Related papers: deepSIP: Linking Type Ia Supernova Spectra to Phot…

A Probabilistic Autoencoder for Type Ia Supernovae Spectral Time Series

We construct a physically-parameterized probabilistic autoencoder (PAE) to learn the intrinsic diversity of type Ia supernovae (SNe Ia) from a sparse set of spectral time series. The PAE is a two-stage generative model, composed of an…

Cosmology and Nongalactic Astrophysics · Physics 2022-08-17 George Stein , Uros Seljak , Vanessa Bohm , G. Aldering , P. Antilogus , C. Aragon , S. Bailey , C. Baltay , S. Bongard , K. Boone , C. Buton , Y. Copin , S. Dixon , D. Fouchez , E. Gangler , R. Gupta , B. Hayden , W. Hillebrandt , M. Karmen , A. G. Kim , M. Kowalski , D. Kusters , P. F. Leget , F. Mondon , J. Nordin , R. Pain , E. Pecontal , R. Pereira , S. Perlmutter , K. A. Ponder , D. Rabinowitz , M. Rigault , D. Rubin , K. Runge , C. Saunders , G. Smadja , N. Suzuki , C. Tao , R. C. Thomas , M. Vincenzi

The Dark Energy Survey: Cosmology Results With ~1500 New High-redshift Type Ia Supernovae Using The Full 5-year Dataset

We present cosmological constraints from the sample of Type Ia supernovae (SN Ia) discovered during the full five years of the Dark Energy Survey (DES) Supernova Program. In contrast to most previous cosmological samples, in which SN are…

Cosmology and Nongalactic Astrophysics · Physics 2025-07-22 DES Collaboration , T. M. C. Abbott , M. Acevedo , M. Aguena , A. Alarcon , S. Allam , O. Alves , A. Amon , F. Andrade-Oliveira , J. Annis , P. Armstrong , J. Asorey , S. Avila , D. Bacon , B. A. Bassett , K. Bechtol , P. H. Bernardinelli , G. M. Bernstein , E. Bertin , J. Blazek , S. Bocquet , D. Brooks , D. Brout , E. Buckley-Geer , D. L. Burke , H. Camacho , R. Camilleri , A. Campos , A. Carnero Rosell , D. Carollo , A. Carr , J. Carretero , F. J. Castander , R. Cawthon , C. Chang , R. Chen , A. Choi , C. Conselice , M. Costanzi , L. N. da Costa , M. Crocce , T. M. Davis , D. L. DePoy , S. Desai , H. T. Diehl , M. Dixon , S. Dodelson , P. Doel , C. Doux , A. Drlica-Wagner , J. Elvin-Poole , S. Everett , I. Ferrero , A. Ferté , B. Flaugher , R. J. Foley , P. Fosalba , D. Friedel , J. Frieman , C. Frohmaier , L. Galbany , J. García-Bellido , M. Gatti , E. Gaztanaga , G. Giannini , K. Glazebrook , O. Graur , D. Gruen , R. A. Gruendl , G. Gutierrez , W. G. Hartley , K. Herner , S. R. Hinton , D. L. Hollowood , K. Honscheid , D. Huterer , B. Jain , D. J. James , N. Jeffrey , E. Kasai , L. Kelsey , S. Kent , R. Kessler , A. G. Kim , R. P. Kirshner , E. Kovacs , K. Kuehn , O. Lahav , J. Lee , S. Lee , G. F. Lewis , T. S. Li , C. Lidman , H. Lin , U. Malik , J. L. Marshall , P. Martini , J. Mena-Fernández , F. Menanteau , R. Miquel , J. J. Mohr , J. Mould , J. Muir , A. Möller , E. Neilsen , R. C. Nichol , P. Nugent , R. L. C. Ogando , A. Palmese , Y. -C. Pan , M. Paterno , W. J. Percival , M. E. S. Pereira , A. Pieres , A. A. Plazas Malagón , B. Popovic , A. Porredon , J. Prat , H. Qu , M. Raveri , M. Rodríguez-Monroy , A. K. Romer , A. Roodman , B. Rose , M. Sako , E. Sanchez , D. Sanchez Cid , M. Schubnell , D. Scolnic , I. Sevilla-Noarbe , P. Shah , J. Allyn. Smith , M. Smith , M. Soares-Santos , E. Suchyta , M. Sullivan , N. Suntzeff , M. E. C. Swanson , B. O. Sánchez , G. Tarle , G. Taylor , D. Thomas , C. To , M. Toy , M. A. Troxel , B. E. Tucker , D. L. Tucker , S. A. Uddin , M. Vincenzi , A. R. Walker , N. Weaverdyck , R. H. Wechsler , J. Weller , W. Wester , P. Wiseman , M. Yamamoto , F. Yuan , B. Zhang , Y. Zhang

A Precise Distance Indicator: Type Ia Supernova Multicolor Light Curve Shapes

We present an empirical method that uses multicolor light curve shapes (MLCS) to estimate the luminosity, distance, and total line-of-sight extinction of Type Ia supernovae (SN Ia). The empirical correlation between the MLCS and the…

Astrophysics · Physics 2009-07-09 Adam Riess , William Press , Robert Kirshner

Type Ia Supernova Light Curve Inference: Hierarchical Bayesian Analysis in the Near Infrared

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…

Cosmology and Nongalactic Astrophysics · Physics 2009-10-21 Kaisey S. Mandel , W. Michael Wood-Vasey , Andrew S. Friedman , Robert P. Kirshner

Comparing the light curves of simulated Type Ia Supernovae with observations using data-driven models

We propose a robust, quantitative method to compare the synthetic light curves of a Type Ia Supernova (SNIa) explosion model with a large set of observed SNeIa, and derive a figure of merit for the explosion model's agreement with…

Cosmology and Nongalactic Astrophysics · Physics 2013-08-05 Benedikt Diemer , Richard Kessler , Carlo Graziani , George C. Jordan , Donald Q. Lamb , Min Long , Daniel R. van Rossum

AI-Driven Reconstruction of Large-Scale Structure from Combined Photometric and Spectroscopic Surveys

Galaxy surveys are crucial for studying large-scale structure (LSS) and cosmology, yet they face limitations--imaging surveys provide extensive sky coverage but suffer from photo-$z$ uncertainties, while spectroscopic surveys yield precise…

Instrumentation and Methods for Astrophysics · Physics 2025-08-26 Wenying Du , Xiaolin Luo , Zhujun Jiang , Xu Xiao , Qiufan Lin , Xin Wang , Yang Wang , Fenfen Yin , Le Zhang , Xiao-Dong Li

Spectral properties of Type Ia supernovae up to z~0.3

Aims: Spectroscopic observations of Type Ia supernovae obtained at the New Technology Telescope (NTT) and the Nordic Optical Telescope (NOT), in conjunction with the SDSS-II Supernova Survey, are analysed. We use spectral indicators…

Cosmology and Nongalactic Astrophysics · Physics 2015-05-20 J. Nordin , L. Ostman , A. Goobar , R. Amanullah , R. C. Nichol , M. Smith , J. Sollerman , B. A. Bassett , J. Frieman , P. M. Garnavich , G. Leloudas , M. Sako , D. P. Schneider

The Complete Light-curve Sample of Spectroscopically Confirmed Type Ia Supernovae from Pan-STARRS1 and Cosmological Constraints from The Combined Pantheon Sample

We present optical light curves, redshifts, and classifications for 365 spectroscopically confirmed Type Ia supernovae (SNe Ia) discovered by the Pan-STARRS1 (PS1) Medium Deep Survey. We detail improvements to the PS1 SN photometry,…

Cosmology and Nongalactic Astrophysics · Physics 2018-06-13 D. M. Scolnic , D. O. Jones , A. Rest , Y. C. Pan , R. Chornock , R. J. Foley , M. E. Huber , R. Kessler , G. Narayan , A. G. Riess , S. Rodney , E. Berger , D. J. Brout , P. J. Challis , M. Drout , D. Finkbeiner , R. Lunnan , R. P. Kirshner , N. E. Sanders , E. Schlafly , S. Smartt , C. W. Stubbs , J. Tonry , W. M. Wood-Vasey , M. Foley , J. Hand , E. Johnson , W. S. Burgett , K. C. Chambers , P. W. Draper , K. W. Hodapp , N. Kaiser , R. P. Kudritzki , E. A. Magnier , N. Metcalfe , F. Bresolin , E. Gall , R. Kotak , M. McCrum , K. W. Smith

Light and color curve properties of type Ia supernovae: Theory vs. Observations

We study optical light curve(LC) relations of type Ia supernovae(SNe~Ia) for their use in cosmology using high-quality photometry published by the Carnegie-Supernovae-Project (CSP-I). We revisit the classical luminosity-decline-rate…

Solar and Stellar Astrophysics · Physics 2017-09-06 P. Hoeflich , E. Y. Hsiao , C. Ashall , C. R. Burns , T. R. Diamond , M. M. Phillips , D. Sand , M. D. Stritzinger , N. Suntzeff , C. Contreras , K. Krisciunas , N. Morrell , L. Wang

The Sloan Digital Sky Survey-II Supernova Survey: Technical Summary

The Sloan Digital Sky Survey-II (SDSS-II) has embarked on a multi-year project to identify and measure light curves for intermediate-redshift (0.05 < z < 0.35) Type Ia supernovae (SNe Ia) using repeated five-band (ugriz) imaging over an…

Astrophysics · Physics 2008-11-26 Joshua A. Frieman , B. Bassett , A. Becker , C. Choi , D. Cinabro , F. DeJongh , D. L. Depoy , B. Dilday , M. Doi , P. M. Garnavich , C. J. Hogan , J. Holtzman , M. Im , S. Jha , R. Kessler , K. Konishi , H. Lampeitl , J. Marriner , J. L. Marshall , D. McGinnis , G. Miknaitis , R. C. Nichol , J. L. Prieto , A. G. Riess , M. W. Richmond , R. Romani , M. Sako , D. P. Schneider , M. Smith , N. Takanashi , K. Tokita , K. van der Heyden , N. Yasuda , C. Zheng , J. Adelman-McCarthy , J. Annis , R. J. Assef , J. Barentine , R. Bender , R. D. Blandford , W. N. Boroski , M. Bremer , H. Brewington , C. A. Collins , A. Crotts , J. Dembicky , J. Eastman , A. Edge , E. Edmondson , E. Elson , M. E. Eyler , A. V. Filippenko , R. J. Foley , S. Frank , A. Goobar , T. Gueth , J. E. Gunn , M. Harvanek , U. Hopp , Y. Ihara , Ž. Ivezić , S. Kahn , J. Kaplan , S. Kent , W. Ketzeback , S. J. Kleinman , W. Kollatschny , R. G. Kron , J. Krzesiński , D. Lamenti , G. Leloudas , H. Lin , D. C. Long , J. Lucey , R. H. Lupton , E. Malanushenko , V. Malanushenko , R. J. McMillan , J. Mendez , C. W. Morgan , T. Morokuma , A. Nitta , L. Ostman , K. Pan , C. M. Rockosi , A. K. Romer , P. Ruiz-Lapuente , G. Saurage , K. Schlesinger , S. A. Snedden , J. Sollerman , C. Stoughton , M. Stritzinger , M. SubbaRao , D. Tucker , P. Vaisanen , L. C. Watson , S. Watters , J. C. Wheeler , B. Yanny , D. York

The Carnegie Supernova Project: The Low-Redshift Survey

Supernovae are essential to understanding the chemical evolution of the Universe. Type Ia supernovae also provide the most powerful observational tool currently available for studying the expansion history of the Universe and the nature of…

Astrophysics · Physics 2016-08-16 Mario Hamuy , Gastón Folatelli , Nidia I. Morrell , Mark M. Phillips , Nicholas B. Suntzeff , S. E. Persson , Miguel Roth , Sergio Gonzalez , Wojtek Krzeminski , Carlos Contreras , Wendy L. Freedman , D. C. Murphy , Barry F. Madore , P. Wyatt , José Maza , Alexei V. Filippenko , Weidong Li , P. A. Pinto

Comparisons of Type Ia Supernova Light Curves in the UV and Optical with the Swift Ultra-Violet/Optical Telescope

We examine the light curve parameters of 97 nearby Type Ia supernovae in the ultraviolet and optical using observations from the Swift Ultra-Violet/Optical Telescope. Our light curve models used a linear combinations of templates, which…

Solar and Stellar Astrophysics · Physics 2022-05-18 Yaswant Devarakonda , Peter J. Brown

Photometric Data-driven Classification of Type Ia Supernovae in the Open Supernova Catalog

We propose a novel approach for a machine-learning-based detection of the type Ia supernovae using photometric information. Unlike other approaches, only real observation data is used during training. Despite being trained on a relatively…

Instrumentation and Methods for Astrophysics · Physics 2021-05-24 Stanislav Dobryakov , Konstantin Malanchev , Denis Derkach , Mikhail Hushchyn

DeepISP: Towards Learning an End-to-End Image Processing Pipeline

We present DeepISP, a full end-to-end deep neural model of the camera image signal processing (ISP) pipeline. Our model learns a mapping from the raw low-light mosaiced image to the final visually compelling image and encompasses low-level…

Image and Video Processing · Electrical Eng. & Systems 2019-02-05 Eli Schwartz , Raja Giryes , Alex M. Bronstein

Artificial Neural Network for Constructing Type Ia Supernovae Spectrum Evolution Model

We construct and train an artificial neural network called the back-propagation neural network to describe the evolution of the type Ia supernova spectrum by using the data from the CfA Supernova Program. This network method has many…

Cosmology and Nongalactic Astrophysics · Physics 2018-06-27 Qiao-Bin Cheng , Chao-Jun Feng , Xiang-Hua Zhai , Xin-Zhou Li

Measurements of the Rate of Type Ia Supernovae at Redshift z < ~0.3 from the SDSS-II Supernova Survey

We present a measurement of the volumetric Type Ia supernova (SN Ia) rate based on data from the Sloan Digital Sky Survey II (SDSS-II) Supernova Survey. The adopted sample of supernovae (SNe) includes 516 SNe Ia at redshift z \lesssim 0.3,…

Cosmology and Nongalactic Astrophysics · Physics 2010-05-07 Benjamin Dilday , Mathew Smith , Bruce Bassett , Andrew Becker , Ralf Bender , Francisco Castander , David Cinabro , Alexei V. Filippenko , Joshua A. Frieman , Lluis Galbany , Peter M. Garnavich , Ariel Goobar , Ulrich Hopp , Yutaka Ihara , Saurabh W. Jha , Richard Kessler , Hubert Lampeitl , John Marriner , Ramon Miquel , Mercedes Molla , Robert C. Nichol , Jakob Nordin , Adam G. Riess , Masao Sako , Donald P. Schneider , Jesper Sollerman , J. Craig Wheeler , Linda Ostman , Dmitry Bizyaev , Howard Brewington , Elena Malanushenko , Viktor Malanushenko , Dan Oravetz , Kaike Pan , Audrey Simmons , Stephanie Snedden

HOLISMOKES XVII: Detecting strongly lensed SNe Ia from time series of multi-band LSST-like imaging data

Strong gravitationally lensed supernovae (LSNe), though rare, are exceptionally valuable probes for cosmology and astrophysics. Upcoming time-domain surveys like the Vera Rubin Observatory's Legacy Survey of Space and Time (LSST) offer a…

Instrumentation and Methods for Astrophysics · Physics 2026-03-04 Satadru Bag , Raoul Canameras , Sherry H. Suyu , Stefan Schuldt , Stefan Taubenberger , Irham Taufik Andika , Alejandra Melo

The Sloan Digital Sky Survey-II: Photometry and Supernova Ia Light Curves from the 2005 data

We present ugriz light curves for 146 spectroscopically confirmed or spectroscopically probable Type Ia supernovae from the 2005 season of the SDSS-II Supernova survey. The light curves have been constructed using a photometric technique…

Cosmology and Nongalactic Astrophysics · Physics 2014-11-20 Jon A. Holtzman , John Marriner , Richard Kessler , Masao Sako , Ben Dilday , Joshua A. Frieman , Donald P. Schneider , Bruce Bassett , Andrew Becker , David Cinabro , Fritz DeJongh , Darren L. Depoy , Mamoru Doi , Peter M. Garnavich , Craig J. Hogan , Saurabh Jha , Kohki Konishi , Hubert Lampeitl , Jennifer L. Marshall , David McGinnis , Gajus Miknaitis , Robert C. Nichol , Jose Luis Prieto , Adam G. Reiss , Michael W. Richmond , Roger Romani , Mathew Smith , Naohiro Takanashi , Kouichi Tokita , Kurt van der Heyden , Naoki Yasuda , Chen Zheng

Progressive DeepSSM: Training Methodology for Image-To-Shape Deep Models

Statistical shape modeling (SSM) is an enabling quantitative tool to study anatomical shapes in various medical applications. However, directly using 3D images in these applications still has a long way to go. Recent deep learning methods…

Computer Vision and Pattern Recognition · Computer Science 2023-10-04 Abu Zahid Bin Aziz , Jadie Adams , Shireen Elhabian

Hyperspectral Image Classification using Spectral-Spatial Mixer Network

This paper introduces SS-MixNet, a lightweight and effective deep learning model for hyperspectral image (HSI) classification. The architecture integrates 3D convolutional layers for local spectral-spatial feature extraction with two…

Computer Vision and Pattern Recognition · Computer Science 2025-11-20 Mohammed Q. Alkhatib