The Supernovae Analysis Application (SNAP)

Bayless, Amanda J.; Fryer, Chris L.; Wollaeger, Ryan; Wiggins, Brandon; Even, Wesley; Rosa, Janie De La; Roming, P. W. A.; Frey, Lucille H.; Young, Patrick; Thorpe, Rob; Powell, Luke; Landers, Rachel; Persson, Heather D.; Hay, Rebecca

doi:10.3847/1538-4357/aa831d

Cited by 3 publications

(3 citation statements)

References 46 publications

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“…But while the availability of time-domain data has improved significantly, publicly accessible models of transients have remained elusive (we are aware of one other service that offers conditional public access to supernova models, SNAP, Bayless et al 2017). Individual works have focused on small subsets of data, offering descriptions of either light curve shapes or distributions of physical parameters for a given set of transients, but the specific data products depend heavily on the scientific motivations of the study in question.…”

Section: Introductionmentioning

confidence: 99%

MOSFiT: Modular Open Source Fitter for Transients

Guillochon

Nicholl

Villar

et al. 2018

ApJS

214

181

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Much of the progress made in time-domain astronomy is accomplished by relating observational multiwavelength time series data to models derived from our understanding of physical laws. This goal is typically accomplished by dividing the task in two: collecting data (observing), and constructing models to represent that data (theorizing). Owing to the natural tendency for specialization, a disconnect can develop between the best available theories and the best available data, potentially delaying advances in our understanding new classes of transients. We introduce MOSFiT: the Modular Open-Source Fitter for Transients, a Python-based package that downloads transient datasets from open online catalogs (e.g., the Open Supernova Catalog), generates Monte Carlo ensembles of semianalytical light curve fits to those datasets and their associated Bayesian parameter posteriors, and optionally delivers the fitting results back to those same catalogs to make them available to the rest of the community. MOSFiT is designed to help bridge the gap between observations and theory in time-domain astronomy; in addition to making the application of existing models and creation of new models as simple as possible, MOSFiT yields statistically robust predictions for transient characteristics, with a standard output format that includes all the setup information necessary to reproduce a given result. As large-scale surveys such as LSST discover entirely new classes of transients, tools such as MOSFiT will be critical for enabling rapid comparison of models against data in statistically consistent, reproducible, and scientifically beneficial ways.

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Section: Introductionmentioning

confidence: 99%

MOSFiT: Modular Open Source Fitter for Transients

Guillochon

Nicholl

Villar

et al. 2018

ApJS

214

181

View full text Add to dashboard Cite

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“…We also had access to the Supernova Analysis Application (SNAP) database (Bayless et al 2017), which currently contains several hundred observed SNe from space and ground observations 2 . However, the SNPhotCC dataset has several advantages over SNAP for this area of research.…”

Section: Datamentioning

confidence: 99%

Using Random Forest Machine Learning Algorithms in Binary Supernovae Classification

Markel,

Bayless

2019

Preprint

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We consider random forest machine learning algorithms applied to the classification of Type Ia and core-collapse supernovae (CCSNe) by full light curves.We also quantitatively show the potential of early-epoch classification using the same machine learning techniques. The algorithm uses the shape and magnitude of the light curve peak to determine the classification. This an initial study to essentially determine SN type with as few data points as possible. New all-sky surveys will discover new transients at a rapid rate and decisions will have to be made on very little data which transients to follow-up. Here we present an initial method where as few as five data points near peak, we an identify whether it is a Type Ia or CCSNe with better than 80% accuracy. Furthermore, we provide an introduction into the use of machine learning for classification of astrophysical transients, including potential pitfalls, best practices, and areas in need of future research.

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“…They will be able to follow the evolution of the SN as the energy output shifts into the optical. From the time-resolved multi-wavelength data of the SBO curves, we will constrain the radii, ejecta mass, explosion energy, and density profile (Bayless et al, 2017).…”

Section: Sbo Of Ccsnementioning

confidence: 99%

Understanding the Death of Massive Stars Using an Astrophysical Transients Observatory

Roming

Baron

Bayless

et al. 2018

Front. Astron. Space Sci.

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The death of massive stars, manifested as gamma-ray bursts and core-collapse supernovae, critically influence how the universe formed and evolves. Despite their fundamental importance, our understanding of these enigmatic objects is severely limited. We have performed a concept study of an Astrophysical Transient Observatory (ATO) that will rapidly facilitate an expansion of our understanding of these objects. ATO combines a very wide-field X-ray telescope, a near-infrared telescope, a multi-mode ultraviolet instrument, and a rapidly slewing spacecraft to realize two primary goals: (1) characterize the highest-redshift massive stars and their environments, and (2) constrain the poorly understood explosion mechanism of massive stars. The goals are met by observing the first massive stars to explode as gamma-ray bursts and to probe their environments, and by observing the shock breakout of core-collapse supernovae to measure the outer envelope parameters of massive stars. Additionally, ATO will observe the shock breakout of Type Ia supernovae and their shock interaction with a companion, Roming et al.Astrophysical Transient Observatory electromagnetic counterparts to gravitational wave sources, kilonovae, tidal disruption events, cataclysmic variables, X-ray transients, flares from exoplanet host stars, and the escape of ionizing radiation from star-forming galaxies. A description of the ATO instruments, the mission simulation, and technology readiness level is provided.

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The Supernovae Analysis Application (SNAP)

Cited by 3 publications

References 46 publications

MOSFiT: Modular Open Source Fitter for Transients

MOSFiT: Modular Open Source Fitter for Transients

Using Random Forest Machine Learning Algorithms in Binary Supernovae Classification

Understanding the Death of Massive Stars Using an Astrophysical Transients Observatory

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