Related papers: CCSNscore: A multi-input deep learning tool for cl…
Hyperspectral image (HSI) classification is widely used for the analysis of remotely sensed images. Hyperspectral imagery includes varying bands of images. Convolutional Neural Network (CNN) is one of the most frequently used deep learning…
The new generation of wide-field time-domain surveys has made it feasible to study the clustering of supernova (SN) host galaxies in the large-scale structure (LSS) for the first time. We investigate the LSS environment of SN populations,…
Type-I Superluminous Supernovae (SLSNe) are an exotic class of core-collapse SN (CCSN) that can be up to 100 times brighter and more slowly-evolving than normal CCSNe. SLSNe represent the end-stages of the most massive stripped stars, and…
The ability to discover new transients via image differencing without direct human intervention is an important task in observational astronomy. For these kind of image classification problems, machine Learning techniques such as…
The 5-year project Supernova Legacy Survey (SNLS) delivers $\sim 100$ Type-Ia supernovae (SNe Ia) per year, in the redshift range $0.3 < z < 1.0$, with well-sampled $g'r'i'z'$ lightcurves. The SNLS Collaboration uses the 1 deg$^2$ Megacam…
Strongly gravitationally lensed supernovae (LSNe) are promising probes for providing absolute distance measurements using gravitational-lens time delays. Spatially unresolved LSNe offer an opportunity to enhance the sample size for…
Most Neural Networks (NNs) for classification are trained using Cross-Entropy as a loss function. This approach requires the model to have an explicit classification layer. However, there exist alternative approaches, such as Contrastive…
Gravitationally lensed supernovae (glSNe) are a powerful tool for exploring the realms of astronomy and cosmology. Time-delay measurements and lens modeling of glSNe can provide a robust and independent method for constraining the expansion…
With the development of deep learning, the performance of hyperspectral image (HSI) classification has been greatly improved in recent years. The shortage of training samples has become a bottleneck for further improvement of performance.…
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…
We present a systematic analysis of 191 stripped-envelope supernovae (SE SNe), aimed to compute their $^{56}$Ni masses from the luminosity in their radioactive tails ($M_\mathrm{Ni}^\mathrm{tail}$) and/or in their maximum light, and the…
The observational properties of core-collapse supernovae (CC-SNe) are shaped by the envelopes of their progenitors. In massive binary systems, mass-transfer alters the pre-SN structures compared to single stars, leading to a diversity in SN…
Core-collapse supernovae (CCSNe) may contribute a significant amount of dust in the early universe. Freshly formed coolant molecules (e.g., CO) and warm dust can be found in CCSNe as early as ~100 d after the explosion, allowing the study…
We use the first compilation of 72 core-collapse supernovae (SNe) from the Palomar Transient Factory (PTF) to study their observed subtype distribution in dwarf galaxies compared to giant galaxies. Our sample is the largest single-survey,…
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…
A growing subset of Type Ia supernovae (SNe Ia) show evidence for unexpected interaction with a dense circumstellar medium (SNe Ia-CSM). The precise nature of the progenitor, however, remains debated owing to spectral ambiguities arising…
The analysis of core-collapse supernova (CCSN) environments can provide important information on the life cycle of massive stars and constrain the progenitor properties of these powerful explosions. The MUSE instrument at the VLT enables…
We develop a hybrid framework to identify kilonovae (KNe), using single-epoch, medium-band spectral energy distributions from the 7-Dimensional Telescope (7DT). The framework integrates an unsupervised anomaly classifier (\texttt{Isolation…
Type II supernovae (SNe II), which show abundant hydrogen in their spectra, belong to a class of SNe with diverse observed properties. It is commonly accepted that SNe II are produced by core collapse and explosion of massive stars.…
Type Ibn/Icn supernovae (SNe Ibn/Icn), which are characterized by narrow helium or carbon lines originated in hydrogen-poor dense circumstellar medium (CSM), provide new insights into the final evolution of massive stars. While SNe Ibn/Icn…