arXiv subjects

Crescenzo Tortora

Publications and source records attributed to Crescenzo Tortora.

At least 73 records · Page 4Linked to original sources

arXiv2020

Rejection criteria based on outliers in the KiDS photometric redshifts and PDF distributions derived by machine learning

The Probability Density Function (PDF) provides an estimate of the photometric redshift (zphot) prediction error. It is crucial for current and future sky surveys, characterized by strict requirements on the zphot precision, reliability and completeness. The present work stands on the assumption that properly defined rejection criteria, capable of identifying and rejecting potential outliers, can increase the precision of zphot estimates and of their cumulative PDF, without sacrificing much in terms of completeness of the sample. We provide a way to assess rejection through proper cuts on the shape descriptors of a PDF, such as the width and the height of the maximum PDF's peak. In this work we tested these rejection criteria to galaxies with photometry extracted from the Kilo Degree Survey (KiDS) ESO Data Release 4, proving that such approach could lead to significant improvements to the zphot quality: e.g., for the clipped sample showing the best trade-off between precision and completeness, we achieve a reduction in outliers fraction of $\simeq 75\%$ and an improvement of $\simeq 6\%$ for NMAD, with respect to the original data set, preserving the $\simeq 93\%$ of its content.

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arXiv2020

Anomaly detection in Astrophysics: a comparison between unsupervised Deep and Machine Learning on KiDS data

Every field of Science is undergoing unprecedented changes in the discovery process, and Astronomy has been a main player in this transition since the beginning. The ongoing and future large and complex multi-messenger sky surveys impose a wide exploiting of robust and efficient automated methods to classify the observed structures and to detect and characterize peculiar and unexpected sources. We performed a preliminary experiment on KiDS DR4 data, by applying to the problem of anomaly detection two different unsupervised machine learning algorithms, considered as potentially promising methods to detect peculiar sources, a Disentangled Convolutional Autoencoder and an Unsupervised Random Forest. The former method, working directly on images, is considered potentially able to identify peculiar objects like interacting galaxies and gravitational lenses. The latter instead, working on catalogue data, could identify objects with unusual values of magnitudes and colours, which in turn could indicate the presence of singularities.

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arXiv2020

Scaling relations and baryonic cycling in local star-forming galaxies: I. The sample

Metallicity and gas content are intimately related in the baryonic exchange cycle of galaxies, and galaxy evolution scenarios can be constrained by quantifying this relation. To this end, we have compiled a sample of ~400 galaxies in the Local Universe, dubbed "MAGMA" (Metallicity And Gas for Mass Assembly), which covers an unprecedented range in parameter space, spanning more than 5 orders of magnitude in stellar mass (Mstar), star-formation rate (SFR), and gas mass (Mgas), and a factor of ~60 in metallicity [Z, 12+log(O/H)]. Stellar masses and SFRs have been recalculated for all the galaxies using IRAC, WISE and GALEX photometry, and 12+log(O/H) has been transformed, where necessary, to a common metallicity calibration. To assess the true dimensionality of the data, we have applied multi-dimensional principal component analyses (PCAs) to our sample. In confirmation of previous work, we find that even with the vast parameter space covered by MAGMA, the relations between Mstar, SFR, Z and Mgas (MHI+MH2) require only two dimensions to describe the hypersurface. To accommodate the curvature in the Mstar-Z relation, we have applied a piecewise 3D PCA that successfully predicts observed 12+log(O/H) to an accuracy of ~0.1dex. MAGMA is a representative sample of isolated star-forming galaxies in the Local Universe, and can be used as a benchmark for cosmological simulations and to calibrate evolutionary trends with redshift.

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arXiv2020

Building the largest spectroscopic sample of ultra-compact massive galaxies with the Kilo Degree Survey

Ultra-compact massive galaxies UCMGs, i.e. galaxies with stellar masses $M_{*} > 8 \times 10^{10} M_{\odot}$ and effective radii $R_{e} < 1.5$ kpc, are very rare systems, in particular at low and intermediate redshifts. Their origin as well as their number density across cosmic time are still under scrutiny, especially because of the paucity of spectroscopically confirmed samples. We have started a systematic census of UCMG candidates within the ESO Kilo Degree Survey, together with a large spectroscopic follow-up campaign to build the largest possible sample of confirmed UCMGs. This is the third paper of the series and the second based on the spectroscopic follow-up program. Here, we present photometrical and structural parameters of 33 new candidates at redshifts $0.15 \lesssim z \lesssim 0.5$ and confirm 19 of them as UCMGs, based on their nominal spectroscopically inferred $M_{*}$ and $R_{e}$. This corresponds to a success rate of $\sim 58\%$, nicely consistent with our previous findings. The addition of these 19 newly confirmed objects, allows us to fully assess the systematics on the system selection, and finally reduce the number density uncertainties. Moreover, putting together the results from our current and past observational campaigns and some literature data, we build the largest sample of UCMGs ever collected, comprising 92 spectroscopically confirmed objects at $0.1 \lesssim z \lesssim 0.5$. This number raises to 116, allowing for a $3σ$ tolerance on the $M_{*}$ and $R_{e}$ thresholds for the UCMG definition. For all these galaxies we have estimated the velocity dispersion values at the effective radii which have been used to derive a preliminary mass-velocity dispersion correlation.

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arXiv2019

KiDS-SQuaD II: Machine learning selection of bright extragalactic objects to search for new gravitationally lensed quasars

The KiDS Strongly lensed QUAsar Detection project (KiDS-SQuaD) aims at finding as many previously undiscovered gravitational lensed quasars as possible in the Kilo Degree Survey. This is the second paper of this series where we present a new, automatic object classification method based on machine learning technique. The main goal of this paper is to build a catalogue of bright extragalactic objects (galaxies and quasars), from the KiDS Data Release 4, with a minimum stellar contamination, preserving the completeness as much as possible, to then apply morphological methods to select reliable gravitationally lensed (GL) quasar candidates. After testing some of the most used machine learning algorithms, decision trees based classifiers, we decided to use CatBoost, that was specifically trained with the aim of creating a sample of extragalactic sources as clean as possible from stars. We discuss the input data, define the training sample for the classifier, give quantitative estimates of its performances, and finally describe the validation results with Gaia DR2, AllWISE, and GAMA catalogues. We have built and make available to the scientific community the KiDS Bright EXtraGalactic Objects catalogue (KiDS-BEXGO), specifically created to find gravitational lenses. This is made of $\approx6$ millions of sources classified as quasars ($\approx 200\,000$) and galaxies ($\approx 5.7$M), up to $r<22^m$. From this catalog we selected 'Multiplets': close pairs of quasars or galaxies surrounded by at least one quasar, presenting the 12 most reliable gravitationally lensed quasar candidates, to demonstrate the potential of the catalogue, which will be further explored in a forthcoming paper. We compared our search to the previous one, presented in the first paper from this series, showing that employing a machine learning method decreases the stars-contaminators within the GL candidates.

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arXiv2019

The Strong Gravitational Lens Finding Challenge

Large scale imaging surveys will increase the number of galaxy-scale strong lensing candidates by maybe three orders of magnitudes beyond the number known today. Finding these rare objects will require picking them out of at least tens of millions of images and deriving scientific results from them will require quantifying the efficiency and bias of any search method. To achieve these objectives automated methods must be developed. Because gravitational lenses are rare objects reducing false positives will be particularly important. We present a description and results of an open gravitational lens finding challenge. Participants were asked to classify 100,000 candidate objects as to whether they were gravitational lenses or not with the goal of developing better automated methods for finding lenses in large data sets. A variety of methods were used including visual inspection, arc and ring finders, support vector machines (SVM) and convolutional neural networks (CNN). We find that many of the methods will be easily fast enough to analyse the anticipated data flow. In test data, several methods are able to identify upwards of half the lenses after applying some thresholds on the lens characteristics such as lensed image brightness, size or contrast with the lens galaxy without making a single false-positive identification. This is significantly better than direct inspection by humans was able to do. (abridged)

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arXiv2018

Statistical analysis of probability density functions for photometric redshifts through the KiDS-ESO-DR3 galaxies

Despite the high accuracy of photometric redshifts (zphot) derived using Machine Learning (ML) methods, the quantification of errors through reliable and accurate Probability Density Functions (PDFs) is still an open problem. First, because it is difficult to accurately assess the contribution from different sources of errors, namely internal to the method itself and from the photometric features defining the available parameter space. Second, because the problem of defining a robust statistical method, always able to quantify and qualify the PDF estimation validity, is still an open issue. We present a comparison among PDFs obtained using three different methods on the same data set: two ML techniques, METAPHOR (Machine-learning Estimation Tool for Accurate PHOtometric Redshifts) and ANNz2, plus the spectral energy distribution template fitting method, BPZ. The photometric data were extracted from the KiDS (Kilo Degree Survey) ESO Data Release 3, while the spectroscopy was obtained from the GAMA (Galaxy and Mass Assembly) Data Release 2. The statistical evaluation of both individual and stacked PDFs was done through quantitative and qualitative estimators, including a dummy PDF, useful to verify whether different statistical estimators can correctly assess PDF quality. We conclude that, in order to quantify the reliability and accuracy of any zphot PDF method, a combined set of statistical estimators is required.

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arXiv2018

SEAGLE - I: A pipeline for simulating and modeling strong lenses from cosmological hydrodynamic simulations

In this paper we introduce the SEAGLE (i.e. Simulating EAGLE LEnses) program, that approaches the study of galaxy formation through strong gravitational lensing, using a suite of high-resolution hydrodynamic simulations, Evolution and Assembly of GaLaxies and their Environments (EAGLE) project. We introduce the simulation and analysis pipeline and present the first set of results from our analysis of early-type galaxies. We identify and extract an ensemble of simulated lens galaxies and use the GLAMER ray-tracing lensing code to create mock lenses similar to those observed in the SLACS and SL2S surveys, using a range of source parameters and galaxy orientations, including observational effects such as the Point-Spread-Function (PSF), pixelization and noise levels, representative of single-orbit observations with the Hubble Space Telescope (HST) using the ACS-F814W filter. We subsequently model these mock lenses using the code LENSED, treating them in the same way as observed lenses. We also estimate the mass model parameters directly from the projected surface mass density of the simulated galaxy, using an identical mass model family. We perform a three-way comparison of all the measured quantities with real lenses. We find the average total density slope of EAGLE lenses, $t=2.26\; (0.25\; \rm{rms})$ to be higher than SL2S, $t=2.16$ or SLACS, $t=2.08$. We find a very strong correlation between the external shear ($γ$) and the complex ellipticity ($ε$), with $γ\sim ε/4$. This correlation indicates a degeneracy in the lens mass modeling. We also see a dispersion between lens modeling and direct fitting results, indicating systematical biases.

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arXiv2017

Probability density estimation of photometric redshifts based on machine learning

Photometric redshifts (photo-z's) provide an alternative way to estimate the distances of large samples of galaxies and are therefore crucial to a large variety of cosmological problems. Among the various methods proposed over the years, supervised machine learning (ML) methods capable to interpolate the knowledge gained by means of spectroscopical data have proven to be very effective. METAPHOR (Machine-learning Estimation Tool for Accurate PHOtometric Redshifts) is a novel method designed to provide a reliable PDF (Probability density Function) of the error distribution of photometric redshifts predicted by ML methods. The method is implemented as a modular workflow, whose internal engine for photo-z estimation makes use of the MLPQNA neural network (Multi Layer Perceptron with Quasi Newton learning rule), with the possibility to easily replace the specific machine learning model chosen to predict photo-z's. After a short description of the software, we present a summary of results on public galaxy data (Sloan Digital Sky Survey - Data Release 9) and a comparison with a completely different method based on Spectral Energy Distribution (SED) template fitting.

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arXiv2017

METAPHOR: Probability density estimation for machine learning based photometric redshifts

We present METAPHOR (Machine-learning Estimation Tool for Accurate PHOtometric Redshifts), a method able to provide a reliable PDF for photometric galaxy redshifts estimated through empirical techniques. METAPHOR is a modular workflow, mainly based on the MLPQNA neural network as internal engine to derive photometric galaxy redshifts, but giving the possibility to easily replace MLPQNA with any other method to predict photo-z's and their PDF. We present here the results about a validation test of the workflow on the galaxies from SDSS-DR9, showing also the universality of the method by replacing MLPQNA with KNN and Random Forest models. The validation test include also a comparison with the PDF's derived from a traditional SED template fitting method (Le Phare).

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arXiv2017

Cooperative photometric redshift estimation

In the modern galaxy surveys photometric redshifts play a central role in a broad range of studies, from gravitational lensing and dark matter distribution to galaxy evolution. Using a dataset of about 25,000 galaxies from the second data release of the Kilo Degree Survey (KiDS) we obtain photometric redshifts with five different methods: (i) Random forest, (ii) Multi Layer Perceptron with Quasi Newton Algorithm, (iii) Multi Layer Perceptron with an optimization network based on the Levenberg-Marquardt learning rule, (iv) the Bayesian Photometric Redshift model (or BPZ) and (v) a classical SED template fitting procedure (Le Phare). We show how SED fitting techniques could provide useful information on the galaxy spectral type which can be used to improve the capability of machine learning methods constraining systematic errors and reduce the occurrence of catastrophic outliers. We use such classification to train specialized regression estimators, by demonstrating that such hybrid approach, involving SED fitting and machine learning in a single collaborative framework, is capable to improve the overall prediction accuracy of photometric redshifts.

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arXiv2016

A cooperative approach among methods for photometric redshifts estimation: an application to KiDS data

Photometric redshifts (photo-z's) are fundamental in galaxy surveys to address different topics, from gravitational lensing and dark matter distribution to galaxy evolution. The Kilo Degree Survey (KiDS), i.e. the ESO public survey on the VLT Survey Telescope (VST), provides the unprecedented opportunity to exploit a large galaxy dataset with an exceptional image quality and depth in the optical wavebands. Using a KiDS subset of about 25,000 galaxies with measured spectroscopic redshifts, we have derived photo-z's using i) three different empirical methods based on supervised machine learning, ii) the Bayesian Photometric Redshift model (or BPZ), and iii) a classical SED template fitting procedure (Le Phare). We confirm that, in the regions of the photometric parameter space properly sampled by the spectroscopic templates, machine learning methods provide better redshift estimates, with a lower scatter and a smaller fraction of outliers. SED fitting techniques, however, provide useful information on the galaxy spectral type which can be effectively used to constrain systematic errors and to better characterize potential catastrophic outliers. Such classification is then used to specialize the training of regression machine learning models, by demonstrating that a hybrid approach, involving SED fitting and machine learning in a single collaborative framework, can be effectively used to improve the accuracy of photo-z estimates.

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arXiv2016

METAPHOR: A machine learning based method for the probability density estimation of photometric redshifts

A variety of fundamental astrophysical science topics require the determination of very accurate photometric redshifts (photo-z's). A wide plethora of methods have been developed, based either on template models fitting or on empirical explorations of the photometric parameter space. Machine learning based techniques are not explicitly dependent on the physical priors and able to produce accurate photo-z estimations within the photometric ranges derived from the spectroscopic training set. These estimates, however, are not easy to characterize in terms of a photo-z Probability Density Function (PDF), due to the fact that the analytical relation mapping the photometric parameters onto the redshift space is virtually unknown. We present METAPHOR (Machine-learning Estimation Tool for Accurate PHOtometric Redshifts), a method designed to provide a reliable PDF of the error distribution for empirical techniques. The method is implemented as a modular workflow, whose internal engine for photo-z estimation makes use of the MLPQNA neural network (Multi Layer Perceptron with Quasi Newton learning rule), with the possibility to easily replace the specific machine learning model chosen to predict photo-z's. We present a summary of results on SDSS-DR9 galaxy data, used also to perform a direct comparison with PDF's obtained by the Le Phare SED template fitting. We show that METAPHOR is capable to estimate the precision and reliability of photometric redshifts obtained with three different self-adaptive techniques, i.e. MLPQNA, Random Forest and the standard K-Nearest Neighbors models.

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arXiv2015

The first and second data releases of the Kilo-Degree Survey

The Kilo-Degree Survey (KiDS) is an optical wide-field imaging survey carried out with the VLT Survey Telescope and the OmegaCAM camera. KiDS will image 1500 square degrees in four filters (ugri), and together with its near-infrared counterpart VIKING will produce deep photometry in nine bands. Designed for weak lensing shape and photometric redshift measurements, the core science driver of the survey is mapping the large-scale matter distribution in the Universe back to a redshift of ~0.5. Secondary science cases are manifold, covering topics such as galaxy evolution, Milky Way structure, and the detection of high-redshift clusters and quasars. KiDS is an ESO Public Survey and dedicated to serving the astronomical community with high-quality data products derived from the survey data, as well as with calibration data. Public data releases will be made on a yearly basis, the first two of which are presented here. For a total of 148 survey tiles (~160 sq.deg.) astrometrically and photometrically calibrated, coadded ugri images have been released, accompanied by weight maps, masks, source lists, and a multi-band source catalog. A dedicated pipeline and data management system based on the Astro-WISE software system, combined with newly developed masking and source classification software, is used for the data production of the data products described here. The achieved data quality and early science projects based on the data products in the first two data releases are reviewed in order to validate the survey data. Early scientific results include the detection of nine high-z QSOs, fifteen candidate strong gravitational lenses, high-quality photometric redshifts and galaxy structural parameters for hundreds of thousands of galaxies. (Abridged)

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arXiv2015

Machine Learning based photometric redshifts for the KiDS ESO DR2 galaxies

We estimated photometric redshifts (zphot) for more than 1.1 million galaxies of the ESO Public Kilo-Degree Survey (KiDS) Data Release 2. KiDS is an optical wide-field imaging survey carried out with the VLT Survey Telescope (VST) and the OmegaCAM camera, which aims at tackling open questions in cosmology and galaxy evolution, such as the origin of dark energy and the channel of galaxy mass growth. We present a catalogue of photometric redshifts obtained using the Multi Layer Perceptron with Quasi Newton Algorithm (MLPQNA) model, provided within the framework of the DAta Mining and Exploration Web Application REsource (DAMEWARE). These photometric redshifts are based on a spectroscopic knowledge base which was obtained by merging spectroscopic datasets from GAMA (Galaxy And Mass Assembly) data release 2 and SDSS-III data release 9. The overall 1 sigma uncertainty on Delta z = (zspec - zphot) / (1+ zspec) is ~ 0.03, with a very small average bias of ~ 0.001, a NMAD of ~ 0.02 and a fraction of catastrophic outliers (| Delta z | > 0.15) of ~0.4%.

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arXiv2012

An inventory of the stellar initial mass function in early-type galaxies

Given a flurry of recent claims for systematic variations in the stellar initial mass function (IMF), we carry out the first inventory of the observational evidence using different approaches. This includes literature results, as well as our own new findings from combined stellar-populations synthesis (SPS) and Jeans dynamical analyses of data on $\sim$~4500 early-type galaxies (ETGs) from the SPIDER project. We focus on the mass-to-light ratio mismatch relative to the Milky Way IMF, \dimf, correlated against the central stellar velocity dispersion, \sigs. We find a strong correlation between \dimf\ and \sigs, for a wide set of dark matter (DM) model profiles. These results are robust if a uniform halo response to baryons is adopted across the sample. The overall normalization of \dimf, and the detailed DM profile, are less certain, but the data are consistent with standard cold-DM halos, and a central DM fraction that is roughly constant with \sigs. For a variety of related studies in the literature, using SPS, dynamics, and gravitational lensing, similar results are found. Studies based solely on spectroscopic line diagnostics agree on a Salpeter-like IMF at high \sigs, but differ at low \sigs. Overall, we find that multiple independent lines of evidence appear to be converging on a systematic variation in the IMF, such that high-\sigs\ ETGs have an excess of low-mass stars relative to spirals and low-\sigs\ ETGs. Robust verification of super-Salpeter IMFs in the highest-\sigs\ galaxies will require additional scrutiny of scatter and systematic uncertainties. The implications for the distribution of DM are still inconclusive.

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arXiv2012

Constraining decaying dark energy density models with the CMB temperature-redshift relation

We discuss the thermodynamic and dynamical properties of a variable dark energy model with density scaling as $ρ_x \propto (1+z)^{m}$, z being the redshift. These models lead to the creation/disruption of matter and radiation, which affect the cosmic evolution of both matter and radiation components in the Universe. In particular, we have studied the temperature-redshift relation of radiation, which has been constrained using a recent collection of cosmic microwave background (CMB) temperature measurements up to $z \sim 3$. We find that, within the uncertainties, the model is indistinguishable from a cosmological constant which does not exchange any particles with other components. Future observations, in particular measurements of CMB temperature at large redshift, will allow to give firmer bounds on the effective equation of state parameter $w_{eff}$ for such types of dark energy models.

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arXiv2012

Dark matter and alternative recipes for the missing mass

Within the standard cosmological scenario the Universe is found to be filled by obscure components (dark matter and dark energy) for ~95% of its energy budget. In particular, almost all the matter content in the Universe is given by dark matter, which dominates the mass budget and drives the dynamics of galaxies and clusters of galaxies. Unfortunately, dark matter and dark energy have not been detected and no direct or indirected observations have allowed to prove their existence and amount. For this reason, some authors have suggested that a modification of Einstein Relativity or the change of the Newton's dynamics law (within a relativistic and classical framework, respectively) could allow to replace these unobserved components. We will start discussing the role of dark matter in the early-type galaxies, mainly in their central regions, investigating how its content changes as a function of the mass and the size of each galaxy and few considerations about the stellar Initial mass function have been made. In the second part of the paper we have described, as examples, some ways to overcome the dark matter hypothesis, by fitting to the observations the modified dynamics coming out from general relativistic extended theories and the MOdyfied Newtonian dynamics (MOND).

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