NOMAI: A real-time photometric classifier for superluminous supernova identification

Russeil, E ORCID: 0000-0001-9923-2407, Lunnan, R ORCID: 0000-0001-9454-4639, Peloton, J ORCID: 0000-0002-8560-4449, Schulze, S ORCID: 0000-0001-6797-1889, Pessi, PJ ORCID: 0000-0002-8041-8559, Perley, D ORCID: 0000-0001-8472-1996, Sollerman, J ORCID: 0000-0003-1546-6615, Gkini, A ORCID: 0009-0000-9383-2305, Hu, Y ORCID: 0000-0002-9744-3910, Chen, T-W ORCID: 0000-0002-1066-6098, Bellm, EC, Chen, TX ORCID: 0000-0001-9152-6224 and Rusholme, B ORCID: 0000-0001-7648-4142 (2026) NOMAI: A real-time photometric classifier for superluminous supernova identification. Astronomy & Astrophysics, 713. ISSN 0004-6361

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Abstract

Superluminous supernovae (SLSNe) are one of the most luminous stellar explosions known, yet they remain poorly understood. Because they are intrinsically rare, efficiently identifying them in the large alert streams produced by modern time-domain surveys is essential for building larger observational samples and enabling spectroscopic follow-up. We present NOMAI, a machine-learning classifier designed to identify SLSN candidates directly from photometric alerts in the ZTF stream, using light curves accumulated over at least 30 days. It does not require any spectroscopic redshift and is running in real time within the Fink broker. ZTF light curves were transformed into a set of physically motivated features derived primarily from model-fitting procedures using SALT2 and Rainbow, a blackbody-based multiband fitting framework. These features were used to train an XGBoost classifier on a curated dataset of labeled ZTF sources constructed using literature samples of SLSNe, along with TNS and internal ZTF labeled sources. The final dataset contains 5280 unique sources, including 225 spectroscopically classified SLSNe. Averaged over 100 bootstrap realizations, the classifier reaches 66% completeness and 58% purity. Deployed within the Fink broker, NOMAI has been running continuously since 18 December 2025 on the ZTF alert stream and publicly reports SLSN candidates every night by automatically posting them to dedicated communication channels. Based on this, we also report the first two-month evaluation period, where the classifier successfully recovered 22 of the 24 active SLSNe reported on the Transient Name Server, providing an upper limit on its real-time completeness. The achieved performances, particularly the high completeness in real-time operations, demonstrate that the classifier provides a valuable tool for experts to efficiently scan the alert stream and identify promising candidates. In the near future, NOMAI is intended to be adapted to operate on the Legacy Survey of Space and Time conducted by the Vera C. Rubin Observatory, which is expected to uncover an unprecedented number of transients, making machine-learning based photometric classification essential.

Item Type:	Article
Uncontrolled Keywords:	5109 Space Sciences; 51 Physical Sciences; Machine Learning and Artificial Intelligence; 0201 Astronomical and Space Sciences; Astronomy & Astrophysics; 5101 Astronomical sciences; 5107 Particle and high energy physics; 5109 Space sciences
Subjects:	Q Science > QB Astronomy Q Science > QC Physics
Divisions:	Astrophysics Research Institute
Publisher:	EDP Sciences
Date of acceptance:	12 July 2026
Date of first compliant Open Access:	28 September 2026
Date Deposited:	28 Sep 2026 10:55
Last Modified:	28 Sep 2026 10:55
DOI or ID number:	10.1051/0004-6361/202660399
URI:	https://researchonline.ljmu.ac.uk/id/eprint/29544

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