ALBERT

Description: The Southern Photometric Local Universe Survey (S-PLUS) is imaging ∼9300 deg2 of the celestial sphere in 12 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 × 9.2k e2v detector with 10 $ m {mu m}$ pixels, resulting in a field of view of 2 deg2 with a plate scale of 0.55 arcsec pixel−1. The survey consists of four main subfields, which include two non-contiguous fields at high Galactic latitudes (|b| 〉 30°, 8000 deg2) and two areas of the Galactic Disc and Bulge (for an additional 1300 deg2). S-PLUS uses the Javalambre 12-band magnitude system, which includes the 5 ugriz broad-band filters and 7 narrow-band filters centred on prominent stellar spectral features: the Balmer jump/[OII], Ca H + K, H δ, G band, Mg b triplet, H α, and the Ca triplet. S-PLUS delivers accurate photometric redshifts (δz/(1 + z) = 0.02 or better) for galaxies with r 〈 19.7 AB mag and z 〈 0.4, thus producing a 3D map of the local Universe over a volume of more than $1, (mathrm{Gpc}/h)^3$. 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 deg2 of the Stripe 82 area, in 12 bands, to a limiting magnitude of r = 21, available at datalab.noao.edu/splus.

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 bacterial genus Burkholderia comprises species occupying several habitats, including a group of symbionts of leguminous plants—also called beta-rhizobia—that has been recently ascribed to the new genus Paraburkholderia . We used common bean ( Phaseolus vulgaris L.) plants to trap rhizobia from an undisturbed soil of the Brazilian Cerrado under the vegetation type ‘Cerradão’. Genetic characterization started with the analyses of 181 isolates by BOX-PCR, where the majority revealed unique profiles, indicating high inter- and intra-species diversity. Restriction fragment length polymorphism-PCR of the 16S rRNA of representative strains of the BOX-PCR groups indicated two main clusters, and gene-sequencing analysis identified the minority (27%) as Rhizobium and the majority (73%) as Paraburkholderia . Phylogenetic analyses of the 16S rRNA and housekeeping ( recA and gyrB ) genes positioned all strains of the second cluster in the species P. nodosa , and the phylogeny of a symbiotic gene— nodC —was in agreement with the conserved genes. All isolates were stable vis-à-vis nodulating common bean, but, in general, with a low capacity for fixing N 2 , although some effective strains were identified. The predominance of P. nodosa might be associated with the edaphic properties of the Cerrado biome, and might represent an important role in terms of maintenance of the ecosystem, which is characterized by acid soils with high saturation of aluminum and low N 2 content.

Description: A novel structure of nonautonomous long terminal repeat (LTR) retrotransposons called terminal repeat with GAG domain (TR-GAG) has been described in plants, both in monocotyledonous, dicotyledonous and basal angiosperm genomes. TR-GAGs are relatively short elements in length (〈4 kb) showing the typical features of LTR-retrotransposons. However, they carry only one open reading frame coding for the GAG precursor protein involved for instance in transposition, the assembly, and the packaging of the element into the virus-like particle. GAG precursors show similarities with both Copia and Gypsy GAG proteins, suggesting evolutionary relationships of TR-GAG elements with both families. Despite the lack of the enzymatic machinery required for their mobility, strong evidences suggest that TR-GAGs are still active. TR-GAGs represent ubiquitous nonautonomous structures that could be involved in the molecular diversities of plant genomes.

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: The near-infrared spectral region is becoming a very useful wavelength range to detect and quantify the stellar population of galaxies. Models are developing to predict the contribution of the thermally pulsating stars on the asymptotic giant branch stars that should dominate the near-infrared region (NIR) spectra of populations 0.3 to 2 Gyr old. When present in a given stellar population, these stars leave unique signatures that can be used to detect them unambiguously. However, these models have to be tested in a homogeneous data base of star-forming galaxies, to check if the results are consistent with what is found from different wavelength ranges. In this work, we performed stellar population synthesis on the nuclear and extended regions of 23 star-forming galaxies to understand how the star formation tracers in the NIR can be used in practice. The stellar population synthesis shows that for the galaxies with strong emission in the NIR, there is an important fraction of young/intermediate population contributing to the spectra, which is probably the ionization source in these galaxies. Galaxies that had no emission lines measured in the NIR were found to have older average ages and less contribution of young populations. Although the stellar population synthesis method proved to be very effective to find the young ionizing population in these galaxies, no clear correlation between these results and the NIR spectral indexes were found. Thus, we believe that, in practice, the use of these indexes is still very limited due to observational limitations.

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: Methicillin-resistant Staphylococcus aureus (MRSA) is still one of the most important hospital pathogen globally. The multiresistant isolates of the ST239-SCC mec III lineage are spread over large geographic regions, colonizing and infecting hospital patients in virtually all continents. The balance between fitness (adaptability) and virulence potential is likely to represent an important issue in the clonal shift dynamics leading the success of some specific MRSA clones over another. The accessory gene regulator ( agr ) is the master quorum sensing system of staphylococci playing a role in the global regulation of key virulence factors. Consequently, agr inactivation in S. aureus may represent a significant mechanism of genetic variability in the adaptation of this healthcare-associated pathogen. We report here the complete genome sequence of the methicillin-resistant S. aureus , isolate HC1335, a variant of the ST239 lineage, which presents a natural insertion of an IS 256 transposase element in the agrC gene encoding AgrC histidine kinase receptor.