] [Google Scholar]
  88. Kewley, L. J., Dopita, M. A., Sutherland, R. S., Heisler, C. A., & Trevena, J. 2001, ApJ, 556, 121 [Google Scholar]
  89. Kewley, L. J., Groves, B., Kauffmann, G., & Heckman, T. 2006, MNRAS, 372, 961 [Google Scholar]
  90. Kewley, L. J., Dopita, M. A., Leitherer, C., et al. 2013, ApJ, 774, 100 [Google Scholar]
  91. Klamer, I. J., Ekers, R. D., Sadler, E. M., & Hunstead, R. W. 2004, ApJ, 612, L97 [NASA ADS] [CrossRef] [Google Scholar]
  92. Lacerda, E. A. D., Cid Fernandes, R., Couto, G. S., et al. 2018, MNRAS, 474, 3727 [Google Scholar]
  93. Lacerda, E. A. D., Sánchez, S. F., Cid Fernandes, R., et al. 2020, MNRAS, 492, 3073 [Google Scholar]
  94. Lang, D. 2014, AJ, 147, 108 [Google Scholar]
  95. Lang, D., Hogg, D. W., & Schlegel, D. J. 2016, AJ, 151, 36 [Google Scholar]
  96. Law, D. R., Cherinka, B., Yan, R., et al. 2016, AJ, 152, 83 [Google Scholar]
  97. Leslie, S. K., Kewley, L. J., Sanders, D. B., & Lee, N. 2016, MNRAS, 455, L82 [Google Scholar]
  98. López-Cobá, C., Sánchez, S. F., Bland-Hawthorn, J., et al. 2019, MNRAS, 482, 4032 [Google Scholar]
  99. López-Cobá, C., Sánchez, S. F., Anderson, J. P., et al. 2020, AJ, 159, 167 [Google Scholar]
  100. Magliocchetti, M., Lutz, D., Santini, P., et al. 2016, MNRAS, 456, 431 [NASA ADS] [CrossRef] [Google Scholar]
  101. Magliocchetti, M., Popesso, P., Brusa, M., & Salvato, M. 2018, MNRAS, 473, 2493 [NASA ADS] [CrossRef] [Google Scholar]
  102. Martig, M., Bournaud, F., Teyssier, R., & Dekel, A. 2009, ApJ, 707, 250 [Google Scholar]
  103. McNamara, B. R., & Nulsen, P. E. J. 2007, ARA&A, 45, 117 [Google Scholar]
  104. Morselli, L., Renzini, A., Popesso, P., & Erfanianfar, G. 2016, MNRAS, 462, 2355 [NASA ADS] [CrossRef] [Google Scholar]
  105. Mullaney, J. R., Daddi, E., Béthermin, M., et al. 2012, ApJ, 753, L30 [Google Scholar]
  106. Mullaney, J. R., Alexander, D. M., Aird, J., et al. 2015, MNRAS, 453, L83 [Google Scholar]
  107. Nesvadba, N. P. H., Lehnert, M. D., De Breuck, C., Gilbert, A. M., & van Breugel, W. 2008, A&A, 491, 407 [Google Scholar]
  108. Nesvadba, N. P. H., Boulanger, F., Salomé, P., et al. 2010, A&A, 521, A65 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  109. Nesvadba, N. P. H., Bicknell, G. V., Mukherjee, D., & Wagner, A. Y. 2020, A&A, 639, L13 [EDP Sciences] [Google Scholar]
  110. Netzer, H. 2009, MNRAS, 399, 1907 [CrossRef] [Google Scholar]
  111. Noeske, K. G., Faber, S. M., Weiner, B. J., et al. 2007, ApJ, 660, L47 [Google Scholar]
  112. Oh, K., Yi, S. K., Schawinski, K., et al. 2015, ApJS, 219, 1 [Google Scholar]
  113. Osterbrock, D. E., & Ferland, G. J. 2006, Astrophysics of Gaseous Nebulae and Active Galactic Nuclei (Sausalito: University Science Books) [Google Scholar]
  114. Papaderos, P., Gomes, J. M., Vílchez, J. M., et al. 2013, A&A, 555, L1 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  115. Parikh, T., Thomas, D., Maraston, C., et al. 2021, MNRAS, 502, 5508 [NASA ADS] [CrossRef] [Google Scholar]
  116. Peng, Y.-J., Lilly, S. J., Kovač, K., et al. 2010, ApJ, 721, 193 [Google Scholar]
  117. Pillepich, A., Nelson, D., Springel, V., et al. 2019, MNRAS, 490, 3196 [Google Scholar]
  118. Pitchford, L. K., Hatziminaoglou, E., Feltre, A., et al. 2016, MNRAS, 462, 4067 [NASA ADS] [CrossRef] [Google Scholar]
  119. Rembold, S. B., Shimoia, J. S., Storchi-Bergmann, T., et al. 2017, MNRAS, 472, 4382 [Google Scholar]
  120. Rich, J. A., Kewley, L. J., & Dopita, M. A. 2011, ApJ, 734, 87 [Google Scholar]
  121. Rodríguez Zaurín, J., Holt, J., Tadhunter, C. N., & González Delgado, R. M. 2007, MNRAS, 375, 1133 [CrossRef] [Google Scholar]
  122. Rosario, D. J., Santini, P., Lutz, D., et al. 2012, A&A, 545, A45 [Google Scholar]
  123. Roy, N., Bundy, K., Cheung, E., et al. 2018, ApJ, 869, 117 [NASA ADS] [CrossRef] [Google Scholar]
  124. Sabater, J., Best, P. N., Hardcastle, M. J., et al. 2019, A&A, 622, A17 [Google Scholar]
  125. Sadler, E. M., Jackson, C. A., Cannon, R. D., et al. 2002, MNRAS, 329, 227 [Google Scholar]
  126. Salpeter, E. E. 1955, ApJ, 121, 161 [Google Scholar]
  127. Sánchez, S. F. 2020, ARA&A, 58, 99 [Google Scholar]
  128. Sánchez, S. F., Pérez, E., Sánchez-Blázquez, P., et al. 2016a, Rev. Mex. Astron. Astrofis., 52, 21 [NASA ADS] [Google Scholar]
  129. Sánchez, S. F., Pérez, E., Sánchez-Blázquez, P., et al. 2016b, Rev. Mex. Astron. Astrofis., 52, 171 [Google Scholar]
  130. Sánchez, S. F., Avila-Reese, V., Hernandez-Toledo, H., et al. 2018, Rev. Mex. Astron. Astrofis., 54, 217 [Google Scholar]
  131. Sartori, L. F., Schawinski, K., Trakhtenbrot, B., et al. 2018, MNRAS, 476, L34 [NASA ADS] [CrossRef] [Google Scholar]
  132. Sarzi, M., Shields, J. C., Schawinski, K., et al. 2010, MNRAS, 402, 2187 [Google Scholar]
  133. Savage, B. D., & Mathis, J. S. 1979, ARA&A, 17, 73 [Google Scholar]
  134. Schaye, J., Crain, R. A., Bower, R. G., et al. 2015, MNRAS, 446, 521 [Google Scholar]
  135. Schirmer, M., Malhotra, S., Levenson, N. A., et al. 2016, MNRAS, 463, 1554 [NASA ADS] [CrossRef] [Google Scholar]
  136. Shimizu, T. T., Mushotzky, R. F., Meléndez, M., Koss, M., & Rosario, D. J. 2015, MNRAS, 452, 1841 [Google Scholar]
  137. Shimwell, T. W., Röttgering, H. J. A., Best, P. N., et al. 2017, A&A, 598, A104 [Google Scholar]
  138. Shimwell, T. W., Tasse, C., Hardcastle, M. J., et al. 2019, A&A, 622, A1 [Google Scholar]
  139. Shimwell, T. W., Hardcastle, M. J., Tasse, C., et al. 2022, A&A, 659, A1 [Google Scholar]
  140. Shin, J., Woo, J.-H., Chung, A., et al. 2019, ApJ, 881, 147 [Google Scholar]
  141. Silverman, J. D., Kovač, K., Knobel, C., et al. 2009, ApJ, 695, 171 [NASA ADS] [CrossRef] [Google Scholar]
  142. Singh, R., van de Ven, G., Jahnke, K., et al. 2013, A&A, 558, A43 [Google Scholar]
  143. Smee, S. A., Gunn, J. E., Uomoto, A., et al. 2013, AJ, 146, 32 [Google Scholar]
  144. Smolčić, V., Novak, M., Bondi, M., et al. 2017, A&A, 602, A1 [Google Scholar]
  145. Speagle, J. S., Steinhardt, C. L., Capak, P. L., & Silverman, J. D. 2014, ApJS, 214, 15 [Google Scholar]
  146. Stanley, F., Harrison, C. M., Alexander, D. M., et al. 2015, MNRAS, 453, 591 [Google Scholar]
  147. Stasińska, G., Cid Fernandes, R., Mateus, A., Sodré, L., & Asari, N. V. 2006, MNRAS, 371, 972 [Google Scholar]
  148. Stasińska, G., Vale Asari, N., Cid Fernandes, R., et al. 2008, MNRAS, 391, L29 [NASA ADS] [Google Scholar]
  149. Stern, J., & Laor, A. 2013, MNRAS, 431, 836 [Google Scholar]
  150. Stoughton, C., Lupton, R. H., Bernardi, M., et al. 2002, AJ, 123, 485 [Google Scholar]
  151. Thomas, D., Steele, O., Maraston, C., et al. 2013, MNRAS, 431, 1383 [Google Scholar]
  152. van Haarlem, M. P., Wise, M. W., Gunst, A. W., et al. 2013, A&A, 556, A2 [Google Scholar]
  153. Venturi, G., Cresci, G., Marconi, A., et al. 2021, A&A, 648, A17 [Google Scholar]
  154. Vulcani, B., Fritz, J., Poggianti, B. M., et al. 2020, ApJ, 892, 146 [Google Scholar]
  155. Wright, E. L., Eisenhardt, P. R. M., Mainzer, A. K., et al. 2010, AJ, 140, 1868 [Google Scholar]
  156. Wylezalek, D., Zakamska, N. L., Greene, J. E., et al. 2018, MNRAS, 474, 1499 [Google Scholar]
  157. Wylezalek, D., Flores, A. M., Zakamska, N. L., Greene, J. E., & Riffel, R. A. 2020, MNRAS, 492, 4680 [Google Scholar]
  158. Yan, R., & Blanton, M. R. 2012, ApJ, 747, 61 [Google Scholar]
  159. Yan, R., Tremonti, C., Bershady, M. A., et al. 2016a, AJ, 151, 8 [Google Scholar]
  160. Yan, R., Bundy, K., Law, D. R., et al. 2016b, AJ, 152, 197 [Google Scholar]
  161. York, D. G., Adelman, J., Anderson, J. E., Jr., et al. 2000, AJ, 120, 1579 [Google Scholar]
  162. Young, J. E., Eracleous, M., Shemmer, O., et al. 2014, MNRAS, 438, 217 [NASA ADS] [CrossRef] [Google Scholar]
  163. Yuan, F., & Narayan, R. 2014, ARA&A, 52, 529 [Google Scholar]
  164. Zhang, K., Wang, T., Dong, X., & Lu, H. 2008, ApJ, 685, L109 [NASA ADS] [CrossRef] [Google Scholar]
  165. Zheng, Z., Wang, H., Ge, J., et al. 2017, MNRAS, 465, 4572 [Google Scholar]

Appendix A: Extra Tables

Table A.1.

MaNGA plateifu for RDAGN host galaxies and their assigned control galaxy.

Table A.2.

MaNGA galaxies excluded from the final RDAGN sample.

All Tables

Table 1.

Classification of galaxies detected in the MaNGA and LoTSS surveys.

Table 2.

Classification of galaxies in the MaNGA-LoTSS cross catalog.

Table 3.

Median distance from the SFMS (left), and the average light-weighted age gradient (right).

Table 4.

Dominant ionizing mechanism at 0.2, 0.6 and 1.0 Re.

Table A.1.

MaNGA plateifu for RDAGN host galaxies and their assigned control galaxy.

Table A.2.

MaNGA galaxies excluded from the final RDAGN sample.

All Figures

thumbnail Fig. 1.

Location of the RDAGN host galaxies on the four diagnostic diagrams used to separate galaxies whose radio emission was from SF from those galaxies likely powered by AGN. Top row from left to right: Dn4000 versus L1.4 GHz/M* from Best et al. (2005b), the [NII]/Hα BPT diagram (Baldwin et al. 1981). Bottom row from left to right: LHα versus L150 MHz, WISE W1 − W2 versus W2 − W3 color-color diagram. The lines in each diagram represent division between SF/radio-quiet AGN, intermediate, and radio AGN. The final RDAGN sample, obtained following our criteria described in Sect. 2, is indicated by green “x’s”. The gray circles represent the full sample of MaNGA-LoTSS galaxies. Classical RLAGN are represented on the LHα versus L150 MHz diagram with dark gray diamonds.

In the text
thumbnail Fig. 2.

Overlay of LOFAR 150 MHz radio contours on optical SDSS three color image of late-type RDAGN 8978−9101 and early-type RDAGN 8244−6103 (bottom). The magenta hexagon represents the MaNGA IFU footprint. Positive contours are defined by rms noise × [3, 6, 12, 24, 48, 96, 192, 384, 768, 1536, 3072]. Negative contours are shown by the gray, dashed line and represent the rms noise × [−3, −6, −12]. The LOFAR beam size is shown in the lower left corner of each image.

In the text
thumbnail Fig. 3.

Distribution of measured properties of the RDAGN (green), control sample (brown), the classical RLAGN (black), and RLAGN control galaxies (red). The median value for each sample is indicated by the dashed, vertical lines. Top row from left to right: local galaxy overdensity evaluated at the fifth nearest neighbor (δ5), M*. Bottom row from left to right: z, SFR as measured by Pipe3D.

In the text
thumbnail Fig. 4.

Distribution of the L150 MHz for the RDAGN (green) and RLAGN (gray) samples. The median value is indicated by the dashed, vertical line.

In the text
thumbnail Fig. 5.

Relation between SFR and M* for existing MaNGA AGN catalogs. The gray-colored image represents the density of MaNGA galaxies in the plot. The RDAGN studied in this work are indicated in green. Dark yellow contours represent the density of AGN host galaxies for from the Comerford et al. (2020) AGN catalog, the dark blue contours represent the density of galaxies from the Sánchez et al. (2018) AGN catalog, the Wylezalek et al. (2018) AGN catalog are represented by orange contours, and the red contours exhibit the density of Rembold et al. (2017) AGN catalog. The SFR–M* space is divided into 50 × 50 bins, and the contours are drawn at 25, 50, 75, and 100% of the maximum number density. The dotted line represents the SFMS derived for SDSS-IV MaNGA galaxies derived by Cano-Díaz et al. (2019), the dark gray shading represents the errors on slope, and the light gray shading represents the standard deviation.

In the text
thumbnail Fig. 6.

Relation between SFR and M* for RDAGN host galaxies (green), the full control sample (brown), RLAGN subsample (black x’s), RLAGN control galaxies (red x’s), and entire Pipe3D catalog (gray). For reference, the SFMS derived for SDSS-IV MaNGA galaxies is indicated by the dotted line (Cano-Díaz et al. 2019), the dark gray shading represents the errors on slope, and the light gray shading represents the standard deviation. The best-fit relations for radio-quiet AGN (dashed line) and radio-mode AGN (solid line) are from Comerford et al. (2020).

In the text
thumbnail Fig. 7.

Distribution for the fractional difference in the SFR as measured by Pipe3D of the RDAGN and its control galaxy for the entire sample, LTGs, and ETGs (green shading). The same values are also shown for the classical RLAGN subsample and its controls (black shading). The vertical, dashed lines represent the median of the distribution. A one-to-one line at zero is represented by the red, dotted line.

In the text
thumbnail Fig. 8.

Example of the diagrams used to determine the gas excitation mechanisms across the surface of each galaxy. Each point on the diagrams represents a spaxel. Top row from left to right: [NII] BPT diagram and [SII] BPT diagram of a late-type RDAGN galaxy example. The solid line represents the maximum starburst line from Kewley et al. (2001). The dashed line on the [NII] BPT diagram represents the Kauffmann et al. (2003) line, which separates pure SF galaxies from composite galaxies. The dotted line on the [SII] BPT diagram separates Seyfert-like excitation from LINER-like excitation (Kewley et al. 2006). Bottom row from left to right: WHAN diagram and final ionization classification map of RDAGN galaxy 8978−9101. The [NII] BPT was used to separate HII (brown) and composite (beige) excitation, the [SII] diagram was used to distinguish Seyfert-like excitation (green) from LINER-like excitation (light green), and the WHAN diagram was used to differentiate LIER-like excitation (dark green) from LINER-like excitation. The red ellipses represent, from the inside outward, 0.2 R/Re, 0.6 R/Re, 1 R/Re.

In the text
thumbnail Fig. 9.

Surface distribution of gas excitation mechanisms (middle panel) and ∑SFR (bottom panel) for late-type RDAGN 8978−9101 and its control galaxy 9881−12705 (top) and early-type RDAGN 8244−6103 and its control galaxy 8483-6104 (bottom). For the early-type RDAGN and its control, the ∑SFR maps are blank because those galaxies do not contain SF or composite spaxels. The optical SDSS image overlaid with the MaNGA IFU footprint (magenta hexagon) is shown on the top panel of the figure.

In the text
thumbnail Fig. 10.

Percentage of galaxies that have emission typical of SF, Composite, LINER, Seyfert, and LIER activity within 0.2, 0.6, and 1.0 effective radius (Re) from the nucleus of the galaxy. We use a broken y-axis for the top and bottom rows.

In the text
thumbnail Fig. 11.

Left of the black vertical line: distribution of the median ∑SFR (top panel) and for the total SFR (bottom panel) for RDAGN galaxies (green) and their controls (brown). We calculate these quantities using all spaxels with a S/N > 0 in all emission-lines used for classification. Right of the black vertical line from left to right: distribution of the median ∑SFR (top panel) and for the total SFR (bottom panel) for RDAGN galaxies (green) and their controls (brown) for the entire sample, LTGs, and ETGs. These quantities are derived from spaxels with a S/N > 3. When no spaxels in the galaxy meet the relevant criteria, the median or total SFR is set to “NaN”.

In the text
thumbnail Fig. 12.

Stellar, light-weighted age gradient (α) in M* bins of 0.2 dex for RDAGN host galaxies (green), the full control sample (brown), RLAGN galaxies (black), RLAGN control galaxies (red), and the entire Pipe3D VAC (gray). A horizontal line is plotted at α = 0 for reference. The x’s represent the median value in each bin and the error bars represent the standard deviation of the sample. We provide the median α values for radio-quiet (α ∼ 0) and radio-mode (α ∼ −0.15) AGN from Comerford et al. (2020; C+20) in the solid and dotted blue lines, respectively.

In the text

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.