Related papers: A Probabilistic Approach to Classifying Supernovae…
Time-domain astronomy is entering a new era as wide-field surveys with higher cadences allow for more discoveries than ever before. The field has seen an increased use of machine learning and deep learning for automated classification of…
A quantitative data-driven comparison among supernovae (SNe) based on their spectral time series combined with multi-band photometry is presented. We use an unsupervised Random Forest algorithm as a metric on a set of 82 well-documented SNe…
We discuss what is an appropriate set of explanatory variables in order to predict the absolute magnitude at the maximum of Type Ia supernovae. In order to have a good prediction, the error for future data, which is called the…
Large numbers of supernovae (SNe) have been discovered in recent years, and many more will be found in the near future. Once discovered, further study of a SN and its possible use as an astronomical tool (e.g., as a distance estimator)…
The dense shell method for the determination of distances to type IIn supernovae has been briefly reviewed. Applying our method to SN 2006gy, SN 2009ip, and SN 2010jl supernovae, we have obtained distances in excellent agreement with the…
We present a new empirical method for fitting multicolor light curves of Type Ia supernovae. Our method combines elements from two widely used techniques in the literature: the delta_m15 template fitting method and the Multicolor…
Thermonuclear (Type Ia) supernovae are bright stellar explosions, the light curves of which can be calibrated to allow for use as "standard candles" for measuring cosmological distances. Contemporary research investigates how the brightness…
In the era of large all-sky surveys, there will be a need for rapid, automatic classifications of newly discovered transient objects. Our focus here is the classification of supernovae (SNe). We consider random forest machine learning…
Even at extragalactic distances, the shape of supernova ejecta can be effectively diagnosed by spectropolarimetry. We present here results for 17 Type Ia supernovae that allow a statistical study of the correlation among the geometric…
The fitting of the observed redshifts and magnitudes of type Ia supernovae to what we would see in homogeneous cosmological models has led to constraints on cosmological parameters. However, in doing such fits it is assumed that the sampled…
Observed data is often contaminated by undiscovered interlopers, leading to biased parameter estimation. Here we present BEAMS (Bayesian Estimation Applied to Multiple Species) which significantly improves on the standard maximum likelihood…
We present a Monte Carlo radiative transfer technique for calculating synthetic spectropolarimetry for multi-dimensional supernova explosion models. The approach utilises "virtual-packets" that are generated during the propagation of the…
In this paper, the suitability of fast-declining Type Ia supernovae (SNe Ia) as cosmological standard candles is examined utilizing a Hubble Flow sample of 43 of these objects observed by the Carnegie Supernova Project (CSP). We confirm…
In astrophysical systems, radiation-matter interactions are important in transferring energy and momentum between the radiation field and the surrounding material. This coupling often makes it necessary to consider the role of radiation…
Photometric classification of Type Ia supernovae is essential for modern time-domain surveys, where spectroscopic confirmation is not always feasible for the full transient sample. In this work, we investigate a compact and physically…
Cosmologists are facing the problem of the analysis of a huge quantity of data when observing the sky. The methods used in cosmology are, for the most of them, relying on astrophysical models, and thus, for the classification, they usually…
In this paper we present and discuss a new Monte Carlo approach aimed at correcting the observed Supernova (SN) rates for the effects of host galaxy dust extinction. The problem is addressed in a general way and the model includes SN…
We present a Hubble diagram of type II supernovae using corrected magnitudes derived only from photometry, with no input of spectral information. We use a data set from the Carnegie Supernovae Project I (CSP) for which optical and…
We present a new solution to the problem of classifying Type Ia supernovae from their light curves alone given a spectroscopically confirmed but biased training set, circumventing the need to obtain an observationally expensive unbiased…
Upcoming photometric surveys will discover tens of thousands of Type Ia supernovae (SNe Ia), vastly outpacing the capacity of our spectroscopic resources. In order to maximize the science return of these observations in the absence of…