Application of machine learning to the study of QCD transition in heavy ion collisions
Bibliographic record
Abstract
In high-energy heavy ion collisions, quarks and gluons are released from the colliding nucleus to form a new state of nuclear matter called deconfined quark gluon plasma (QGP). To study the transition from normal nuclear matter or hadron resonance gas to QGP, non-perturbative quantum chromodynamics (QCD) must be solved on supercomputers using the lattice numerical method (lattice Quantum Chromodynamics, lattice QCD). However, lattice QCD only works for zero and small baryon chemical potential regions that can be described by the Taylor expansion and provides the nuclear equation of state (EoS) and QCD transition in these regions. For large baryon chemical potential regions that cannot be described by the Taylor expansion, lattice QCD fails to provide the nuclear EoS and QCD transition owing to the famous sign problem. Machine learning helps to study the nuclear EoS and QCD phase transition. First, machine learning can determine the nuclear EoS and QCD transition using the momentum distribution of final state hadrons in heavy-ion collisions, with data from both heavy-ion collision experiments and relativistic hydrodynamic simulations. Second, it can contribute to the direct solution of the sign problem in lattice QCD. The present paper reviews the applications of machine learning to the study of the QCD phase transition in heavy-ion collisions. This study (1) introduces nuclear EoS and QCD transition as well as the difficulty of the lattice QCD method, (2) analyzes the nuclear EoS using Bayesian analysis, (3) identifies the nuclear EoS and QCD phase transition using different types of deep neural networks (e.g., convolutional neural network, point cloud network, and many-event averaging), (4) searches for critical self-similarity using a dynamical edge convolution-based graph neural network, (5) learns the quasi-particle mass using a physically informed network and auto-differentiation, (6) discards unphysical regions in the nuclear EoS with a critical endpoint using active learning, (7) discusses unsupervised learning for the nuclear liquid-gas phase transition, (8) determines the nuclear symmetry energy in heavy-ion collisions, (9) investigates Mach cones using deep learning assisted jet tomography, and (10) accelerates the sampling of lattice QCD configurations using a physically constrained neural network while solving the sign problem in lattice QCD using deep learning.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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 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".