A systematic search for rapid transients in the Subaru HSC-SSP transient survey
Seiji Toshikage, Masaomi Tanaka, Naoki Yasuda, Takashi J. Moriya,, Ichiro Takahashi, Ji-an Jiang, Mitsuru Kokubo, Naoki Matsumoto, Keiichi, Maeda, Tomoki Morokuma, Nao Suzuki, and Nozomu Tominaga

TL;DR
This study systematically searches for rapid transients at medium-high redshifts using Subaru HSC-SSP data, employing machine learning to classify candidates and estimate their rates, revealing diverse properties and potential progenitors.
Contribution
Introduces a machine learning-based classification method for rapid transients in a large survey, and provides the first rate estimates for these events at medium-high redshifts.
Findings
Identified 14 rapid transient candidates at redshifts 0.34 to 1.85.
Estimated the event rate of rapid transients as ~6,000 events per year per Gpc^3.
Detected candidates of Type Ibn supernovae and superluminous supernovae at high redshifts.
Abstract
Recent high-cadence transient surveys have discovered rapid transients whose light curve timescales are shorter than those of typical supernovae. In this paper, we present a systematic search for rapid transients at medium-high redshifts among 3381 supernova candidates obtained from the Subaru HSC-SSP transient survey. We developed a machine learning classifier to classify the supernova candidates into four types (Type Ia, Ibc, II supernovae, and rapid transients) based on the features derived from the light curves. By applying this classifier to the 3381 supernova candidates and by further applying the quality cut, we selected 14 rapid transient samples. They are located at a wide range of redshifts () and show a wide range of the peak absolute magnitude (). The event rate of the rapid transients is estimated to be $\sim…
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Taxonomy
TopicsIonosphere and magnetosphere dynamics · Superconducting Materials and Applications · Magnetic confinement fusion research
