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Models of physics beyond the Standard Model often contain a large number of parameters. These form a high-dimensional space that is computationally intractable to fully explore. Experimental results project onto a subspace of parameters that are consistent with those observations, but mapping these constraints to the underlying parameters is also typically intractable. Instead, physicists often resort to scanning small subsets of the full parameter space and testing for experimental consistency. We propose an alternative approach that uses generative models to significantly improve the computational efficiency of sampling high-dimensional parameter spaces. To demonstrate this, we sample the constrained and phenomenological Minimal Supersymmetric Standard Models subject to the requirement that the sampled points are consistent with the measured Higgs boson mass. Our method achieves orders of magnitude improvements in sampling efficiency compared to a brute force search.

We analyse strongly lensed images in eight galaxy clusters to measure their dark matter density profiles in the radial region between 10 kpc and 150 kpc, and use this to constrain the self-interaction cross-section of dark matter (DM) particles. We infer the mass profiles of the central DM haloes, bright central galaxies, key member galaxies, and DM subhaloes for the member galaxies for all eight clusters using the qlens code. The inferred DM halo surface densities are fit to a self-interacting dark matter model, which allows us to constrain the self-interaction cross-section over mass σ/m. When our full method is applied to mock data generated from two clusters in the Illustris-TNG simulation, we find results consistent with no dark matter self-interactions as expected. For the eight observed clusters with average relative velocities of $1458_{-81}^{+80}$ km s−1, we infer $\sigma /m = 0.082_{-0.021}^{+0.027} \rm cm^2\, g^{ -1}$ and $\sigma /m \lt 0.13~ \rm cm^2\, g^{ -1}$ at the 95 per cent confidence level.

ABSTRACT Self-interacting dark matter (SIDM) models have received great attention over the past decade as solutions to the small-scale puzzles of astrophysics. Though there are different implementations of dark matter (DM) self-interactions in N-body codes of structure formation, there has not been a systematic study to compare the predictions of these different implementations. We investigate the implementation of dark matter self-interactions in two simulation codes:gizmo and arepo. We begin with identical initial conditions for an isolated 1010 M⊙ dark matter halo and investigate the evolution of the density and velocity dispersion profiles in gizmo and arepo for SIDM cross-section over mass of 1, 5, and 50 $\rm cm^2\, g^{-1}$. Our tests are restricted to the core expansion phase, where the core density decreases and core radius increases with time. We find better than 30 per cent agreement between the codes for the density profile in this phase of evolution, with the agreement improving at higher resolution. We find that varying code-specific SIDM parameters changes the central halo density by less than 10 per cent outside of the convergence radius. We argue that SIDM core formation is robust across the two different schemes and conclude that these codes can reliably differentiate between cross-sections of 1, 5, and 50 $\rm cm^2\,more »g^{-1}$, but finer distinctions would require further investigation.« less