Stratification Criteria for Machine Learning Pattern Discovery in Particle Physics: Preparing for the AlphaFold Moment
Abstract
Machine learning capabilities are expanding into scientific domains at an accelerating pace. When applied to high-energy physics pattern discovery, they will generate candidates faster than traditional evaluation can absorb. ML finds patterns in past data it is inherently post hoc. Whether those patterns reflect structure or coincidence is unknowable at discovery time; this limitation applies equally to human and computational pattern-finding. What differs is scale: ML candidate generation is effectively unbounded, while human evaluation capacity remains fixed. When generation rate exceeds evaluation bandwidth, binary accept/reject degenerates to random sampling. Information-theoretically, the only response that preserves ranking under finite evaluation budget is stratification. By focusing on stratification rather than binary filtering, rule adjustments can be made retroactively, thresholds tuned as results accumulate, and evaluation bandwidth focused on top-ranked candidates. This paper attempts to codify those criteria, proposing seven computationally evaluable standards for stratifying ML-generated patterns. The goal is not to deliver verdicts but to prioritize which candidates merit pre-registration and longitudinal tracking. The framework preserves the essential paradigm: pattern plus theory equals potentially real physics. Patterns alone, however striking, remain candidates until theoretical understanding arrives. Making these criteria explicit enables prefiltering at scale while creating a collaborative resource rather than a competitive one. ML capabilities extend what physicists can search while preserving how physicists evaluate. We offer this provisional framework for community calibration, with the goal of developing validation infrastructure before the capability fully arrives.
Versions
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BibTeX
@misc{brilliant2026stratification,
title = {{Stratification Criteria for Machine Learning Pattern Discovery in Particle Physics: Preparing for the AlphaFold Moment}},
author = {Andrew Michael Brilliant},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.06015},
eprint = {2609.06015},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.06015},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - Stratification Criteria for Machine Learning Pattern Discovery in Particle Physics: Preparing for the AlphaFold Moment
AU - Andrew Michael Brilliant
PY - 2026
DA - 2026/09/05
PB - arXiv
DO - 10.48550/arxiv.2609.06015
UR - https://arxiv.org/abs/2609.06015
AB - Machine learning capabilities are expanding into scientific domains at an accelerating pace. When applied to high-energy physics pattern discovery, they will generate candidates faster than traditional evaluation can absorb. ML finds patterns in past data it is inherently post hoc. Whether those patterns reflect structure or coincidence is unknowable at discovery time; this limitation applies equally to human and computational pattern-finding. What differs is scale: ML candidate generation is effectively unbounded, while human evaluation capacity remains fixed. When generation rate exceeds evaluation bandwidth, binary accept/reject degenerates to random sampling. Information-theoretically, the only response that preserves ranking under finite evaluation budget is stratification. By focusing on stratification rather than binary filtering, rule adjustments can be made retroactively, thresholds tuned as results accumulate, and evaluation bandwidth focused on top-ranked candidates. This paper attempts to codify those criteria, proposing seven computationally evaluable standards for stratifying ML-generated patterns. The goal is not to deliver verdicts but to prioritize which candidates merit pre-registration and longitudinal tracking. The framework preserves the essential paradigm: pattern plus theory equals potentially real physics. Patterns alone, however striking, remain candidates until theoretical understanding arrives. Making these criteria explicit enables prefiltering at scale while creating a collaborative resource rather than a competitive one. ML capabilities extend what physicists can search while preserving how physicists evaluate. We offer this provisional framework for community calibration, with the goal of developing validation infrastructure before the capability fully arrives.
ER -