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The aim of this work is to present a semi-analytical light curve modeling code which can be used for estimating physical properties of core collapse supernovae (SNe) in a quick and efficient way. To verify our code we fit light curves of…

High Energy Astrophysical Phenomena · Physics 2014-11-19 Andrea P. Nagy , Andras Ordasi , Jozsef Vinko , J. Craig Wheeler

We introduce Preconditioned Monte Carlo (PMC), a novel Monte Carlo method for Bayesian inference that facilitates efficient sampling of probability distributions with non-trivial geometry. PMC utilises a Normalising Flow (NF) in order to…

Instrumentation and Methods for Astrophysics · Physics 2022-08-24 Minas Karamanis , Florian Beutler , John A. Peacock , David Nabergoj , Uros Seljak

Dramatically increasing data volumes are forcing astronomers to adopt automated methods for the identification and classification of astronomical objects. Although deep-learning models are often well-suited to this task, obtaining a measure…

Instrumentation and Methods for Astrophysics · Physics 2026-03-23 Alex Walls , James Barry , Devina Mohan , Anna M. M. Scaife

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 supernovae (SNe Ia) have been intensively investigated due to its great homogeneity and high luminosity, which make it possible to use them as standardizable candles for the determination of cosmological parameters. In 2011, the…

Cosmology and Nongalactic Astrophysics · Physics 2016-11-04 Rodrigo C. V. Coelho , Maurício O. Calvão , Ribamar R. R. Reis , Beatriz B. Siffert

Models for complex systems are often built with more parameters than can be uniquely identified by available data. Because of the variety of causes, identifying a lack of parameter identifiability typically requires mathematical…

Methodology · Statistics 2013-10-03 David Campbell , Subhash Lele

Sequential Monte Carlo methods, also known as particle methods, are a popular set of techniques for approximating high-dimensional probability distributions and their normalizing constants. These methods have found numerous applications in…

Computation · Statistics 2021-06-23 Jeremy Heng , Adrian N. Bishop , George Deligiannidis , Arnaud Doucet

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 photometric properties and distance measurements of 252 high redshift Type Ia supernovae (0.15 < z < 1.1) discovered during the first three years of the Supernova Legacy Survey (SNLS). These events were detected and their…

The LaSilla/QUEST Variability Survey (LSQ) and the Carnegie Supernova Project (CSP II) are collaborating to discover and obtain photometric light curves for a large sample of low redshift (z < 0.1) Type Ia supernovae. The supernovae are…

Context: Type II supernovae provide a direct way to estimate distances through the expanding photosphere method, which is independent of the cosmic distance ladder. A recently introduced Gaussian process-based method allows for a fast and…

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 investigate the prospects of future supernova searches to get meaningful constraints about the cosmic star formation history (CSFH) and the delay time of type Ia supernovae from star formation (tau_{Ia}), based only on supernova data.…

Astrophysics · Physics 2009-11-11 Takeshi Oda , Tomonori Totani

We explore the effect of contamination of intermediate redshift Type Ia supernova samples by Type Ibc supernovae. Simulating observed samples of Ia and mixed Ibc/Ia populations at a range of redshifts for an underlying cosmological…

Astrophysics · Physics 2009-11-10 N. L. Homeier

Study of radio supernovae over the past 25 years includes two dozen detected objects and more than 100 upper limits. From this work it is possible to identify classes of radio properties, demonstrate conformance to and deviations from…

Observations of Type Ia supernovae used to map the expansion history of the Universe suffer from systematic uncertainties that need to be propagated into the estimates of cosmological parameters. We propose an iterative Monte-Carlo…

Astrophysics · Physics 2009-06-23 Jakob Nordin , Ariel Goobar , Jakob Jonsson

Type-Ia supernova observations yield estimates of the luminosity distance, which includes not only the background luminosity distance, but also the fluctuation due to inhomogeneities in the Universe. In particular, the spatial correlation…

Cosmology and Nongalactic Astrophysics · Physics 2020-02-11 Jaiyul Yoo

We present photometric follow-up observations of the supernova candidate AT 2018we, which was discovered by the Gaia photometric alerts system. The first Gaia detection of the object was made Feb 12, 2018. Our observations, made in Cousins…

Solar and Stellar Astrophysics · Physics 2018-09-18 T. Willamo , J. Ala-Könni , J. Arvo , I. Pippa , T. Salo

Type Ia Supernovae (SNe Ia) are the best standard candles known today. At high redshift ($z\sim1$) SNe Ia are used to determine the Cosmological Constant Lambda with great success. However the most serious concern is raised by thepossible…

Astrophysics · Physics 2007-05-23 P. Erni , G. A. Tammann

In the upcoming decade large astronomical surveys will discover millions of transients raising unprecedented data challenges in the process. Only the use of the machine learning algorithms can process such large data volumes. Most of the…

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