Flinch: A Differentiable Framework for Field-Level Inference of Cosmological parameters from curved sky data

TL;DR

Flinch is a differentiable, scalable framework for direct field-level inference of cosmological parameters from curved sky data, offering improved accuracy and efficiency over traditional methods.

Contribution

It introduces a novel differentiable framework that enables direct inference from maps, enhancing flexibility and reducing computational costs in cosmology.

Findings

Achieves up to 40% tighter constraints than traditional methods.

Validates the approach with simulated CMB maps and power spectra.

Demonstrates significant efficiency gains with MicroCanonical Langevin Monte Carlo.

Abstract

We present Flinch, a fully differentiable and high-performance framework for field-level inference on angular maps, developed to improve the flexibility and scalability of current methodologies. Flinch is integrated with differentiable cosmology tools, allowing gradients to propagate from individual map pixels directly to the underlying cosmological parameters. This architecture allows cosmological inference to be carried out directly from the map itself, bypassing the need to specify a likelihood for intermediate summary statistics. Using simulated, masked CMB temperature maps, we validate our pipeline by reconstructing both maps and angular power spectra, and we perform cosmological parameter inference with competitive precision. In comparison with the standard pseudo-$C_\ell$ approach, Flinch delivers substantially tighter constraints, with error bars reduced by up to 40%. Among the gradient-based samplers routinely employed in field-level analyses, we further show that MicroCanonical Langevin Monte Carlo provides orders-of-magnitude improvements in sampling efficiency over currently employed Hamiltonian Monte Carlo samplers, greatly reducing computational expense.