MALLORN: many artificial LSST light curves based on observations of real nuclear transients

Journal article

The Vera C. Rubin Observatory’s 10-yr Legacy Survey of Space and Time (LSST) is expected to produce a hundredfold increase in the number of transients we observe. However, there are insufficient spectroscopic resources to follow up on all of the wealth of targets that LSST will provide. As such it is necessary to be able to prioritize objects for follow-up observations or inclusion in sample studies based purely on their LSST photometry. We are particularly keen to identify tidal disruption events (TDEs) with LSST. TDEs are immensely useful for determining black hole parameters and probing our understanding of accretion physics. To assist in these efforts, we present the Many Artificial LSST Light curves based on the Observations of Real Nuclear transients (MALLORN) data set and the corresponding classifier challenge for identifying TDEs. MALLORN comprises 10 178 simulated LSST light curves, constructed from real Zwicky Transient Facility (ZTF) observations of 64 TDEs, 727 nuclear supernovae and 1407 AGN with spectroscopic labels using Gaussian process fitting, empirically motivated spectral energy distributions from SNCosmo and the baseline from the Rubin Survey Simulator. Our novel approach can be easily adapted to simulate transients for any photometric survey using observations from another, requiring only the limiting magnitudes and an estimate of the cadence of observations. The MALLORN Astronomical Classification Challenge, launched on Kaggle on 2025 October 15, will allow competitors to test their photometric classifiers on simulated LSST data to find TDEs and improve upon their capabilities prior to the start of LSST.

Authors: Magill, D; Nicholl, M; Anilkumar, V; van Velzen, S; Sheng, X

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Oxford University Press
• Journal: RAS Techniques and Instruments
• Volume: 5
• Pages: rzag019
• Article number: rzag019
• Publication date: 2026-03-10
• Acceptance date: 2026-03-03
• DOI: 10.1093/rasti/rzag019
• EISSN: 2752-8200
• ISSN: 2752-8200
• Language: English
• Keywords: Time domain astronomy; Data methods; Transient sources; Machine learning