PyTorch Neural Networks and Track Analysis for Top Quark Tagging
Bibliographic record
Abstract
The identification of top quarks is motivated by their high mass and strong coupling to the Higgs mechanism. Boosted top quarks also allow for improved measurements of the Standard Model in the high momentum tails of event feature distributions. Neural networks have been proven as an effective method for distinguishing top quarks from Quantum ChromoDynamic (QCD) events using jet constituent features from the ATLAS and CMS calorimeters. In this project Deep Neural Networks (DNN’s) and Long Short-Term Memory (LSTM) networks were built in PyTorch to compare their performances to previously tested Keras models. After applying similar preprocessing and optimization techniques, the performance of the PyTorch models was found to be highly comparable to the Keras models. Track features from the inner tracker offer promising new information to improve the performance of top tagging neural networks by utilizing information typically used in b-jet identification. Track features were analyzed and incorporated in processing scripts used to prepare the data for input to neural networks. It was found that keeping 100 "p_T" ordered tracks with "p_T" greater than 1 GeV could retain relevant information for jet classification while minimizing noise and computing time.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".