Identifying Galaxy Mergers in Observations and Simulations with Deep Learning

Mergers are an important aspect of galaxy formation and evolution. We aim to test whether deep learning techniques can be used to reproduce visual classification of observations, physical classification of simulations and highlight any differences between these two classifications. With one of the main difficulties of merger studies being the lack of a truth sample, we can use our method to test biases in visually identified merger catalogues. A convolutional neural network architecture was developed and trained in two ways: one with observations from SDSS and one with simulated galaxies from EAGLE, processed to mimic the SDSS observations. The SDSS images were also classified by the simulation trained network and the EAGLE images classified by the observation trained network. The observationally trained network achieves an accuracy of 91.5% while the simulation trained network achieves 65.2% on the visually classified SDSS and physically classified EAGLE images respectively. Classifying the SDSS images with the simulation trained network was less successful, only achieving an accuracy of 64.6%, while classifying the EAGLE images with the observation network was very poor, achieving an accuracy of only 53.0% with preferential assignment to the non-merger classification. This suggests that most of the simulated mergers do not have conspicuous merger features and visually identified merger catalogues from observations are incomplete and biased towards certain merger types. The networks trained and tested with the same data perform the best, with observations performing better than simulations, a result of the observational sample being biased towards conspicuous mergers. Classifying SDSS observations with the simulation trained network has proven to work, providing tantalizing prospects for using simulation trained networks for galaxy identification in large surveys.Comment: Submitted to A&A, revised after first referee report. 20 pages, 22 figures, 14 tables, 1 appendi

Identifying galaxy mergers in observations and simulations with deep learning

Context. Mergers are an important aspect of galaxy formation and evolution. With large upcoming surveys, such as Euclid and LSST, accurate techniques that are fast and efficient are needed to identify galaxy mergers for further study. Aims: We aim to test whether deep learning techniques can be used to reproduce visual classification of observations, physical classification of simulations and highlight any differences between these two classifications. As one of the main difficulties of merger studies is the lack of a truth sample, we can use our method to test biases in visually identified merger catalogues. Methods: We developed a convolutional neural network architecture and trained it in two ways: one with observations from SDSS and one with simulated galaxies from EAGLE, processed to mimic the SDSS observations. The SDSS images were also classified by the simulation trained network and the EAGLE images classified by the observation trained network. Results: The observationally trained network achieves an accuracy of 91.5% while the simulation trained network achieves 65.2% on the visually classified SDSS and physically classified EAGLE images respectively. Classifying the SDSS images with the simulation trained network was less successful, only achieving an accuracy of 64.6%, while classifying the EAGLE images with the observation network was very poor, achieving an accuracy of only 53.0% with preferential assignment to the non-merger classification. This suggests that most of the simulated mergers do not have conspicuous merger features and visually identified merger catalogues from observations are incomplete and biased towards certain merger types. Conclusions: The networks trained and tested with the same data perform the best, with observations performing better than simulations, a result of the observational sample being biased towards conspicuous mergers. Classifying SDSS observations with the simulation trained network has proven to work, providing tantalising prospects for using simulation trained networks for galaxy identification in large surveys

Small-scale galaxy clustering in the eagle simulation

We study present-day galaxy clustering in the EAGLE cosmological hydrodynamical simulation. EAGLE’s galaxy formation parameters were calibrated to reproduce the redshift z = 0.1 galaxy stellar mass function, and the simulation also reproduces galaxy colours well. The simulation volume is too small to correctly sample large-scale fluctuations and we therefore concentrate on scales smaller than a few mega parsecs. We find very good agreement with observed clustering measurements from the Galaxy And Mass Assembly (GAMA) survey, when galaxies are binned by stellar mass, colour or luminosity. However, low-mass red galaxies are clustered too strongly, which is at least partly due to limited numerical resolution. Apart from this limitation, we conclude that EAGLE galaxies inhabit similar dark matter haloes as observed GAMA galaxies, and that the radial distribution of satellite galaxies, as a function of stellar mass and colour, is similar to that observed as well

The diverse evolutionary pathways of post-starburst galaxies

About 35 years ago a class of galaxies with unusually strong Balmer absorption lines and weak emission lines was discovered in distant galaxy clusters(1,2). These objects, alternatively referred to as post-starburst, E+A or k+a galaxies, are now known to occur in all environments and at all redshifts(3-7), with many exhibiting compact morphologies and low-surface brightness features indicative of past galaxy mergers(3,8). They are commonly thought to represent galaxies that are transitioning from blue to red sequence, making them critical to our understanding of the origins of galaxy bimodality(9-14). However, recent observational studies have questioned this simple interpretation(15-18). From observations alone, it is challenging to disentangle the different mechanisms that lead to the quenching of star formation in galaxies. Here we present examples of three different evolutionary pathways that lead to galaxies with strong Balmer absorption lines in the Evolution and Assembly of Galaxies and their Environments (EAGLE) simulation(19,20): classical blue -> red quenching, blue -> blue cycle and red -> red rejuvenation. The first two are found in both post-starburst galaxies and galaxies with truncated star formation. Each pathway is consistent with scenarios hypothesized for observational samples(2,15,18,21,22). The fact that 'post-starburst' signatures can be attained via various evolutionary channels explains the diversity of observed properties, and lends support to the idea that slower quenching channels are important at low redshift(23,24).Peer reviewe

From the far-ultraviolet to the far-infrared – galaxy emission at 0 ≤ z ≤ 10 in the shark semi-analytic model

We combine the shark semi-analytic model of galaxy formation with the prospect software tool for spectral energy distribution (SED) generation to study the multiwavelength emission of galaxies from the far-ultraviolet (FUV) to the far-infrared (FIR) at 0 ≤ z ≤ 10. We produce a physical model for the attenuation of galaxies across cosmic time by combining a local Universe empirical relation to compute the dust mass of galaxies from their gas metallicity and mass, attenuation curves derived from radiative transfer calculations of galaxies in the eagle hydrodynamic simulation suite, and the properties of shark galaxies. We are able to produce a wide range of galaxies, from the z = 8 star-forming galaxies with almost no extinction, z = 2 submillimetre galaxies, down to the normal star-forming and red-sequence galaxies at z = 0. Quantitatively, we find that shark reproduces the observed (i) z = 0 FUV-to-FIR, (ii) 0 ≤ z ≤ 3 rest-frame K-band, and (iii) 0 ≤ z ≤ 10 rest-frame FUV luminosity functions, (iv) z ≤ 8 UV slopes, (v) the FUV-to-FIR number counts (including the widely disputed 850 μm), (vi) redshift distribution of bright 850μm galaxies, and (vii) the integrated cosmic SED from z = 0 to 1 to an unprecedented level. This is achieved without the need to invoke changes in the stellar initial mass function, dust-to-metal mass ratio, or metal enrichment time-scales. Our model predicts star formation in galaxy discs to dominate in the FUV-to-optical, while bulges dominate at the NIR at all redshifts. The FIR sees a strong evolution in which discs dominate at z ≤ 1 and starbursts (triggered by both galaxy mergers and disc instabilities, in an even mix) dominate at higher redshifts, even out to z = 10.Publisher PDFPeer reviewe

The MAGPI Survey -- science goals, design, observing strategy, early results and theoretical framework

© The Author(s), 2021. Published by Cambridge University Press on behalf of the Astronomical Society of Australia. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1017/pasa.2021.25We present an overview of the Middle Ages Galaxy Properties with Integral Field Spectroscopy (MAGPI) survey, a Large Program on ESO/VLT. MAGPI is designed to study the physical drivers of galaxy transformation at a lookback time of 3-4 Gyr, during which the dynamical, morphological, and chemical properties of galaxies are predicted to evolve significantly. The survey uses new medium-deep adaptive optics aided MUSE observations of fields selected from the GAMA survey, providing a wealth of publicly available ancillary multi-wavelength data. With these data, MAGPI will map the kinematic and chemical properties of stars and ionised gas for a sample of 60 massive (> 7 x 10^10 M_Sun) central galaxies at 0.25 < zPeer reviewe

Galaxy and Mass Assembly (GAMA): Variation in Galaxy Structure Across the Green Valley

Using a sample of 472 local Universe (z < 0.06) galaxies in the stellar mass range 10.25 < log M*/MG < 10.75, we explore the variation in galaxy structure as a function of morphology and galaxy colour. Our sample of galaxies is sub-divided into red, green and blue colour groups and into elliptical and non-elliptical (disk-type) morphologies. Using KiDS and VIKING derived postage stamp images, a group of eight volunteers visually classified bars, rings, morphological lenses, tidal streams, shells and signs of merger activity for all systems. We find a significant surplus of rings (2.3σ) and lenses (2.9σ) in disk-type galaxies as they transition across the green valley. Combined, this implies a joint ring/lens green valley surplus significance of 3.3σ relative to equivalent disk-types within either the blue cloud or the red sequence. We recover a bar fraction of ∼ 44% which remains flat with colour, however, we find that the presence of a bar acts to modulate the incidence of rings and (to a lesser extent) lenses, with rings in barred disk-type galaxies more common by ∼ 20 − 30 percentage points relative to their unbarred counterparts, regardless of colour. Additionally, green valley disk-type galaxies with a bar exhibit a significant 3.0σ surplus of lenses relative to their blue/red analogues. The existence of such structures rules out violent transformative events as the primary end-of-life evolutionary mechanism, with a more passive scenario the favoured candidate for the majority of galaxies rapidly transitioning across the green valley. Key words: galaxies: elliptical and lenticular, cD – galaxies: spiral – galaxies: evo- lution – galaxies: star formation – galaxies: statistics – galaxies: structur

Radial distribution of dust, stars, gas, and star-formation rate in DustPedia face-on galaxies

Aims. The purpose of this work is the characterization of the radial distribution of dust, stars, gas, and star-formation rate (SFR) in a sub-sample of 18 face-on spiral galaxies extracted from the DustPedia sample. Methods. This study is performed by exploiting the multi-wavelength DustPedia database, from ultraviolet (UV) to sub-millimeter bands, in addition to molecular (12CO) and atomic (Hi) gas maps and metallicity abundance information available in the literature. We fitted the surface-brightness profiles of the tracers of dust and stars, the mass surface-density profiles of dust, stars, molecular gas, and total gas, and the SFR surface-density profiles with an exponential curve and derived their scale-lengths. We also developed a method to solve for the CO-to-H2 conversion factor (αCO) per galaxy by using dust- and gas-mass profiles. Results. Although each galaxy has its own peculiar behavior, we identified a common trend of the exponential scale-lengths versus wavelength. On average, the scale-lengths normalized to the B-band 25 mag/arcsec2 radius decrease from UV to 70 μm, from 0.4 to 0.2, and then increase back up to ~0.3 at 500 microns. The main result is that, on average, the dust-mass surface-density scale-length is about 1.8 times the stellar one derived from IRAC data and the 3.6 μm surface brightness, and close to that in the UV. We found a mild dependence of the scale-lengths on the Hubble stage T: the scale-lengths of the Herschel bands and the 3.6 μm scale-length tend to increase from earlier to later types, the scale-length at 70 μm tends to be smaller than that at longer sub-mm wavelength with ratios between longer sub-mm wavelengths and 70 μm that decrease with increasing T. The scale-length ratio of SFR and stars shows a weak increasing trend towards later types. Our αCO determinations are in the range (0.3−9) M⊙ pc-2 (K km s-1)-1, almost invariant by using a fixed dust-to-gas ratio mass (DGR) or a DGR depending on metallicity gradient

Multi-wavelength de-blended Herschel view of the statistical properties of dusty star-forming galaxies across cosmic time

International audience Aims: We study the statistical properties of dusty star-forming galaxies across cosmic time, such as their number counts, luminosity functions (LF), and the dust-obscured star formation rate density (SFRD). Methods: We used the most recent de-blended Herschel catalogue in the COSMOS field to measure the number counts and LFs at far-infrared (FIR) and sub-millimetre (sub-mm) wavelengths. The de-blended catalogue was generated by combining the Bayesian source extraction tool XID+ and an informative prior derived from the associated deep multi-wavelength photometric data. Results: Through our de-confusion technique and based on the deep multi-wavelength photometric information, we are able to achieve more accurate measurements while at the same time probing roughly ten times below the Herschel confusion limit. Our number counts at 250 μm agree well with previous Herschel studies. However, our counts at 350 and 500 μm are below previous Herschel results because previous Herschel studies suffered from source confusion and blending issues. Our number counts at 450 and 870 μm show excellent agreement with previous determinations derived from single-dish and interferometric observations. Our measurements of the LF at 250 μm and the total IR LF agree well with previous results in the overlapping redshift and luminosity range. The increased dynamic range of our measurements allows us to better measure the faint-end of the LF and measure the dust-obscured SFRD out to z ∼ 6. We find that the fraction of obscured star formation activity is at its highest (>80%) around z ∼ 1. We do not find a shift of balance between z ∼ 3 and z ∼ 4 in the SFRD from being dominated by unobscured star formation at higher redshift to obscured star formation at lower redshift. However, we do find 3 < z < 4 to be an interesting transition period as the portion of the total SFRD that is obscured by dust is significantly lower at higher redshifts