Related papers: Optimal Classification and Outlier Detection for S…

Stripped-envelope supernovae (SESNe) mark the deaths of massive stars without hydrogen-rich envelopes. Most SESNe likely originate from binary systems where a companion stripped the progenitor of its envelope. Years of HST imaging of nearby…

We present a comparative study of 22 core-collapse supernovae (SNe), selected to explore a novel, multidimensional ranking scheme aimed at identifying the best supernova. Each SN is evaluated based on three principal criteria: (1) inferred…

In the era of large astronomical surveys, photometric classification of supernovae (SNe) has become an important research field due to limited spectroscopic resources for candidate follow-up and classification. In this work, we present a…

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 present GHOST, a database of 16,175 spectroscopically classified supernovae and the properties of their host galaxies. We have developed a host galaxy association method using image gradients that achieves fewer misassociations for low-z…

We present a method for selecting high-redshift type Ia supernovae (SNe Ia) located via rolling SN searches. The technique, using both color and magnitude information of events from only 2-3 epochs of multi-band real-time photometry, is…

The dominant mechanism and time scales over which stripped-envelope supernovae (SNe) progenitor stars shed their hydrogen envelopes are uncertain. Observations of Type Ib and Ic SNe at late phases could reveal the optical signatures of…

In order to assess qualitatively the ejecta geometry of stripped-envelope core-collapse supernovae, we investigate 98 late-time spectra of 39 objects, many of them previously unpublished. We perform a Gauss-fitting of the [O I] 6300, 6364…

Spectroscopy is an important tool for providing insights into the structure of core-collapse supernova explosions. We use the Monte Carlo radiative transfer code ARTIS to compute synthetic spectra and light curves based on a two-dimensional…

A new class of core-collapse supernovae (SNe) has been discovered in recent years by optical/infrared surveys; these SNe suggest the presence of one or more extremely dense (~10^5-10^11 cm^-3) shells of circumstellar material (CSM) on…

Type IIb supernovae (SNe IIb) present a unique opportunity for investigating the evolutionary channels and mechanisms governing the evolution of stripped-envelope SN progenitors due to a variety of observational constraints available.…

The $\gamma$-ray deposition history in an expanding supernova (SN) ejecta has been mostly used to constrain models for Type Ia SN. Here we expand this methodology to core-collapse SNe, including stripped envelope (SE; Type Ib/Ic/IIb) and…

In this work, we employ two publicly available analysis tools to study four hydrogen(H)--stripped core--collapse supernovae (CCSNe) namely, SN 2009jf, iPTF13bvn, SN 2015ap, and SN 2016bau. We use the Modular Open-Source Fitter for…

We study supernova (SN) classification using the machine learning method of the Recurrent Neural Network (RNN) in the Chinese Space Station Survey Telescope Ultra-Deep Field (CSST-UDF) photometric survey, and explore the improvement of the…

Stripped-envelope supernovae (SE-SNe) show a wide variety of photometric and spectroscopic properties. This is due to the different potential formation channels and the stripping mechanism that allows for a large diversity within the…

We present an algorithm to identify the types of supernova spectra, and determine their redshift and phase. This algorithm, based on the correlation techniques of Tonry & Davis, is implemented in the SuperNova IDentification code (SNID). It…

We introduce a transformer-based neural network for the accurate classification of real and bogus transient detections in astronomical images. This network advances beyond the conventional convolutional neural network (CNN) methods, widely…

We present an application of computer vision methods to classify the light curves of eclipsing binaries (EB). We have used pre-trained models based on convolutional neural networks ($\textit{ResNet50}$) and vision transformers…

In the upcoming decade large astronomical surveys will discover millions of transients raising unprecedented data challenges in the process. Only the use of the machine learning algorithms can process such large data volumes. Most of the…

We investigate the expected rates and bolometric light-curve properties of stripped-envelope electron-capture supernovae (ECSNe) using stellar models from the Binary Population and Spectral Synthesis (BPASS) code. We find that 0.8 per cent…