arXiv subjects

Roberto E. González

Publications and source records attributed to Roberto E. González.

4 recordsLinked to original sources

arXiv2023

Not Hydro: Using Neural Networks to estimate galaxy properties on a Dark-Matter-Only simulation

Using data from TNG300-2, we train a neural network (NN) to recreate the stellar mass ($M^*$) and star formation rate (SFR) of central galaxies in a dark-matter-only simulation. We consider 12 input properties from the halo and sub-halo hosting the galaxy and the near environment. $M^*$ predictions are robust, but the machine does not fully reproduce its scatter. The same happens for SFR, but the predictions are not as good as for $M^*$. We chained neural networks, improving the predictions on SFR to some extent. For SFR, we time-averaged this value between $z=0$ and $z=0.1$, which improved results for $z=0$. Predictions of both variables have trouble reproducing values at lower and higher ends. We also study the impact of each input variable in the performance of the predictions using a leave-one-covariate-out approach, which led to insights about the physical and statistical relation between input variables. In terms of metrics, our machine outperforms similar studies, but the main discoveries in this work are not linked with the quality of the predictions themselves, but to how the predictions relate to the input variables. We find that previously studied relations between physical variables are meaningful to the machine. We also find that some merger tree properties strongly impact the performance of the machine. %We highlight the value of machine learning (ML) methods in helping understand the information contained in different variables, since with its help we were able to obtain useful insights resulting from studying the impact of input variables on the resulting behaviour of galaxy properties. We conclude that ML models are useful tools to understand the significance of physical different properties and their impact on target characteristics, as well as strong candidates for potential simulation methods.

astro-ph.GA↗

arXiv2018

Galaxy detection and identification using deep learning and data augmentation

We present a method for automatic detection and classification of galaxies which includes a novel data-augmentation procedure to make trained models more robust against the data taken from different instruments and contrast-stretching functions. This method is shown as part of AstroCV, a growing open source computer vision repository for processing and analyzing big astronomical datasets, including high performance Python and C++ algorithms used in the areas of image processing and computer vision. The underlying models were trained using convolutional neural networks and deep learning techniques, which provide better results than methods based on manual feature engineering and SVMs in most of the cases where training datasets are large. The detection and classification methods were trained end-to-end using public datasets such as the Sloan Digital Sky Survey (SDSS), the Galaxy Zoo, and private datasets such as the Next Generation Virgo (NGVS) and Fornax (NGFS) surveys. Training results are strongly bound to the conversion method from raw FITS data for each band into a 3-channel color image. Therefore, we propose data augmentation for the training using 5 conversion methods. This greatly improves the overall galaxy detection and classification for images produced from different instruments, bands and data reduction procedures. The detection and classification methods were trained using the deep learning framework DARKNET and the real-time object detection system YOLO. These methods are implemented in C language and CUDA platform, and makes intensive use of graphical processing units (GPU). Using a single high-end Nvidia GPU card, it can process a SDSS image in 50 milliseconds and a DECam image in less than 3 seconds.

astro-ph.IM↗

arXiv2017

Galactic conformity measured in semi-analytic models

We study the correlation between the specific star formation rate of central galaxies and neighbour galaxies, also known as 'galactic conformity', out to 20 Mpc/h using three semi-analytic models (SAMs, one from L-GALAXIES and other two from GALFORM). The aim is to establish whether SAMs are able to show galactic conformity using different models and selection criteria. In all the models, when the selection of primary galaxies is based on an isolation criterion in real space, the mean fraction of quenched galaxies around quenched primary galaxies is higher than that around star-forming primary galaxies of the same stellar mass. The overall signal of conformity decreases when we remove satellites selected as primary galaxies, but the effect is much stronger in GALFORM models compared with the L-GALAXIES model. We find this difference is partially explained by the fact that in GALFORM once a galaxy becomes a satellite remains as such, whereas satellites can become centrals at a later time in L-GALAXIES. The signal of conformity decreases down to 60% in the L-GALAXIES model after removing central galaxies that were ejected from their host halo in the past. Galactic conformity is also influenced by primary galaxies at fixed stellar mass that reside in dark matter haloes of different masses. Finally, we explore a proxy of conformity between distinct haloes. In this case the conformity is weak beyond ~ 3 Mpc/h (<3% in L-GALAXIES, <1-2% in GALFORM models). Therefore, it seems difficult that conformity is directly related with a long-range effect.

astro-ph.GA↗

arXiv2014

The origin of spin in galaxies: clues from simulations of atomic cooling halos

In order to elucidate the origin of spin in both dark matter and baryons in galaxies, we have performed hydrodynamical simulations from cosmological initial conditions. We study atomic cooling haloes in the redshift range $100 > z > 9$ with masses of order $10^9{\rm M_{\odot}}$ at redshift $z=10$. We assume that the gas has primordial composition and that ${\rm H_2}$-cooling and prior star-formation in the haloes have been suppressed. We present a comprehensive analysis of the gas and dark matter properties of four halos with very low ($λ\approx 0.01$), low ($λ\approx 0.04$), high ($λ\approx 0.06$) and very high ($λ\approx 0.1$) spin parameter. Our main conclusion is that the spin orientation and magnitude is initially well described by tidal torque linear theory, but later on is determined by the merging and accretion history of each halo. We provide evidence that the topology of the merging region, i.e. the number of colliding filaments, gives an accurate prediction for the spin of dark matter and gas: halos at the center of knots will have low spin while those in the center of filaments will have high spin. The spin of a halo is given by $λ\approx 0.05 \times \left(\frac{7.6}{\rm number\,\,\, of \,\,\, filaments}\right)^{5.1}$.

astro-ph.CO↗