Talks

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We will present KARMMA, a field-level inference designed to enable joint inference of cosmology and the convergence field from cosmic shear data. KARMMA is a full-sky Bayesian algorithm that forward-models the convergence field as an augmented lognormal realization. Using N-body simulations, we generate mock cosmic shear observations and use these to validate the cosmological inferences from KARMMA. We find field-level inference with LSST Y1-like data has the potential to improve constraints on the dark energy equation of state by a factor of six relative to a standard power-spectrum approach.

Upcoming Stage-IV galaxy surveys will map the large-scale structure of the Universe with unprecedented precision, requiring analysis methods that can exploit information beyond traditional two-point statistics. Field-level inference offers a principled path forward by working directly with the observed fields, capturing non-Gaussian signatures that summary statistics discard. However, existing approaches typically address either cosmological parameter estimation or field reconstruction in isolation, or rely on explicit inference frameworks that require differentiable forward models and costly MCMC sampling. We present a diffusion-model-based method that performs joint inference of weak lensing convergence maps and cosmological parameters in a single, unified framework. Built on a pixel-space vision transformer, our model learns the joint posterior distribution over both the convergence field and cosmological parameters, conditioned on noisy shear observations. By operating within the implicit inference paradigm, our approach bypasses the need for a differentiable forward model, opening the door to arbitrarily complex simulators. We demonstrate our method on simulated LSST Year-10 weak lensing data generated with log-normal convergence fields in a wCDM cosmology, showing that our approach can recover accurate joint posteriors over both the mass map and cosmological parameters from a single amortized model, validated against MCMC baselines and coverage diagnostics.

Simulation-based inference (SBI) provides a powerful framework for extracting rich information from nonlinear scales in current and upcoming cosmological surveys, and ensuring its robustness requires stringent validation of forward models. In this work, we recast forward model validation as an out-of-distribution (OoD) detection problem within the framework of machine learning (ML)-based SBI. We employ probability density as the metric for OoD detection, and compare various density estimation techniques, demonstrating that field-level probability density estimation via continuous time flow models (CTFM) significantly outperforms feature-level approaches that combine scattering transform (ST) or convolutional neural networks (CNN) with normalizing flows (NFs), as well as NF-based field-level estimators, as quantified by the area under the receiver operating characteristic curve (AUROC). Our analysis shows that CTFM not only excels in detecting OoD samples but also provides a robust metric for model selection. Additionally, we verified CTFM maintains consistent efficacy across different cosmologies while mitigating the inductive biases inherent in NF architectures. Although our proof-of-concept study employs simplified forward modeling and noise settings, our framework establishes a promising pathway for identifying unknown systematics in the cosmology datasets.

Simulation-based inference (SBI) has recently emerged as a promising avenue for inferring cosmological parameters from the 3D distribution of galaxies from large spectroscopic surveys (DESI, PFS, DESI-II). The first iteration of the SimBIG project demonstrated a proof-of-concept for this approach, using a forward-modeling pipeline built from high-fidelity QUIJOTE N-body simulations applied to a subset of the BOSS survey. The initial SimBIG results were promising, producing $H_0$ and $S_8$ constraints up to 1.5 and 1.9x tighter than traditional power spectra analyses. However, scaling this approach to larger survey volumes presents a major challenge, as the computational costs of high-resolution N-body simulations quickly becomes prohibitive. In this work, we present a scalable SimBIG-II approach. We leverage relatively inexpensive simulations from augmented Lagrangian perturbation theory (ALPT) with a highly flexible local and non-local bias prescription. We combine these simulations with a probabilistic emulator so that they retain the predictive power of N-body + HOD simulations. I will present the preliminary results of our sensitivity analysis of the SimBIG-II pipeline. Then, I will discuss the implications for our initial application to the full BOSS survey volume, as well as the subsequent SimBIG-II analyses of DESI galaxy samples.

Weak lensing convergence maps are expected to be significantly non-Gaussian on small scales, causing the power spectrum to fail as a sufficient statistic for cosmological parameters by discarding information encoded in higher-order correlations. This limitation is particularly pressing in the context of next-generation surveys, which will improve the signal-to-noise ratio and grant access to deeply non-linear scales. To tighten constraints on cosmological parameters, neural-network-based full-field inference methods have recently emerged as a promising avenue; however, they require large suites of computationally expensive simulations. To address this challenge, we propose a new approach to building pixel-level emulators (Zeghal et al., 2025). By learning a minimal conditional transformation between cheap and costly simulations using Conditional Optimal Transport Flow Matching (COT-FM, Kerrigan et al., 2024), we show that we can generate new high-fidelity simulations whose statistics match those of the expensive simulator across cosmological parameters. We validate this by performing full-field inference on the emulated simulations and demonstrate that the resulting posteriors are in excellent agreement with those obtained from the true costly simulations. Notably, our emulator can operate on unpaired datasets. This flexibility enabled us to secure second place in the Weak Lensing Uncertainty Challenge at NeurIPS 2025, where only the simulations and their corresponding cosmological parameters were provided, without access to the initial conditions needed to pair them with their cheap counterparts.