相关论文: Observational and Physical Classification of Super…

We propose a new strategy of finding strongly-lensed supernovae (SNe) by monitoring known galaxy-scale strong-lens systems. Strongly lensed SNe are potentially powerful tools for the study of cosmology, galaxy evolution, and stellar…

We study the nature of 39 unconfirmed supernovae (SNe) from the sky area covered by the Sloan Digital Sky Survey (SDSS) Data Release 8 (DR8), using available photometric and imaging data and intensive literature search. We confirm that 21…

- Constraining the cosmological parameters and understanding Dark Energy have tremendous implications for the nature of the Universe and its physical laws. - The pervasive limit of systematic uncertainties reached by cosmography based on…

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…

Type Ia supernovae (SNe Ia) have been intensively investigated due to its great homogeneity and high luminosity, which make it possible to use them as standardizable candles for the determination of cosmological parameters. In 2011, the…

Supernova remnants (SNRs) arise from the interaction between the ejecta of a supernova (SN) explosion and the surrounding circumstellar and interstellar medium. Some SNRs, mostly nearby SNRs, can be studied in great detail. However, to…

Type II supernovae (SNe II) are the most common terminal stellar explosions in the Universe. With SNe now being detected within days after explosion, there is growing evidence that the majority of Type II SNe show signs of interaction with…

All types of supernovae (SNe), except Type Ia, have been observed to interact with their immediate circumstellar medium (CSM). This interaction can reveal their progenitor's histories, and constrain our ideas about the evolution of massive…

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…

Automated classification of supernovae (SNe) based on optical photometric light curve information is essential in the upcoming era of wide-field time domain surveys, such as the Legacy Survey of Space and Time (LSST) conducted by the Rubin…

We investigate the identification of hydrogen-poor superluminous supernovae (SLSNe I) using a photometric analysis, without including an arbitrary magnitude threshold. We assemble a homogeneous sample of previously classified SLSNe I from…

We review the existing set of optical/UV/IR observations of Supernova 1993J, concentrating heavily on optical data because these are by far the most plentiful. Some results from theoretical modeling of the observations are also discussed.…

The SuperNovae Analysis aPplication (SNAP) is a new tool for the analysis of SN observations and validation of SN models. SNAP consists of an open source relational database with (a) observational light curve, (b) theoretical light curve,…

We present the results of a systematic analysis of a group of Type II plateau supernovae that span a large range in luminosities, from faint objects like SN 1997D and 1999br to very luminous events like SN 1992am. The physical properties of…

Type Ia supernovae are generally agreed to arise from thermonuclear explosions of carbon-oxygen white dwarfs. The actual path to explosion, however, remains elusive, with numerous plausible parent systems and explosion mechanisms suggested.…

Ongoing searches for supernovae (SNe) at cosmological distances have recently started to provide a link between SN Ia statistics and galaxy evolution. We use recent estimates of the global history of star formation to compute the…

Large photometric surveys with the aim of identifying many Type Ia supernovae (SNe) at moderate redshift are challenged in separating these SNe from other SN types. We are motivated to identify Type Ia SNe based only on broadband…

What are Type II-Linear supernovae (SNe II-L)? This class, which has been ill defined for decades, now receives significant attention -- both theoretically, in order to understand what happens to stars in the ~15-25Mo range, and…

We present a new method for probabilistically classifying supernovae (SNe) without using SN spectral or photometric data. Unlike all previous studies to classify SNe without spectra, this technique does not use any SN photometry. Instead,…

We present a novel method of classifying Type Ia supernovae using convolutional neural networks, a neural network framework typically used for image recognition. Our model is trained on photometric information only, eliminating the need for…