A comprehensive simulation framework for multi-modal kilonova observations from all-sky surveys
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 multimodal datasets are necessary for training. Due to a lack of numerous kilonova observations, we propose 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.
Related
- Galaxy-Galaxy Strong Lensing simulation with the GPU acceleration across surveys and multi-bands
- A Comprehensive AGN Inventory for the Dwarf-Dominated RESOLVE and ECO Surveys: Mass-Dependent AGN Types and Occupation Fractions
- A Comprehensive Active Galactic Nuclei Inventory for the Dwarf-dominated RESOLVE and ECO Surveys: Mass-dependent Active Galactic Nuclei Types and Occupation Fractions
- Cracking into RACS - I. Measuring the cosmic dipole in the RACS-low3 and RACS-mid1 radio surveys with simulation-based inference
- Cracking into RACS - I. Measuring the cosmic dipole in the RACS-low3 and RACS-mid1 radio surveys with simulation-based inference
BibTeX
@misc{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},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.02088},
eprint = {2610.02088},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.02088},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - A comprehensive simulation framework for multi-modal kilonova observations from all-sky surveys
AU - Felipe Fontinele Nunes
AU - Andrew Toivonen
AU - Farhana Taiyebah
AU - Leonard Lupin-Jimenez
AU - Skylar Callis
AU - Malhar Kulkarni
AU - Soumi De
AU - Michael W. Coughlin
PY - 2026
DA - 2026/10/01
PB - arXiv
DO - 10.48550/arxiv.2610.02088
UR - https://arxiv.org/abs/2610.02088
AB - 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 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.
ER -