arXiv subjects

Asa F. L. Bluck

Publications and source records attributed to Asa F. L. Bluck.

At least 37 records · Page 2Linked to original sources

arXiv2022

The Molecular-Gas Main Sequence and Schmidt-Kennicutt relation are fundamental, the Star-Forming Main Sequence is a (useful) byproduct

We investigate the relationship between the star formation rate (SFR), stellar mass ($M_*$) and molecular gas mass ($M_{H_2}$) for local star-forming galaxies. We further investigate these relationships for high-z (z=1-3) galaxies and for the hosts of a local sample of Active Galactic Nuclei (AGN). We explore which of these dependencies are intrinsic and which are an indirect by-product by employing partial correlation coefficients and random forest regression. We find that for local star-forming galaxies, high-z galaxies, and AGN host galaxies, the Schmidt-Kennicutt relation (SK, between $M_{H_2}$ and SFR), and the Molecular Gas Main Sequence (MGMS, between $M_{H_2}$ and $M_*$) are intrinsic primary relations, while the relationship between $M_*$ and SFR, i.e. the Star-Forming Main Sequence (SFMS), is an indirect by-product of the former two. Hence the Star-Forming Main Sequence is not a fundamental scaling relation for local or high-redshift galaxies. We find evidence for both the evolution of the MGMS and SK relation over cosmic time, where, at a given stellar mass, the higher the redshift, the greater the molecular gas mass and the star formation efficiency. We offer a parameterisation of both the MGMS and SK relation's evolution with redshift, showing how they combine to form the observed evolution of the SFMS. In addition, we find that the local AGN host galaxies follow an AGN-MGMS relation (as well as a AGN-SK relation), where the MGMS is offset to lower $M_{H_2}$ for a given $M_*$ compared to local SF galaxies.

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arXiv2022

Resolved Molecular Gas Observations of MaNGA Post-starbursts Reveal a Tumultuous Past

Post-starburst galaxies (PSBs) have recently and rapidly quenched their star-formation, thus they are an important way to understand how galaxies transition from star-forming late-types to quiescent early-types. The recent discovery of large cold gas reservoirs in PSBs calls into question the theory that galaxies must lose their gas to become quiescent. Optical Integral Field Spectroscopy (IFS) surveys have revealed two classes of PSBs: central PSBs with central quenching regions and ring PSBs with quenching in their outskirts. We analyze a sample of 13 nearby (z < 0.1) PSBs with spatially resolved optical IFS data from the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey and matched resolution Atacama Large (sub-)Millimeter Array (ALMA) observations of $^{12}$CO(1-0). Disturbed stellar kinematics in 7/13 of our PSBs and centrally concentrated molecular gas is consistent with a recent merger for most of our sample. In galaxies without merger evidence, alternate processes may funnel gas inwards and suppress star-formation, which may include outflows, stellar bars, and minor mergers or interactions. The star-formation efficiencies of the post-starburst regions in nearly half our galaxies are suppressed while the gas fractions are consistent with star-forming galaxies. AGN feedback may drive this stabilization, and we observe AGN-consistent emission in the centers of 5/13 galaxies. Finally, our central and ring PSBs have similar properties except the ionized and molecular gas in central PSBs is more disturbed. Overall, the molecular gas in our PSBs tends to be compact and highly disturbed, resulting in concentrated gas reservoirs unable to form stars efficiently.

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arXiv2022

The ALMaQUEST Survey X: What powers merger induced star formation?

Galaxy mergers are known to trigger both extended and central star formation. However, what remains to be understood is whether this triggered star formation is facilitated by enhanced star formation efficiencies, or an abundance of molecular gas fuel. This work presents spatially resolved measurements of CO emission collected with the Atacama Large Millimetre Array (ALMA) for 20 merging galaxies (either pairs or post-mergers) selected from the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey. Eleven additional merging galaxies are selected from the ALMA MaNGA QUEnching and STar formation (ALMaQUEST) survey, resulting in a set of 31 mergers at various stages of interaction and covering a broad range of star formation rates (SFR). We investigate galaxy-to-galaxy variations in the resolved Kennicutt-Schmidt relation (rKS: $Σ_{H_2}$ vs. $Σ_{SFR}$), the resolved molecular gas main sequence (rMGMS: $Σ_{\star}$ vs. $Σ_{H_2}$), and the resolved star-forming main sequence (rSFMS: $Σ_{\star}$ vs. $Σ_{SFR}$). We quantify offsets from these resolved relations to determine if star formation rate, molecular gas fraction, and/or star formation efficiency (SFE) is enhanced in different regions of an individual galaxy. By comparing offsets in all three parameters we can discern whether gas fraction or SFE powers an enhanced $Σ_{SFR}$. We find that merger-induced star formation can be driven by a variety of mechanisms, both within a galaxy and between different mergers, regardless of interaction stage.

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arXiv2022

The quenching of galaxies, bulges, and disks since cosmic noon: A machine learning approach for identifying causality in astronomical data

We present an analysis of the quenching of star formation in galaxies, bulges, and disks throughout the bulk of cosmic history, from $z=2-0$. We utilise observations from the SDSS and MaNGA at low redshifts. We complement these data with observations from CANDELS at high redshifts. Additionally, we compare the observations to detailed predictions from the LGalaxies semi-analytic model. To analyse the data, we developed a machine learning approach utilising a Random Forest classifier. We first demonstrate that this technique is extremely effective at extracting causal insight from highly complex and inter-correlated model data, before applying it to various observational surveys. Our primary observational results are as follows: At all redshifts studied in this work, we find bulge mass to be the most predictive parameter of quenching, out of the photometric parameter set (incorporating bulge mass, disk mass, total stellar mass, and $B/T$ structure). Moreover, we also find bulge mass to be the most predictive parameter of quenching in both bulge and disk structures, treated separately. Hence, intrinsic galaxy quenching must be due to a stable mechanism operating over cosmic time, and the same quenching mechanism must be effective in both bulge and disk regions. Despite the success of bulge mass in predicting quenching, we find that central velocity dispersion is even more predictive (when available in spectroscopic data sets). In comparison to the LGalaxies model, we find that all of these observational results may be consistently explained through quenching via preventative `radio-mode' active galactic nucleus (AGN) feedback. Furthermore, many alternative quenching mechanisms (including virial shocks, supernova feedback, and morphological stabilisation) are found to be inconsistent with our observational results and those from the literature.

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arXiv2022

The ALMaQUEST Survey IX: The nature of the resolved star forming main sequence

We investigate the nature of the scaling relations between the surface density of star formation rate ($Σ_{\rm SFR}$), stellar mass ($Σ_*$), and molecular gas mass ($Σ_{\rm H_2}$), aiming at distinguishing between the relations that are primary, i.e. more fundamental, and those which are instead an indirect by-product of the other relations. We use the ALMaQUEST survey and analyse the data by using both partial correlations and Random Forest regression techniques. We unambiguously find that the strongest intrinsic correlation is between $Σ_{\rm SFR}$ and $Σ_{\rm H_2}$ (i.e. the resolved Schmidt-Kennicutt relation), followed by the correlation between $Σ_{\rm H_2}$ and $Σ_*$ (resolved Molecular Gas Main Sequence, rMGMS). Once these two correlations are taken into account, we find that there is no evidence for any intrinsic correlation between $Σ_{\rm SFR}$ and $Σ_*$, implying that SFR is entirely driven by the amount of molecular gas, while its dependence on stellar mass (i.e. the resolved Star Forming Main Sequence, rSFMS) simply emerges as a consequence of the relationship between molecular gas and stellar mass.

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arXiv2022

What drives galaxy quenching? A deep connection between galaxy kinematics and quenching in the local Universe

We develop a 2D inclined rotating disc model, which we apply to the stellar velocity maps of 1862 galaxies taken from the MaNGA survey (SDSS public Data Release 15). We use a random forest classifier to identify the kinematic parameters that are most connected to galaxy quenching. We find that kinematic parameters that relate predominantly to the disc (such as the mean rotational velocity) and parameters that characterise whether a galaxy is rotation- or dispersion-dominated (such as the ratio of rotational velocity to velocity dispersion) are not fundamentally linked to the quenching of star formation. Instead, we find overwhelmingly that it is the absolute level of velocity dispersion (a property that relates primarily to a galaxy's bulge/spheroidal component) that is most important for separating star forming and quenched galaxies. Furthermore, a partial correlation analysis shows that many commonly discussed correlations between galaxy properties and quenching are spurious, and that the fundamental correlation is between quenching and velocity dispersion. In particular, we find that at fixed velocity dispersion, there is only a very weak dependence of quenching on the disc properties, whereby more discy galaxies are slightly more likely to be forming stars. By invoking the tight relationship between black hole mass and velocity dispersion, and noting that black hole mass traces the total energy released by AGN, we argue that these data support a scenario in which quenching occurs by preventive feedback from AGN. The kinematic measurements from this work are publicly available.

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arXiv2021

On the quenching of star formation in observed and simulated central galaxies: Evidence for the role of integrated AGN feedback

In this paper we investigate how massive central galaxies cease their star formation by comparing theoretical predictions from cosmological simulations: EAGLE, Illustris and IllustrisTNG with observations of the local Universe from the Sloan Digital Sky Survey (SDSS). Our machine learning (ML) classification reveals supermassive black hole mass ($M_{\rm BH}$) as the most predictive parameter in determining whether a galaxy is star forming or quenched at redshift $z=0$ in all three simulations. This predicted consequence of active galactic nucleus (AGN) quenching is reflected in the observations, where it is true for a range of indirect estimates of $M_{\rm BH}$ via proxies as well as its dynamical measurements. Our partial correlation analysis shows that other galactic parameters lose their strong association with quiescence, once their correlations with $M_{\rm BH}$ are accounted for. In simulations we demonstrate that it is the integrated power output of the AGN, rather than its instantaneous activity, which causes galaxies to quench. Finally, we analyse the change in molecular gas content of galaxies from star forming to passive populations. We find that both gas fractions ($f_{\rm gas}$) and star formation efficiencies (SFEs) decrease upon transition to quiescence in the observations but SFE is more predictive than $f_{\rm gas}$ in the ML passive/star-forming classification. These trends in the SDSS are most closely recovered in IllustrisTNG and are in direct contrast with the predictions made by Illustris. We conclude that a viable AGN feedback prescription can be achieved by a combination of preventative feedback and turbulence injection which together quench star formation in central galaxies.

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arXiv2021

Mapping the Diversity of Galaxy Spectra with Deep Unsupervised Machine Learning

Modern spectroscopic surveys of galaxies such as MaNGA consist of millions of diverse spectra covering different regions of thousands of galaxies. We propose and implement a deep unsupervised machine learning method to summarize the entire diversity of MaNGA spectra onto a 15x15 map (DESOM-1), where neighbouring points on the map represent similar spectra. We demonstrate our method as an alternative to conventional full spectral fitting for deriving physical quantities, as well as their full probability distributions, much more efficiently than traditional resource-intensive Bayesian methods. Since spectra are grouped by similarity, the distribution of spectra onto the map for a single galaxy, i.e., its "fingerprint", reveals the presence of distinct stellar populations within the galaxy indicating smoother or episodic star-formation histories. We further map the diversity of galaxy fingerprints onto a second map (DESOM-2). Using galaxy images and independent measures of galaxy morphology, we confirm that galaxies with similar fingerprints have similar morphologies and inclination angles. Since morphological information was not used in the mapping algorithm, relating galaxy morphology to the star-formation histories encoded in the fingerprints is one example of how the DESOM maps can be used to make scientific inferences.

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arXiv2021

Towards robust determination of non-parametric morphologies in marginal astronomical data: resolving uncertainties with cosmological hydrodynamical simulations

Quantitative morphologies, such as asymmetry and concentration, have long been used as an effective way to assess the distribution of galaxy starlight in large samples. Application of such quantitative indicators to other data products could provide a tool capable of capturing the 2-dimensional distribution of a range of galactic properties, such as stellar mass or star-formation rate maps. In this work, we utilize galaxies from the Illustris and IllustrisTNG simulations to assess the applicability of concentration and asymmetry indicators to the stellar mass distribution in galaxies. Specifically, we test whether the intrinsic values of concentration and asymmetry (measured directly from the simulation stellar mass particle maps) are recovered after the application of measurement uncertainty and a point spread function (PSF). We find that random noise has a non-negligible systematic effect on asymmetry that scales inversely with signal-to-noise, particularly at signal-to-noise less than 100. We evaluate different methods to correct for the noise contribution to asymmetry at very low signal-to-noise, where previous studies have been unable to explore due to systematics. We present algebraic corrections for noise and resolution to recover the intrinsic morphology parameters. Using Illustris as a comparison dataset, we evaluate the robustness of these fits in the presence of a different physics model, and confirm these correction methods can be applied to other datasets. Lastly, we provide estimations for the uncertainty on different correction methods at varying signal-to-noise and resolution regimes.

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arXiv2020

The ALMaQUEST Survey: V. The non-universality of kpc-scale star formation relations and the factors that drive them

Using a sample of ~15,000 kpc-scale star-forming spaxels in 28 galaxies drawn from the ALMA-MaNGA QUEnching and STar formation (ALMaQUEST) survey, we investigate the galaxy-to-galaxy variation of the `resolved' Schmidt-Kennicutt relation (rSK; Sigma_H2 - Sigma_SFR), the `resolved' star forming main sequence (rSFMS; Sigma_* - Sigma_SFR) and the `resolved' molecular gas main sequence (rMGMS; Sigma_* - Sigma_H2). The rSK relation, rSFMS and rMGMS all show significant galaxy-to-galaxy variation in both shape and normalization, indicating that none of these relations is universal between galaxies. The rSFMS shows the largest galaxy-to-galaxy variation and the rMGMS the least. By defining an `offset' from the average relations, we compute a Delta_rSK, Delta_rSFMS, Delta_rMGMS for each galaxy, to investigate correlations with global properties. We find the following correlations with at least 2 sigma significance: the rSK is lower (i.e. lower star formation efficiency) in galaxies with higher M_*, larger Sersic index and lower specific SFR (sSFR); the rSFMS is lower (i.e. lower sSFR) in galaxies with higher M_* and larger Sersic index; the rMGMS is lower (i.e. lower gas fraction) in galaxies with lower sSFR. In the ensemble of all 15,000 data points, the rSK relation and rMGMS show equally tight scatters and strong correlation coefficients, compared with a larger scatter and weaker correlation in the rSFMS. Moreover, whilst there is no correlation between Delta_rSK and Delta_rMGMS in the sample, the offset of a galaxy's rSFMS does correlate with both of the other two offsets. Our results therefore indicate that the rSK and rMGMS are independent relations, whereas the rSFMS is a result of their combination.

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arXiv2020

The ALMaQUEST Survey: VI. The molecular gas main sequence of `retired' regions in galaxies

In order to investigate the role of gas in the demise of star formation on kpc-scales, we compare the resolved molecular gas main sequence (rMGMS: Sigma_* vs Sigma_H2) of star-forming regions to the sequence of `retired' regions that have ceased to form new stars. Using data from the ALMaQUEST survey, we find that retired spaxels form a rMGMS that is distinct from that of star-forming spaxels, offset to lower Sigma_H2 at fixed Sigma_* by a factor of ~5. We study the rMGMS of star-forming and retired spaxels on a galaxy-by-galaxy basis for eight individual ALMaQUEST galaxies. Six of these galaxies have their retired spaxels concentrated within the central few kpc. Molecular gas is detected in 40-100% of retired spaxels in the eight galaxies in our sample. Both the star-forming and retired rMGMS show a diversity in normalization from galaxy-to-galaxy. However, in any given galaxy, the rMGMS for retired regions is found to be distinct from the star-forming sequence and gas fractions of retired spaxels are up to an order of magnitude lower than the star-forming spaxels. We conclude that quenching is associated with a depletion (but not absence) of molecular gas via a mechanism that typically begins in the centre of the galaxy.

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arXiv2020

Spatially resolved star formation and fuelling in galaxy interactions

We investigate the spatial structure and evolution of star formation and the interstellar medium (ISM) in interacting galaxies. We use an extensive suite of parsec-scale galaxy merger simulations (stellar mass ratio = 2.5:1), which employs the "Feedback In Realistic Environments-" model (fire-2). This framework resolves star formation, feedback processes, and the multi-phase structure of the ISM. We focus on the galaxy-pair stages of interaction. We find that close encounters substantially augment cool (HI) and cold-dense (H2) gas budgets, elevating the formation of new stars as a result. This enhancement is centrally-concentrated for the secondary galaxy, and more radially extended for the primary. This behaviour is weakly dependent on orbital geometry. We also find that galaxies with elevated global star formation rate (SFR) experience intense nuclear SFR enhancement, driven by high levels of either star formation efficiency (SFE) or available cold-dense gas fuel. Galaxies with suppressed global SFR also contain a nuclear cold-dense gas reservoir, but low SFE levels diminish SFR in the central region. Concretely, in the majority of cases, SFR-enhancement in the central kiloparsec is fuel-driven (55% for the secondary, 71% for the primary) -- whilst central SFR-suppression is efficiency-driven (91% for the secondary, 97% for the primary). Our numerical predictions underscore the need of substantially larger, and/or merger-dedicated, spatially-resolved galaxy surveys -- capable of examining vast and diverse samples of interacting systems -- coupled with multi-wavelength campaigns aimed to capture their internal ISM structure.

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arXiv2020

How do central and satellite galaxies quench? -- Insights from spatially resolved spectroscopy in the MaNGA survey

We investigate how star formation quenching proceeds within central and satellite galaxies using spatially resolved spectroscopy from the SDSS-IV MaNGA DR15. We adopt a complete sample of star formation rate surface densities ($Σ_{\rm SFR}$), derived in Bluck et al. (2020), to compute the distance at which each spaxel resides from the resolved star forming main sequence ($Σ_{\rm SFR} - Σ_*$ relation): $ΔΣ_{\rm SFR}$. We study galaxy radial profiles in $ΔΣ_{\rm SFR}$, and luminosity weighted stellar age (${\rm Age_L}$), split by a variety of intrinsic and environmental parameters. Via several statistical analyses, we establish that the quenching of central galaxies is governed by intrinsic parameters, with central velocity dispersion ($σ_c$) being the most important single parameter. High mass satellites quench in a very similar manner to centrals. Conversely, low mass satellite quenching is governed primarily by environmental parameters, with local galaxy over-density ($δ_5$) being the most important single parameter. Utilising the empirical $M_{BH}$ - $σ_c$ relation, we estimate that quenching via AGN feedback must occur at $M_{BH} \geq 10^{6.5-7.5} M_{\odot}$, and is marked by steeply rising $ΔΣ_{\rm SFR}$ radial profiles in the green valley, indicating `inside-out' quenching. On the other hand, environmental quenching occurs at over-densities of 10 - 30 times the average galaxy density at z$\sim$0.1, and is marked by steeply declining $ΔΣ_{\rm SFR}$ profiles, indicating `outside-in' quenching. Finally, through an analysis of stellar metallicities, we conclude that both intrinsic and environmental quenching must incorporate significant starvation of gas supply.

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arXiv2020

Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging

There are several supervised machine learning methods used for the application of automated morphological classification of galaxies; however, there has not yet been a clear comparison of these different methods using imaging data, or a investigation for maximising their effectiveness. We carry out a comparison between several common machine learning methods for galaxy classification (Convolutional Neural Network (CNN), K-nearest neighbour, Logistic Regression, Support Vector Machine, Random Forest, and Neural Networks) by using Dark Energy Survey (DES) data combined with visual classifications from the Galaxy Zoo 1 project (GZ1). Our goal is to determine the optimal machine learning methods when using imaging data for galaxy classification. We show that CNN is the most successful method of these ten methods in our study. Using a sample of $\sim$2,800 galaxies with visual classification from GZ1, we reach an accuracy of $\sim$0.99 for the morphological classification of Ellipticals and Spirals. The further investigation of the galaxies that have a different ML and visual classification but with high predicted probabilities in our CNN usually reveals an the incorrect classification provided by GZ1. We further find the galaxies having a low probability of being either spirals or ellipticals are visually Lenticulars (S0), demonstrating that supervised learning is able to rediscover that this class of galaxy is distinct from both Es and Spirals. We confirm that $\sim$2.5\% galaxies are misclassified by GZ1 in our study. After correcting these galaxies' labels, we improve our CNN performance to an average accuracy of over 0.99 (accuracy of 0.994 is our best result).

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arXiv2020

The ALMaQUEST Survey: II. What drives central starbursts at z~0?

Starburst galaxies have elevated star formation rates (SFRs) for their stellar mass. In Ellison et al. (2018) we used integral field unit (IFU) maps of star formation rate surface density (Sigma_SFR) and stellar mass surface density (Sigma_*) to show that starburst galaxies in the local universe are driven by SFRs that are preferentially boosted in their central regions. Here, we present molecular gas maps obtained with the Atacama Large Millimeter Array (ALMA) observatory for 12 central starburst galaxies at z~0 drawn from the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey. The ALMA and MaNGA data are well matched in spatial resolution, such that the ALMA maps of molecular gas surface density (Sigma_H2) can be directly compared with MaNGA maps at kpc-scale resolution. The combination of Sigma_H2, Sigma_* and Sigma_SFR at the same resolution allow us to investigate whether central starbursts are driven primarily by enhancements in star formation efficiency (SFE) or by increased gas fractions. By computing offsets from the resolved Kennicutt-Schmidt relation (Sigma_H2 vs. Sigma_SFR) and the molecular gas main sequence (Sigma_* vs. Sigma_H2), we conclude that the primary driver of the central starburst is an elevated SFE. We also show that the enhancement in Sigma_SFR is accompanied by a dilution in O/H, consistent with a triggering that is induced by metal poor gas inflow. These observational signatures are found in both undisturbed (9/12 galaxies in our sample) and recently merged galaxies, indicating that both interactions and secular mechanisms contribute to central starbursts.

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arXiv2019

The ALMaQUEST Survey: III. Scatter in the resolved star forming main sequence is primarily due to variations in star formation efficiency

Using a sample of 11,478 spaxels in 34 galaxies with molecular gas, star formation and stellar maps taken from the ALMA-MaNGA QUEnching and STar formation (ALMaQUEST) survey, we investigate the parameters that correlate with variations in star formation rates on kpc scales. We use a combination of correlation statistics and an artificial neural network to quantify the parameters that drive both the absolute star formation rate surface density (Sigma_SFR), as well as its scatter around the resolved star forming main sequence (Delta Sigma_SFR). We find that Sigma_SFR is primarily regulated by molecular gas surface density (Sigma_H2) with a secondary dependence on stellar mass surface density (Sigma_*), as expected from an `extended Kennicutt-Schmidt relation'. However, Delta Sigma_SFR is driven primarily by changes in star formation efficiency (SFE), with variations in gas fraction playing a secondary role. Taken together, our results demonstrate that whilst the absolute rate of star formation is primarily set by the amount of molecular gas, the variation of star formation rate above and below the resolved star forming main sequence (on kpc scales) is primarily due to changes in SFE.

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arXiv2019

Towards a deeper understanding of the physics driving galaxy quenching -- inferring trends in the gas content via extinction

In order to investigate the importance of different proposed quenching mechanisms, we use an indirect method to estimate gas masses for ~62,000 SDSS DR7 galaxies. We infer gas surface densities from dust column densities as traced by extinction within the fibre, applying a metallicity correction to account for varying dust-to-gas ratios. We find that both gas fraction and star formation efficiency (SFE) decrease moving away from the star forming main sequence (MS) towards quiescence for all galaxy masses. We further show that both quantities correlate similarly strongly with the departure from the MS, implying the need for any physical model of quenching to invoke a change in $\textit{both}$ gas fraction and SFE. Our results call for a better understanding of the physical processes driving the decrease in star formation efficiency, which has received relatively little attention in the theory of quenching until now.

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arXiv2019

Are galactic star formation and quenching governed by local, global or environmental phenomena?

We present an analysis of star formation and quenching in the SDSS-IV MaNGA-DR15, utilising over 5 million spaxels from $\sim$3500 local galaxies. We estimate star formation rate surface densities ($Σ_{\rm SFR}$) via dust corrected $Hα$ flux where possible, and via an empirical relationship between specific star formation rate (sSFR) and the strength of the 4000 Angstrom break (D4000) in all other cases. We train a multi-layered artificial neural network (ANN) and a random forest (RF) to classify spaxels into `star forming' and `quenched' categories given various individual (and groups of) parameters. We find that global parameters (pertaining to the galaxy as a whole) perform collectively the best at predicting when spaxels will be quenched, and are substantially superior to local/ spatially resolved and environmental parameters. Central velocity dispersion is the best single parameter for predicting quenching in central galaxies. We interpret this observational fact as a probable consequence of the total integrated energy from AGN feedback being traced by the mass of the black hole, which is well known to correlate strongly with central velocity dispersion. Additionally, we train both an ANN and RF to estimate $Σ_{\rm SFR}$ values directly via regression in star forming regions. Local/ spatially resolved parameters are collectively the most predictive at estimating $Σ_{\rm SFR}$ in these analyses, with stellar mass surface density at the spaxel location ($Σ_*$) being by far the best single parameter. Thus, quenching is fundamentally a global process but star formation is governed locally by processes within each spaxel.

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