Related papers: HyperGal: hyperspectral scene modeling for superno…

Although all Type II supernovae (SNe) originate from massive stars possessing a hydrogen-rich envelope, their light curve morphology is diverse, reflecting poorly characterised heterogeneity in the physical properties of their progenitor…

While conventional Type Ia supernova (SN Ia) cosmology analyses rely primarily on rest-frame optical light curves to determine distances, SNe Ia are excellent standard candles in near-infrared (NIR) light, which is significantly less…

Recently, 3D Gaussian Splatting (3DGS) has excelled in novel view synthesis (NVS) with its real-time rendering capabilities and superior quality. However, it encounters challenges for high-resolution novel view synthesis (HRNVS) due to the…

The Legacy Survey of Space and Time (LSST) at the Vera C. Rubin Observatory will discover tens of thousands of extragalactic transients each night. The high volume of alerts demands immediate classification of transient types in order to…

Modern astronomical surveys, such as the Zwicky Transient Facility (ZTF), are capable of detecting thousands of transient events per year, necessitating the use of automated and scalable data analysis techniques. Recent advances in machine…

The Pan-STARRS (PS1) Medium Deep Survey discovered over 5,000 likely supernovae (SNe) but obtained spectral classifications for just 10% of its SN candidates. We measured spectroscopic host galaxy redshifts for 3,147 of these likely SNe and…

We present Keck high-quality rest-frame ultraviolet (UV) through optical spectra of 21 Type Ia supernovae (SNe Ia) in the redshift range 0.11 < z < 0.37 and a mean redshift of 0.22 that were discovered during the Sloan Digital Sky Survey-II…

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…

Upcoming photometric surveys will discover tens of thousands of Type Ia supernovae (SNe Ia), vastly outpacing the capacity of our spectroscopic resources. In order to maximize the science return of these observations in the absence of…

The revolutionary discovery of dark energy and accelerating cosmic expansion was made with just 42 type Ia supernovae (SNe Ia) in 1999. Since then, large synoptic surveys, e.g., Dark Energy Survey (DES), have observed thousands more SNe Ia…

We report on the development of SuperCam, a 64 pixel, superheterodyne camera designed for operation in the astrophysically important 870 micron atmospheric window. SuperCam will be used to answer fundamental questions about the physics and…

The nature of the progenitor systems and explosion mechanisms that give rise to Type Ia supernovae (SNe Ia) are still debated. The interaction signature of circumstellar material (CSM) being swept up by expanding ejecta can constrain the…

Both multi-messenger astronomy and new high-throughput wide-field surveys require flexible tools for the selection and analysis of astrophysical transients. We here introduce the Alert Management, Photometry and Evaluation of Lightcurves…

A number of Type I (hydrogenless) superluminous supernova (SLSN) events have been discovered recently. However, their nature remains debatable. One of the most promising ideas is the shock-interaction mechanism, but only simplified…

CO observations allow estimations of the gas content of molecular clouds, which trace the reservoir of cold gas fuelling star formation, as well as to determine extinction via H$_2$ column density, N(H$_2$). Here, we studied millimetric and…

This work proposes a new image analysis tool called Label Consistent Transform Learning (LCTL). Transform learning is a recent unsupervised representation learning approach; we add supervision by incorporating a label consistency…

Helium is expected to be present in the ejecta of some hydrogen-poor superluminous supernovae (SLSN-I). However, so far only one event, PTF10hgi has been identified with He features in its photospheric spectra (Quimby et al. 2018). We…

Hyperspectral salient object detection (HSOD) aims to extract targets or regions with significantly different spectra from hyperspectral images. While existing deep learning-based methods can achieve good detection results, they generally…

Hyperspectral imaging sensors are becoming increasingly popular in robotics applications such as agriculture and mining, and allow per-pixel thematic classification of materials in a scene based on their unique spectral signatures.…

Deep neural networks face several challenges in hyperspectral image classification, including high-dimensional data, sparse distribution of ground objects, and spectral redundancy, which often lead to classification overfitting and limited…