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We present a sample of 485 photometrically identified Type Ia supernova candidates mined from the first three years of data of the CFHT SuperNova Legacy Survey (SNLS). The images were submitted to a deferred processing independent of the…

Cosmological parameter estimation techniques that robustly account for systematic measurement uncertainties will be crucial for the next generation of cosmological surveys. We present a new analysis method, superABC, for obtaining…

Cosmology and Nongalactic Astrophysics · Physics 2016-11-11 Elise Jennings , Rachel Wolf , Masao Sako

Type Ia supernovae (SNe Ia) are more precise standardizable candles when measured in the near-infrared (NIR) than in the optical. With this motivation, from 2012-2017 we embarked on the RAISIN program with the Hubble Space Telescope (HST)…

Vast amounts of astronomical photometric data are generated from various projects, requiring significant effort to identify variable stars and other object classes. In light of this, a general, widely applicable classification framework…

Instrumentation and Methods for Astrophysics · Physics 2024-09-23 Kaiming Cui , D. J. Armstrong , Fabo Feng

We present the analysis of the first set of low-redshift Type Ia supernovae (SNe Ia) by the Carnegie Supernova Project. Well-sampled, high-precision optical (ugriBV) and near-infrared (NIR; YJHKs) light curves obtained in a well-understood…

We have publicly released a blinded mix of simulated SNe, with types (Ia, Ib, Ic, II) selected in proportion to their expected rate. The simulation is realized in the griz filters of the Dark Energy Survey (DES) with realistic observing…

Instrumentation and Methods for Astrophysics · Physics 2010-04-29 Richard Kessler , Alex Conley , Saurabh Jha , Stephen Kuhlmann

Over the past years type Ia supernovae (SNe Ia) have become a major tool to determine the expansion history of the Universe, and considerable attention has been given to, both, observations and models of these events. However, until now,…

High Energy Astrophysical Phenomena · Physics 2017-01-18 Michele Sasdelli , W. Hillebrandt , M. Kromer , E. E. O. Ishida , F. K. Roepke , S. A. Simm , R. Pakmor

We study the utility of a large sample of type Ia supernovae that might be observed in an imaging survey that rapidly scans a large fraction of the sky for constraining dark energy. We consider information from the traditional luminosity…

Astrophysics · Physics 2011-02-11 Andrew R. Zentner , Suman Bhattacharya

Correctly interpreting observations of sources such as type Ia supernovae (SNe Ia) require knowledge of the power spectrum of matter on AU scales - which is very hard to model accurately. Because under-dense regions account for much of the…

Cosmology and Nongalactic Astrophysics · Physics 2013-11-22 V. C. Busti , R. F. L. Holanda , C. Clarkson

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…

Strongly lensed type Ia supernovae (SNe Ia) provide a unique cosmological probe to address the Hubble tension problem in cosmology. In addition to the sensitivity of the time delays to the value of the Hubble constant, the transient and…

Cosmology and Nongalactic Astrophysics · Physics 2025-06-27 Prajakta Mane , Anupreeta More , Surhud More

We analyze a set of 89 Type Ia supernovae (SN Ia) that have both optical and near-infrared (NIR) photometry to derive distances and construct low redshift ($z < 0.04$) Hubble diagrams. We construct mean light curve (LC) templates using a…

Cosmology and Nongalactic Astrophysics · Physics 2020-01-08 Arturo Avelino , Andrew S. Friedman , Kaisey S. Mandel , David O. Jones , Peter J. Challis , Robert P. Kirshner

Supernovae (SNe) come in various flavors and are classified into different types based on emission and absorption lines in their spectra. SN candidates are now abundant with the advent of large systematic sky surveys like the Zwicky…

Type Ia supernova (SN Ia) cosmology analyses include a luminosity step function in their distance standardization process to account for an observed yet unexplained difference in the post-standardization luminosities of SNe Ia originating…

Cosmology and Nongalactic Astrophysics · Physics 2024-01-17 G. Taylor , C. Lidman , B. Popovic , H. Abbot

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…

One of the brightest objects in the universe, supernovae (SNe) are powerful explosions marking the end of a star's lifetime. Supernova (SN) type is defined by spectroscopic emission lines, but obtaining spectroscopy is often logistically…

Instrumentation and Methods for Astrophysics · Physics 2022-07-20 Helen Qu , Masao Sako , Anais Moller , Cyrille Doux

We report on work to increase the number of well-measured Type Ia supernovae (SNe Ia) at high redshifts. Light curves, including high signal-to-noise HST data, and spectra of six SNe Ia that were discovered during 2001 are presented.…

Modern time-domain surveys, such as the Zwicky Transient Facility (ZTF), detect far more extragalactic transients than can be spectroscopically classified. Photometric classification offers a scalable alternative, enabling the…

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

In response to a recently reported observation of evidence for two classes of Type Ia Supernovae (SNe Ia) distinguished by their brightness in the rest-frame near ultraviolet (NUV), we search for the phenomenon in publicly available…

Cosmology and Nongalactic Astrophysics · Physics 2017-01-16 David Cinabro , Daniel Scolnic , Richard Kessler , Ashley Li , Jake Miller