The timing analysis of transient events allows for investigating numerous still open areas of modern astrophysics. The article explores all the mathematical and physical tools required to estimate delays and associated errors between two Times of Arrival (ToA) lists, by exploiting Cross Correlation Function (CCF) techniques. The CCF permits the establishment of the delay between two observed signals and is defined on two continuous functions. A detector does not directly measure the intensity of the electromagnetic signal (interacting with its material) but rather detects each photon ToA through a probabilistic process. Since the CCF is defined on continuous functions, the crucial step is to obtain a continuous rate curve from a list of ToA. This step is treated in the article and the constructed rate functions are light curves that are continuous functions. This allows, in principle, the estimation of delays with any desired resolution. Due to the statistical nature of the measurement process, two independent detections of the same signal yield different photon times. Consequently, light curves derived from these lists differ due to Poisson fluctuations, leading the CCF between them to fluctuate around the true theoretical delay. This article describes a Monte Carlo technique that enables reliable delay estimation by providing a robust measure of the uncertainties induced by Poissonian fluctuations. GRB data are considered as they offer optimal test cases for the proposed techniques. The developed techniques provides a significant computational advantage and are useful analyzing of data characterized by low-count statistics (i.e., low photon count rates in c/s), as they allow overcoming the limitations associated with traditional fixed bin-size methods.
Sensipy: simulate gamma-ray observations of transient astrophysical sources
Max-Planck-Institut für Physik（马克斯·普朗克物理研究所）；Technische Universität München（慕尼黑工业大学）；University of Pisa（比萨大学）；INAF - Osservatorio Astronomico di Roma（意大利国家天体物理研究所罗马天文台）；Cherenkov Telescope Array Observatory（切伦科夫望远镜阵列观测站）；Institut de Física d’Altes Energies (IFAE)（高能物理研究所）
GW-YOLO: Multi-transient segmentation in LIGO using computer vision
LIGO Laboratory, Massachusetts Institute of Technology（麻省理工学院 LIGO 实验室）；University of California, Riverside（加州大学河滨分校）；Inter-University Centre for Astronomy and Astrophysics（天文与天体物理跨大学中心）
Representation Learning for Time-Domain High-Energy Astrophysics: Discovery of Extragalactic Fast X-ray Transient XRT 200515
Institute for Computational and Mathematical Engineering, Stanford University（斯坦福大学计算与数学工程研究所）；University of Cambridge（剑桥大学）；Center for Astrophysics | Harvard & Smithsonian（哈佛-史密森天体物理中心）；INAF – Osservatorio Astrofisico di Torino（意大利国家天体物理研究所都灵天文台）；The University of Sydney（悉尼大学）
Simulating Transient Noise Bursts in LIGO with gengli
Institute for Gravitational and Subatomic Physics (GRASP), Utrecht University（乌得勒支大学引力与亚原子物理研究所（GRASP））；Nikhef（荷兰国家核与粒子物理研究所）；University of Massachusetts, Dartmouth（马萨诸塞大学达特茅斯分校）
