Related papers: Classifying Supernovae Using Only Galaxy Data
Due to high-cadence automated surveys, we can now detect and classify supernovae (SNe) within a few days after explosion, if not earlier. Early-time spectra of young SNe directly probe the outermost layers of the ejecta, providing insights…
We have publicly released a blinded mix of simulated SNe, with types (Ia, Ib, Ic, II) selected in proportion to their expected rate. The simulation is realized in the griz filters of the Dark Energy Survey (DES) with realistic observing…
While many studies have shown a correlation between properties of the light curves of Type Ia SN (SNe Ia) and properties of their host galaxies, it remains unclear what is driving these correlations. We introduce a new direct method to…
We present new estimates of the observed rates of SNe determined with the {\em control time} method applied to the files of observations of two long term, photographic SN searches carried out at the Asiago and Sternberg Observatories. Our…
While significant advances have been made in photometric classification ahead of the millions of transient events and hundreds of supernovae (SNe) each night that the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will…
Current and future cosmological analyses with Type Ia Supernovae (SNe Ia) face three critical challenges: i) measuring redshifts from the supernova or its host galaxy; ii) classifying SNe without spectra; and iii) accounting for…
The Vera C. Rubin Observatory will increase the number of observed supernovae (SNe) by an order of magnitude; however, it is impossible to spectroscopically confirm the class for all the SNe discovered. Thus, photometric classification is…
We present the summary of our last results on the spatial distribution and relative frequencies of Supernovae (SNe) in a large number of host galaxies from the Sloan Digital Sky Survey (SDSS). We use the locations of SNe in order to study…
We apply deep recurrent neural networks, which are capable of learning complex sequential information, to classify supernovae\footnote{Code available at \href{https://github.com/adammoss/supernovae}{https://github.com/adammoss/supernovae}}.…
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…
Aim: We present new extraction and identification techniques for supernova (SN) spectra developed within the Supernova Legacy Survey (SNLS) collaboration. Method: The new spectral extraction method takes full advantage of photometric…
This is the third paper of a series in which we present new measurements of the observed rates of supernovae (SNe) in the local Universe, determined from the Lick Observatory Supernova Search (LOSS). We have considered a sample of about…
This chapter describes the current classification scheme of supernovae (SNe). This scheme has evolved over many decades and now includes numerous SN Types and sub-types. Many of these are universally recognized, while there are…
The Pan-STARRS1 (PS1) survey has obtained imaging in 5 bands (grizy_P1) over 10 Medium Deep Survey (MDS) fields covering a total of 70 square degrees. This paper describes the search for apparently hostless supernovae (SNe) within the first…
The efficient classification of different types of supernova is one of the most important problems for observational cosmology. However, spectroscopic confirmation of most objects in upcoming photometric surveys, such as the The Rubin…
With large numbers of transients discovered by current and future imaging surveys, machine learning is increasingly applied to light curve and host galaxy properties to select events for follow-up. However, finding rare types of transients…
This is the second paper of a series in which we present new measurements of the observed rates of supernovae (SNe) in the local Universe, determined from the Lick Observatory Supernova Search (LOSS). In this paper, a complete SN sample is…
We present a technique to measure lightcurves of time-variable point sources on a spatially structured background from imaging data. The technique was developed to measure light curves of SNLS supernovae in order to infer their distances.…
Observations of high redshift type Ia supernovae (SNe) will enable us to probe the structure of galaxy halos and the composition of dark matter. The future prospects for this field are briefly discussed here. First the ability of SN…
We discuss the extent to which photometric measurements alone can be used to identify Type Ia supernovae (SNIa) and to determine redshift and other parameters of interest for cosmological studies. We fit the light curve data of the type…