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Forecasting supernova observations with the CSST: I. Photometric samples.

Authors :
Liu, Chengqi
Xu, Youhua
Meng, Xianmin
Zhang, Xin
Li, Shi-Yu
Fu, Yuming
Wang, Xiaofeng
Liu, Shufei
Luo, Zun
Wang, Guanghuan
Zhan, Hu
Source :
SCIENCE CHINA Physics, Mechanics & Astronomy; Nov2024, Vol. 67 Issue 11, p1-14, 14p
Publication Year :
2024

Abstract

The China Space Station Telescope (CSST, also known as Xuntian) is a serviceable two-meter-aperture wide-field telescope operating in the same orbit as the China Space Station. The CSST plans to survey a sky area of 17,500 deg<superscript>2</superscript> of the medium-to-high Galactic latitude to a depth of 25–26 AB mag in at least 6 photometric bands over 255–1,000 nm. Within such a large sky area, slitless spectra will also be taken over the same wavelength range as the imaging survey. Even though the CSST survey is not dedicated to time-domain studies, it would still detect a large number of transients, such as supernovae (SNe). In this paper, we simulate photometric SN observations based on a strawman survey plan using the Sncosmo package. During its 10-year survey, the CSST is expected to observe about 5 million SNe of various types. With quality cuts, we obtain a “gold” sample that comprises roughly 7,400 SNe Ia, 2,200 SNe Ibc, and 6,500 SNe II candidates with correctly classified percentages reaching 91%, 63%, and 93% (formally defined as classification precision), respectively. The same survey can also trigger alerts for the detection of about 15,500 SNe Ia (precision 61%) and 2,100 SNe II (precision 49%) candidates at least two days before the light maxima. Moreover, the near-ultraviolet observations of the CSST will be able to catch hundreds of shock-cooling events serendipitously every year. These results demonstrate that the CSST can make a potentially significant contribution to SN studies. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
16747348
Volume :
67
Issue :
11
Database :
Complementary Index
Journal :
SCIENCE CHINA Physics, Mechanics & Astronomy
Publication Type :
Academic Journal
Accession number :
179517351
Full Text :
https://doi.org/10.1007/s11433-024-2456-x