Orateur
Description
Real-time follow-up of high-energy transients requires rapid decisions under uncertainty: the source position, temporal evolution, spectrum, and detectability are often only partially constrained when an alert is received. This is particularly challenging for VHE gamma-ray observatories such as H.E.S.S. and CTAO. Existing follow-up strategies often do not fully account for source time evolution, spectral properties, instrument specific constraints, and real-time updates. The proposed framework starts from an incoming alert, such as a GRB, gravitational-wave event, or high-energy neutrino alert, and extracts the relevant event context. It then performs feasibility and visibility filtering, source hypothesis sampling, and evaluates the expected detectability using a machine learning model trained on simulated transient observations. Finally, the pipeline estimates the detection probability for candidate positions within the uncertainty region, ranks them, and uses this ranking to construct an executable follow-up strategy for observatories. This work aims to develop a framework to enhance the detection of gamma-ray transients with CTAO. The successful implementation of this framework promises to provide deeper insights into high-energy astrophysical phenomena and enhance the scientific return from CTAO observations.