The Cosmic Graph: Optimal Information Extraction from Large-Scale Structure using Catalogues
OJA 2022
Abstract:
We present an implicit likelihood approach to quantifying cosmological information over discrete catalogue data, assembled as graphs. To do so, we explore cosmological inference using mock dark matter halo catalogues. We employ Information Maximising Neural Networks (IMNNs) to quantify Fisher information extraction as a function of graph representation. We a) demonstrate the high sensitivity of modular graph structure to the underlying cosmology in the noise-free limit, b) show that networks automatically combine mass and clustering information through comparisons to traditional statistics, c) demonstrate that graph neural networks can still extract information when catalogues are subject to noisy survey cuts, and d) illustrate how nonlinear IMNN summaries can be used as asymptotically optimal compressed statistics for Bayesian implicit likelihood inference. We reduce the area of joint Ωm,σ8 parameter constraints with small (∼100 object) halo catalogues by a factor of 42 over the two-point correlation function, and demonstrate that the networks automatically combine mass and clustering information. This work utilises a new IMNN implementation over graph data in Jax, which can take advantage of either numerical or auto-differentiability. We also show that graph IMNNs successfully compress simulations far from the fiducial model at which the network is fitted, indicating a promising alternative to n-point statistics in catalogue-based analyses.
QUBIC Experiment Toward the First Light
Journal of Low Temperature Physics Springer Nature 209:5-6 (2022) 839-848
Constraints on dark matter annihilation and decay from the large-scale structure of the nearby Universe
Physical Review D American Physical Society 106:10 (2022) 103526
Abstract:
Decaying or annihilating dark matter particles could be detected through gamma-ray emission from the species they decay or annihilate into. This is usually done by modeling the flux from specific dark matter-rich objects such as the Milky Way halo, Local Group dwarfs, and nearby groups. However, these objects are expected to have significant emission from baryonic processes as well, and the analyses discard gamma-ray data over most of the sky. Here we construct full-sky templates for gamma-ray flux from the large-scale structure within ∼200 Mpc by means of a suite of constrained N-body simulations (csiborg) produced using the Bayesian Origin Reconstruction from Galaxies algorithm. Marginalizing over uncertainties in this reconstruction, small-scale structure, and parameters describing astrophysical contributions to the observed gamma-ray sky, we compare to observations from the Fermi Large Area Telescope to constrain dark matter annihilation cross sections and decay rates through a Markov chain Monte Carlo analysis. We rule out the thermal relic cross section for s-wave annihilation for all mχ7 GeV/c2 at 95% confidence if the annihilation produces gluons or quarks less massive than the bottom quark. We infer a contribution to the gamma-ray sky with the same spatial distribution as dark matter decay at 3.3σ. Although this could be due to dark matter decay via these channels with a decay rate Γ≈6×10-28 s-1, we find that a power-law spectrum of index p=-2.75-0.46+0.71, likely of baryonic origin, is preferred by the data.VINTERGATAN-GM: The cosmological imprints of early mergers on Milky-Way-mass galaxies
(2022)
Evidence for non-merger co-evolution of galaxies and their supermassive black holes
(2022)