Imperial College London
Portrait Picture

ProfessorAlanHeavens

Faculty of Natural Sciences, Department of Physics

Chair in Astrostatistics
 
 
 
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Contact

 

+44 (0)20 7594 2930a.heavens Website

 
 
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Location

 

1018EBlackett LaboratorySouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Heavens:2022:10.21105/astro.2207.05202,
author = {Heavens, A and Makinen, TL and Lemos, P and Porqueres, N and Wandelt, B},
doi = {10.21105/astro.2207.05202},
journal = {Open Journal of Astrophysics},
pages = {1--16},
title = {The cosmic graph: optimal information extraction from large-scale structure using catalogues},
url = {http://dx.doi.org/10.21105/astro.2207.05202},
volume = {5},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - We present an implicit likelihood approach to quantifying cosmological information over discrete catalogue data, assembled as graphs. To do so, we explore cosmological parameter constraints 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 graph neural network summaries automatically combine mass and clustering information through comparisons to traditional statistics, c) demonstrate that 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 simulation-based 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 away from the fiducial model at which the network is fitted, indicating a promising alternative to n-point statistics in catalogue simulation-based analyses.
AU  - Heavens,A
AU  - Makinen,TL
AU  - Lemos,P
AU  - Porqueres,N
AU  - Wandelt,B
DO  - 10.21105/astro.2207.05202
EP  - 16
PY  - 2022///
SP  - 1
TI  - The cosmic graph: optimal information extraction from large-scale structure using catalogues
T2  - Open Journal of Astrophysics
UR  - http://dx.doi.org/10.21105/astro.2207.05202
UR  - https://astro.theoj.org/article/57787-the-cosmic-graph-optimal-information-extraction-from-large-scale-structure-using-catalogues
UR  - http://hdl.handle.net/10044/1/101619
VL  - 5
ER  -
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