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SuperRAENN :
SuperRAENN : A Semisupervised Supernova Photometric Classification Pipeline Trained on Pan-STARRS1 Medium-Deep Survey Supernovae
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Villar, V. Ashley (författare)
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Hosseinzadeh, Griffin (författare)
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Berger, Edo (författare)
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Ntampaka, Michelle (författare)
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Jones, David O. (författare)
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Challis, Peter (författare)
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Chornock, Ryan (författare)
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Drout, Maria R. (författare)
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Foley, Ryan J. (författare)
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Kirshner, Robert P. (författare)
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- Lunnan, Ragnhild (författare)
- Stockholms universitet,Institutionen för astronomi,Oskar Klein-centrum för kosmopartikelfysik (OKC)
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Margutti, Raffaella (författare)
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Milisavljevic, Dan (författare)
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Sanders, Nathan (författare)
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Pan, Yen-Chen (författare)
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Rest, Armin (författare)
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Scolnic, Daniel M. (författare)
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Magnier, Eugene (författare)
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Metcalfe, Nigel (författare)
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Wainscoat, Richard (författare)
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Waters, Christopher (författare)
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(creator_code:org_t)
- 2020-12-17
- 2020
- Engelska.
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Ingår i: Astrophysical Journal. - : American Astronomical Society. - 0004-637X .- 1538-4357. ; 905:2
- Relaterad länk:
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https://iopscience.i...
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https://urn.kb.se/re...
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https://doi.org/10.3...
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Abstract
Ämnesord
Stäng
- Automated classification of supernovae (SNe) based on optical photometric light-curve information is essential in the upcoming era of wide-field time domain surveys, such as the Legacy Survey of Space and Time (LSST) conducted by the Rubin Observatory. Photometric classification can enable real-time identification of interesting events for extended multiwavelength follow-up, as well as archival population studies. Here we present the complete sample of 5243 SN-like light curves (in g(P1)r(P1)i(P1)z(P1)) from the Pan-STARRS1 Medium-Deep Survey (PS1-MDS). The PS1-MDS is similar to the planned LSST Wide-Fast-Deep survey in terms of cadence, filters, and depth, making this a useful training set for the community. Using this data set, we train a novel semisupervised machine learning algorithm to photometrically classify 2315 new SN-like light curves with host galaxy spectroscopic redshifts. Our algorithm consists of an RF supervised classification step and a novel unsupervised step in which we introduce a recurrent autoencoder neural network (RAENN). Our final pipeline, dubbed SuperRAENN, has an accuracy of 87% across five SN classes (Type Ia, Ibc, II, IIn, SLSN-I) and macro-averaged purity and completeness of 66% and 69%, respectively. We find the highest accuracy rates for SNe Ia and SLSNe and the lowest for SNe Ibc. Our complete spectroscopically and photometrically classified samples break down into 62.0% Type Ia (1839 objects), 19.8% Type II (553 objects), 4.8% Type IIn (136 objects), 11.7% Type Ibc (291 objects), and 1.6% Type I SLSNe (54 objects).
Ämnesord
- NATURVETENSKAP -- Fysik (hsv//swe)
- NATURAL SCIENCES -- Physical Sciences (hsv//eng)
Nyckelord
- Supernovae
- Astrostatistics
- Light curve classification
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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Till lärosätets databas
- Av författaren/redakt...
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Villar, V. Ashle ...
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Hosseinzadeh, Gr ...
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Berger, Edo
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Ntampaka, Michel ...
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Jones, David O.
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Challis, Peter
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visa fler...
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Chornock, Ryan
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Drout, Maria R.
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Foley, Ryan J.
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Kirshner, Robert ...
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Lunnan, Ragnhild
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Margutti, Raffae ...
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Milisavljevic, D ...
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Sanders, Nathan
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Pan, Yen-Chen
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Rest, Armin
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Scolnic, Daniel ...
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Magnier, Eugene
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Metcalfe, Nigel
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Wainscoat, Richa ...
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Waters, Christop ...
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visa färre...
- Om ämnet
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- NATURVETENSKAP
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NATURVETENSKAP
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och Fysik
- Artiklar i publikationen
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Astrophysical Jo ...
- Av lärosätet
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Stockholms universitet