Kilonova Seekers：面向时域天体物理的实时公民科学项目
天体物理仪器与方法
2024-07-25 v2 高能天体物理现象
摘要
时域天体物理领域发展迅猛，新的观测项目大幅增加数据卷数。针对机器学习分类器训练集， democratized、分布式的方法至关重要，以充分利用这场发现浪潮——公民科学方法已被证明有效地满足这些要求。本文描述了创建及初始结果，该项目名为 Kilonova Seekers，旨在实时发现 GOTO 望远镜捕获的暂现现象。Kilonova Seekers 于 2023 年 7 月启动，期间收集了约 2000 名志愿者提交的 60 万+ 项分类结果，覆盖 LIGO-Virgo-KAGRA O4a 观测运行。在此期间，项目发现了 20 例天体，生成了 17,682 项检测的“黄金标准”训练集，用于增强深度学习分类器，测量了 Zooniverse 志愿者在真伪分类中的表现和偏差。该项目将持续到 GOTO 项目生命周期结束，不断提高候选对象的检索频率，直接促进当前开发的下一代分类算法。
引用
@article{arxiv.2406.02334,
title = {$\textit{Kilonova Seekers}$: the GOTO project for real-time citizen science in time-domain astrophysics},
author = {T. L. Killestein and L. Kelsey and E. Wickens and L. Nuttall and J. Lyman and C. Krawczyk and K. Ackley and M. J. Dyer and F. Jiménez-Ibarra and K. Ulaczyk and D. O'Neill and A. Kumar and D. Steeghs and D. K. Galloway and V. S. Dhillon and P. O'Brien and G. Ramsay and K. Noysena and R. Kotak and R. P. Breton and E. Pallé and D. Pollacco and S. Awiphan and S. Belkin and P. Chote and P. Clark and D. Coppejans and C. Duffy and R. Eyles-Ferris and B. Godson and B. Gompertz and O. Graur and P. Irawati and D. Jarvis and Y. Julakanti and M. R. Kennedy and H. Kuncarayakti and A. Levan and S. Littlefair and M. Magee and S. Mandhai and D. Mata Sánchez and S. Mattila and J. McCormac and J. Mullaney and J. Munday and M. Patel and M. Pursiainen and J. Rana and U. Sawangwit and E. Stanway and R. Starling and B. Warwick and K. Wiersema},
journal= {arXiv preprint arXiv:2406.02334},
year = {2024}
}
备注
20 pages, 15 figures. Accepted in MNRAS