Search

We employ a Generalised Additive Model (GAM) to address the limitations inherent in radial metallicity gradients predicted by chemical evolution models, thereby facilitating the estimation of birth radii for the thin disc stars in our sample based on their ages and chemical composition. We then juxtapose the birth radius predictions derived from the GAM with the calculated guiding radii, among other dynamic parameters. Metal-rich stars, formed in the inner regions of the Milky Way, seem to be predominantly churned outward. Their metal-poor counterparts, formed in the outer thin disc, exhibit the opposite behaviour. The proportion of blurred/undisturbed stars generally increases with decreasing metallicity when compared to their churned counterparts. Approximately $3/4$ of the sample has been affected by (inward or outward) churning, while the remaining $\sim 1/4$ has either been influenced by blurring or remained undisturbed. These percentages vary considerably across different metallicity-stratified groups. Also, a large age gap is identified between churned and blurred/undisturbed sub-samples within each HC-based group, being the outward-churned stars systematically the oldest, inward-churned stars the youngest, and blurred/undisturbed stars in intermediate ages. We also detect significant differences in angular momenta in the $z$ component, for stars that have either churned inward or outward, when compared to their blurred/undisturbed counterparts. Additionally, we observe the potential effects of the pericentric passage of the Sagittarius dwarf galaxy in our most metal-poor subset of stars, formed in the outer disc. Finally, we estimate that the Sun's most probable birth radius is $7.08 \pm 0.24$ kpc, with a 3$\sigma$ range spanning from 6.46 to 7.81 kpc, in agreement with previous studies., Comment: Accepted for publication in Astronomy & Astrophysics (A&A). 35 pages; 21 figures in the main text + 18 figures in the appendix. Minor formatting changes were made to the arXiv version to ensure proper compilation, but the scientific content remains unchanged. Abdriged abstract to meet arXiv's length requirements; we strongly recommend reading the full abstract

Context. Transient astronomical events that exhibit no discernible association with a host galaxy are commonly referred to as hostless. These rare phenomena are associated with extremely energetic events, and they can offer unique insights into the properties and evolution of stars and galaxies. However, the sheer number of transients captured by contemporary high-cadence astronomical surveys renders the manual identification of all potential hostless transients impractical. Therefore, creating a systematic identification tool is crucial for studying these elusive events. Aims. We present the ExtragaLactic alErt Pipeline for Hostless AstroNomical Transients (ELEPHANT), a framework for filtering hostless transients in astronomical data streams. Methods. We used Fink to access all the ZTF alerts produced between January/2022 and December/2023, selecting only those associated with extragalactic transients. We then processed the associated stamps using a sequence of image analysis techniques to retrieve hostless candidates. Results. We find that less than 2% of all analyzed transients are potentially hostless. Among them, approximately 10% have a spectroscopic class reported on TNS, with Type Ia supernova being the most common class, followed by SLSN. Among the hostless candidates retrieved by our pipeline, there was SN 2018ibb, which has been proposed to be a PISN candidate; and SN 2022ann, one of only five known SNe Icn. When no class is reported on TNS, the dominant classes are QSO and SN candidates, the former obtained from SIMBAD and the latter inferred using the Fink ML classifier. Conclusions. ELEPHANT represents an effective strategy to filter extragalactic events within large and complex astronomical alert streams. There are many applications for which this pipeline will be useful, ranging from transient selection for follow-up to studies of transient environments., Comment: 15 pages, 11 figures

We describe the fast transient classification algorithm in the center of the kilonova (KN) science module currently implemented in the Fink broker and report classification results based on simulated catalogs and real data from the ZTF alert stream. We used noiseless, homogeneously sampled simulations to construct a basis of principal components (PCs). All light curves from a more realistic ZTF simulation were written as a linear combination of this basis. The corresponding coefficients were used as features in training a random forest classifier. The same method was applied to long (>30 days) and medium (<30 days) light curves. The latter aimed to simulate the data situation found within the ZTF alert stream. Classification based on long light curves achieved 73.87% precision and 82.19% recall. Medium baseline analysis resulted in 69.30% precision and 69.74% recall, thus confirming the robustness of precision results when limited to 30 days of observations. In both cases, dwarf flares and point Type Ia supernovae were the most frequent contaminants. The final trained model was integrated into the Fink broker and has been distributing fast transients, tagged as KN_candidates, to the astronomical community, especially through the GRANDMA collaboration. We showed that features specifically designed to grasp different light curve behaviors provide enough information to separate fast (KN-like) from slow (non-KN-like) evolving events. This module represents one crucial link in an intricate chain of infrastructure elements for multi-messenger astronomy which is currently being put in place by the Fink broker team in preparation for the arrival of data from the Vera Rubin Observatory Legacy Survey of Space and Time., Comment: 8 Pages, 12 Figures, submitted to Astronomy and Astrophysics

We present GLADE+, an extended version of the GLADE galaxy catalogue introduced in our previous paper for multimessenger searches with advanced gravitational-wave detectors. GLADE+ combines data from six separate but not independent astronomical catalogues: the GWGC, 2MPZ, 2MASS XSC, HyperLEDA, and WISExSCOSPZ galaxy catalogues, and the SDSS-DR16Q quasar catalogue. To allow corrections of CMB-frame redshifts for peculiar motions, we calculated peculiar velocities along with their standard deviations of all galaxies having $B$-band magnitude data within redshift $z=0.05$ using the "Bayesian Origin Reconstruction from Galaxies" formalism. GLADE+ is complete up to luminosity distance $d_L=47^{+4}_{-2}$ Mpc in terms of the total expected $B$-band luminosity of galaxies, and contains all of the brightest galaxies giving 90\% of the total $B$-band and $K$-band luminosity up to $d_L\simeq 130$ Mpc. We include estimations of stellar masses and individual binary neutron star merger rates for galaxies with $W1$ magnitudes. These parameters can help in ranking galaxies in a given gravitational wave localization volume in terms of their likelihood of being hosts, thereby possibly reducing the number of pointings and total integration time needed to find the electromagnetic counterpart., Comment: 9 pages, 4 figures, accepted for publication in MNRAS

Context: In spiral galaxies, star formation tends to trace features of the spiral pattern, including arms, spurs, feathers, and branches. However, in our own Milky Way, it has been challenging to connect individual star-forming regions to their larger Galactic environment owing to our perspective from within the disk. One feature in nearly all modern models of the Milky Way is the Sagittarius Arm, located inward of the Sun with a pitch angle of ~12 deg. Aims: We map the 3D locations and velocities of star-forming regions in a segment of the Sagittarius Arm using young stellar objects (YSOs) from the Spitzer/IRAC Candidate YSO (SPICY) catalog to compare their distribution to models of the arm. Methods: Distances and velocities for these objects are derived from Gaia EDR3 astrometry and molecular line surveys. We infer parallaxes and proper motions for spatially clustered groups of YSOs and estimate their radial velocities from the velocities of spatially associated molecular clouds. Results: We identify 25 star-forming regions in the Galactic longitude range l~4.0-18.5 deg arranged in a narrow, ~1 kpc long linear structure with a high pitch angle of $\psi = 56$ deg and a high aspect ratio of ~7:1. This structure includes massive star-forming regions such as M8, M16, M17, and M20. The motions in the structure are remarkably coherent, with velocities in the direction of Galactic rotation of $240\pm3$ km/s (slightly higher than average) and slight drifts toward the Galactic center (-4.3 km/s) and in the negative Z direction (-2.9 km/s). The rotational shear experienced by the structure is 4.6 km/s/kpc. Conclusions: The observed 56 deg pitch angle is remarkably high for a segment of the Sagittarius Arm. We discuss possible interpretations of this feature as a substructure within the lower pitch angle Sagittarius Arm, as a spur, or as an isolated structure., Comment: Accepted for publication in A&A Letters

The so-called ultraviolet (UV) upturn of elliptical galaxies is a phenomenon characterised by the up-rise of their fluxes in bluer wavelengths, typically in the 1,200-2,500A range. This work aims at estimating the rate of occurrence of the UV upturn over the entire red-sequence population of galaxies that show significant UV emission. This assessment is made considering it as function of three parameters: redshift, stellar mass, and -- what may seem counter-intuitive at first -- emission-line classification. We built a multiwavelength spectro-photometric catalogue from the Galaxy Mass Assembly survey, together with aperture-matched data from Galaxy Evolution Explorer Medium-Depth Imaging Survey (MIS) and Sloan Digital Sky Survey, covering the redshift range between 0.06 and 0.40. From this sample, we analyse the UV emission among UV bright galaxies, by selecting those that occupy the red-sequence locus in the (NUV-r) x (FUV-NUV) chart; then, we stratify the sample by their emission-line classes. To that end, we make use of emission-line diagnostic diagrams, focusing the analysis in retired/passive lineless galaxies. Then, a Bayesian logistic model was built to simultaneously deal with the effects of all galaxy properties (including emission-line classification or lack thereof). The main results show that retired/passive systems host an up-rise in the fraction of UV upturn or redshifts between 0.06 and 0.25, followed by an in-fall up to 0.35. Additionally, we show that the fraction of UV upturn hosts rises with increasing stellar mass., Comment: 17 pages, 19 figures, accepted for publication in MNRAS

In this paper we present a thorough discussion about the photometric redshift (photo-z) performance of the Southern Photometric Local Universe Survey (S-PLUS). This survey combines a 7 narrow + 5 broad passband filter system, with a typical photometric-depth of r$\sim$21 AB. For this exercise, we utilize the Data Release 1 (DR1), corresponding to 336 deg$^{2}$ from the Stripe-82 region. We rely on the \texttt{BPZ2} code to compute our estimates, using a new library of SED models, which includes additional templates for quiescent galaxies. When compared to a spectroscopic redshift control sample of $\sim$100k galaxies, we find a precision of $\sigma_{z}<$0.8\%, $<$2.0\% or $<$3.0\% for galaxies with magnitudes r$<$17, $<$19 and $<$21, respectively. A precision of 0.6\% is attained for galaxies with the highest \texttt{Odds} values. These estimates have a negligible bias and a fraction of catastrophic outliers inferior to 1\%. We identify a redshift window (i.e., 0.26$

The Southern Photometric Local Universe Survey (S-PLUS) is imaging ~9300 deg^2 of the celestial sphere in twelve optical bands using a dedicated 0.8 m robotic telescope, the T80-South, at the Cerro Tololo Inter-American Observatory, Chile. The telescope is equipped with a 9.2k by 9.2k e2v detector with 10 um pixels, resulting in a field-of-view of 2 deg^2 with a plate scale of 0.55"/pixel. The survey consists of four main subfields, which include two non-contiguous fields at high Galactic latitudes (8000 deg^2 at |b| > 30 deg) and two areas of the Galactic plane and bulge (for an additional 1300 deg^2). S-PLUS uses the Javalambre 12-band magnitude system, which includes the 5 u, g, r, i, z broad-band filters and 7 narrow-band filters centered on prominent stellar spectral features: the Balmer jump/[OII], Ca H+K, H-delta, G-band, Mg b triplet, H-alpha, and the Ca triplet. S-PLUS delivers accurate photometric redshifts (delta_z/(1+z) = 0.02 or better) for galaxies with r < 20 AB mag and redshift < 0.5, thus producing a 3D map of the local Universe over a volume of more than 1 (Gpc/h)^3. The final S-PLUS catalogue will also enable the study of star formation and stellar populations in and around the Milky Way and nearby galaxies, as well as searches for quasars, variable sources, and low-metallicity stars. In this paper we introduce the main characteristics of the survey, illustrated with science verification data highlighting the unique capabilities of S-PLUS. We also present the first public data release of ~336 deg^2 of the Stripe-82 area, which is available at http://datalab.noao.edu/splus., Comment: Updated to reflect the published version (MNRAS, 489, 241). For a short introductory video of the S-PLUS project, see https://youtu.be/yc5kHrHU9Jk - The S-PLUS Data Release 1 is available at http://datalab.noao.edu/splus

Over the past century, major advances in astronomy and astrophysics have been largely driven by improvements in instrumentation and data collection. With the amassing of high quality data from new telescopes, and especially with the advent of deep and large astronomical surveys, it is becoming clear that future advances will also rely heavily on how those data are analyzed and interpreted. New methodologies derived from advances in statistics, computer science, and machine learning are beginning to be employed in sophisticated investigations that are not only bringing forth new discoveries, but are placing them on a solid footing. Progress in wide-field sky surveys, interferometric imaging, precision cosmology, exoplanet detection and characterization, and many subfields of stellar, Galactic and extragalactic astronomy, has resulted in complex data analysis challenges that must be solved to perform scientific inference. Research in astrostatistics and astroinformatics will be necessary to develop the state-of-the-art methodology needed in astronomy. Overcoming these challenges requires dedicated, interdisciplinary research. We recommend: (1) increasing funding for interdisciplinary projects in astrostatistics and astroinformatics; (2) dedicating space and time at conferences for interdisciplinary research and promotion; (3) developing sustainable funding for long-term astrostatisics appointments; and (4) funding infrastructure development for data archives and archive support, state-of-the-art algorithms, and efficient computing., Comment: Submitted to the Astro2020 Decadal Survey call for science white papers

Open clusters (OCs) are popular tracers of the structure and evolutionary history of the Galactic disk. The OC population is often considered to be complete within 1.8 kpc of the Sun. The recent Gaia Data Release 2 (DR2) allows the latter claim to be challenged. We perform a systematic search for new OCs in the direction of Perseus using precise and accurate astrometry from Gaia DR2. We implement a coarse-to-fine search method. First, we exploit spatial proximity using a fast density-aware partitioning of the sky via a k-d tree in the spatial domain of Galactic coordinates, (l, b). Secondly, we employ a Gaussian mixture model in the proper motion space to quickly tag fields around OC candidates. Thirdly, we apply an unsupervised membership assignment method, UPMASK, to scrutinise the candidates. We visually inspect colour-magnitude diagrams to validate the detected objects. Finally, we perform a diagnostic to quantify the significance of each identified overdensity in proper motion and in parallax space We report the discovery of 41 new stellar clusters. This represents an increment of at least 20% of the previously known OC population in this volume of the Milky Way. We also report on the clear identification of NGC 886, an object previously considered an asterism. This letter challenges the previous claim of a near-complete sample of open clusters up to 1.8 kpc. Our results reveal that this claim requires revision, and a complete census of nearby open clusters is yet to be found., Comment: accepted for publication in A&A

The dark ages of the Universe end with the formation of the first generation of stars residing in primeval galaxies. These objects were the first to produce ultraviolet ionizing photons in a period when the cosmic gas changed from a neutral state to an ionized one, known as Epoch of Reionization (EoR). A pivotal aspect to comprehend the EoR is to probe the intertwined relationship between the fraction of ionizing photons capable to escape dark haloes, also known as the escape fraction ($f_{esc}$), and the physical properties of the galaxy. This work develops a sound statistical model suitable to account for such non-linear relationships and the non-Gaussian nature of $f_{esc}$. This model simultaneously estimates the probability that a given primordial galaxy starts the ionizing photon production and estimates the mean level of the $f_{esc}$ once it is triggered. The model was employed in the First Billion Years simulation suite, from which we show that the baryonic fraction and the rate of ionizing photons appear to have a larger impact on $f_{esc}$ than previously thought. A naive univariate analysis of the same problem would suggest smaller effects for these properties and a much larger impact for the specific star formation rate, which is lessened after accounting for other galaxy properties and non-linearities in the statistical model.

We report a framework for spectroscopic follow-up design for optimizing supernova photometric classification. The strategy accounts for the unavoidable mismatch between spectroscopic and photometric samples, and can be used even in the beginning of a new survey -- without any initial training set. The framework falls under the umbrella of active learning (AL), a class of algorithms that aims to minimize labelling costs by identifying a few, carefully chosen, objects which have high potential in improving the classifier predictions. As a proof of concept, we use the simulated data released after the Supernova Photometric Classification Challenge (SNPCC) and a random forest classifier. Our results show that, using only 12\% the number of training objects in the SNPCC spectroscopic sample, this approach is able to double purity results. Moreover, in order to take into account multiple spectroscopic observations in the same night, we propose a semi-supervised batch-mode AL algorithm which selects a set of $N=5$ most informative objects at each night. In comparison with the initial state using the traditional approach, our method achieves 2.3 times higher purity and comparable figure of merit results after only 180 days of observation, or 800 queries (73% of the SNPCC spectroscopic sample size). Such results were obtained using the same amount of spectroscopic time necessary to observe the original SNPCC spectroscopic sample, showing that this type of strategy is feasible with current available spectroscopic resources. The code used in this work is available in the COINtoolbox: https://github.com/COINtoolbox/ActSNClass ., Comment: 18 pages, 15 figures - replace to match journal version

Astronomical observations of extended sources, such as cubes of integral field spectroscopy (IFS), encode auto-correlated spatial structures that cannot be optimally exploited by standard methodologies. This work introduces a novel technique to model IFS datasets, which treats the observed galaxy properties as realizations of an unobserved Gaussian Markov random field. The method is computationally efficient, resilient to the presence of low-signal-to-noise regions, and uses an alternative to Markov Chain Monte Carlo for fast Bayesian inference, the Integrated Nested Laplace Approximation (INLA). As a case study, we analyse 721 IFS data cubes of nearby galaxies from the CALIFA and PISCO surveys, for which we retrieve the maps of the following physical properties: age, metallicity, mass and extinction. The proposed Bayesian approach, built on a generative representation of the galaxy properties, enables the creation of synthetic images, recovery of areas with bad pixels, and an increased power to detect structures in datasets subject to substantial noise and/or sparsity of sampling. A snippet code to reproduce the analysis of this paper is available in the COIN toolbox, together with the field reconstructions of the CALIFA and PISCO samples., Comment: 13 pages, 10 figures, submitted to MNRAS (comments welcome)

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., Comment: 2 pages, to appear in Proceedings of the IAU Symposium 330, "Astrometry and Astrophysics in the Gaia Sky", held in April 2017, Nice, France

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 $\rm W_{H\alpha}$ vs. [NII]/H$\alpha$ (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$\beta$, $\log$ [NII]/H$\alpha$, and $\log$ EW(H${\alpha}$), 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/)., Comment: Accepted for publication in MNRAS

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)., Comment: 19 pages, 10 figures. Minor revision accepted by MNRAS on 2017 March 16

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., Comment: 11 pages, 6 figures, accepted in MNRAS

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)., Comment: 16 pages, 12 figures, accepted for publication in MNRAS

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., Comment: Refereed Proceeding of "The Universe of Digital Sky Surveys" conference held at the INAF - Observatory of Capodimonte, Naples, on 25th-28th November 2014, to be published in the Astrophysics and Space Science Proceedings, edited by Longo, Napolitano, Marconi, Paolillo, Iodice, 6 pages, and 1 figure

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., Comment: 14 pages, 12 figures. Accepted for publication in MNRAS

Concerted effort is currently ongoing to open up the Epoch of Reionization (EoR) ($z\sim$15-6) for studies with IR and radio telescopes. Whereas IR detections have been made of sources (Lyman-$\alpha$ 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)., Comment: Accepted for publication in the SKA Science Book 'Advancing Astrophysics with the Square Kilometre Array', to appear in 2015. PoS(AASKA14)001

Approximate Bayesian Computation (ABC) enables parameter inference for complex physical systems in cases where the true likelihood function is unknown, unavailable, or computationally too expensive. It relies on the forward simulation of mock data and comparison between observed and synthetic catalogues. Here we present cosmoabc, a Python ABC sampler featuring a Population Monte Carlo (PMC) variation of the original ABC algorithm, which uses an adaptive importance sampling scheme. The code is very flexible and can be easily coupled to an external simulator, while allowing to incorporate arbitrary distance and prior functions. As an example of practical application, we coupled cosmoabc with the numcosmo library and demonstrate how it can be used to estimate posterior probability distributions over cosmological parameters based on measurements of galaxy clusters number counts without computing the likelihood function. cosmoabc is published under the GPLv3 license on PyPI and GitHub and documentation is available at http://goo.gl/SmB8EX, Comment: Add missing variable to algorithm 1 description. To see all frames in figure 4 download the pdf and open with Adobe Reader (or equivalent) and use the controls bellow the plot

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., Comment: 20 pages, 10 figures, 3 tables, accepted for publication in Astronomy and Computing

Machine learning techniques offer a precious tool box for use within astronomy to solve problems involving so-called big data. They provide a means to make accurate predictions about a particular system without prior knowledge of the underlying physical processes of the data. In this article, and the companion papers of this series, we present the set of Generalized Linear Models (GLMs) as a fast alternative method for tackling general astronomical problems, including the ones related to the machine learning paradigm. To demonstrate the applicability of GLMs to inherently positive and continuous physical observables, we explore their use in estimating the photometric redshifts of galaxies from their multi-wavelength photometry. Using the gamma family with a log link function we predict redshifts from the PHoto-z Accuracy Testing simulated catalogue and a subset of the Sloan Digital Sky Survey from Data Release 10. We obtain fits that result in catastrophic outlier rates as low as ~1% for simulated and ~2% for real data. Moreover, we can easily obtain such levels of precision within a matter of seconds on a normal desktop computer and with training sets that contain merely thousands of galaxies. Our software is made publicly available as an user-friendly package developed in Python, R and via an interactive web application (https://cosmostatisticsinitiative.shinyapps.io/CosmoPhotoz). This software allows users to apply a set of GLMs to their own photometric catalogues and generates publication quality plots with minimum effort from the user. By facilitating their ease of use to the astronomical community, this paper series aims to make GLMs widely known and to encourage their implementation in future large-scale projects, such as the Large Synoptic Survey Telescope., Comment: 19 pages, 7 figures, 1 table, accepted for publication in Astronomy and Computing

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., Comment: 17 pages, 11 figures. Accepted for publication in MNRAS

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., Comment: 9 pages, 5 figures, accepted for publication in MNRAS

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 $\sigma_{(z_{\rm phot} - z_{\rm spec})/(1+z_{\rm spec})}\lesssim 0.045$ 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., Comment: 6 pages, 4 figures. Accepted for publication in MNRAS Letters

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., Comment: 9 pages, 8 figures. Minor corrections added to match published version

(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, $B(r) \propto \rho_{g}^{\alpha}$, to seek generality we assume $\alpha$ 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, $P_{\rm turb} = {1/3}\rho_{\rm g}(\sigma_{r}^{2}+\sigma_{t}^{2})$. 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)., Comment: 14 pages, 4 figures, accepted for publication in A&A