此外，透過將快速演化的瞬變天體候選者與千級新星（kilonova）模型及 AT~2017gfo 觀測基準進行比對，我們亦成功尋獲千級新星候選者 AT~2020bgp。DETECT 充分展現了在如薇拉·魯賓天文台時空遺產巡天（LSST）等新一代大規模巡天，歷史光譜資源將如何有力地支援瞬變天體快速分類、稀有事件識別以及族群統計研究。;Wide-field time-domain surveys now report large numbers of new transients each day， while spectroscopic follow-up remains available for only a small fraction of them. We present DETECT (DESI and Transient Event Cross-matching Tool)， an automated pipeline that provides rapid contextual characterization of newly reported transients by cross-matching them with host galaxies in the Dark Energy Spectroscopic Instrument (DESI) spectroscopic archive. DETECT operates daily， ingests reports from the Transient Name Server (TNS)， and identifies nearby DESI galaxy counterparts to estimate host redshifts and transient absolute magnitudes. We assess the pipeline using archival data from 2020 to 2024 and recover host spectroscopic redshifts for $\sim15\%$ of the TNS sample. From this data set， we construct a ``Gold Sample′′ of 5，634 transients with high-quality light curves processed using \texttt{Haffet}. We show that combining WISE mid-infrared information with optical luminosity estimates enables an efficient photometric high-contrast selection of intrinsically luminous transients ($M < -21$ mag) that substantially outshine their hosts， thereby isolating superluminous supernovae (SNe) and highlighting unusually bright systems， including candidate lensed SNe， such as SN~2025wny. By comparing fast transient candidates against kilonova models and the AT~2017gfo baseline， we also recover the kilonova candidate AT~2020bgp. DETECT demonstrates how archival spectroscopic resources can support transient classification， rare-event identification， and population studies in the era of large imaging surveys such as the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST).
