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

The existence of multiple subclasses of type Ia supernovae (SNeIa) has been the subject of great debate in the last decade. One major challenge inevitably met when trying to infer the existence of one or more subclasses is the time consuming, and subjective, process of subclass definition. In this work, we show how machine learning tools facilitate identification of subtypes of SNeIa through the establishment of a hierarchical group structure in the continuous space of spectral diversity formed by these objects. Using Deep Learning, we were capable of performing such identification in a 4 dimensional feature space (+1 for time evolution), while the standard Principal Component Analysis barely achieves similar results using 15 principal components. This is evidence that the progenitor system and the explosion mechanism can be described by a small number of initial physical parameters. As a proof of concept, we show that our results are in close agreement with a previously suggested classification scheme and that our proposed method can grasp the main spectral features behind the definition of such subtypes. This allows the confirmation of the velocity of lines as a first order effect in the determination of SNIa subtypes, followed by 91bg-like events. Given the expected data deluge in the forthcoming years, our proposed approach is essential to allow a quick and statistically coherent identification of SNeIa subtypes (and outliers). All tools used in this work were made publicly available in the Python package Dimensionality Reduction And Clustering for Unsupervised Learning in Astronomy (DRACULA) and can be found within COINtoolbox (https://github.com/COINtoolbox/DRACULA).Comment: 16 pages, 12 figures, accepted for publication in MNRA

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

The existence of multiple subclasses of type Ia supernovae (SNeIa) has been the subject of great debate in the last decade. One major challenge inevitably met when trying to infer the existence of one or more subclasses is the time consuming, and subjective, process of subclass definition. In this work, we show how machine learning tools facilitate the automatic discovery of sub-populations of SNIa; to that end we introduce the DRACULA Python package (Dimensionality Reduction And Clustering for Unsupervised Learning in Astronomy). Our approach is divided in three steps: (i) Transfer Learning, which takes advantage of all available spectra (even those without an epoch estimate) as an information source, (ii) dimensionality reduction through Deep Learning and (iii) unsupervised learning (clustering) using K-Means. Results match a previously suggested classification scheme, showing that the proposed method is able to grasp the main spectral features behind the definition of such subclasses. Moreover, our methodology is capable of automatically identifying a hierarchical structure of spectral features. This allows the confirmation of the velocity of lines as a first order effect in the determination of SNIa sub-classes, followed by 91bg-like events. In this context, SNIa spectra are described by a space of 4 dimensions + 1 for the time evolution of objects. We interpreted this as evidence that the progenitor system and the explosion mechanism should be described by a small number of initial physical parameters. Given the expected data deluge in the forthcoming years, our proposed approach is essential to allow a quick and statistically coherent identification of subclasses (and outliers). DRACULA is publicly available within COINtoolbox (https://github.com/COINtoolbox/DRACULA)

Testing the isotropy of the Dark Energy Survey's extreme trans-Neptunian objects

We test whether the population of "extreme" trans-Neptunian objects (eTNOs) detected in the Y4 Dark Energy Survey (DES) data exhibit azimuthal asymmetries which might be evidence of gravitational perturbations from an unseen super-Earth in a distant orbit. By rotating the orbits of the detected eTNOs, we construct a synthetic population which, when subject to the DES selection function, reproduces the detected distribution of eTNOs in the orbital elements $a,e,$ and $i$ as well as absolute magnitude $H$, but has uniform distributions in mean anomaly $M$, longitude of ascending node $\Omega,$ and argument of perihelion $\omega.$ We then compare the detected distributions in each of $\Omega, \omega,$ and $\varpi\equiv\Omega+\omega$ to those expected from the isotropic population, using Kuiper's variant of the Kolmogorov-Smirnov test. The three angles are tested for each of 4 definitions of the eTNO population, choosing among $a>(150,250)$ AU and perihelion $q>(30,37)$ AU. These choices yield 3--7 eTNOs in the DES Y4 sample. Among the twelve total tests, two have the likelihood of drawing the observed angles from the isotropic population at $p250, q>37$ AU, and the 4 detections at $a>250, q>30$ AU, have $\Omega$ distribution with $p=0.03$ of coming from the isotropic construction, but this is not strong evidence of anisotropy given the 12 different tests. The DES data taken on their own are thus consistent with azimuthal isotropy and do not require a "Planet 9" hypothesis. The limited sky coverage and object count mean, however, that the DES data by no means falsify this hypothesis.Comment: Accepted on PS

Detection and identification of Xanthomonas pathotypes associated with citrus diseases using comparative genomics and multiplex PCR

Background. In Citrus cultures, three species of Xanthomonas are known to cause distinct diseases. X. citri subsp. citri patothype A, X. fuscans subsp. aurantifolii pathotypes B and C, and X. alfalfae subsp. citrumelonis, are the causative agents of cancrosis A, B, C, and citrus bacterial spots, respectively. Although these species exhibit different levels of virulence and aggressiveness, only limited alternatives are currently available for proper and early detection of these diseases in the fields. The present study aimed to develop a new molecular diagnostic method based on genomic sequences derived from the four species of Xanthomonas. Results. Using comparative genomics approaches, primers were synthesized for the identification of the four causative agents of citrus diseases. These primers were validated for their specificity to their target DNA by both conventional and multiplex PCR. Upon evaluation, their sensitivity was found to be 0.02 ng/mu l in vitro and 1.5 x 10(4) CFU ml(-1) in infected leaves. Additionally, none of the primers were able to generate amplicons in 19 other genomes of Xanthomonas not associated with Citrus and one species of Xylella, the causal agent of citrus variegated chlorosis (CVC). This denotes strong specificity of the primers for the different species of Xanthomonas investigated in this study. Conclusions. We demonstrated that these markers can be used as potential candidates for performing in vivo molecular diagnosis exclusively for citrus-associated Xanthomonas. The bioinformatics pipeline developed in this study to design specific genomic regions is capable of generating specific primers. It is freely available and can be utilized for any other model organism.7CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQCOORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPESFUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE MINAS GERAIS - FAPEMIG481226/2013-3CFP 51/2013; 3385/2013APQ-02387-1

Testing the isotropy of the dark energy Survey's extreme trans-neptunian objects

We test whether the population of "extreme"trans-Neptunian objects (eTNOs) detected in the first four years of the Dark Energy Survey (DES Y4) data exhibit azimuthal asymmetries that might be evidence of gravitational perturbations from an unseen super-Earth in a distant orbit. By rotating the orbits of the detected eTNOs, we construct a synthetic population that, when subject to the DES selection function, reproduces the detected distribution of eTNOs in the orbital elements a, e, and i as well as absolute magnitude H, but has uniform distributions in mean anomaly M, longitude of ascending node Ω, and argument of perihelion ω. We then compare the detected distributions in each of Ω, ω, and the longitude of perihelion {equation presented} to those expected from the isotropic population, using Kuiper's variant of the Kolmogorov-Smirnov test. The three angles are tested for each of four definitions of the eTNO population, choosing among a > (150, 250) au and perihelion q > (30, 37) au. These choices yield 3-7 eTNOs in the DES Y4 sample. Among the 12 total tests, two have the likelihood of drawing the observed angles from the isotropic population at p 250 and q > 37 au and the four detections at a > 250 and q > 30 au have a Ω distribution with p ≈ 0.03 coming from the isotropic construction, but this is not strong evidence of anisotropy given the 12 different tests. The DES data taken on their own are thus consistent with azimuthal isotropy and do not require a "Planet 9"hypothesis. The limited sky coverage and object count mean, however, that the DES data by no means falsify this hypothesis

Photometric Properties of Jupiter Trojans Detected by the Dark Energy Survey

The Jupiter Trojans are a large group of asteroids that are coorbiting with Jupiter near its L4 and L5 Lagrange points. The study of Jupiter Trojans is crucial for testing different models of planet formation that are directly related to our understanding of solar system evolution. In this work, we select known Jupiter Trojans listed by the Minor Planet Center from the full six years data set (Y6) of the Dark Energy Survey (DES) to analyze their photometric properties. The DES data allow us to study Jupiter Trojans with a fainter magnitude limit than previous studies in a homogeneous survey with griz band measurements. We extract a final catalog of 573 unique Jupiter Trojans. Our sample include 547 asteroids belonging to L5. This is one of the largest analyzed samples for this group. By comparing with the data reported by other surveys we found that the color distribution of L5 Trojans is similar to that of L4 Trojans. We find that L5 Trojans' g - i and g - r colors become less red with fainter absolute magnitudes, a trend also seen in L4 Trojans. Both the L4 and L5 clouds consistently show such a color-size correlation over an absolute magnitude range 11 < H < 18. We also use DES colors to perform taxonomic classifications. C- and P-type asteroids outnumber D-type asteroids in the L5 Trojans DES sample, which have diameters in the 5-20 km range. This is consistent with the color-size correlation

The Dark Energy Survey Supernova Program: Corrections on Photometry Due to Wavelength-dependent Atmospheric Effects

Wavelength-dependent atmospheric effects impact photometric supernova flux measurements for ground-based observations. We present corrections on supernova flux measurements from the Dark Energy Survey Supernova Program’s 5YR sample (DES-SN5YR) for differential chromatic refraction (DCR) and wavelength-dependent seeing, and we show their impact on the cosmological parameters w and Ωm . We use g − i colors of Type Ia supernovae to quantify astrometric offsets caused by DCR and simulate point-spread functions (PSFs) using the GalSIM package to predict the shapes of the PSFs with DCR and wavelength-dependent seeing. We calculate the magnitude corrections and apply them to the magnitudes computed by the DES-SN5YR photometric pipeline. We find that for the DES-SN5YR analysis, not accounting for the astrometric offsets and changes in the PSF shape cause an average bias of +0.2 mmag and −0.3 mmag, respectively, with standard deviations of 0.7 mmag and 2.7 mmag across all DES observing bands (griz) throughout all redshifts. When the DCR and seeing effects are not accounted for, we find that w and Ωm are lower by less than 0.004 ± 0.02 and 0.001 ± 0.01, respectively, with 0.02 and 0.01 being the 1σ statistical uncertainties. Although we find that these biases do not limit the constraints of the DES-SN5YR sample, future surveys with much higher statistics, lower systematics, and especially those that observe in the u band will require these corrections as wavelength-dependent atmospheric effects are larger at shorter wavelengths. We also discuss limitations of our method and how they can be better accounted for in future surveys

A sample of dust attenuation laws for Dark Energy Survey supernova host galaxies

CONTEXT: Type Ia supernovae (SNe Ia) are useful distance indicators in cosmology, provided their luminosity is standardized by applying empirical corrections based on light-curve properties. One factor behind these corrections is dust extinction, which is accounted for in the color–luminosity relation of the standardization. This relation is usually assumed to be universal, which can potentially introduce systematics into the standardization. The “mass step” observed for SN Ia Hubble residuals has been suggested as one such systematic. AIMS: We seek to obtain a more complete view of dust attenuation properties for a sample of 162 SN Ia host galaxies and to probe their link to the mass step. METHODS: We inferred attenuation laws toward hosts from both global and local (4 kpc) Dark Energy Survey photometry and composite stellar population model fits. RESULTS: We recovered a relation between the optical depth and the attenuation slope, best explained by differing star-to-dust geometry for different galaxy orientations, which is significantly different from the optical depth and extinction slope relation observed directly for SNe. We obtain a large variation of attenuation slopes and confirm these change with host properties, such as the stellar mass and age, meaning a universal SN Ia correction should ideally not be assumed. Analyzing the cosmological standardization, we find evidence for a mass step and a two-dimensional “dust step”, both more pronounced for red SNe. Although comparable, the two steps are not found to be completely analogous. CONCLUSIONS: We conclude that host galaxy dust data cannot fully account for the mass step, using either an alternative SN standardization with extinction proxied by host attenuation or a dust-step approach

Building an Efficient Cluster Cosmology Software Package for Modeling Cluster Counts and Lensing

We introduce a software suite developed for galaxy cluster cosmological analysis with the Dark Energy Survey Data. Cosmological analyses based on galaxy cluster number counts and weak-lensing measurements need efficient software infrastructure to explore an increasingly large parameter space, and account for various cosmological and astrophysical effects. Our software package is designed to model the cluster observables in a wide-field optical survey, including galaxy cluster counts, their averaged weak-lensing masses, or the cluster's averaged weak-lensing radial signals. To ensure maximum efficiency, this software package is developed in C++ in the CosmoSIS software framework, making use of the CUBA integration library. We also implement a testing and validation scheme to ensure the quality of the package. We demonstrate the effectiveness of this development by applying the software to the Dark Energy Survey Year 1 galaxy cluster cosmological data sets, and acquired cosmological constraints that are consistent with the fiducial Dark Energy Survey analysis