Speaker
Description
The structure of dark matter haloes is often described by radial density profiles motivated by cosmological simulations. These are
typically assumed to have a fixed functional form (e.g. NFW), with some free parameters. However, relying on simulations has
the disadvantage that the resulting profiles depend on the dark matter model and the baryonic physics implementation, which are
highly uncertain. Instead, we present a method to constrain halo density profiles directly from observations. This is done using a
symbolic regression algorithm called Exhaustive Symbolic Regression (ESR). ESR searches for the optimal analytic expression
to fit data, combining both accuracy and simplicity. We apply ESR to a sample of 149 galaxy clusters from the HSC-XXL
survey to identify which functional forms perform best across the entire sample of clusters. We identify density profiles that
statistically outperform NFW under a minimum-description-length criterion. Within the radial range probed by the weak-lensing
data (𝑅 ∼ 0.3 − 3 h−1 Mpc), the highest-ranked ESR profiles exhibit shallow inner behaviour and a maximum in the density
profile. As a practical application, we show how the best-fitting ESR models can be used to obtain enclosed mass estimates. We
find masses that are, on average, higher than those derived using NFW, highlighting a source of potential bias when assuming the
wrong density profile. These results have important knock-on effects for analyses that utilise clusters, for example cosmological
constraints on 𝜎8 and Ωm from cluster abundance and clustering. Beyond the HSC dataset, the method is applicable to any data
constraining the dark matter distribution in galaxies and galaxy clusters, such as other weak lensing surveys, galactic rotation
curves, or complementary probes.