Search for Astrophysical Transients on Limiting Time Scales and Their Classification Based on INTEGRAL Data
G. Yu. Mozgunov (1), A. S. Pozanenko (1,2), P. Yu. Minaev (1), I. V. Chelovekov (1), S. A. Grebenev (1), A. G. Demin (3), A. V. Ridnaya (3), D. S. Svinkin (3), Yu. R. Temiraev (4), and D. D. Frederiks (3) ((1) Space Research Institute, Moscow

TL;DR
This study searches for ultra-long gamma-ray transients using INTEGRAL data, develops an automated classification algorithm, and confirms 16 astrophysical transient candidates, including four ultra-long gamma-ray bursts, advancing detection methods.
Contribution
The paper introduces a novel automated light curve processing algorithm and applies machine learning to classify and confirm ultra-long gamma-ray transients in INTEGRAL data.
Findings
Confirmed 16 astrophysical transient candidates.
Identified four ultra-long gamma-ray bursts.
Up to 270 probable gamma-ray bursts identified.
Abstract
We have searched for ultra-long (> 100 s) gamma-ray transients in the data from the anticoincidence shield (ACS) of the SPI gamma-ray spectrometer onboard the INTEGRAL orbital observatory and classified them by machine learning methods. We have found about 4364 candidates for such events in the SPI-ACS data by the `blind' threshold search method. We have developed an algorithm for automatic processing of their light curves that distinguishes a candidate for transients on various time scales and allows its duration and fluence to be determined. The algorithm has been applied to calculate (and compare) the fluxes in the light curves recorded by various INTEGRAL detectors: IREM, SPI-ACS, SPI, ISGRI, and PICsIT. These fluxes have been used to train the classifier based on gradient boosting. Subsequently, we have performed a cluster analysis of the candidates found by the dimensionality…
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