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Test Sets for Jet Anomaly Detection at the LHC

Authors: Cheng, Taoli (10322928);

Test Sets for Jet Anomaly Detection at the LHC

Abstract

A few datasets are updated in Version 2.1. These datasets are tagged with 'new' in the file names. - pT~1.2TeV top jets, the mother Z' mass is slightly adjusted to better match pT peaks around 1.2TeV - Especially there was a bug in the previous version for top jets with mass 80 GeV `*top_m80_100k*` (the top jet mass was not correctly set). So please be careful when you use those datasets. 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 and 1200 GeV (with prefix "pt1200_" in file names). 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(\(\nu \nu\)); \(m_{W} = 59, 80, 120, 174 ~ \textrm{GeV}\) pp -> Z' -> t t~; \(m_t=80, 174 ~ \textrm{GeV}\). For m_t=80 GeV, the decay product W mass is set to 20 GeV. pp -> HH -> (hh) (hh), (h -> jj); \(m_H=174 ~ \textrm{GeV}\), \(m_h = 20, 80 ~ \textrm{GeV}\) 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 the anti-kt algorithm with the 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 the higher-pt jet first) `obs[:, n_j - 1]`: jet four vectors and n-subjettiness for the \(n_j\)-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 the \(n_j\)-th jet `constituents[:, n_j - 1]`: \(\{ E_i, P_{xi}, P_{yi}, P_{zi}, \textrm{PID}_i\}\) (PID: PDG for tracks; [22] for photons; [0] for neutral hadrons) 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 the 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. Each file was generated in 100K original Madgraph events. After the preselection, a small fraction of events is discarded. Contact we are welcoming any feedback, suggestions, or requests on new test samples. Please contact chengtaoli.1990@gmail.com for more information.

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Keywords

Evolutionary Biology, Science Policy, W jet samples, Immunology, Jet Anomaly Detection, Marine Biology, Cell Biology, HH, Biochemistry, Microbiology, decay product W mass, PDG, Higgs jets, h 5 files, Infectious Diseases, Genetics, Particle flow objects, LHC, jet constituents information, Developmental Biology, Cancer

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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