Multi-modal transmission forecasting for the Vera C. Rubin Observatory
Abstract
In the age of large automated surveys conducted at facilities like the NSF-DOE Vera C. Rubin Observatory, delivering precision science goals demands highly efficient scheduling strategies that remain agile and dynamic to changing environments on the summit. On site, a variety of telemetry informs the scheduler of the best next pointing given the atmosphere's transmission. This live monitoring is provided by the Dutch Rubin Enhanced Atmospheric Monitor (DREAM) all-sky camera. Currently, dynamic scheduling uses short-term predictive algorithms, such as tracking cloud cross-correlations, to anticipate movement at short scheduling timescales. However, many of Rubin's core science objectives require survey strategies to commit to observations 20--40,min in advance (pair mode), which remains out of reach because clouds at those timescales can arrive from beyond the horizon. We developed a single monolithic multimodal strategy that folds asynchronous data from both the DREAM camera and weather satellite imagery to forecast multi-horizon sky transmission at 90,s, 5,min, 20,min, and 30,min in advance. We demonstrate that at 20-minute timescales, our model reduces transmission error by 21% and false-clear misses by 22% versus Rubin's frozen-flow tracker, and reduces false-clear misses by 26% versus persistence. We also demonstrate empirically, through ablations, that the DREAM all-sky camera provides short-horizon predictive power while satellite imagery provides long-horizon predictive power. We estimate the optimistic order-of-magnitude gain for survey efficiency improvement to be between 0.1% to 1%.
Related
- Investigation and Mitigation of a Prominent Off-Axis Stray Light Path in Rubin Observatory Commissioning
- Prospects for Astrobiology and Technosignature Searches with the Vera C. Rubin Observatory Legacy Survey of Space and Time
- Extracting Asteroid Physical Properties from Vera C. Rubin Observatory Science Validation Survey Data: Rotational Periods, Colors, and Taxonomies
- The "Rubin 2036: Opportunities for Discovery" Workshop: A Community Roadmap for the Future of the Vera C. Rubin Observatory After the LSST
- Measuring the Vertical Structure of Active Galactic Nuclei Disks with Transformer Models and the Vera C. Rubin Observatory
BibTeX
@misc{ma2026multimodal,
title = {{Multi-modal transmission forecasting for the Vera C. Rubin Observatory}},
author = {Peter Xiangyuan Ma and Joshua S. Bloom},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.22725},
eprint = {2610.22725},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.22725},
note = {CC0},
}
RIS
TY - UNPB
TI - Multi-modal transmission forecasting for the Vera C. Rubin Observatory
AU - Peter Xiangyuan Ma
AU - Joshua S. Bloom
PY - 2026
DA - 2026/10/15
PB - arXiv
DO - 10.48550/arxiv.2610.22725
UR - https://arxiv.org/abs/2610.22725
AB - In the age of large automated surveys conducted at facilities like the NSF-DOE Vera C. Rubin Observatory, delivering precision science goals demands highly efficient scheduling strategies that remain agile and dynamic to changing environments on the summit. On site, a variety of telemetry informs the scheduler of the best next pointing given the atmosphere's transmission. This live monitoring is provided by the Dutch Rubin Enhanced Atmospheric Monitor (DREAM) all-sky camera. Currently, dynamic scheduling uses short-term predictive algorithms, such as tracking cloud cross-correlations, to anticipate movement at short scheduling timescales. However, many of Rubin's core science objectives require survey strategies to commit to observations 20--40,min in advance (pair mode), which remains out of reach because clouds at those timescales can arrive from beyond the horizon. We developed a single monolithic multimodal strategy that folds asynchronous data from both the DREAM camera and weather satellite imagery to forecast multi-horizon sky transmission at 90,s, 5,min, 20,min, and 30,min in advance. We demonstrate that at 20-minute timescales, our model reduces transmission error by 21% and false-clear misses by 22% versus Rubin's frozen-flow tracker, and reduces false-clear misses by 26% versus persistence. We also demonstrate empirically, through ablations, that the DREAM all-sky camera provides short-horizon predictive power while satellite imagery provides long-horizon predictive power. We estimate the optimistic order-of-magnitude gain for survey efficiency improvement to be between 0.1% to 1%.
ER -