Contributed
2024
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Lightening blackbox models in cosmology.X Meeting on Fundamental Cosmology, Sevilla, Andalucía, SpainThe next generation of galaxy surveys has the potential to significantly deepen our understanding of the Universe, though this potential hinges on our ability to rigorously address systematic uncertainties. This was previously beyond reach in field-based implicit likelihood cosmological inference frameworks. We aim at inferring the initial matter power spectrum after recombination to diagnose a variety of systematic effects in galaxy surveys prior to inferring the cosmological parameters. Our approach is built upon a two-step framework. First, we employ the SELFI algorithm to infer the initial matter power spectrum, which we utilise to comprehensively investigate and disentangle how systematic effects influence the power spectrum reconstruction, using a single set of N-body simulations. Second, we obtain posterior cosmological parameters via implicit likelihood inference, recycling the simulations from the first step for data compression. We rely on a model of large-scale spectroscopic galaxy surveys that incorporates fully non-linear gravitational evolution and simulates multiple systematic effects typically encountered in astrophysical surveys. We demonstrate along with a practical guide how the SELFI posterior can be utilised to thoroughly assess the impact of misspecified linear galaxy bias parameters, selection functions, survey masks and inaccurate redshifts on the initial power spectrum after recombination. We show that a subtly misspecified model can lead to a bias greater than 2σ in the (Ωm, σ8) plane, which we are able to detect and avoid using SELFI prior to inferring the cosmological parameters. This framework has the potential to significantly enhance the robustness of physical information extraction from full-forward models of large-scale galaxy surveys such as DESI, Euclid, and LSST.
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Implicit Likelihood Inference in cosmology while checking for survey systematics.Euclid-France 12th Symposium, Lyon, FranceWe present methodological advances to perform implicit likelihood inference of cosmology from arbitrarily complex models of galaxy surveys, while efficiently checking for systematics. This novel approach makes it possible to fully utilise our prior theoretical understanding of the initial matter power spectrum, in order to investigate the effects of known sources of systematics at play in the complex data generating process. It is currently being used for Additional Galaxy Clustering probes in preparation for the first Euclid data release.
2023
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Implicit Likelihood Inference in cosmology while checking for survey systematics.Action Dark Energy, Annecy, FranceWe present methodological advances to perform implicit likelihood inference of cosmology from any forward model of galaxy surveys, while efficiently checking for systematics. The approach is based on a two-steps framework, and does not require any inner knowledge of the forward data model. First, we use SELFI (Simulator expansion for likelihood-free inference) to infer the initial matter power spectrum from any probe, and we use it to check whether all systematics are correctly accounted for based on qualitative and quantitative criteria. Second, cosmological parameters are inferred using implicit likelihood inference. Simulations used in the first step are recycled for optimal data compression, which is required for the second step. We show that mis-modelled systematic effects that would result in a biased posterior are unambiguously detected before performing the inference of cosmological parameters. The method is currently being used for Additional Galaxy Clustering probes in preparation for the first Euclid data release.
Others
2024
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Lightening black-box models.Seminar PhD students at the IAP, Paris, FranceNext-generation large-scale optical surveys, such as Euclid and LSST, promise to unveil the nature of dark energy, elucidate the processes driving cosmic inflation, and constrain neutrino masses with unprecedented precision. Despite the wealth of data these surveys will provide, they will be dominated by systematic rather than statistical uncertainty, challenging standard Bayesian inference methods that heavily rely on the accuracy of the forward models involved. Notably, even slightly misspecified models can significantly bias or overconcentrate the credible posteriors on cosmological parameters, risking false discoveries. Furthermore, even if systematic effects arising from survey strategies were fully understood and controlled, theoretical challenges like galaxy biasing and non-linear structure growth at late times would persist, reducing the detectability of cosmological signatures and therefore calling for novel inference techniques. In this talk, I will present recent methodological advances to perform implicit likelihood inference of cosmology using arbitrarily complex black-box models of cosmological surveys. These advances enable efficient and thorough checks for systematic effects by leveraging our theoretical understanding of the primordial matter power spectrum after inflation, or any other latent variable in the forward model of cosmological observables.
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Novel methods for implicit likelihood inference in cosmology.PhD day at the IAP, Paris, FranceWe present methodological advances to perform implicit likelihood inference in cosmology from arbitrarily complex probabilistic forward models of cosmological surveys.
- Implicit Likelihood Inference in cosmology while checking for survey systematics.The Elbereth Conference, Paris, France
We present methodological advances to perform implicit likelihood inference of cosmology from arbitrarily complex models of cosmological surveys, while efficiently and extensively checking for systematics. This novel approach makes it possible to fully utilise our prior theoretical understanding of the initial matter power spectrum after inflation, in order to investigate the effects of known sources of systematics at play in the data generating process.