Detecting unresolved lensed SNe Ia in LSST using blended light curves

¹ Technical University of Munich, TUM School of Natural Sciences, Physics Department, James-Franck-Straße 1, 85748 Garching, Germany
² Max-Planck-Institut für Astrophysik, Karl-Schwarzschild Straße 1, 85748 Garching, Germany
³ Academia Sinica Institute of Astronomy and Astrophysics (ASIAA), 11F of ASMAB, No. 1, Section 4, Roosevelt Road, Taipei 10617, Taiwan
⁴ Oskar Klein Centre, Department of Physics, Stockholm University, 106 91 Stockholm, Sweden
⁵ Aix Marseille Univ, CNRS, CNES, LAM, Marseille, France
⁶ Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA
⁷ Berkeley Center for Cosmological Physics, University of California, Berkeley, CA 94720, USA
⁸ Korea Astronomy and Space Science Institute (KASI), 776 Daedeok-daero, Yuseong-gu, Daejeon 34055, Korea
⁹ KASI Campus, University of Science and Technology, 217 Gajeong-ro, Yuseong-gu, Daejeon 34113, Korea
¹⁰ Inter-University Centre for Astronomy and Astrophysics, Post Bag 4, Ganeshkhind, Pune 411007, India
¹¹ Kavli Institute for the Physics and Mathematics of the Universe (IPMU), 5-1-5 Kashiwanoha, Kashiwa-shi, Chiba 277-8583, Japan
¹² Dipartimento di Fisica, Università degli Studi di Milano, via Celoria 16, 20133 Milano, Italy
¹³ INAF - IASF Milano, via A. Corti 12, 20133 Milano, Italy

^★ Corresponding author; satadru.bag@tum.de

Received: 23 April 2024
Accepted: 20 August 2024

Abstract

Strongly gravitationally lensed supernovae (LSNe) are promising probes for providing absolute distance measurements using gravitational-lens time delays. Spatially unresolved LSNe offer an opportunity to enhance the sample size for precision cosmology. We predict that there will be approximately three times as many unresolved as resolved LSNe Ia in the Legacy Survey of Space and Time (LSST) by the Rubin Observatory. In this article, we explore the feasibility of detecting unresolved LSNe Ia from a pool of preclassified SNe Ia light curves using the shape of the blended light curves with deep-learning techniques. We find that ∼30% unresolved LSNe Ia can be detected with a simple 1D convolutional neural network (CNN) using well-sampled rizy-band light curves (with a false-positive rate of ∼3%). Even when the light curve is well observed in only a single band among r, i, and z, detection is still possible with false-positive rates ranging from ∼4 to 7% depending on the band. Furthermore, we demonstrate that these unresolved cases can be detected at an early stage using light curves up to ∼20 days from the first observation with well-controlled false-positive rates, providing ample opportunity to trigger follow-up observations. Additionally, we demonstrate the feasibility of time-delay estimations using solely LSST-like data of unresolved light curves, particularly for doubles, when excluding systems with low time delays and magnification ratios. However, the abundance of such systems among those unresolved in LSST poses a significant challenge. This approach holds potential utility for upcoming wide-field surveys, and overall results could significantly improve with enhanced cadence and depth in the future surveys.