Saved in:
Bibliographic Details
Main Authors: D'Amico, Giacomo, Doro, Michele, De Caria, Michela
Format: Preprint
Published: 2025
Subjects:
Online Access:https://arxiv.org/abs/2510.23168
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1866912682860347392
author D'Amico, Giacomo
Doro, Michele
De Caria, Michela
author_facet D'Amico, Giacomo
Doro, Michele
De Caria, Michela
contents We present a novel method for both forecasting and recasting upper limits (ULs) on dark matter (DM) annihilation cross sections, $\left< σv \right>^{UL}$, or decay lifetime $τ^{LL}$ . 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 $b\bar{b}$, $W^+W^-$, $τ^+τ^-$, and $μ^+μ^-$, and apply it to several major gamma-ray experiments, including MAGIC, \textit{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 (e.g., 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.
format Preprint
id arxiv_https___arxiv_org_abs_2510_23168
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Recasting and Forecasting Dark Matter Limits Without Raw Data: A Generalized Algorithm for Gamma-Ray Telescopes
D'Amico, Giacomo
Doro, Michele
De Caria, Michela
High Energy Astrophysical Phenomena
High Energy Physics - Phenomenology
We present a novel method for both forecasting and recasting upper limits (ULs) on dark matter (DM) annihilation cross sections, $\left< σv \right>^{UL}$, or decay lifetime $τ^{LL}$ . 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 $b\bar{b}$, $W^+W^-$, $τ^+τ^-$, and $μ^+μ^-$, and apply it to several major gamma-ray experiments, including MAGIC, \textit{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 (e.g., 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.
title Recasting and Forecasting Dark Matter Limits Without Raw Data: A Generalized Algorithm for Gamma-Ray Telescopes
topic High Energy Astrophysical Phenomena
High Energy Physics - Phenomenology
url https://arxiv.org/abs/2510.23168