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An Open Catalog for Supernova Data
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We present the Open Supernova Catalog, an online collection of observations and metadata for presently 36,000+ supernovae and related candidates. The catalog is freely available on the web (https://sne.space), with its main interface having been designed to be a user-friendly, rapidly-searchable table accessible on desktop and mobile devices. In addition to the primary catalog table containing supernova metadata, an individual page is generated for each supernova which displays its available metadata, light curves, and spectra spanning X-ray to radio frequencies. The data presented in the catalog is automatically rebuilt on a daily basis and is constructed by parsing several dozen sources, including the data presented in the supernova literature and from secondary sources such as other web-based catalogs. Individual supernova data is stored in the hierarchical, human- and machine-readable JSON format, with the entirety of each supernova's data being contained within a single JSON file bearing its name. The setup we present here, which is based upon open source software maintained via git repositories hosted on github, enables anyone to download the entirety of the supernova dataset to their home computer in minutes, and to make contributions of their own data back to the catalog via git. As the supernova dataset continues to grow, especially in the upcoming era of all-sky synoptic telescopes which will increase the total number of events by orders of magnitude, we hope that the catalog we have designed will be a valuable tool for the community to analyze both historical and contemporary supernovae.
Forward citations
Cited by 4 Pith papers
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On the Gamma-ray Efficiency of Superluminous Supernovae: Potential Detections and Population-Level Constraints
No significant GeV emission from 223 SLSNe constrains GeV-to-optical efficiency to η < 1.3×10^{-3}, with <0.7% of events allowed above 10^{-2}; SN 2017egm shows a ~4σ excess favoring magnetar origin while SN 2018bsz does not.
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Rapid and robust simulation-based inference for kilonovae
Simulation-based inference with a Gaussian process emulator trained on ~1300 POSSIS simulations enables rapid, robust kilonova parameter estimation that avoids MCMC biases from likelihood misspecification.
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Rapid and robust simulation-based inference for kilonovae
A simulation-based inference method with Gaussian process emulators trained on 1300 kilonova simulations recovers parameters accurately and rapidly while avoiding MCMC biases from likelihood misspecification.
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