arXiv subjects

R. S. de Souza

Publications and source records attributed to R. S. de Souza.

32 records · Page 2Linked to original sources

arXiv2017

The relation between velocity dispersions and chemical abundances in RAVE giants

We developed a Bayesian framework to determine in a robust way the relation between velocity dispersions and chemical abundances in a sample of stars. Our modelling takes into account the uncertainties in the chemical and kinematic properties. We make use of RAVE DR5 radial velocities and abundances together with Gaia DR1 proper motions and parallaxes (when possible, otherwise UCAC4 data is used). We found that, in general, the velocity dispersions increase with decreasing [Fe/H] and increasing [Mg/Fe]. A possible decrease in velocity dispersion for stars with high [Mg/Fe] is a property of a negligible fraction of stars and hardly a robust result. At low [Fe/H] and high [Mg/Fe] the sample is incomplete, affected by biases, and likely not representative of the underlying stellar population.

astro-ph.GA↗

arXiv2017

A probabilistic approach to emission-line galaxy classification

We invoke a Gaussian mixture model (GMM) to jointly analyse two traditional emission-line classification schemes of galaxy ionization sources: the Baldwin-Phillips-Terlevich (BPT) and $\rm W_{Hα}$ vs. [NII]/H$α$ (WHAN) diagrams, using spectroscopic data from the Sloan Digital Sky Survey Data Release 7 and SEAGal/STARLIGHT datasets. We apply a GMM to empirically define classes of galaxies in a three-dimensional space spanned by the $\log$ [OIII]/H$β$, $\log$ [NII]/H$α$, and $\log$ EW(H$α$), optical parameters. The best-fit GMM based on several statistical criteria suggests a solution around four Gaussian components (GCs), which are capable to explain up to 97 per cent of the data variance. Using elements of information theory, we compare each GC to their respective astronomical counterpart. GC1 and GC4 are associated with star-forming galaxies, suggesting the need to define a new starburst subgroup. GC2 is associated with BPT's Active Galaxy Nuclei (AGN) class and WHAN's weak AGN class. GC3 is associated with BPT's composite class and WHAN's strong AGN class. Conversely, there is no statistical evidence -- based on four GCs -- for the existence of a Seyfert/LINER dichotomy in our sample. Notwithstanding, the inclusion of an additional GC5 unravels it. The GC5 appears associated to the LINER and Passive galaxies on the BPT and WHAN diagrams respectively. Subtleties aside, we demonstrate the potential of our methodology to recover/unravel different objects inside the wilderness of astronomical datasets, without lacking the ability to convey physically interpretable results. The probabilistic classifications from the GMM analysis are publicly available within the COINtoolbox (https://cointoolbox.github.io/GMM\_Catalogue/).

astro-ph.GA↗

arXiv2017

On the realistic validation of photometric redshifts, or why Teddy will never be Happy

Two of the main problems encountered in the development and accurate validation of photometric redshift (photo-z) techniques are the lack of spectroscopic coverage in feature space (e.g. colours and magnitudes) and the mismatch between photometric error distributions associated with the spectroscopic and photometric samples. Although these issues are well known, there is currently no standard benchmark allowing a quantitative analysis of their impact on the final photo-z estimation. In this work, we present two galaxy catalogues, Teddy and Happy, built to enable a more demanding and realistic test of photo-z methods. Using photometry from the Sloan Digital Sky Survey and spectroscopy from a collection of sources, we constructed datasets which mimic the biases between the underlying probability distribution of the real spectroscopic and photometric sample. We demonstrate the potential of these catalogues by submitting them to the scrutiny of different photo-z methods, including machine learning (ML) and template fitting approaches. Beyond the expected bad results from most ML algorithms for cases with missing coverage in feature space, we were able to recognize the superiority of global models in the same situation and the general failure across all types of methods when incomplete coverage is convoluted with the presence of photometric errors - a data situation which photo-z methods were not trained to deal with up to now and which must be addressed by future large scale surveys. Our catalogues represent the first controlled environment allowing a straightforward implementation of such tests. The data are publicly available within the COINtoolbox (https://github.com/COINtoolbox/photoz_catalogues).

astro-ph.GA↗

arXiv2016

Is the cluster environment quenching the Seyfert activity in elliptical and spiral galaxies?

We developed a hierarchical Bayesian model (HBM) to investigate how the presence of Seyfert activity relates to their environment, herein represented by the galaxy cluster mass, $M_{200}$, and the normalized cluster-centric distance, $r/r_{200}$. We achieved this by constructing an unbiased sample of galaxies from the Sloan Digital Sky Survey, with morphological classifications provided by the Galaxy Zoo Project. A propensity score matching approach is introduced to control for the effects of confounding variables: stellar mass, galaxy colour, and star formation rate. The connection between Seyfert-activity and environmental properties in the de-biased sample is modelled within an HBM framework using the so-called logistic regression technique, suitable for the analysis of binary data (e.g., whether or not a galaxy hosts an AGN). Unlike standard ordinary least square fitting methods, our methodology naturally allows modelling the probability of Seyfert-AGN activity in galaxies on their natural scale, i.e. as a binary variable. Furthermore, we demonstrate how an HBM can incorporate information of each particular galaxy morphological type in a unified framework. In elliptical galaxies, our analysis indicates a strong correlation of Seyfert-AGN activity with $r/r_{200}$, and a weaker correlation with the mass of the host. In spiral galaxies these trends do not appear, suggesting that the link between Seyfert activity and the properties of spiral galaxies are independent of the environment.

astro-ph.GA↗

arXiv2016

Exploring the spectroscopic diversity of type Ia supernovae with DRACULA: a machine learning approach

The existence of multiple subclasses of type Ia supernovae (SNeIa) has been the subject of great debate in the last decade. One major challenge inevitably met when trying to infer the existence of one or more subclasses is the time consuming, and subjective, process of subclass definition. In this work, we show how machine learning tools facilitate identification of subtypes of SNeIa through the establishment of a hierarchical group structure in the continuous space of spectral diversity formed by these objects. Using Deep Learning, we were capable of performing such identification in a 4 dimensional feature space (+1 for time evolution), while the standard Principal Component Analysis barely achieves similar results using 15 principal components. This is evidence that the progenitor system and the explosion mechanism can be described by a small number of initial physical parameters. As a proof of concept, we show that our results are in close agreement with a previously suggested classification scheme and that our proposed method can grasp the main spectral features behind the definition of such subtypes. This allows the confirmation of the velocity of lines as a first order effect in the determination of SNIa subtypes, followed by 91bg-like events. Given the expected data deluge in the forthcoming years, our proposed approach is essential to allow a quick and statistically coherent identification of SNeIa subtypes (and outliers). All tools used in this work were made publicly available in the Python package Dimensionality Reduction And Clustering for Unsupervised Learning in Astronomy (DRACULA) and can be found within COINtoolbox (https://github.com/COINtoolbox/DRACULA).

astro-ph.SR↗

arXiv2015

Probing the stellar initial mass function with high-$z$ supernovae

The first supernovae will soon be visible at the edge of the observable universe, revealing the birthplaces of Population III stars. With upcoming near-infrared missions, a broad analysis of the detectability of high-$z$ supernovae is paramount. We combine cosmological and radiation transport simulations, instrument specifications, and survey strategies to create synthetic observations of primeval core-collapse, Type IIn and pair-instability supernovae with the James Webb Space Telescope ($JWST$). We show that a dedicated observational campaign with the $JWST$ can detect up to $\sim 15$ pair-instability explosions, $\sim 300$ core-collapse supernovae, but less than one Type IIn explosion per year, depending on the Population III star formation history. Our synthetic survey also shows that $\approx 1-2 \times10^2$ supernovae detections, depending on the accuracy of the classification, are sufficient to discriminate between a Salpeter and flat mass distribution for high redshift stars with a confidence level greater than 99.5 per cent. We discuss how the purity of the sample affects our results and how supervised learning methods may help to discriminate between CC and PI SNe.

astro-ph.CO↗

arXiv2015

The Overlooked Potential of Generalized Linear Models in Astronomy-III: Bayesian Negative Binomial Regression and Globular Cluster Populations

In this paper, the third in a series illustrating the power of generalized linear models (GLMs) for the astronomical community, we elucidate the potential of the class of GLMs which handles count data. The size of a galaxy's globular cluster population $N_{\rm GC}$ is a prolonged puzzle in the astronomical literature. It falls in the category of count data analysis, yet it is usually modelled as if it were a continuous response variable. We have developed a Bayesian negative binomial regression model to study the connection between $N_{\rm GC}$ and the following galaxy properties: central black hole mass, dynamical bulge mass, bulge velocity dispersion, and absolute visual magnitude. The methodology introduced herein naturally accounts for heteroscedasticity, intrinsic scatter, errors in measurements in both axes (either discrete or continuous), and allows modelling the population of globular clusters on their natural scale as a non-negative integer variable. Prediction intervals of 99% around the trend for expected $N_{\rm GC}$comfortably envelope the data, notably including the Milky Way, which has hitherto been considered a problematic outlier. Finally, we demonstrate how random intercept models can incorporate information of each particular galaxy morphological type. Bayesian variable selection methodology allows for automatically identifying galaxy types with different productions of GCs, suggesting that on average S0 galaxies have a GC population 35% smaller than other types with similar brightness.

astro-ph.IM↗

arXiv2015

Using gamma regression for photometric redshifts of survey galaxies

Machine learning techniques offer a plethora of opportunities in tackling big data within the astronomical community. We present the set of Generalized Linear Models as a fast alternative for determining photometric redshifts of galaxies, a set of tools not commonly applied within astronomy, despite being widely used in other professions. With this technique, we achieve catastrophic outlier rates of the order of ~1%, that can be achieved in a matter of seconds on large datasets of size ~1,000,000. To make these techniques easily accessible to the astronomical community, we developed a set of libraries and tools that are publicly available.

astro-ph.IM↗

arXiv2015

The Cosmic Dawn and Epoch of Reionization with the Square Kilometre Array

Concerted effort is currently ongoing to open up the Epoch of Reionization (EoR) ($z\sim$15-6) for studies with IR and radio telescopes. Whereas IR detections have been made of sources (Lyman-$α$ emitters, quasars and drop-outs) in this redshift regime in relatively small fields of view, no direct detection of neutral hydrogen, via the redshifted 21-cm line, has yet been established. Such a direct detection is expected in the coming years, with ongoing surveys, and could open up the entire universe from $z\sim$6-200 for astrophysical and cosmological studies, opening not only the EoR, but also its preceding Cosmic Dawn ($z\sim$30-15) and possibly even the later phases of the Dark Ages ($z\sim$200-30). All currently ongoing experiments attempt statistical detections of the 21-cm signal during the EoR, with limited signal-to-noise. Direct imaging, except maybe on the largest (degree) scales at lower redshifts, as well as higher redshifts will remain out of reach. The Square Kilometre Array(SKA) will revolutionize the field, allowing direct imaging of neutral hydrogen from scales of arc-minutes to degrees over most of the redshift range $z\sim$6-28 with SKA1-LOW, and possibly even higher redshifts with the SKA2-LOW. In this SKA will be unique, and in parallel provide enormous potential of synergy with other upcoming facilities (e.g. JWST). In this chapter we summarize the physics of 21-cm emission, the different phases the universe is thought to go through, and the observables that the SKA can probe, referring where needed to detailed chapters in this volume (Abridged).

astro-ph.CO↗

arXiv2015

The Overlooked Potential of Generalized Linear Models in Astronomy - I: Binomial Regression

Revealing hidden patterns in astronomical data is often the path to fundamental scientific breakthroughs; meanwhile the complexity of scientific inquiry increases as more subtle relationships are sought. Contemporary data analysis problems often elude the capabilities of classical statistical techniques, suggesting the use of cutting edge statistical methods. In this light, astronomers have overlooked a whole family of statistical techniques for exploratory data analysis and robust regression, the so-called Generalized Linear Models (GLMs). In this paper -- the first in a series aimed at illustrating the power of these methods in astronomical applications -- we elucidate the potential of a particular class of GLMs for handling binary/binomial data, the so-called logit and probit regression techniques, from both a maximum likelihood and a Bayesian perspective. As a case in point, we present the use of these GLMs to explore the conditions of star formation activity and metal enrichment in primordial minihaloes from cosmological hydro-simulations including detailed chemistry, gas physics, and stellar feedback. We predict that for a dark mini-halo with metallicity $\approx 1.3 \times 10^{-4} Z_{\bigodot}$, an increase of $1.2 \times 10^{-2}$ in the gas molecular fraction, increases the probability of star formation occurrence by a factor of 75%. Finally, we highlight the use of receiver operating characteristic curves as a diagnostic for binary classifiers, and ultimately we use these to demonstrate the competitive predictive performance of GLMs against the popular technique of artificial neural networks.

astro-ph.IM↗

arXiv2014

The first analytical expression to estimate photometric redshifts suggested by a machine

We report the first analytical expression purely constructed by a machine to determine photometric redshifts ($z_{\rm phot}$) of galaxies. A simple and reliable functional form is derived using $41,214$ galaxies from the Sloan Digital Sky Survey Data Release 10 (SDSS-DR10) spectroscopic sample. The method automatically dropped the $u$ and $z$ bands, relying only on $g$, $r$ and $i$ for the final solution. Applying this expression to other $1,417,181$ SDSS-DR10 galaxies, with measured spectroscopic redshifts ($z_{\rm spec}$), we achieved a mean $\langle (z_{\rm phot} - z_{\rm spec})/(1+z_{\rm spec})\rangle\lesssim 0.0086$ and a scatter $σ_{(z_{\rm phot} - z_{\rm spec})/(1+z_{\rm spec})}\lesssim 0.045$ when averaged up to $z \lesssim 1.0$. The method was also applied to the PHAT0 dataset, confirming the competitiveness of our results when faced with other methods from the literature. This is the first use of symbolic regression in cosmology, representing a leap forward in astronomy-data-mining connection.

astro-ph.CO↗

arXiv2014

Robust PCA and MIC statistics of baryons in early mini-haloes

We present a novel approach, based on robust principal components analysis (RPCA) and maximal information coefficient (MIC), to study the redshift dependence of halo baryonic properties. Our data are composed of a set of different physical quantities for primordial minihaloes: dark-matter mass ($M_{\mathrm{dm}}$), gas mass ($M_{\mathrm{gas}}$), stellar mass ($M_{\mathrm{star}}$), molecular fraction (${\mathrm{x_{mol}}}$), metallicity ($Z$), star formation rate (SFR) and temperature. We find that $M_{\mathrm{dm}}$ and $M_{\mathrm{gas}}$ are dominant factors for variance, particularly at high redshift. Nonetheless, with the emergence of the first stars and subsequent feedback mechanisms, ${\mathrm{x_{mol}}}$, SFR and $Z$ start to have a more dominant role. Standard PCA gives three principal components (PCs) capable to explain more than 97 per cent of the data variance at any redshift (two PCs usually accounting for no less than 92 per cent), whilst the first PC from the RPCA analysis explains no less than 84 per cent of the total variance in the entire redshift range (with two PCs explaining $ \gtrsim 95$ per cent anytime). Our analysis also suggests that all the gaseous properties have a stronger correlation with $M_{\mathrm{gas}}$ than with $M_{\mathrm{dm}}$, while $M_{\mathrm{gas}}$ has a deeper correlation with ${\mathrm{x_{mol}}}$ than with $Z$ or SFR. This indicates the crucial role of gas molecular content to initiate star formation and consequent metal pollution from Population III and Population II/I regimes in primordial galaxies. Finally, a comparison between MIC and Spearman correlation coefficient shows that the former is a more reliable indicator when halo properties are weakly correlated.

astro-ph.CO↗

arXiv2013

Detectability of the First Cosmic Explosions

We present a fully self-consistent simulation of a synthetic survey of the furthermost cosmic explosions. The appearance of the first generation of stars (Population III) in the Universe represents a critical point during cosmic evolution, signaling the end of the dark ages, a period of absence of light sources. Despite their importance, there is no confirmed detection of Population III stars so far. A fraction of these primordial stars are expected to die as pair-instability supernovae (PISNe), and should be bright enough to be observed up to a few hundred million years after the big bang. While the quest for Population III stars continues, detailed theoretical models and computer simulations serve as a testbed for their observability. With the upcoming near-infrared missions, estimates of the feasibility of detecting PISNe are not only timely but imperative. To address this problem, we combine state-of-the-art cosmological and radiative simulations into a complete and self-consistent framework, which includes detailed features of the observational process. We show that a dedicated observational strategy using $\lesssim 8$ per cent of total allocation time of the James Webb Space Telescope mission can provide us up to $\sim 9-15$ detectable PISNe per year.

astro-ph.CO↗

arXiv2009

On the influence of non-thermal pressure on the mass determination of galaxy clusters

(Abridged) The main purpose of this paper is to consider the contribution of all three non-thermal components to total mass measurements of galaxy clusters: cosmic rays, turbulence and magnetic pressures. To estimate the thermal pressure we used public XMM-\textit{Newton} archival data of 5 Abell clusters. To describe the magnetic pressure, we assume a radial distribution for the magnetic field, $B(r) \propto ρ_{g}^α$, to seek generality we assume $α$ within the range of 0.5 to 0.9, as indicated by observations and numerical simulations. For the turbulent component, we assumed an isotropic pressure, $P_{\rm turb} = {1/3}ρ_{\rm g}(σ_{r}^{2}+σ_{t}^{2})$. We also consider the contribution of cosmic ray pressure, $P_{cr}\propto r^{-0.5}$. It follows that a consistent description for the non-thermal component could yield variation in mass estimates that vary from 10% up to $\sim$30%. We verified that in the inner parts of cool-core clusters the cosmic ray component is comparable to the magnetic pressure, while in non cool-core cluster the cosmic ray component is dominant. For cool-core clusters the magnetic pressure is the dominant component, contributing with more than 50% of total mass variation due to non-thermal pressure components. However, for non cool-core clusters, the major influence comes from the cosmic ray pressure that accounts with more than 80% of total mass variation due to non-thermal pressure effects. For our sample, the maximum influence of the turbulent component to total mass variation can be almost 20%. We show that this analysis can be regarded as a starting point for a more detailed and refined exploration of the influence of non-thermal pressure in the intra-cluster medium (ICM).

astro-ph.CO↗