Paper Detail
Felipe Fontinele Nunes, Andrew Toivonen, Farhana Taiyebah, Leonard Lupin-Jimenez, Skylar Callis, Malhar Kulkarni, Soumi De, Michael W. Coughlin
Current all-sky surveys such as Vera Rubin's Legacy Survey of Space and Time and the Zwicky Transient Facility (ZTF) promise a wealth of scientific gains that are contained in the millions of astrophysical transient candidates produced each night. Kilonovae, one such transient of interest, will be challenging to identify in the alert stream and will require efficient artificial intelligence models to parse the large, real-time influx of data. In order to build these large models, comprehensive multimodal datasets are necessary for training. Due to a lack of numerous kilonova observations, we propose $\texttt{kilonova-multimodal-emulator}$ -- a simulation pipeline for realistic, multimodal kilonova observations comprised of photometry, spectra, and images. We demonstrate this pipeline for ZTF-type observations, based on historical cadence and limiting magnitude information from the Bright Transient Survey (BTS) and using the latest radiative transfer kilonova models to produce a comprehensive dataset meant for the training of large artificial intelligence model.
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@article{nunes2026comprehensive,
title = {A comprehensive simulation framework for multi-modal kilonova observations from all-sky surveys},
author = {Felipe Fontinele Nunes and Andrew Toivonen and Farhana Taiyebah and Leonard Lupin-Jimenez and Skylar Callis and Malhar Kulkarni and Soumi De and Michael W. Coughlin},
year = {2026},
abstract = {Current all-sky surveys such as Vera Rubin's Legacy Survey of Space and Time and the Zwicky Transient Facility (ZTF) promise a wealth of scientific gains that are contained in the millions of astrophysical transient candidates produced each night. Kilonovae, one such transient of interest, will be challenging to identify in the alert stream and will require efficient artificial intelligence models to parse the large, real-time influx of data. In order to build these large models, comprehensive m},
url = {https://arxiv.org/abs/2610.02088},
keywords = {astro-ph.IM},
eprint = {2610.02088},
archiveprefix = {arXiv},
}
{}