Efficient Probabilistic Inference in the Quest for Physics Beyond the\n Standard Model
Why this work is in the frame
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Bibliographic record
Abstract
We present a novel probabilistic programming framework that couples directly\nto existing large-scale simulators through a cross-platform probabilistic\nexecution protocol, which allows general-purpose inference engines to record\nand control random number draws within simulators in a language-agnostic way.\nThe execution of existing simulators as probabilistic programs enables highly\ninterpretable posterior inference in the structured model defined by the\nsimulator code base. We demonstrate the technique in particle physics, on a\nscientifically accurate simulation of the tau lepton decay, which is a key\ningredient in establishing the properties of the Higgs boson. Inference\nefficiency is achieved via inference compilation where a deep recurrent neural\nnetwork is trained to parameterize proposal distributions and control the\nstochastic simulator in a sequential importance sampling scheme, at a fraction\nof the computational cost of a Markov chain Monte Carlo baseline.\n
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it