Talks

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Simulation-Based Inference (SBI) overcomes problems of likelihood-based estimators allowing for the extraction of information at full-field level. I present a proof-of-concept SBI pipeline to marginally constrain the cosmological parameters $\{\Omega_m, \sigma_8\}$ from large-scale structure observables. Our approach combines fast dark matter simulations with neural emulators that generate galaxy and HI maps. We perform inference both on the power spectrum as a summary statistics and directly on field-level representations of the data, using either one single probe or multiple fields at the same time, while marginalising over baryonic nuisance parameters. We assess systematically the impact of data compression and multi-tracers information on cosmological constraints. We find that multi-probe analyses, combining galaxy and HI fields, improve constraints with respect to single-probe cases. Moving from summary statistics to field-level inference leads to a significant gain in constraining power, with the full 3D approach providing the most accurate and well-calibrated posteriors. When marginalizing over astrophysical effects, field-level methods retain substantially more cosmological information than power-spectrum-based analyses. These results highlight the potential of combining multi-probes observations and full field information into SBI to fully exploit the information content of forthcoming surveys, and provide a baseline for more realistic observational data-based analyses.

Field-Level Inference (FLI) needs to be made more tractable and robust at survey scale. To this purpose, I developed fast, differentiable cosmological simulators and introduced a standardized benchmark, showing how to reduce the required model evaluations by orders of magnitude. Building on this, I am working toward FLI constraints on local Primordial Non-Gaussianity (PNG) from DESI. I will show the validation of the pipeline on N-body simulations with HOD-populated galaxies, carefully assessing model fidelity and calibration, progressively incorporating survey realism at the field-level, and in parallel extending the analysis to a multi-probe framework combining galaxy clustering with CMB lensing.

Understanding the complex interplay of gas and dark matter is critical for the next generation of cosmological surveys and related inference. While many techniques seek to marginalize over these uncertainties, there is a wealth of information from across astrophysics than can better constrain properties like galaxy formation and evolution. In this talk, I will introduce recent developments in pushing field level inference into these nonlinear scales. These efforts include differentiable models for full dark-matter dynamics (i.e. TreePM), hydrodynamical physics, subgrid physics, and halos/group finding. Not only can differentiability be used for explicit likelihood analysis, but also to greatly accelerate existing simulation-based pipelines. Time permitting, I will also discuss how these techniques will be used for an integrated analysis in the ongoing Prime Focus Spectrograph survey, which will study galaxy evolution from z ~ 7.2 to the present day.

Simulation-based inference (SBI) is increasingly used to extract cosmological information from complex observables, with reliability typically validated through coverage-based diagnostics such as simulation-based calibration (SBC) and the coverage test of accuracy with random points (TARP). These tests check whether posteriors contain the true parameter value with the expected frequency; a necessary condition for accuracy. However, coverage is insensitive to posterior shapes, so an estimator can pass such tests while exhibiting systematic tail biases or realization-level discrepancies that go undetected. In this talk I present our work where we systematically compare posteriors obtained through likelihood-based inference (LBI) and SBI with contrastive neural ratio estimation (CNRE) for constraining local primordial non-Gaussianity ($f_{\rm NL}^{\rm local}$) from dark matter halo statistics in the Quijote-PNG simulations. Using the power spectrum ($P$), bispectrum ($B$), and wavelet scattering transform (WST) coefficients, we compare posterior distributions across 1000 test realizations, examining higher-order moments, credible interval shapes, and tail behavior. We also use the Wasserstein-2 distance as a shape-sensitive diagnostic to capture discrepancies that aren't captured by other metrics. We show that the $P+B$ SBI posterior is systematically under-confident relative to that from LBI, a miscalibration invisible to standard diagnostics, demonstrating concretely that passing coverage tests does not guarantee posterior faithfulness. We also find that WST coefficients improve $f_{\rm NL}^{\rm local}$ constraints beyond $P+B$ even at large scales ($k_{\rm max} = 0.14\, h\, {\rm Mpc}^{-1}$), supporting field-level summaries as probes of primordial physics.

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