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).
Abstract
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 the feature space (e.g. ...colours and magnitudes) and the mismatch between the 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 data sets that 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 the 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).
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 × 102 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.
We present spectroscopic and photometric observations for the Type Ibn supernova (SN) dubbed PSN J07285387+3349106. Using data provided by amateur astronomers, we monitored the photometric rise of ...the SN to maximum light, occurred on 2015 February 18.8 ut (JDmax(V) = 245 7072.0 ± 0.8). PSN J07285387+3349106 exploded in the inner region of an infrared luminous galaxy, and is the most reddened SN Ibn discovered so far. We apply multiple methods to derive the total reddening to the SN, and determine a total colour excess E(B − V)tot = 0.99 ± 0.48 mag. Accounting for the reddening correction, which is affected by a large uncertainty, we estimate a peak absolute magnitude of M
V
= −20.30 ± 1.50. The spectra are dominated by continuum emission at early phases, and He i lines with narrow P-Cygni profiles are detected. We also identify weak Fe iii and N ii features. All these lines show an absorption component which is blueshifted by about 900–1000 km s−1. The spectra also show relatively broad He i line wings with low contrast, which extend to above 3000 km s−1. From about two weeks past maximum, broad lines of O i, Mg ii and the Ca ii near-infrared triplet are identified. The composition and the expansion velocity of the circumstellar material, and the presence of He i and α-elements in the SN ejecta indicate that PSN J07285387+3349106 was produced by the core collapse of a stripped-envelope star. We suggest that the precursor was WNE-type Wolf–Rayet star in its dense, He-rich circumstellar cocoon.
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.
Astronomical observations of extended sources, such as cubes of integral field spectroscopy (IFS), encode autocorrelated spatial structures that cannot be optimally exploited by standard ...methodologies. This work introduces a novel technique to model IFS data sets, 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. 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 data sets 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.
ABSTRACT
The new generation of deep photometric surveys requires unprecedentedly precise shape and photometry measurements of billions of galaxies to achieve their main science goals. At such depths, ...one major limiting factor is the blending of galaxies due to line-of-sight projection, with an expected fraction of blended galaxies of up to 50 per cent. This proof-of-concept work explores for the first time the use of deep neural networks to estimate the photometry of blended pairs of galaxies in space-based monochrome images similar to the ones that will be delivered by the Euclidspace telescope under simplified idealized conditions. Using a clean sample of isolated galaxies from the CANDELS survey, we artificially blend them and train two different network models to recover the photometry of the two galaxies. We show that our approach can recover the original photometry of the galaxies before being blended with $\sim 7{{\ \rm per\ cent}}$ mean absolute percentage error on flux estimations without any human intervention and without any assumption on the galaxy shape. This represents an improvement of at least a factor of 4 compared to the classical SExtractor approach. We also show that, forcing the network to simultaneously estimate fractional segmentation maps results in a slightly improved photometry. All data products and codes have been made public to ease the comparison with other approaches on a common data set. See https://github.com/aboucaud/coindeblend.
A new method to study the intrinsic colour and luminosity of Type Ia supernovae (SNe Ia) is presented. A metric space built using principal component analysis on a spectral series for SNe Ia between ...−12.5 and +17.5 d from the B maximum is used as a set of predictors. This metric space is built to be insensitive to reddening. Hence, it does not predict the part of the colour excess due to dust extinction. At the same time, the rich variability of SN Ia spectra is a good predictor of a large fraction of the intrinsic colour variability. Such a metric space is a good predictor of the epoch when the maximum in the B − V colour curve is reached. Multivariate partial least-squares regression predicts the intrinsic B-band light curve and the intrinsic B − V colour curve up to a month after the maximum. This allows us to study the relation between the light curves of SNe Ia and their spectra. The total-to-selective extinction ratio RV
in the host galaxy of SNe Ia is found, on average, to be consistent with typical Milky Way values. This analysis shows the importance of collecting spectra to study SNe Ia, even with a large sample publicly available. Future automated surveys, such as the Large Synoptic Survey Telescope, will provide a large number of light curves. The analysis shows that observing accompanying spectra for a significant number of SNe will be important even for normal SNe Ia.
This work determines the degree to which a traditional analysis of the standard model of cosmology (ΛCDM) based on type Ia supernovae can identify deviations from a cosmological constant in the form ...of a redshift-dependent dark energy equation of state w(z). We introduce and apply a novel random curve generator to simulate instances of w(z) from constraint families with increasing distinction from a cosmological constant. After producing a series of mock catalogs of binned type Ia supernovae corresponding to each w(z) curve, we perform a standard ΛCDM analysis to estimate the corresponding posterior densities of the absolute magnitude of type Ia supernovae, the present-day matter density, and the equation of state parameter. Using the Kullback-Leibler divergence between posterior densities as a difference measure, we demonstrate that a standard type Ia supernova cosmology analysis has limited sensitivity to extensive redshift dependencies of the dark energy equation of state. In addition, we report that larger redshift-dependent departures from a cosmological constant do not necessarily manifest easier-detectable incompatibilities with the ΛCDM model. Our results suggest that physics beyond the standard model may simply be hidden in plain sight.
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We present a detailed statistical analysis of possible non-linearities in the period–luminosity (PL), period–Wesenheit (PW) and period–colour (PC) relations for Cepheid variables in the Large ...Magellanic Cloud (LMC) at optical (VI) and near-infrared (JHKs
) wavelengths. We test for the presence of possible non-linearities and determine their statistical significance by applying a variety of robust statistical tests (F-test, random-walk, testimator and the Davies test) to optical data from third phase of the Optical Gravitational Lensing Experiment and near-infrared data from Large Magellanic Cloud Near-Infrared Synoptic Survey. For fundamental-mode Cepheids, we find that the optical PL, PW and PC relations are non-linear at 10 d. The near-infrared PL and the
$W^H_{V,I}$
relations are non-linear around 18 d; this break is attributed to a distinct variation in mean Fourier amplitude parameters near this period for longer wavelengths as compared to optical bands. The near-infrared PW relations are also non-linear except for the
$W_{H,K_s}$
relation. For first-overtone mode Cepheids, a significant change in the slope of PL, PW and PC relations is found around 2.5 d only at optical wavelengths. We determine a global slope of −3.212 ± 0.013 for the
$W^H_{V,I}$
relation by combining our LMC data with observations of Cepheids in Supernovae host galaxies. We find this slope to be consistent with the corresponding LMC relation at short periods, and significantly different to the long-period value. We do not find any significant difference in the slope of the global-fit solution using a linear or non-linear LMC PL relation as calibrator, but the linear version provides a two times better constraint on the slope and metallicity coefficient.
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