相关论文: Unsupervised clustering of Type II supernova light…

We investigate a new empirical fitting method for the optical light curves of Type Ia supernovae (SNe~Ia) that is able to estimate the first-light time of SNe~Ia, even when they are not discovered extremely early. With an improved ability…

Distant supernovae can now be detected routinely. To date 34 supernovae at $z > 0.1$ have been discovered. Among them are 12 Type~Ia supernovae confirmed spectroscopically and suited to measure the cosmic deceleration when appropriately…

SN 2006bt displays characteristics unlike those of any other known Type Ia supernova (SN Ia). We present optical light curves and spectra of SN 2006bt which demonstrate the peculiar nature of this object. SN 2006bt has broad, slowly…

SN 2002es is a peculiar subluminous Type Ia supernova (SN Ia) with a combination of observed characteristics never before seen in a SN Ia. At maximum light, SN 2002es shares spectroscopic properties with the underluminous SN 1991bg subclass…

Clustering analysis is one of the critical tasks in machine learning. Traditionally, clustering has been an independent task, separate from outlier detection. Due to the fact that the performance of clustering can be significantly eroded by…

We present a comprehensive statistical analysis of the properties of Type Ia SN light curves in the near infrared using recent data from PAIRITEL and the literature. We construct a hierarchical Bayesian framework, incorporating several…

We investigate polarization spectra of hydrogen-rich core-collapse supernovae (Type~II SNe). The polarization signal from SNe contains two independent components: intrinsic SN polarization and interstellar polarization (ISP). From these…

Upcoming high-cadence transient survey programmes will produce a wealth of observational data for Type Ia supernovae. These data sets will contain numerous events detected very early in their evolution, shortly after explosion. Here, we…

The classification of stripped-envelope supernovae (SE-SNe) is revisited using modern data-sets. Spectra are analysed using an empirical method to "blindly" categorise SNe according to spectral feature strength and appearance. This method…

Robust fast methods to classify variable light curves in large sky surveys are becoming increasingly important. While it is relatively straightforward to identify common periodic stars and particular transient events (supernovae, novae,…

- 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…

The present study is the first of a series of three papers where we characterise the type II supernovae (SNe~II) from the Carnegie Supernova Project-I to understand their diversity in terms of progenitor and explosion properties. In this…

We present a methodology to discover outliers in catalogs of periodic light-curves. We use cross-correlation as measure of ``similarity'' between two individual light-curves and then classify light-curves with lowest average ``similarity''…

In astronomical surveys, such as the Zwicky Transient Facility, supernovae (SNe) are relatively uncommon objects compared to other classes of variable events. Along with this scarcity, the processing of multi-band light-curves is a…

Evolutionary effects with redshift of core collapse supernovae and their application to cosmology have been studied based on an extensive grid of stellar models between 13 and 25 Mo, and their light curves after the explosion. With…

The early rise of Type IIP supernovae (SN IIP) provides important information for constraining the properties of their progenitors. This can in turn be compared to pre-explosion imaging constraints and stellar models to develop a more…

Fast-declining Type Ia supernovae (SN Ia) separate into two categories based on their bolometric and near-infrared (NIR) properties. The peak bolometric luminosity ($\mathrm{L_{max}}$), the phase of the first maximum relative to the…

We explore and demonstrate the capabilities of LSST to study Type I superluminous supernovae (SLSNe). We first fit the light curves of 58 known SLSNe at z~0.1-1.6, using an analytical magnetar spin-down model implemented in MOSFiT. We then…

During the last decade, a considerable amount of effort has been made to classify variable stars using different machine learning techniques. Typically, light curves are represented as vectors of statistical descriptors or features that are…

We present $BVi$ multi-band high-cadence observations of a Type II supernova (SN) KSP-SN-2022c from a star-forming galaxy at $z$ $\simeq$ 0.041 from its infant to nebular phase. Early light curve fitting with a single power-law is…