Recasting and forecasting dark matter limits without raw data: A generalized algorithm for gamma-ray telescopes

We present a novel method for both forecasting and recasting upper limits ULs on dark matter DM annihilation cross sections sigmav UL or decay lifetime tau LL. The forecasting method relies solely on the instrument response functions IRFs to predict ULs for a given observational setup without the need for full analysis pipelines. The recasting procedure uses published ULs to reinterpret constraints for alternative DM models or channels. We demonstrate its utility across a range of canonical annihilation channels including bb bar W plus W minus tau plus tau minus and mu plus mu minus 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. We apply this method to reinterpret ULs derived from standard spectra for example PPPC4DMID in terms of more recent DM scenarios including a Higgsino like model with mixed final states and spectra generated with the CosmiXs model. Extensive Monte Carlo simulations and direct comparison with published results confirm the robustness and accuracy of our method with discrepancies remaining within statistical uncertainties. The algorithm is generally applicable to any scenario where the expected signal model is parametric offering a powerful tool for reinterpreting existing gamma ray limits and efficiently exploring the DM parameter space in current and future indirect detection experiments.