Optimizing 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, LogisticRegression, Support Vector Machine, Random Forest, and Neural Networks) by using DarkEnergy 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 _2,800 galaxies with visual classification from GZ1, we reach an accuracy of _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 _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)

The origin of the mass scales for maximal star formation efficiency and quenching: the critical role of supernovae

We use the Henriques et al. version of the Munich galaxy formation model (L-GALAXIES) to investigate why the halo and stellar mass scales above which galaxies are quenched are constant with redshift and coincide with the scale, where baryons are most efficiently converted into stars. This model assumes that central galaxies are quenched by active galactic nucleus (AGN) feedback when hot halo gas accretes on to a supermassive black hole. Nevertheless, we find that supernova (SN) feedback sets both mass scales. As haloes grow above a threshold mass, SNe can no longer eject material so their hot gas content increases, enhancing the cooling rate on to the central galaxy, its cold gas content, its star formation rate, and the growth rate of its central black hole. Strong AGN feedback terminates this short-lived phase by suppressing the fuel supply for star formation. Despite strong evolution of the halo mass – temperature relation, quenching occurs at a redshift-independent halo and stellar mass that coincides with the mass where baryons have been converted into stars with maximal efficiency. These regularities and coincidences are a result of the specific parameters selected by Monte Carlo Markov Chain (MCMC) tuning of the model to fit the observed abundance and passive fraction of galaxies over the redshift range 0 ≤ z ≤ 3. Thus they are required by the observed evolution of the galaxy population, at least in the context of models of this type.ISSN:0035-8711ISSN:1365-296

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 (SFEs), 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 (SFRs). We investigate galaxy-to-galaxy variations in the resolved Kennicutt-Schmidt relation, (rKS: Sigma(H2) versus Sigma(SFR)), the resolved molecular gas main sequence (rMGMS: Sigma(*) versus Sigma(SF)(R)), and the resolved star-forming main sequence (rSFMS: Sigma(*) versus Sigma(S)(FR)). We quantify offsets from these resolved relations to determine if SFR, molecular gas fraction, or/and SFE is/are 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 Sigma(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

A catalogue of structural and morphological measurements for DES Y1

We present a structural and morphological catalogue for 45 million objects selected from the first year data of the Dark Energy Survey (DES). Single Sérsic fits and non-parametric measurements are produced for g, r, and i filters. The parameters from the best-fitting Sérsic model (total magnitude, half-light radius, Sérsic index, axis ratio, and position angle) are measured with GALFIT; the non-parametric coefficients (concentration, asymmetry, clumpiness, Gini, M20) are provided using the Zurich Estimator of Structural Types (ZEST+). To study the statistical uncertainties, we consider a sample of state-of-the-art image simulations with a realistic distribution in the input parameter space and then process and analyse them as we do with real data: this enables us to quantify the observational biases due to PSF blurring and magnitude effects and correct the measurements as a function of magnitude, galaxy size, Sérsic index (concentration for the analysis of the non-parametric measurements) and ellipticity. We present the largest structural catalogue to date: we find that accurate and complete measurements for all the structural parameters are typically obtained for galaxies with SEXTRACTORMAG_AUTO_I≤21. Indeed, the parameters in the filters i and r can be overall well recovered up to MAG_AUTO≤21.5, corresponding to a fitting completeness of ∼90 per cent below this threshold, for a total of 25 million galaxies. The combination of parametric and non-parametric structural measurements makes this catalogue an important instrument to explore and understand how galaxies form and evolve.ISSN:0035-8711ISSN:1365-296

Interacting galaxies on FIRE-2: the connection between enhanced star formation and interstellar gas content

We present a comprehensive suite of high-resolution (parsec-scale), idealized (non-cosmological) galaxy merger simulations (24 runs, stellar mass ratio ∼2.5:1) to investigate the connection between interaction-induced star formation and the evolution of the interstellar medium (ISM) in various temperature–density regimes. We use the GIZMO code and the second version of the ‘Feedback in Realistic Environments’ model (FIRE-2), which captures the multiphase structure of the ISM. Our simulations are designed to represent galaxy mergers in the local Universe. In this work, we focus on the ‘galaxy-pair period’ between first and second pericentric passage. We split the ISM into four regimes: hot, warm, cool, and cold-dense, motivated by the hot, ionized, atomic and molecular gas phases observed in real galaxies. We find that, on average, interactions enhance the star formation rate of the pair (∼30 per cent, merger-suite sample average) and elevate their cold-dense gas content (∼18 per cent). This is accompanied by a decrease in warm gas (∼11 per cent), a negligible change in cool gas (∼4 per cent increase), and a substantial increase in hot gas (∼400 per cent). The amount of cold-dense gas with densities above 1000 cm−3 (the cold ultra-dense regime) is elevated significantly (∼240 per cent), but only accounts for ∼0.15 per cent (on average) of the cold-dense gas budget.ISSN:0035-8711ISSN:1365-296

What shapes a galaxy? - unraveling the role of mass, environment, and star formation in forming galactic structure

We investigate the dependence of galaxy structure on a variety of galactic and environmental parameters for ∼500 000 galaxies at z< 0.2, taken from the Sloan Digital Sky Survey data release 7 (SDSS-DR7). We utilize bulge-to-total stellar mass ratio (B/T)* as the primary indicator of galactic structure, which circumvents issues of morphological dependence on waveband. We rank galaxy and environmental parameters in terms of how predictive they are of galaxy structure, using an artificial neural network approach. We find that distance from the star-forming main sequence (ΔSFR), followed by stellar mass (M*), are the most closely connected parameters to (B/T)*, and are significantly more predictive of galaxy structure than global star formation rate (SFR), or any environmental metric considered (for both central and satellite galaxies). Additionally, we make a detailed comparison to the Illustris hydrodynamical simulation and the LGalaxies semi-analytic model. In both simulations, we find a significant lack of bulge-dominated galaxies at a fixed stellar mass, compared to the SDSS. This result highlights a potentially serious problem in contemporary models of galaxy evolution.ISSN:0035-8711ISSN:1365-296