Bibliographic record
Abstract
Data Description These datasets are generated as a series of test sets for anomalous jet tagging at the LHC. They include boosted W jets, Top jets and Higgs jets. Jet transverse momentum is focused around 600 GeV. Each file includes 100k original events from MadGraph, but might have slightly less events in the final h5 files due to fatjet pre-selection. Production processes include: pp -> W' -> W (jj) Z(\(\nu \nu\)); \(m_{W} = 59, 80, 120, 174 ~GeV\) pp -> Z' -> t t~; \(m_t=80, 174 ~GeV\) pp -> HH -> (hh) (hh), (h -> bb); \(m_H=174~GeV\), \(m_h = 20, 80 ~GeV\) Data Generation Jet samples in this dataset are generated with MadGraph, Pythia8 and Delphes (no pile-up effects simulated). Particle flow objects are used to cluster jets. FastJet was used for jet clustering. Jets are clustered using anti-kt algorithm with cone size R=1.0. Leading jet: \(p_T>450 \textrm{GeV}\); sub-leading jet: \(p_T>200 \textrm{GeV}\) Data Structure To get jets: f['objects/jets'] For jets, there are two datasets: ['constituents', 'obs']. (jets information is stored with higher-pt jet first) `obs[:, n_j - 1]`: jet four vectors and n-subjettiness for (n_j -1) th jet (pt, eta, phi, m, tau1, tau2, tau3, tau4, tau5) pt-sorted (highest first) jet constituents information are stored in variable length arrays for (n_j -1) th jet `constituents[:, n_j - 1]`: \(\{ E_i, P_{xi}, P_{yi}, P_{zi}, \textrm{PID}_i\}\) (PID: PDG for tracks; [22] for photon; [0] for neutral hadron) Extra Notes Since the dataset is structured as events, for W jet samples, only leading jet is available; while for Top and Higgs jets, leading and sub-leading jets are both valid. One might need to restrict jet \(p_T\) range at use. e.g. to get leading jet constituents: `f["objects/jets/constituents"][:,0]` The file names are self-explanatory on the corresponding generation process.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.011 |
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".