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Issue
A&A
Volume 635, March 2020
Article Number A177
Number of page(s) 7
Section Extragalactic astronomy
DOI https://doi.org/10.1051/0004-6361/201937193
Published online 31 March 2020

© ESO 2020

1. Introduction

A key element in our understanding of bulge evolution in late-type galaxies (LTGs) involves the termination of their dominant mass assembly phase through star formation quenching (SFQ). This process is thought to result from different, perhaps non mutually exclusive mechanisms, such as morphological quenching (i.e., stabilization of the disk against gas fragmentation once its center becomes dominated by a massive stellar spheroid, Martig et al. 2009; Genzel 2014), the inhibition of inflow of cold gas from the cosmic web due to virial shocks in the galactic halo (Dekel et al. 2009) or the stripping away of the gaseous reservoir of galaxies in clusters (see also, Peng et al. 2015; Larson et al. 1980), or negative feedback by an active galactic nucleus (AGN; e.g., Silk 1997; Di Matteo et al. 2005; Croton et al. 2006; Cattaneo et al. 2009).

Various pieces of evidence suggest that SFQ is initiated once galaxy bulges have grown to a mass of ℳ⋆, B ∼3 × 1010 M (Strateva et al. 2001) and a stellar surface density of Σ⋆, B ∼ 109M kpc−2 (Kauffmann 2003; González Delgado et al. 2016). Indeed, whereas in the local Universe star formation (SF) is almost omnipresent in low-ℳ⋆, B, low-Σ⋆, B bulges, it steeply vanishes above a characteristic mass-density threshold log ≃ (10,9), as documented through multi-band photometry (e.g., Peng et al. 2010; Omand et al. 2014), single-aperture spectroscopy (e.g., Kauffmann 2003; Brinchmann et al. 2004), and, more recently, spatially resolved integral field spectroscopy (IFS; e.g., Fang et al. 2013; Catalán-Torrecilla et al. 2017; Ellison 2018; Woo & Ellison 2019). For instance, Zibetti et al. (2017) report from an analysis of stellar indices for 394 galaxies from the CALIFA IFS survey (Sánchez et al. 2012) a bivariate distribution of galaxy stellar populations on the Σ versus luminosity-weighted age ⟨t/yr⟩ plane, with old (log ⟨t ≃ 10) quiescent E and S0 galaxies that populate a high-density peak (log(Σ) ≥8.3), whereas younger (log ⟨t ≤ 9.5) LTGs are confined to log(Σ) ≤ 8. Breda & Papaderos (2018, hereafter BP18) find from spatially resolved modeling of CALIFA IFS data for a representative sample of local LTGs that the contribution ⟨δμ9G⟩ of stellar populations younger than 9 Gyr to the bulge r-band mean surface brightness ⟨μ⟩ is tightly anticorrelated with ℳ⋆, B and Σ⋆, B, showing a monotonous decrease from ∼60% in the lowest-mass bulges to ≤ 10% in the most massive and dense ones (log(M, Σ)⋆, B ≃ 11.3,9.7). This, and the fact that the bulge-to-disk age contrast increases with increasing ℳ⋆, T (BP18), suggests that the assembly timescale of bulges scales inversely with galaxy mass. This means that the more massive a LTG is, the earlier it has experienced the dominant phase of its bulge build-up (what these authors termed sub-galactic downsizing), which is in agreement with earlier conclusions by Ganda et al. (2007). This trend also adds further support to the picture of inside-out galaxy growth (Eggen et al. 1962; Fall & Efstathiou 1980; van den Bosch 1998; Kepner 1999), in which dense galaxy centers complete their assembly first while the stellar mass continues to build up in the galaxy periphery, which is in agreement with previous findings (e.g., Muñoz-Mateos et al. 2007; Salim et al. 2012; Pérez et al. 2013; González Delgado et al. 2014; Tacchella et al. 2015). At a higher redshift z, the association between log and SFQ is established through abundance matching studies (van Dokkum et al. 2013), pixel-by-pixel fitting of the spectral energy distribution (Wuyts et al. 2012; Lang et al. 2014; Tacchella et al. 2015; Mosleh et al. 2018), or empirical relations between the rest-frame color and the mass-to-light ratio (Szomoru et al. 2012).

The growth of stellar spheroids above log and the ensuing SFQ, appears to be accompanied by a gradual change in the dominant gas excitation mechanism: whereas nebular emission in low-mass bulges is typically powered by SF, the majority of bulges above log (for instance, 94% of those bulges in the sample of BP18) fall in the loci of Seyferts, LINERs (low-ionization nuclear emission-line regions; Heckmann 1980) and Composites. The affinity of quenched, high-Σ stellar spheroids (bulges and early-type galaxies-ETGs) to LINER-specific emission-line ratios (e.g., Annibali et al. 2010; Yan & Blanton 2012, see Kormendy & Ho 2013 for a review), recently documented with IFS out to several kpc from the nucleus (Papaderos et al. 2013; Singh et al. 2013; Gomes et al. 2016a), thereby adding justification to the generic term LIER (e.g., Belfiore et al. 2016), has been traditionally attributed to photoionization by old (≥100 Myr) post-AGB sources (Binette et al. 1994) or a weak AGN (Ho 2008).

Papaderos et al. (2013), on the other hand, argue that even a strong AGN cannot be ruled out in massive spheroids showing merely weak LINER emission (i.e., with an Hα equivalent width EW(Hα) < 0.5 Å): this is because these authors (see also Gomes et al. 2016a) find that, in the absence of absorbing cold gas with a sufficient filling factor, the bulk of Lyman continuum (LyC) radiation from pAGB sources (consequently, also from a putative AGN) is escaping without being reprocessed into nebular emission. Indeed, the paucity of a cold medium in the nuclear region of quenched spheroids can be seen as natural consequence of depletion, thermalization and expulsion of gas by SF and eventually an AGN. If so, SFQ, LyC photon escape, and LI(N)ER emission are actually inseparable and causally linked facets of one and the same phenomenon, namely the partial or complete evacuation of cold gas from bulges and ETGs once they have grown above log, and AGN, if present, has fully unfold its energy impact.

Whereas there is broad consensus that the dense galaxy centers form and quench first (e.g., Tacchella et al. 2015, 2018), the timescale τSFQ of inside-out SFQ (ioSFQ) in LTG bulges is poorly constrained. A circumstantial argument that the onset of accretion-powered nuclear activity above log does not universally initiate a rapid SFQ rests on the co-existence of bulges classified as Composite, LINER and Seyfert above log. This led BP18 to deduce a τSFQ ≥ 2 Gyr, in agreement with estimates by Tacchella et al. (2015) from analysis of massive (10.84 ≤ log ℳ⋆, T ≤ 11.7) galaxies at z ∼2.2. Clearly, quantitative inferences on τSFQ as a function of total and bulge mass are desirable, given that they could help discriminating between different proposed SFQ mechanisms. For instance, a short τSFQ (e.g., on the order of the warm-gas sound crossing time of 600 Myr for a typical LTG bulge with RB∼3 kpc) would be consistent with a single or several intermittent energetic episodes leading to quick gas removal (e.g., most plausibly, a strong AGN outburst or a series of nuclear starbursts), and otherwise with a gradual inside-out depletion of gas or, possibly, morphological quenching, or gas starvation scenarios.

Quick ioSFQ should thus nearly preserve pre-existing stellar age gradients, whereas slow ioSFQ should produce or amplify negative age gradients. Therefore, in the idealized case of a bulge with initially uniform age that experiences an outwardly propagating SFQ front with a constant radial velocity vq, the slope (Gyr/kpc) in stellar age (∝1/vq) could help placing constraints on vq1.

Several observational studies in the past decades have greatly improved our understanding on age and star formation rate (SFR) patterns in galaxies and their bulges. These include, for example, i) the analysis of radial specific SFR (sSFR) profiles, obtained from Hα and EW(Hα) determinations (e.g., Catalán-Torrecilla et al. 2017; Belfiore et al. 2018), age-dating of stellar populations via ii) broad-band colors (e.g., Peletier & Balcells 1996; de Jong 1996; Peletier & de Grijs 1998; Muñoz-Mateos et al. 2007) or iii) Lick indices (e.g., Thomas & Davies 2006; Morelli et al. 2016), and iv) full spectral synthesis of IFS data (e.g., González Delgado et al. 2014, 2016; Sánchez-Blázquez et al. 2014, 2016). These studies consistently report a radial decrease of sSFR in the central parts of galaxies, and a trend for an inversion (though a large scatter) of color profiles from positive (negative) in low (high) mass bulges (Balcells & Peletier 1993, 1994), in qualitative agreement with the presence of negative ⟨t gradients in high-mass galaxies and vice versa (e.g., González Delgado et al. 2014, 2016).

On the other hand, all these approaches are tied to underlying assumptions and subject to limitations. For instance, the EW(Hα) is a proxy to sSFR only as long as the extinction-corrected Hα luminosity is a reliable SFR tracer. This requires quite specific assumptions (continuous SF at a constant SFR since ∼100 Myr and no LyC photon leakage, and, in the case of IFS, that all Hα emission excited by stars is registered). A secondary issue is that negative stellar metallicity gradients in the bulge can readily lead to an outwardly increasing specific LyC production rate by a factor of ∼2, thereby mimicking positive Hα-based sSFR gradients. A further potential caveat of i is that line-of-sight dilution by the triaxial stellar background of the bulge can naturally produce positive EW(Hα) (thus also sSFR) gradients (cf. Papaderos et al. 2002, for an analogy to local blue compact dwarf galaxies), further adding to the previous effect. Regarding ii, extinction-corrected color gradients are not convertible into age gradients without assumptions on the star formation- and chemical enrichment history, and can strongly be affected by nebular emission (Huchra 1977; Krüger et al. 1995; Papaderos et al. 1998; Schaerer & de Barros 2009). As for iii, Lick indices are per se luminosity-weighted, thus primarily reflect the radial distribution of young stars. Since they are designed for instantaneously formed stellar populations (e.g., globular clusters) and not for systems with prolonged SF, and potentially suffer from emission-line infilling, they appear to be of questionable applicability to LTG bulges. Finally, iv is subject to the notorious age-metallicity-extinction degeneracy.

A further concern is that the systematization and inter-comparison of results from studies employing the methods above is partly hindered by the fact that age gradients are mostly inferred within the effective radius Reff instead of within the bulge radius RB. As recently pointed out by BP18, given that Reff shrinks with increasing bulge-to-total ratio and is functionally coupled to the Sérsic index (Trujillo et al. 2001), the usual normalization to it can erase potentially important physical trends or lead to artificial correlations. Likewise, some studies adopt radius normalizations that might optimally serve specific science goals but are less suited to the study of the bulge component. As an example, González Delgado et al. (2016) infer gradients normalized within a50, the semi-major axis of the elliptical aperture containing 50% of the total light at 5635 Å. Since a50 is unrelated to RB and also cannot be converted into a photometric radius R without knowledge of the ellipticity, a transformation of age gradients from this study into ∇(t⋆, B) is not possible.

The goal of our study is to infer stellar age gradients within a homogeneously defined RB for a representative sample of local LTGs, as a step toward the systematization of the physical properties of bulges and the exploration of the mechanisms driving their ioSFQ. In Sect. 2 we briefly present our sample and analysis methodology, and in Sect. 3 we discuss trends between bulge age gradients and galaxy mass, and their possible interpretation in the context of ioSFQ. The main results from this study are summarized in Sect. 4. Throughout we adopt distance estimates from NASA/IPAC Extragalactic Database for H0 = 67.8 km s−1 Mpc−1.

2. Analysis and main results

The sample used for this analysis has been studied in detail by BP18 and comprises 135 non-interacting, nearly face-on (< 40°) local (≤130 Mpc) LTGs from the CALIFA IFS survey (Sánchez et al. 2012, 2016) conducted with the Potsdam Multi-Aperture Spectrometer (PMAS; Roth et al. 2005; Kelz et al. 2006). It spans 2.6 dex in total stellar mass (8.9 ≤ log ℳ⋆, T ≤ 11.5) and 3 dex in bulge mass (8.3 ≤ log ℳ⋆, B ≤ 11.3), therefore can be considered representative of the LTG population in the local Universe.

The reader is referred to BP18 for details on the photometric and spectral modeling analysis. Here, we only recall that the isophotal radius RB of the bulge component in our sample ranges between 25 and 112 (cf. Fig. A.2 in BP18), with only four galaxies (3%) having a bulge diameter < 6″. Therefore, convolution with the point spread function (FWHM ≈ 26, in the case of CALIFA IFS data) does not appreciably affect our study. RB was determined with the code IFIT (cf. BP18 for details Breda et al. 2019) as the radius of the Sérsic model to the bulge at an extinction-corrected surface brightness 24 r mag/▫″. An alternative approach might have been to define RB at the radius where the surface brightness of the bulge equals that of the disk (Sánchez-Blázquez et al. 2014), this would have required, however, image decomposition, which can be prone to methodological uncertainties discussed in BP18.

The spectral modeling of low-resolution (R​ ∼ ​850) CALIFA IFS data in the V500 setup was carried out spaxel-by-spaxel with the pipeline Porto3D (Papaderos et al. 2013; Gomes et al. 2016a), which invokes the population synthesis code STARLIGHT (Cid Fernandes et al. 2005). The simple stellar population libraries used consisted of templates from Bruzual & Charlot (2003) for 38 ages between 1 Myr and 13 Gyr and allowing for a time resolution of 1 Gyr for ages above 7 Gyr. As pointed out in BP18, the intrinsic V-band extinction in the bulge, as inferred with STARLIGHT for a foreground screen model was found to be relatively low (0.3 ± 0.18 mag) and not show a clear trend with ℳ⋆, B.

Single-spaxel (sisp) determinations of the mass- and light-weighted age (⟨t and ⟨t, respectively) were converted into radial profiles using an adaptation of the isophotal annuli (isan) surface photometry technique by Papaderos et al. (2002). The key feature of this method lies in the computation of statistics within logarithmically equidistant isophotal zones, each corresponding to a photometric radius R (″), being defined on a reference image of the emission-line-free pseudo-continuum at 6390–6490 Å.

Following BP18, the mean age and its uncertainty σisan within each isan were determined, respectively, as the arithmetic average and standard deviation about the mean of the individual sisp determinations. One advantage of this approach is that it prevents the highest-luminosity (or highest-Σ) spaxels from dictating the result, since all spaxels subtended within an isan are given equal weight. Typically, three to seven isan are included within RB. As a next step, the mean t⋆, ℳ and t⋆, ℒ were spline-interpolated to a finer radius step, and a linear regression (both non-weighted and weighted by σisan) was computed within RPSF < RRB, with RPSF set to 26 in order to exclude the innermost PSF-affected part of age profiles from fits. From the latter, the mass- and light-weighted stellar age gradient (∇(t⋆, B) and ∇(t⋆, B), respectively) within the bulge was determined. Figure 1 illustrates this procedure on the example of the LTG CGCG 0163-062.

thumbnail Fig. 1.

Illustration of the derivation of ∇(t⋆, B) and ∇(t⋆, B) in the bulge through linear fits (red lines) between RPSF and RB (blue and black vertical dashed lines, respectively) to spline-interpolated stellar age determinations (gray curves) within individual isan (markers). Error bars show the standard deviation about the mean of single-spaxel determinations within each isan.

Figure 2 shows ∇(t⋆, B) and ∇(t⋆, B) vs. LTG mass log(ℳ⋆, T), with error bars depicting formal uncertainties from weighted and non-weighted linear fits (semi-transparent and solid markers, respectively). The color coding corresponds to the tentative subdivision of bulges into the intervals iA, iB and iC (blue, green and red, respectively) according to ⟨δμ9G⟩ (mag), which was defined by BP18 as the difference μ0 Gyr-μ9 Gyr between the mean r band surface brightness of the present-day stellar component and that of stars older than 9 Gyr. Thus, a ⟨δμ9G⟩ ≈ 0 mag characterizes bulges that have completed their make-up earlier than 9 Gyr ago (z ≃ 1.34), whereas a ⟨δμ9G⟩ of, say, −2.5 mag translates into a contribution of 90% from stars younger than 9 Gyr. Breda & Papaderos (2018) showed that ⟨δμ9G⟩ tightly correlates with physical and evolutionary properties of LTG bulges (e.g., stellar age, surface density and mass) and proposed it as a convenient means for their classification. The most massive, dense and old bulges fall onto the iC class (⟨δμ9G⟩ ≥–0.5 mag) whereas bulges classified as iA (⟨δμ9G⟩≤ − 2.5 mag) are the youngest and reside in the least massive LTGs. Spectroscopically classified after Baldwin et al. (1981), nearly all iA bulges fall on the locus of H II regions, whereas almost all bulges classified as LINER (+) and Seyfert (x) are hosted by LTGs with log(ℳ⋆, T) > 10.5, as apparent from Fig. 2.

thumbnail Fig. 2.

Logarithm of total stellar mass ℳ⋆, T (M) vs. radial age gradient within the bulge radius RB in Gyr/RB, as obtained from mass- and light-weighted stellar age determinations (∇(t⋆, B) and ∇(t⋆, B); upper and lower panel, respectively). Symbols are color-coded according to the bulge classification scheme by BP18 (blue, green and red for iA, iB and iC bulges, respectively). Semi-transparent and solid markers correspond, respectively, to determinations from non-weighted and weighted linear fits. Bulges classified by BP18 as LINER (+) and Seyfert (x) are indicated. Dashed-dotted lines show non-weighted linear fits to weighted ∇(t⋆, B) and ∇(t⋆, B) determinations, the arithmetic average of which for the three bulge classes is depicted by stars.

Linear fits to weighted determinations of age gradients (dashed-dotted lines) yield the relations ∇(t⋆, B) = –(1.73 ± 0.15) ⋅ log(ℳ⋆, T) + (17.0 ± 1.56) and ∇(t⋆, B) = –(2.0 ± 0.15) ⋅ log(ℳ⋆, T) + (20.1 ± 1.56).

Despite a large scatter, both panels of Fig. 2 consistently reveal a trend for an inversion of age gradients from positive to negative values with increasing galaxy mass: whereas iA bulges hosted by lower-(M, Σ) LTGs show in their majority flat or positive age gradients, the opposite is the case for iB & iC bulges for which both ∇(t⋆, B) and ∇(t⋆, B) are generally negative with an average value of ∼–2 Gyr/RB above log ℳ⋆, T ≃ 10.5.

We note that this trend is highly unlikely to be driven by the outwardly increasing contribution of the star-forming disk, given that profile decomposition by BP18 yields that in most cases the latter provides a small fraction of the r-band emission at RB, and ⟨t determinations are relatively insensitive to young stellar populations. Moreover, would disk contamination be strongly affecting mass-weighted age gradients within RB, then one would expect low-mass (iA) bulges to be most prone to this effect and show negative ∇(t⋆, B) values, which is the opposite of the evidence from Fig. 2.

3. Discussion

Over the past years, observational evidence has been accumulating for a dependence of the slope of stellar age gradients on galaxy mass and luminosity (cf. Sect. 1). The present study adds further insights into this subject by virtue of the fact that it investigates for the first time a representative sample of local LTGs and determines age gradients within a clear-cut defined bulge radius. On the other hand, one should bear in mind some methodological limitations of this study. Of those, especially important is the age-metallicity-extinction degeneracy being inherent to all purely stellar population spectral synthesis codes (cf. BP18 for a further discussion). In the case of STARLIGHT, typical uncertainties of 0.1-0.15 dex on age determinations (Cid Fernandes et al. 2005, 2014) within individual isan could propagate into errors in ∇(t⋆, B) and ∇(t⋆, B) at a level that we estimate to be ∼20%. The same applies to systematic errors arising from the neglect of nebular continuum emission: as demonstrated by Cardoso et al. (2017), already at a modest level of nebular contamination (EW(Hα) ≃40 − 60 Å) the ⟨t inferred from STARLIGHT can be overestimated by 0.2–0.3 dex. Additionally, several of the here analyzed LTGs show within RB significant departures from a linear increase of stellar age with radius, thus linear fits (cf. Fig. 1) yield in these cases rather coarse estimates to ∇(t⋆, B) and ∇(t⋆, B).

The large scatter (σ ∼1.7 Gyr/RB) in Fig. 2 does not permit to associate the inversion point from positive to negative age gradients with a sharp threshold in ℳ⋆, T. However, judging from the linear fits in Fig. 2, this transition occurs at approximately log(ℳ⋆, T/M) ≃10, that is, the empirically suggested border between SF- and AGN- or LINER-dominated LTG bulges (BP18). This is consistent with the notion that the inversion of stellar age gradients marks the epoch when accretion-powered nuclear activity starts taking over the gas excitation in bulges, then gradually leading to the extinction of their SF activity. The fact that negative age gradients are naturally arising in an ioSFQ scenario reinforces this conjecture, even though this alone is no compelling evidence for a causal link between both phenomena. In the light of the trends in Fig. 2 it appears worthwhile to extend this study with metallicity determinations both in the stellar and nebular component, which is a forthcoming task. Interestingly, Tissera et al. (2016) find from cosmological simulations evidence for inside-out disk formation, with simulated disks with a mass around 1010 M showing steeper negative stellar metallicity gradients, in agreement with observational results (e.g., González Delgado et al. 2015).

One may consider two simple limiting cases for the bulge growth prior to the onset of ioSFQ, the first one employing the assumption that the sSFR within RB has initially been roughly spatially uniform (thus ∇(t⋆, B) ≈ 0) and the second one assuming an inside-out bulge assembly process, thus a negative ∇(t⋆, B) of genetic origin. In a simple gedankenexperiment, one can then convert in the first case the present-day ∇(t⋆, B) into a mean radial velocity vq (km s−1) for a spheric-symmetric outwardly (inwardly) propagating front of SF quenching (shrinking).

As apparent from Fig. 3, for the majority of bulges above log(ℳ⋆, B) ≈ 10 (iB and iC class in the notation by BP18), this vq is in the range of 1–2 km s−1, which is ∼2 dex lower than the central stellar velocity dispersion of LTG bulges (150–200 km s−1, Falcón-Barroso et al. 2016)2. This prima facie points against a brief energetic event (e.g., an AGN-driven blast wave or series of starbursts) that rapidly swept gas out of the bulge, thereby leading to an abrupt cessation of SF out to RB. The ∇(t⋆, B) inversion point at a present-day stellar mass of log(ℳ⋆, T) ≃ 10 translates by the relations log(ℳ⋆, B) = 1.22 ⋅ log(ℳ⋆, T) - 2.87 and ⟨t⋆, B = 2.3 ⋅ log(ℳ⋆, B) - 13.44 by BP18 to an age of ∼9.6 Gyr (z ≈ 0.4) for the onset of ioSFQ. This suggests that, on the statistical average, LTGs with a present-day log(ℳ⋆, T) ≥10 have entered their ioSFQ phase no later than ∼4 Gyr after the Big Bang. To reconcile this with the observed age contrast between bulge center and periphery (∼3 Gyr; cf. Fig. 2) in the most massive (> 1011 M) LTGs, one has then to assume that ioSFQ in these systems has started at a cosmic age of ≤1 Gyr (z ≥ 5.7). The hypothesis that this process was driven by negative AGN feedback appears to be compatible with existing data, even though the peak of the average growth rate of super-massive black holes has occurred only at z ≤3 (e.g., Shankar et al. 2009).

thumbnail Fig. 3.

Logarithm of total stellar mass ℳ⋆, T vs. age gradient (determinations from weighted linear fits) translated into an average inside-out SF quenching velocity (vq; negative values) or outside-in SF shrinking velocity (positive values) in the bulge. Dashed-dotted lines show linear fits. The meaning of symbols is identical to that in Fig. 2.

Admittedly, the assumption of a nearly constant vq is simplistic: for instance, by analogy to a SF-driven supershell that spherically expands against an ambient medium of constant density, one would expect the radius to grow as t2/5 and t3/5 for the case of, respectively, an instantaneous and continuous injection of mechanical energy (e.g., De Young & Heckman 1994, see also McCray & Kafatos 1987). Furthermore, a stable, spheric-symmetrically expanding ioSFQ front is improbable in the presence of a turbulent multi-phase gas medium exposed to strongly directional outflows from an AGN, as several observations indicate (e.g., Kehrig et al. 2012, see Kormendy & Ho 2013 for a review).

As for the second hypothesis, it assumes a superposition of a pre-existing negative ∇(t⋆, B) with that subsequently arising through ioSFQ. Discriminating between both is clearly a formidable task, perhaps only possible through a combined chemodynamical decomposition of stellar populations and the nebular emission they are associated with. Obviously, if the observed ∇(t⋆, B)’s of ∼3 Gyr/RB are primarily of genetic origin, then massive (iB and iC) bulges cannot have formed quasi-monolithically (< 1 Gyr, Kormendy & Kennicutt 2004, for a review), but instead over a prolonged phase of 2–3 Gyr that lasted until z ≈ 2.2, as suggested by BP18. These authors proposed that bulges arise out of galactic disks on a timescale that is inversely related to the present-day LTG mass, with massive iC bulges forming in the most massive galaxies first, whereas the least massive iA bulges still assembling out of gaseous and stellar material from the disk.

This minimum timescale of 2–3 Gyr for the make-up of bulges is probably also consistent with the two-phase galaxy formation scenario by Oser et al. (2010), if the initial (z≥2) dissipational phase of in-situ SF in their simulations is chronologically associated with the dominant phase of bulge formation in the most massive LTGs. The same applies to the timescales predicted by bulge formation scenarios envisaging inward migration and coalescence of massive (∼109 M) SF clumps emerging out of violent disk instabilities (Bournaud et al. 2007; Mandelker et al. 2014). As pointed out in BP18, a natural consequence from inward migration of SF clumps from the disk are negative age gradients in massive iC bulges, in agreement with the evidence from Fig. 2.

The large scatter in ∇(t⋆, B) in Fig. 2), even though partly due to observational and methodological uncertainties, suggests that there are multiple evolutionary pathways for bulges being bracketed by the two limiting cases of slow and quick ioSFQ. The spread of determinations at the l.h.s. of the diagram (∇(t⋆, B) ≥ 0) might reflect a various degree of intensity and confinement of SF to the center of low-mass bulges, possibly regulated by the inflow rate of intra-clump gas from the disk (Hopkins 2012) and influenced by minor wet mergers with gaseous entities from the galaxy itself and its surroundings.

As for bulges with ∇(t⋆, B) <  0, the documented spread in ∇(t⋆, B) might be accounted for by a superposition of different non-mutually exclusive processes. These include both negative and positive AGN feedback, the latter in the sense of fast nuclear outflows plunging into the ambient dense gas and triggering localized SF in the periphery of the bulge, this way promoting its growth (Maiolino et al. 2017) and further steepening negative age gradients. The role of bars for this phase and over the whole bulge evolution is most certainly another important unknown: Gadotti & dos Anjos (2001) found mostly negative U-B and B-V color gradients, with flat or positive gradients mostly in barred galaxies.

The expected imprints of morphological quenching or gas starvation (cf. Sect. 1) on stellar age gradients in the bulge has, to our best knowledge, not been quantitatively explored from the theoretical point of view. Whereas both of these processes act toward shutting off SF all over RB, thus probably have no dominant effect on pre-existing age gradients, it cannot be excluded that they favor, over a certain period at least, a non-uniform sSFR within the bulge, eventually leaving an imprint on ∇(t⋆, B) and contributing to its scatter. The resolution of current cosmological simulations is insufficient for spatially resolving the bulge and carrying out a comparative investigation of the expected ∇(t⋆, B)ℒ, ℳ for the above scenarios. Moreover, contrary to an AGN, one cannot switch on and off gas inflow onto the bulge in order to quantitatively assess the effect of inhibition of gas transfer from the cosmic web (Dekel et al. 2009) or the local galaxy environment (e.g., Peng et al. 2015). Additionally, the investment in computational time for higher-resolution simulations appears justifiable only after an improvement of contemporary models, which in some respects suffer from significant deficits. For instance, simulations on larger spatial scales (out to Reff) by Hirschmann et al. (2013) and Choi et al. (2017) always predict strongly positive age gradients in massive (log(ℳ⋆, T/M) > 10) galaxies (Hirschmann et al. 2015), which indicates that better prescriptions or additional physical processes need to be implemented.

4. Summary and conclusions

The trend for an inversion of the slope of radial stellar age gradients from positive values in young low-mass bulges to negative values for old massive bulges, here solidified through a homogeneous analysis of representative sample of local late-type galaxies from the CALIFA survey, likely encodes crucial information on the physical drivers of bulge growth and the ensuing inside-out cessation of star-forming activity.

For massive bulges (log ℳ⋆, T >  10), we distinguish between two simple limiting cases, the first one attributing stellar age gradients to an inside-out quenching of star formation, and the second one viewing them as relics from the bulge formation process. In the first case, and assuming that prior to ioSFQ bulges had a spatially uniform age, the observed gradients translate into a mean inside-out SFQ velocity of 1–2 km s−1, which is consistent with a gradual evacuation or thermalization a cold gas being susceptible to star formation. If due to negative AGN feedback, this could hint at a mild growth of super-massive black holes in tandem with the bulge. However, alternative mechanisms for the shut-off of star formation (e.g., morphological quenching or inhibition of cold gas inflow from the halo and the cosmic web) cannot be excluded given the lack of quantitative theoretical constraints on the radial age patterns these processes could leave behind.

In the second scenario, that is, assuming that the observed age gradients portray the bulge formation process, then the range of values inferred (∼3 Gyr/RB) argue against a quasi-monolithic build-up and point instead to a prolonged formation phase that could have been driven by a superposition of inward migration of star-forming clumps from the disk and in situ star formation. Quite importantly, if the observed age gradients in bulges are mainly of genetic origin, then one cannot rule out a quick ioSFQ episode following an energetic AGN outburst, given that such a process would leave virtually no imprints on pre-existing age patterns in the stellar component.

As the large scatter (1.7 Gyr/RB) of age gradients at a given mass suggests, the evolutionary pathways of galaxy bulges are far more complex (see Fig. 4), and possibly shaped by a mixture of the two aforementioned limiting scenarios with, for instance, positive AGN feedback, bulge-bar interaction and possibly also morphological quenching and gas starvation. A further exploration of the age gradient vs. stellar mass relation with higher-resolution IFS data and detailed numerical simulations, incorporating realistic recipes for star formation and AGN-driven feedback, appears to be of considerable interest and fundamental to the development of a coherent picture on the assembly history of late-type galaxies and their structural components.

thumbnail Fig. 4.

Effect of different processes shaping the evolution of LTG bulges on the slope of the mass-weighted stellar age gradient ∇(t⋆, B).


1

An analogous case of conversion of stellar age gradients into a mean SF propagation velocity was presented in Papaderos et al. (1998) for the blue compact galaxy SBS 0335-052E (Izotov et al. 1990).

2

Interestingly, in Gomes et al. (2016b) we simulated the formation of ETGs in a toy model assuming inside-out SF since 13.5 Gyr at a mean velocity of 2 km s−1, which was chosen such as to reproduce the typical radius of these galaxies. The fact that this assumed velocity turns out to be fairy close to the here observationally estimated mean vq for LTG bulges, if not a mere coincidence, raises the speculation of whether stellar spheroids both form and quench in an inside-out manner at a speed on the other of the sound speed in the neutral gas.

Acknowledgments

We thank the anonymous referee for valuable comments and suggestions. Additionally, we thank the EU for providing to Portugal a substantial fraction of the financial resources that allowed it to sustain a research infrastructure in astrophysics. Specifically, this work was carried out at an institute whose funding is provided to 85% by the EU via the FCT (Fundação para a Ciência e a Tecnologia) apparatus, through European and national funding via FEDER through COMPETE by the grants UID/FIS/04434/2013 & POCI-01-0145-FEDER-007672 and PTDC/FIS-AST/3214/2012 & FCOMP-01-0124-FEDER-029170. Additionally, this work was supported through FCT grants UID/FIS/04434/2019, UIDB/04434/2020 and UIDP/04434/2020. We further acknowledge support by European Community Programme (FP7/2007-2013) under grant agreement No. PIRSES-GA-2013-612701 (SELGIFS). We are grateful to Dr. Dimitri Gadotti, Prof. Daniel Schaerer and Dr. Andrew Humphrey for valuable comments. I. B. was supported by the FCT PhD::SPACE Doctoral Network (PD/00040/2012) through the fellowship PD/BD/52707/2014 funded by FCT (Portugal) and POPH/FSE (EC) and by the fellowship CAUP-07/2014-BI in the context of the FCT project PTDC/FIS-AST/3214/2012 & FCOMP-01-0124-FEDER-029170. P. P. was supported through Investigador FCT contract IF/01220/2013/CP1191/CT0002 and by a contract that is supported by FCT/MCTES through national funds (PIDDAC) and by grant PTDC/FIS-AST/29245/2017. J. M. G. is supported by the fellowship CIAAUP-04/2016-BPD in the context of the FCT project UID/FIS/04434/2013 & POCI-01-0145-FEDER-007672 and acknowledges the previous support by the fellowships SFRH/BPD/66958/2009 funded by FCT and POPH/FSE (EC) and DL 57/2016/CP1364/CT0003. L. S. M. C. acknowledges support by the project “Enabling Green E-science for the SKA Research Infrastructure (ENGAGE SKA)” (reference POCI-01-0145-FEDER-022217), funded by COMPETE 2020 and FCT. P.L. acknowledges support by DL57/2016/CP1364/CT0010. F. B. acknowledges the support by FCT via the postdoctoral fellowship SFRH/BPD/103958/2014. This study uses data provided by the Calar Alto Legacy Integral Field Area (CALIFA) survey (califa.caha.es), funded by the Spanish Ministry of Science under grant ICTS-2009-10, and the Centro Astronómico Hispano-Alemán. It is based on observations collected at the Centro Astronómico Hispano Alemán (CAHA) at Calar Alto, operated jointly by the Max-Planck-Institut für Astronomie and the Instituto de Astrofísica de Andalucía (CSIC). This research has made use of the NASA/IPAC Extragalactic Database (NED) which is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration.

References

  1. Annibali, F., Bressan, A., Rampazzo, R., et al. 2010, A&A, 519, A40 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  2. Balcells, M., & Peletier, R. F. 1993, Galactic Bulges, 153, 409 [NASA ADS] [CrossRef] [Google Scholar]
  3. Balcells, M., & Peletier, R. F. 1994, AJ, 107, 135 [NASA ADS] [CrossRef] [Google Scholar]
  4. Baldwin, J. A., Phillips, M. M., & Terlevich, R. 1981, PASP, 93, 5 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  5. Belfiore, F., Maiolino, R., Maraston, C., et al. 2016, MNRAS, 461, 311 [Google Scholar]
  6. Belfiore, F., Maiolino, R., Bundy, K., et al. 2018, MNRAS, 477, 3014 [NASA ADS] [CrossRef] [Google Scholar]
  7. Binette, L., Magris, C. G., Stasińska, G., & Bruzual, A. G. 1994, A&A, 292, 13 [NASA ADS] [Google Scholar]
  8. Bournaud, F., Elmegreen, B. G., & Elmegreen, D. M. 2007, ApJ, 670, 237 [Google Scholar]
  9. Breda, I., & Papaderos, P. 2018, A&A, 614, A48 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  10. Breda, I., Papaderos, P., Gomes, J. M., & Amarantidis, S. 2019, A&A, 632, A128 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  11. Brinchmann, J., Charlot, S., White, S. D. M., et al. 2004, MNRAS, 351, 1151 [NASA ADS] [CrossRef] [Google Scholar]
  12. Bruzual, G., & Charlot, S. 2003, MNRAS, 344, 1000 [NASA ADS] [CrossRef] [Google Scholar]
  13. Cardoso, L., Gomes, J. M., & Papaderos, P. 2017, A&A, 622, A56 [Google Scholar]
  14. Catalán-Torrecilla, C., Gil de Paz, A., Castillo-Morales, A., et al. 2017, ApJ, 848, 87 [NASA ADS] [CrossRef] [Google Scholar]
  15. Cattaneo, A., Faber, S. M., Binney, J., Dekel, A., Kormendy, J., et al. 2009, Nature, 460, 213 [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
  16. Choi, E., Ostriker, J. P., Naab, T., et al. 2017, ApJ, 844, 31 [NASA ADS] [CrossRef] [Google Scholar]
  17. Cid Fernandes, R., Mateus, A., Sodré, L., Stasińska, G., & Gomes, J. M. 2005, MNRAS, 358, 363 [NASA ADS] [CrossRef] [Google Scholar]
  18. Cid Fernandes, R., Gonzálex Delgado, R., García Benito, R., et al. 2014, A&A, 561, A130 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  19. Croton, D. J., Springel, V., White, S. D. M., et al. 2006, MNRAS, 365, 11 [NASA ADS] [CrossRef] [Google Scholar]
  20. Dekel, A., Birnboim, Y., Engel, G., et al. 2009, Nature, 457, 451 [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
  21. de Jong, R. S. 1996, A&A, 313, 377 [NASA ADS] [Google Scholar]
  22. De Young, D. S., & Heckman, T. M. 1994, ApJ, 431, 598 [NASA ADS] [CrossRef] [Google Scholar]
  23. Di Matteo, T., Springel, V., & Hernquist, L. 2005, Nature, 433, 604 [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
  24. Eggen, O. J., Lynden-Bell, D., & Sandage, A. R. 1962, ApJ, 136, 748 [NASA ADS] [CrossRef] [Google Scholar]
  25. Ellison, S., Sánchez, S., Medel, H. I., et al. 2018, MNRAS, 474, 2039 [NASA ADS] [CrossRef] [Google Scholar]
  26. Falcón-Barroso, J., Lyubenova, M., van de Ven, G., et al. 2016, A&A, 597, A48 [Google Scholar]
  27. Fall, S. M., & Efstathiou, G. 1980, MNRAS, 193, 189 [NASA ADS] [CrossRef] [Google Scholar]
  28. Fang, J. J., Faber, S. M., Koo, D. C., & Dekel, A. 2013, ApJ, 776, 63 [Google Scholar]
  29. Gadotti, D. A., & dos Anjos, S. 2001, AJ, 122, 1298 [NASA ADS] [CrossRef] [Google Scholar]
  30. Ganda, K., Peletier, R. F., McDermid, R. M., et al. 2007, MNRAS, 380, 506 [NASA ADS] [CrossRef] [Google Scholar]
  31. Genzel, R., Förster schreiber, N. M., Lang, P., et al. 2014, ApJ, 785, 75 [NASA ADS] [CrossRef] [Google Scholar]
  32. Gomes, J. M., Papaderos, P., Kehrig, C., et al. 2016a, A&A, 588, A68 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  33. Gomes, J. M., Papaderos, P., Vílchez, J. M., et al. 2016b, A&A, 586, 22 [Google Scholar]
  34. González Delgado, R. M., Pérez, E., Cid Fernandes, R., et al. 2014, A&A, 562, A47 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  35. González Delgado, R. M., García-Benito, R., Pérez, E., et al. 2015, A&A, 581, A103 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  36. González Delgado, R. M., Cid Fernandes, R., Pérez, E., et al. 2016, A&A, 590, A44 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  37. Heckmann, T. M. 1980, A&A, 87, 152 [NASA ADS] [Google Scholar]
  38. Hirschmann, M., Naab, T., Davé, R., et al. 2013, MNRAS, 436, 2929 [NASA ADS] [CrossRef] [Google Scholar]
  39. Hirschmann, M., Naab, T., & Ostriker, J. P. 2015, MNRAS, 449, 528 [NASA ADS] [CrossRef] [Google Scholar]
  40. Ho, L. C. 2008, ARA&A, 46, 475 [NASA ADS] [CrossRef] [Google Scholar]
  41. Hopkins, P. F., Kereš, D., Murray, N., et al. 2012, MNRAS, 427, 968 [NASA ADS] [CrossRef] [Google Scholar]
  42. Huchra, J. P. 1977, ApJ, 217, 928 [NASA ADS] [CrossRef] [Google Scholar]
  43. Izotov, Y. I., Guseva, N. G., Lipovetsky, V. A., et al. 1990, Nature, 343, 238 [NASA ADS] [CrossRef] [Google Scholar]
  44. Kauffmann, G., Heckman, T. M., White, S. D. M.et al. 2003, MNRAS, 341, 54 [Google Scholar]
  45. Kehrig, C., Monreal-Ibero, A., Papaderos, P., et al. 2012, A&A, 540, A11 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  46. Kelz, A., Verheijen, M. A. W., Roth, M. M., et al. 2006, PASP, 118, 129 [NASA ADS] [CrossRef] [Google Scholar]
  47. Kepner, J. V. 1999, ApJ, 520, 59 [NASA ADS] [CrossRef] [Google Scholar]
  48. Kormendy, J., & Kennicutt, Jr., R. 2004, ARA&A, 42, 603 [NASA ADS] [CrossRef] [Google Scholar]
  49. Kormendy, J., & Ho, L. C. 2013, ARA&A, 51, 511 [Google Scholar]
  50. Krüger, H., Fritze-v Alvensleben, U., & Loose, H. H. 1995, A&A, 303, 41 [NASA ADS] [Google Scholar]
  51. Larson, R. B., Tinsley, B. M., & Caldwell, C. N. 1980, ApJ, 237, 692 [NASA ADS] [CrossRef] [Google Scholar]
  52. Lang, P., Wuyts, S., Somerville, R. S., et al. 2014, ApJ, 788, 11 [NASA ADS] [CrossRef] [Google Scholar]
  53. Maiolino, R., Russell, H. R., Fabian, A. C., et al. 2017, Nature, 544, 202 [NASA ADS] [CrossRef] [Google Scholar]
  54. Mandelker, N., Dekel, A., Ceverino, D., et al. 2014, MNRAS, 443, 3675 [NASA ADS] [CrossRef] [Google Scholar]
  55. Martig, M., Bournaud, F., Teyssier, R., & Dekel, A. 2009, ApJ, 707, 250 [NASA ADS] [CrossRef] [Google Scholar]
  56. McCray, R., & Kafatos, M. 1987, ApJ, 317, 190 [NASA ADS] [CrossRef] [Google Scholar]
  57. Mosleh, M., Tavasoli, S., & Tacchella, S. 2018, ApJ, 861, 101 [NASA ADS] [CrossRef] [Google Scholar]
  58. Morelli, L., Parmiggiani, M., Corsini, E. M., et al. 2016, MNRAS, 463, 4396 [NASA ADS] [CrossRef] [Google Scholar]
  59. Muñoz-Mateos, J. C., Gil de Paz, A., Boissier, S., et al. 2007, ApJ, 658, 1006 [NASA ADS] [CrossRef] [Google Scholar]
  60. Omand, C. M. B., Balogh, M. L., & Poggianti, B. M. 2014, MNRAS, 440, 843 [NASA ADS] [CrossRef] [Google Scholar]
  61. Oser, L., Ostriker, J. P., Naab, T., et al. 2010, ApJ, 725, 2312 [NASA ADS] [CrossRef] [Google Scholar]
  62. Papaderos, P., Izotov, Y. I., Fricke, K. J., et al. 1998, A&A, 338, 43 [NASA ADS] [Google Scholar]
  63. Papaderos, P., Izotov, Y. I., Thuan, T. X., et al. 2002, A&A, 393, 461 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  64. Papaderos, P., Gomes, J., Vílchez, J., et al. 2013, A&A, 555, L1 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  65. Peletier, R. F., & Balcells, M. 1996, AJ, 111, 2238 [NASA ADS] [CrossRef] [Google Scholar]
  66. Peletier, R. F., & de Grijs, R. 1998, MNRAS, 300, L3 [NASA ADS] [CrossRef] [Google Scholar]
  67. Peng, Y.-J., Lilly, S. J., Kovac, K., et al. 2010, ApJ, 721, 193 [NASA ADS] [CrossRef] [Google Scholar]
  68. Peng, Y., Maiolino, R., & Cochrane, R. 2015, Nature, 521, 192 [NASA ADS] [CrossRef] [Google Scholar]
  69. Pérez, E., Cid Fernandes, R., González Delgado, R. M., et al. 2013, ApJ, 764, L1 [NASA ADS] [CrossRef] [Google Scholar]
  70. Roth, M. M., Kelz, A., Fechner, T., et al. 2005, PASP, 117, 620 [NASA ADS] [CrossRef] [Google Scholar]
  71. Salim, S., Fang, J. J., Rich, R. M., et al. 2012, ApJ, 755, 105 [NASA ADS] [CrossRef] [Google Scholar]
  72. Sánchez, S. F., Kennicutt, R. C., Gil de Paz, A., et al. 2012, A&A, 538, A8 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  73. Sánchez, S. F., García-Benito, R., Zibetti, S., et al. 2016, A&A, 594, A36 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  74. Sánchez-Blázquez, P. 2016, in Astrophysics and Space Science Library, eds. E. Laurikainen, R. Peletier, & D. Gadotti, Galactic Bulges, 418, 127 [NASA ADS] [CrossRef] [Google Scholar]
  75. Sánchez-Blázquez, P., Rosales-Ortega, F. F., Méndez-Abreu, J., et al. 2014, A&A, 570, A6 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  76. Schaerer, D., & de Barros, S. 2009, A&A, 502, 423 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  77. Shankar, F., Weinberg, D., & Miralda-Escudé, J. 2009, ApJ, 690, 20 [NASA ADS] [CrossRef] [Google Scholar]
  78. Silk, J. 1997, ApJ, 481, 703 [NASA ADS] [CrossRef] [Google Scholar]
  79. Singh, R., van de Ven, G., Jahnke, K., et al. 2013, A&A, 558, A43 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  80. Strateva, I., Ivvezic, Z., Knapp, G. R., et al. 2001, AJ, 122, 1861 [Google Scholar]
  81. Szomoru, D., Franx, M., & van Dokkum, P. G. 2012, ApJ, 749, 121 [NASA ADS] [CrossRef] [Google Scholar]
  82. Tacchella, S., Carollo, C. M., Renzini, A., et al. 2015, Science, 348, 314 [Google Scholar]
  83. Tacchella, S., Carollo, C. M., Förster Schreiber, N. M., et al. 2018, ApJ, 859, 56 [NASA ADS] [CrossRef] [Google Scholar]
  84. Thomas, D., & Davies, R. 2006, MNRAS, 366, 510 [NASA ADS] [CrossRef] [Google Scholar]
  85. Tissera, P. B., Machado, R. E. G., Sánchez-Blazquez, P., et al. 2016, A&A, 592, A93 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  86. Trujillo, I., Graham, A. W., & Caon, N. 2001, MNRAS, 326, 869 [NASA ADS] [CrossRef] [Google Scholar]
  87. van den Bosch, F. C. 1998, ApJ, 507, 601 [NASA ADS] [CrossRef] [Google Scholar]
  88. van Dokkum, P. G., & Franx, M. 2001, ApJ, 553, 91 [NASA ADS] [CrossRef] [Google Scholar]
  89. van Dokkum, P. G., Leja, J., Nelson, E. J., et al. 2013, ApJ, 771, L35 [NASA ADS] [CrossRef] [Google Scholar]
  90. Yan, R., & Blanton, M. R. 2012, ApJ, 747, 61 [NASA ADS] [CrossRef] [Google Scholar]
  91. Woo, J., & Ellison, S. L. 2019, MNRAS, 487, 1927 [NASA ADS] [CrossRef] [Google Scholar]
  92. Wuyts, S., Förster Schreiber, N., Genzel, R., et al. 2012, ApJ, 753, 114 [NASA ADS] [CrossRef] [Google Scholar]
  93. Zibetti, S., Gallazzi, A. R., Ascasibar, Y., et al. 2017, MNRAS, 468, 1902 [NASA ADS] [Google Scholar]

All Figures

thumbnail Fig. 1.

Illustration of the derivation of ∇(t⋆, B) and ∇(t⋆, B) in the bulge through linear fits (red lines) between RPSF and RB (blue and black vertical dashed lines, respectively) to spline-interpolated stellar age determinations (gray curves) within individual isan (markers). Error bars show the standard deviation about the mean of single-spaxel determinations within each isan.

In the text
thumbnail Fig. 2.

Logarithm of total stellar mass ℳ⋆, T (M) vs. radial age gradient within the bulge radius RB in Gyr/RB, as obtained from mass- and light-weighted stellar age determinations (∇(t⋆, B) and ∇(t⋆, B); upper and lower panel, respectively). Symbols are color-coded according to the bulge classification scheme by BP18 (blue, green and red for iA, iB and iC bulges, respectively). Semi-transparent and solid markers correspond, respectively, to determinations from non-weighted and weighted linear fits. Bulges classified by BP18 as LINER (+) and Seyfert (x) are indicated. Dashed-dotted lines show non-weighted linear fits to weighted ∇(t⋆, B) and ∇(t⋆, B) determinations, the arithmetic average of which for the three bulge classes is depicted by stars.

In the text
thumbnail Fig. 3.

Logarithm of total stellar mass ℳ⋆, T vs. age gradient (determinations from weighted linear fits) translated into an average inside-out SF quenching velocity (vq; negative values) or outside-in SF shrinking velocity (positive values) in the bulge. Dashed-dotted lines show linear fits. The meaning of symbols is identical to that in Fig. 2.

In the text
thumbnail Fig. 4.

Effect of different processes shaping the evolution of LTG bulges on the slope of the mass-weighted stellar age gradient ∇(t⋆, B).

In the text

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