Robust cusps near topological phase transitions: Signatures of Majorana fermions and interactions with fluctuations
Bibliographic record
Abstract
In this paper, we study nonanalyticities or cusps near a topological phase transition driven by changes of global topologies rather than by the formation of conventional (local) order. The particular phase transitions between topologically ordered superfluids and nontopological superfluids studied in this paper are always accompanied by the emergence or disappearance of Majorana boundary modes, while at the critical point these boundary modes are liberated and evolve into the bulk. Proliferation of massless Majorana fermions in the bulk at the transition leaves unique thermodynamic signatures. For a very broad class of isotropic superfluids or superconductors (with or without time-reversal symmetry), the resultant transitions in $d$ spatial dimensions will be of $(d+1)\mathrm{th}$ order. The $(d+1)\mathrm{th}$-order derivatives of the grand potential are either discontinuous in even dimensions or logarithmically divergent in odd dimensions. Bulk quantities such as compressibility can develop a cusp along with the change of topology of the superfluids. We compute quantitatively the cusp structures and further investigate the effect of interactions induced by massive fluctuation fields. These cusps are robust as direct manifestations of Majorana fermions and these interactions, and can be potentially considered as an alternative approach for detecting these puzzling particles.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".