arXiv subjects

Leonardo Ferreira

Publications and source records attributed to Leonardo Ferreira.

58 records · Page 4Linked to original sources

arXiv2021

Relic galaxy analogues in Illustris TNG50: the formation pathways of surviving red nuggets in a cosmological simulation

Relic galaxies are massive compact quiescent galaxies that formed at high-redshift and remained almost unchanged since then. In this work, we search for analogues to relic galaxies in the TNG50 cosmological simulations to understand relic formation and test the ability of TNG50 to reproduce such rare objects. Using stellar mass, age, radius, quiescence and stellar assembly criteria, we find 5 subhalos in TNG50 that could be potential relic analogues. We compare their properties with other constraints imposed by a sample of 13 observed relic galaxies. We find one analogue in TNG50 that simultaneously satisfies most of the available observational constraints, such as metallicity and morphology. It also shows similarities to the confirmed relic NGC 1277, regarding environment and dark matter fraction. By taking into account a degree of relicness, a second relic analogue may also be considered. However, the central parts of the brightness and density profiles of the analogues are less steep than that of real relic galaxies, possibly due to limited numerical resolution. We identify two formation pathways of relic analogues in TNG50 depending on their environment: they either have their remaining gas stripped during the infall into a cluster at $z \lesssim 1.2$ or consume it before $z > 1.5$. They are then deprived of significant star formation, leaving their stellar populations almost unaltered during the last 9 Gyr. We also find that the analogue progenitors at $z \sim 4$ inhabit more massive halos than progenitors of quiescent galaxies with similar stellar mass at $z \sim 0$.

astro-ph.GA↗

arXiv2021

Emission Line Galaxies in the SHARDS Frontier Fields I: Candidate Selection and the Discovery of Bursty Hα Emitters

Emission line galaxies provide a crucial tool for the study of galaxy formation and evolution, providing a means to trace a galaxy's star formation history or metal enrichment, and to identify galaxies at a range of stellar masses. In this paper we present a study of emission line galaxies in the SHARDS Frontier Fields medium-band survey. Through detailed flux calibrations we combine the first results of the SHARDS-FF survey with existing Hubble Frontier Field data to select 1,098 candidate emission line galaxies from the Hubble Frontier Filed clusters Abell 370 and MACS J1149.5+2223. Furthermore, we implement this deep medium-band imaging to update photometric redshift estimates and stellar population parameters and discover 38 predominantly low-mass Hα emitters at redshifts 0.24 < z < 0.46. Overall, 27 of these sources have corresponding UV data from the Hubble Space Telescope which allows us to distinguish these sources and investigate the burstiness of their star formation histories. We find that more than 50% of our sample show an enhancement in Hα over UV, suggesting recent bursts in star formation on time scales of a few, to tens of megayears. We investigate these sources and find that they are typically low-mass disky galaxies with normal sizes. Their structures and star formation suggest that they are not undergoing mergers but are bursting due to alternative causes, such as gas accretion.

astro-ph.GA↗

arXiv2020

Retrieving Internal Kinematics of Galaxies with Deep Learning using Single-Band Optical Images

Using deep machine learning we show that the internal velocities of galaxies can be retrieved from optical images trained using 4596 systems observed with the SDSS-MaNGA survey. Using only $i$-band images we show that the velocity dispersions and the rotational velocities of galaxies can be measured to an accuracy of 29 km~$\rm{s}^{-1}$ and 69 km~$\rm{s}^{-1}$ respectively, close to the resolution limit of the spectroscopic data. This shows that galaxy structures in the optical holds important information concerning the internal properties of galaxies and that the internal kinematics of galaxies are quantitatively reflected in their stellar light distributions beyond a simple rotational vs. dispersion distinction.

astro-ph.IM↗

arXiv2020

Galaxy Merger Rates up to z $\sim$ 3 using a Bayesian Deep Learning Model $-$ A Major-Merger classifier using IllustrisTNG Simulation data

Merging is potentially the dominate process in galaxy formation, yet there is still debate about its history over cosmic time. To address this we classify major mergers and measure galaxy merger rates up to z $\sim$ 3 in all five CANDELS fields (UDS, EGS, GOODS-S, GOODS-N, COSMOS) using deep learning convolutional neural networks (CNNs) trained with simulated galaxies from the IllustrisTNG cosmological simulation. The deep learning architecture used is objectively selected by a Bayesian Optmization process over the range of possible hyperparameters. We show that our model can achieve 90% accuracy when classifying mergers from the simulation, and has the additional feature of separating mergers before the infall of stellar masses from post mergers. We compare our machine learning classifications on CANDELS galaxies and compare with visual merger classifications from Kartaltepe et al. (2015), and show that they are broadly consistent. We finish by demonstrating that our model is capable of measuring galaxy merger rates, $\mathcal{R}$, that are consistent with results found for CANDELS galaxies using close pairs statistics, with $\mathcal{R}(z) = 0.02 \pm 0.004 \times (1 +z) ^ {2.76 \pm 0.21}$. This is the first general agreement between major mergers measured using pairs and structure at z < 3.

astro-ph.GA↗