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The spectral energy distribution (SED) sequence for type Ia supernovae (SN Ia) is modeled by an artificial neural network. The SN Ia luminosity is characterized as a function of phase, wavelength, a color parameter and a decline rate…

Cosmology and Nongalactic Astrophysics · Physics 2021-07-28 Qiao-Bin Cheng , Chao-Jun Feng , Xiang-Hua Zhai , Xin-Zhou Li

We present a sample of Type Icn supernovae (SNe Icn), a newly-discovered class of transients characterized by their interaction with H- and He-poor circumstellar material (CSM). This sample is the largest collection of SNe Icn to date and…

We present here a re-calibration of the photometric systems used in the Pantheon+ sample of Type Ia supernovae (SNe Ia) including those used for the SH0ES distance-ladder measurement of H$_0$. We utilize the large and uniform sky coverage…

Accurate hyperspectral image (HSI) interpretation is critical for providing valuable insights into various earth observation-related applications such as urban planning, precision agriculture, and environmental monitoring. However, existing…

Superluminous supernovae (SLSNe) are a new class of transients with luminosities $\sim10 -100$ times larger than the usual core-collapse supernovae (SNe). Their origin is still unclear and one widely discussed scenario involves a…

High Energy Astrophysical Phenomena · Physics 2026-01-23 Shang Li , Yun-Feng Liang , Neng-Hui Liao , Lei Lei , Yi-Zhong Fan

Autonomous driving vehicles and robotic systems rely on accurate perception of their surroundings. Scene understanding is one of the crucial components of perception modules. Among all available sensors, LiDARs are one of the essential…

Computer Vision and Pattern Recognition · Computer Science 2021-03-17 Ryan Razani , Ran Cheng , Ehsan Taghavi , Liu Bingbing

Supernovae (SNe) with photospheric spectra devoid of Hydrogen and Helium features are generally classified as Type Ic SNe (SNe Ic). However, there is ongoing debate as to whether Helium can be hidden in the ejecta of SNe Ic (that is, Helium…

High Energy Astrophysical Phenomena · Physics 2021-03-03 Marc Williamson , Wolfgang Kerzendorf , Maryam Modjaz

We present an extended grid of multi-epoch 1D nonlocal thermodynamic equilibrium radiative transfer calculations for nebular-phase Type Ibc supernovae (SNe) from He-star explosions. Compared to Dessart+21, we study the spectral evolution…

Solar and Stellar Astrophysics · Physics 2023-08-30 Luc Dessart , D. John Hillier , S. E. Woosley , Hanindyo Kuncarayakti

Remote sensing change detection (RSCD) is a complex task, where changes often appear at different scales and orientations. Convolutional neural networks (CNNs) are good at capturing local spatial patterns but cannot model global semantics…

Computer Vision and Pattern Recognition · Computer Science 2026-01-19 Humza Naveed , Xina Zeng , Mitch Bryson , Nagita Mehrseresht

With a rapidly rising number of transients detected in astronomy, classification methods based on machine learning are increasingly being employed. Their goals are typically to obtain a definitive classification of transients, and for good…

Instrumentation and Methods for Astrophysics · Physics 2022-09-21 N. Miranda , J. C. Freytag , J. Nordin , R. Biswas , V. Brinnel , C. Fremling , M. Kowalski , A. Mahabal , S. Reusch , J. van Santen

Hyperspectral imaging is useful for applications ranging from medical diagnostics to agricultural crop monitoring; however, traditional scanning hyperspectral imagers are prohibitively slow and expensive for widespread adoption. Snapshot…

Image and Video Processing · Electrical Eng. & Systems 2020-09-30 Kristina Monakhova , Kyrollos Yanny , Neerja Aggarwal , Laura Waller

Hyperspectral imaging (HSI) is an advanced sensing modality that simultaneously captures spatial and spectral information, enabling non-invasive, label-free analysis of material, chemical, and biological properties. This Primer presents a…

Computer Vision and Pattern Recognition · Computer Science 2026-02-10 Danfeng Hong , Chenyu Li , Naoto Yokoya , Bing Zhang , Xiuping Jia , Antonio Plaza , Paolo Gamba , Jon Atli Benediktsson , Jocelyn Chanussot

Hyperspectral anomaly detection (HAD) aims to localize pixel points whose spectral features differ from the background. HAD is essential in scenarios of unknown or camouflaged target features, such as water quality monitoring, crop growth…

Computer Vision and Pattern Recognition · Computer Science 2024-01-03 Jitao Ma , Weiying Xie , Yunsong Li

In preparation for the Supernova Survey of the Sloan Digital Sky Survey (SDSS) II, a proposed 3-year extension to the SDSS, we have conducted an early engineering and science run during the fall of 2004, which consisted of approximately 20…

We present a model-independent, photometry-only framework for identifying strongly lensed supernovae when multiple images are unresolved and blended into a single point source. Building on the simulation-based methodology of Bag et al.…

Instrumentation and Methods for Astrophysics · Physics 2026-05-01 Sangwoo Park , Arman Shafieloo , Alex G. Kim , Eric V. Linder , Xiaosheng Huang

Substantial effort has been devoted to the characterization of transient phenomena from photometric information. Automated approaches to this problem have taken advantage of complete phase-coverage of an event, limiting their use for…

Instrumentation and Methods for Astrophysics · Physics 2023-07-06 Alexander Gagliano , Gabriella Contardo , Daniel Foreman-Mackey , Alex I. Malz , Patrick D. Aleo

While the sample of optical Type Ia Supernova (SN Ia) light curves (LCs) usable for cosmological parameter measurements surpasses 2000, the sample of published, cosmologically viable near-infrared (NIR) SN Ia LCs, which have been shown to…

We present the cosmological analysis of 752 photometrically-classified Type Ia Supernovae (SNe Ia) obtained from the full Sloan Digital Sky Survey II (SDSS-II) Supernova (SN) Survey, supplemented with host-galaxy spectroscopy from the…

Context. The SDSS-II Supernova Survey, conducted between 2005 and 2007, was designed to detect a large number of Type Ia supernovae (SNe Ia) around z~0.2, the redshift "gap" between low-z and high-z SN searches. The survey has provided…