Search: (WFRF:(Coughlin Michael W.))
> (2021) >
SNIascore :
SNIascore : Deep-learning Classification of Low-resolution Supernova Spectra
-
Fremling, Christoffer (author)
-
Hall, Xander J. (author)
-
Coughlin, Michael W. (author)
-
show more...
-
Dahiwale, Aishwarya S. (author)
-
Duev, Dmitry A. (author)
-
Graham, Matthew J. (author)
-
Kasliwal, Mansi M. (author)
-
- Kool, Erik C. (author)
- Stockholms universitet,Institutionen för astronomi,Oskar Klein-centrum för kosmopartikelfysik (OKC)
-
Mahabal, Ashish A. (author)
-
Miller, Adam A. (author)
-
Neill, James D. (author)
-
Perley, Daniel A. (author)
-
Rigault, Mickael (author)
-
Rosnet, Philippe (author)
-
Rusholme, Ben (author)
-
Sharma, Yashvi (author)
-
Shin, Kyung Min (author)
-
Shupe, David L. (author)
-
- Sollerman, Jesper (author)
- Stockholms universitet,Institutionen för astronomi,Oskar Klein-centrum för kosmopartikelfysik (OKC)
-
Walters, Richard S. (author)
-
Kulkarni, S. R. (author)
-
show less...
-
(creator_code:org_t)
- 2021-08-05
- 2021
- English.
-
In: Astrophysical Journal Letters. - : American Astronomical Society. - 2041-8205 .- 2041-8213. ; 917:1
- Related links:
-
https://iopscience.i...
-
show more...
-
https://urn.kb.se/re...
-
https://doi.org/10.3...
-
show less...
Abstract
Subject headings
Close
- We present SNIascore, a deep-learning-based method for spectroscopic classification of thermonuclear supernovae (SNe Ia) based on very low-resolution (R similar to 100) data. The goal of SNIascore is the fully automated classification of SNe Ia with a very low false-positive rate (FPR) so that human intervention can be greatly reduced in large-scale SN classification efforts, such as that undertaken by the public Zwicky Transient Facility (ZTF) Bright Transient Survey (BTS). We utilize a recurrent neural network architecture with a combination of bidirectional long short-term memory and gated recurrent unit layers. SNIascore achieves a SNIascore simultaneously performs binary classification and predicts the redshifts of secure SNe Ia via regression (with a typical uncertainty of z = 0.01 to z = 0.12). For the magnitude-limited ZTF BTS survey (approximate to 70% SNe Ia), deploying SNIascore reduces the amount of spectra in need of human classification or confirmation by approximate to 60%. Furthermore, SNIascore allows SN Ia classifications to be automatically announced in real time to the public immediately following a finished observation during the night.
Subject headings
- NATURVETENSKAP -- Fysik (hsv//swe)
- NATURAL SCIENCES -- Physical Sciences (hsv//eng)
Publication and Content Type
- ref (subject category)
- art (subject category)
Find in a library
To the university's database
- By the author/editor
-
Fremling, Christ ...
-
Hall, Xander J.
-
Coughlin, Michae ...
-
Dahiwale, Aishwa ...
-
Duev, Dmitry A.
-
Graham, Matthew ...
-
show more...
-
Kasliwal, Mansi ...
-
Kool, Erik C.
-
Mahabal, Ashish ...
-
Miller, Adam A.
-
Neill, James D.
-
Perley, Daniel A ...
-
Rigault, Mickael
-
Rosnet, Philippe
-
Rusholme, Ben
-
Sharma, Yashvi
-
Shin, Kyung Min
-
Shupe, David L.
-
Sollerman, Jespe ...
-
Walters, Richard ...
-
Kulkarni, S. R.
-
show less...
- About the subject
-
- NATURAL SCIENCES
-
NATURAL SCIENCES
-
and Physical Science ...
- Articles in the publication
-
Astrophysical Jo ...
- By the university
-
Stockholm University