Flinch: A Differentiable Framework for Field-Level Inference of Cosmological parameters from curved sky data
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".