Forecasting supernova observations with the CSST: I. Photometric samples

• Published in: Science China. Physics, mechanics & astronomy mechanics & astronomy, 2024-11, Vol.67 (11), p.119512, Article 119512
• Main Authors: Liu, Chengqi, Xu, Youhua, Meng, Xianmin, Zhang, Xin, Li, Shi-Yu, Fu, Yuming, Wang, Xiaofeng, Liu, Shufei, Luo, Zun, Wang, Guanghuan, Zhan, Hu
• Format: Article
• Language: English
• Subjects: Astronomy; Classical and Continuum Physics; Observations and Techniques; Photometry; Physics; Physics and Astronomy; Polls & surveys; Sky surveys (astronomy); Space stations; Supernovae
• ISSN: 1674-7348; 1869-1927
• DOI: 10.1007/s11433-024-2456-x

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 2 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 S ncosmo 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.