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Prospects of Searching for Type Ia Supernovae with 2.5-m Wide Field Survey Telescope

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arxiv 2212.11535 v1 pith:BRAVOAJS submitted 2022-12-22 astro-ph.SR astro-ph.HE

classification astro-ph.SRastro-ph.HE
keywords wfstsurveywidediscoverexplosionfacilityfieldlimit
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abstract

Type Ia Supernovae (SNe Ia) are the thermonuclear explosion of a carbon-oxygen white dwarf (WD) and are well-known as a distance indicator. However, it is still unclear how WDs increase their mass near the Chandrasekhar limit and how the thermonuclear runaway happens. The observational clues associated with these open questions, such as the photometric data within hours to days since the explosion, are scarce. Thus, an essential way is to discover SNe Ia at specific epochs with optimal surveys. The 2.5-m Wide Field Survey Telescope (WFST) is an upcoming survey facility deployed in western China. In this paper, we assess the detecability of SNe Ia with mock observations of WFST. Followed by the volumetric rate, we generate a spectral series of SNe Ia based on a data-based model and introduce the line-of-sight extinction to calculate the brightness from the observer. By comparing with the detection limit of WFST, which is affected by the observing conditions, we can count the number of SNe Ia discovered by mock WFST observations. We expect that WFST can find more than $3.0\times10^{4}$ pre-maximum SNe Ia within one-year running. In particular, WFST could discover about 45 bright SNe Ia, 99 early-phase SNe Ia, or $1.1\times10^{4}$ well-observed SNe Ia with the hypothesized Wide, Deep, or Medium mode, respectively, suggesting WFST will be an influential facility in time-domain astronomy.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. ZTF SN Ia DR2 follow-up: early excess in Type Ia supernova light curves

    astro-ph.CO 2026-07 conditional novelty 5.0 of 10

    Up to about a quarter of Type Ia supernovae may have an early light-curve bump, but the candidate list depends strongly on the detection method and neither leading explosion model is clearly preferred.

  2. The classification of real and bogus transients using active learning and semi-supervised learning

    astro-ph.IM 2024-12 conditional novelty 4.0 of 10

    RB-C1000, a pipeline combining active learning and semi-supervised pseudo-labeling, achieves roughly 98.8% real/bogus classification accuracy on new ZTF datasets using only 1,000 labels.

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