Photometric data-driven classification of Type Ia supernovae in the open Supernova Catalog

被引：5
|
作者：
Dobryakov, S. [1 ]
Malanchev, K. [1 ,2 ,3 ]
Derkach, D. [1 ]
Hushchyn, M. [1 ]
机构：
[1] Natl Res Univ, Higher Sch Econ, 20 Myasnitskaya Ulitsa, Moscow 101000, Russia
[2] Lomonosov Moscow State Univ, Sternberg Astron Inst, 13 Universitetsky Pr, Moscow 119234, Russia
[3] Univ Illinois, Dept Astron, 1002 West Green St, Urbana, IL 61801 USA
来源：
ASTRONOMY AND COMPUTING | 2021年 / 35卷
关键词：
Supernovae; Machine learning; Data-driven; PAN-STARRS1; SAMPLE;
D O I：
10.1016/j.ascom.2021.100451
中图分类号：
P1 [天文学];
学科分类号：
0704 ;
摘要：
We propose a novel approach for a machine-learning-based detection of the type Ia supernovae using photometric information. Unlike other approaches, only real observation data is used during training. Despite being trained on a relatively small sample, the method shows good results on real data from the Open Supernovae Catalog. We also investigate model transfer from the PLAsTiCC simulations train dataset to real data application, and the reverse, and find the performance significantly decreases in both cases, highlighting the existing differences between simulated and real data. (C) 2021 Elsevier B.V. All rights reserved.
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收藏
页数：10
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