Unsupervised machine learning of quenched gauge symmetries: A proof-of-concept demonstration
Bibliographic record
Abstract
One of the most prominent tasks of machine learning (ML) methods within the field of condensed matter physics has been to classify phases of matter. Given their many successes in performing this task, one may ask whether these methods---particularly unsupervised ones---can go beyond learning the thermodynamic behavior of a system. This question is especially intriguing when considering systems that have a ``hidden order''. In this work we study two random spin systems with a hidden ferromagnetic order that can be exposed by applying a Mattis gauge transformation. We demonstrate that the principal component analysis, perhaps the simplest unsupervised ML method, can detect the hidden order, quantify the corresponding gauge variables, and map the original random models onto simpler gauge-transformed ferromagnetic ones, all without any prior knowledge of the underlying gauge transformation. Our work illustrates that ML algorithms can in principle identify not manifestly obvious symmetries of a system.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".