arXiv subjects

Ranfang Zheng

Publications and source records attributed to Ranfang Zheng.

3 recordsLinked to original sources

arXiv2026

Searching for Type Ia Supernovae in the Dark Energy Spectroscopic Instrument

With the development of large-scale photometric surveys, an increasing number of supernova candidates are being discovered, leading to a rapidly growing demand for supernova spectra. In addition to equipping photometric surveys with follow-up spectroscopic facilities, archival spectra from large multi-object spectroscopic surveys can be mined to provide spectroscopic classifications for candidates and to find supernovae missed by previous surveys. In this work, we combine Principal Component Analysis (PCA), the Local Outlier Factor (LOF) algorithm, and the supernova classification tool SNID to search for Type Ia supernovae among 1,757,303 galaxy spectra from the Dark Energy Spectroscopic Instrument (DESI) Data Release 1 (DR1). We finally obtain 247 Type Ia supernovae and 17 supernovae of other types. Among these, 202 supernovae lack classification records in the Transient Name Server (TNS) and represent newly identified SNe. These results demonstrate the potential of multi-object spectroscopic surveys to supplement supernova samples, particularly for transients missed by traditional photometric surveys.

astro-ph.HE↗

arXiv2026

Two Earliest Optical-UV Tidal Disruption Events Hidden in the SDSS DR7 Catalog Unveiled by the Transformer-Based Spectrum Classifier

Optical spectroscopic features are decisive in the current identification of optical-UV tidal disruption events (TDEs). Regarding that the TDE estimated occurrence rate is $10^{-5}-10^{-4}$ galaxy$^{-1}$ yr$^{-1}$ by both theoretical and observational methods, large optical spectroscopic catalogs with >10$^5$ galaxy spectra can include some serendipitous spectra with TDE spectroscopic features, which can be found after building a useful selection method. We hereby introduce a principal component analysis enhanced Transformer TDE spectrum classifier which achieves a precision of 0.88 and a recall of 0.99 on our evaluation dataset, and report its inspiring discoveries in the widely-used SDSS DR7 catalog: two newly discovered TDEs and one reported likely TDE. For SDSS J124225.39+642919.0, we confirm the presence of a UV transient in GALEX catalog when the spectrum was taken, and its occurrence time should be earlier than the spectrum observation time, MJD < 52316 (February 11, 2002), making it the earliest optical-UV TDE discovered by now. For SDSS J152459.70+045423.1, its spectrum matches all features of the TDE-H+He spectrum, and was taken during an optical outburst recorded by the Catalina Real-time Transient Survey. The start of this outburst lies in 54269 < MJD < 54476 (June 18, 2007 - January 11, 2008), making it one of the earliest among the reported optical TDEs. The discovery of two new TDEs highlights the power of machine-learning based classifiers in digging out buried treasures in large-volume catalogs, and marks a new method for discovering optical-UV TDEs.

astro-ph.HE↗

arXiv2025

TTC: Transformer-based TDE Classifier for the Wide Field Survey Telescope (WFST)

We propose the Transformer-based Tidal disruption events (TDE) Classifier (\texttt{TTC}), specifically designed to operate effectively with both real-time alert streams and archival data of the Wide Field Survey Telescope (WFST). It aims to minimize the reliance on external catalogs and find TDE candidates from pure light curves, which is more suitable for finding TDEs in faint and distant galaxies. \texttt{TTC} consists of two key modules that can work independently: (1) A light curve parametric fitting module and (2) a Transformer (\texttt{Mgformer})-based classification network. The training of the latter module and evaluation for each module utilize a light curve dataset of 7413 spectroscopically classified transients from the Zwicky Transient Facility (ZTF). The \texttt{Mgformer}-based module is superior in performance and flexibility. Its representative recall and precision values are 0.79 and 0.76, respectively, and can be modified by adjusting the threshold. It can also efficiently find TDE candidates within 30 days from the first detection. For comparison, the parametric fitting module yields values of 0.72 and 0.40, respectively, while it is $>$10 times faster in average speed. Hence, the setup of modules allows a trade-off between performance and time, as well as precision and recall. \texttt{TTC} has successfully picked out all spectroscopically identified TDEs among ZTF transients in a real-time classification test, and selected $\sim$20 TDE candidates in the deep field survey data of WFST. The discovery rate will greatly increase once the differential database for the wide field survey is ready.

astro-ph.IM↗