Ponente
Descripción
We present a novel algorithm for both forecasting and recasting upper limits (ULs) on dark matter (DM) annihilation cross sections. The forecasting method relies solely on the instrument response functions (IRFs) to predict ULs for a given observational setup. The recasting procedure uses published ULs to reinterpret results for alternative DM models or channels, without requiring access to raw data or full analysis pipelines. We demonstrate its utility across a range of annihilation channels and apply it to several major gamma-ray experiments, including MAGIC, Fermi-LAT, and CTAO. Notably, we develop a recasting approach that remains effective even when the IRF is unavailable by extracting generalized IRF-dependent coefficients from benchmark channels. Through Monte Carlo simulations and comparison with published results, we validate the robustness and accuracy of our approach, achieving good agreement within statistical uncertainties. Our framework offers a powerful tool for reinterpreting existing gamma-ray limits and efficiently exploring the DM parameter space in current and future indirect detection experiments.
