ALBERT

Description: The existence of multiple subclasses of Type Ia supernovae (SNe Ia) 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 SNe Ia 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 four-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 SN Ia 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 SNe Ia 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 ).

Description: 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 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 an 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 cluster. 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.

Description: The problem of supernova photometric identification will be extremely important for large surveys in the next decade. In this work, we propose the use of kernel principal component analysis (KPCA) combined with k  = 1 nearest neighbour algorithm (1NN) as a framework for supernovae (SNe) photometric classification. The method does not rely on information about redshift or local environmental variables, so it is less sensitive to bias than its template fitting counterparts. The classification is entirely based on information within the spectroscopic confirmed sample and each new light curve is classified one at a time. This allows us to update the principal component (PC) parameter space if a new spectroscopic light curve is available while also avoids the need of re-determining it for each individual new classification. We applied the method to different instances of the Supernova Photometric Classification Challenge (SNPCC) data set. Our method provides good purity results in all data sample analysed, when signal-to-noise ratio (SNR) ≥ 5. Therefore, we can state that if a sample as the post-SNPCC was available today, we would be able to classify 15 per cent of the initial data set with purity 90 per cent ( D 7 +SNR3). Results from the original SNPCC sample, reported as a function of redshift, show that our method provides high purity (up to 97 per cent), especially in the range of 0.2 ≤ z  〈 0.4, when compared to results from the SNPCC, while maintaining a moderate figure of merit (0.25). This makes our algorithm ideal for a first approach to an unlabelled data set or to be used as a complement in increasing the training sample for other algorithms. We also present results for SNe photometric classification using only pre-maximum epochs, obtaining 63 per cent purity and 77 per cent successful classification rates (SNR ≥ 5). In a tougher scenario, considering only SNe with MLCS2k2 fit probability 〉0.1, we demonstrate that KPCA+1NN is able to improve the classification results up to 〉95 per cent (SNR ≥ 3) purity without the need of redshift information. Results are sensitive to the information contained in each light curve, as a consequence, higher quality data points lead to higher successful classification rates. The method is flexible enough to be applied to other astrophysical transients, as long as a training and a test sample are provided.

Description: 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 dm ), gas mass ( M gas ), stellar mass ( M star ), molecular fraction ( x mol ), metallicity ( Z ), star formation rate (SFR) and temperature. We find that M dm and M gas are dominant factors for variance, particularly at high redshift. Nonetheless, with the emergence of the first stars and subsequent feedback mechanisms, 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 95 per cent anytime). Our analysis also suggests that all the gaseous properties have a stronger correlation with M gas than with M dm , while M gas has a deeper correlation with 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.

Description: The first supernovae (SNe) 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 SNe is paramount. We combine cosmological and radiation transport simulations, instrument specifications and survey strategies to create synthetic observations of primeval core-collapse (CC), Type IIn and pair-instability (PI) SNe with the James Webb Space Telescope ( JWST ). We show that a dedicated observational campaign with the JWST can detect up to ~15 PI explosions, ~300 CC SNe, but less than one Type IIn explosion per year, depending on the Population III star formation history. Our synthetic survey also shows that 1–2 10 2 SNe 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.

Description: We report the first analytical expression purely constructed by a machine to determine photometric redshifts ( z 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 1417 181 SDSS-DR10 galaxies, with measured spectroscopic redshifts ( z spec ), we achieved a mean 〈( z phot – z spec )/(1 + z spec )〉 0.0086 and a scatter ( z phot – z spec )/(1 + z spec ) 0.045 when averaged up to z 1.0. The method was also applied to the PHAT0 data set, 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.

Description: Structures in warm dark matter (WDM) models are exponentially suppressed below a certain scale, characterized by the dark matter particle mass, m x . Since structures form hierarchically, the presence of collapsed objects at high redshifts can set strong lower limits on m x . We place robust constraints on m x using recent results from the Swift data base of high-redshift gamma-ray bursts (GRBs). We parametrize the redshift evolution of the ratio between the cosmic GRB rate and star formation rate (SFR) as (1 + z ) α , thereby allowing astrophysical uncertainties to partially mimic the cosmological suppression of structures in WDM models. Using a maximum-likelihood estimator on two different z  〉 4 GRB subsamples (including two bursts at z  〉 8), we constrain m x   1.6–1.8 keV at 95 per cent CL, when marginalized over a flat prior in α. We further estimate that 5 years of a Sino-French space-based multi-band astronomical variable objects monitor like mission would tighten these constraints to m x   2.3 keV. Our results show that GRBs are a powerful probe of high-redshift structures, providing robust and competitive constraints on m x .

Description: 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, signalling 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 8 per cent of the total allocation time of the James Webb Space Telescope mission can provide us with up to ~9–15 detectable PISNe per year.