NAJADS: a self-contained framework for the direct determination of astrophysical J-factors

TL;DR

NAJADS is a new framework that directly derives astrophysical J-factors from cosmological simulation data, reducing uncertainties in dark matter indirect detection analyses by accounting for complex 3D structures and density fluctuations.

Contribution

It introduces a self-contained method to compute J-factors directly from raw simulation data, improving accuracy over traditional semi-analytical models.

Findings

Successfully validated the NAJADS framework.

Generated all-sky J-factor maps considering 3D structures.

Compared numerical results with semi-analytical methods.

Abstract

Cosmological simulations play a pivotal role in understanding the properties of the dark matter (DM) distribution in both galactic and galaxy-cluster environments. The characterization of DM structures is crucial for informing indirect DM searches, aiming at the detection of the annihilation (or decay) products of DM particles. A fundamental quantity in these analyses is the astrophysical J-factor. In the DM phenomenology community, J-factors are typically computed through the semi-analytical modelling of the DM mass distribution, which is affected by large uncertainties. With the scope of addressing and possibly reducing these uncertainties, we present NAJADS, a self-contained framework to derive the DM J-factor directly from the raw simulations data. We show how this framework can be used to compute all-sky maps of the J-factor, automatically accounting for the complex 3D structure of the simulated halos and for the boosting of the signal due to the density fluctuations along the line of sight. After validating our code, we present a proof-of-concept application of NAJADS to a realistic halo from the IllustrisTNG suite, and exploit it to make a thorough comparison between our numerical approach and traditional semi-analytical methods.