LHCb-topo-trigger

LHCb RUN-II topological trigger upgrading

Links

• If you are interested in details, see paper

LHCb trigger system RUN-I

LHCb trigger system RUN-II: upgrading

For RUN-II new scheme is applied:

• HLT1 track
• HLT1 2-body
• HLT2 n-body

LHC

• Sample: one proton-proton bunches collision, called Event (40MHz)
• Event consists of the secondary vertices (SVR) or tracks, where particles are produced
• Features: an SVR, tracks and its products physical characteristics reconstructed from the detectors (momentum, mass, angles, impact parameter)

Data

• Training data are set of SVRs for HLT2 n-body and HLT1 2-body or trakcs for HLT1 track all events
• Monte Carlo 2015 data (signal data) were simulated for various types of interesting events (different decays):
  • all decays are used in HLT1 2-body and HLT1 track training
  • six types of decays are used for HLT2 n-body training and all for testing
• Minimum bias data (real data for a small period of time) are used as background data
• Event is interesting from physical point of view if it contains at least one SVR, where searched decay happens

Features:

• SumPT (sumpt): sum of transverse momentums (pt) for all tracks in the SVR;
• MCOR (mcor): "corrected" mass of the SVR;
• IPChi2 (ipchi2): impact parameter chi2 of the SVR;
• MinPT (minpt): the minimum of tracks pt in the SVR;
• FDChi2 (fdchi2): flight distance chi2 of the SVR from the p-p collision;
• NIPChi2LT16 (nlt16):  number of tracks in the primary vertex with IPChi2 < 16;
• N (n): number of tracks in the SVR;
• NHLT1 (n1trk): number of tracks passing HLT1 (high level trigger first stage);
• VChi2 (chi2): vertex chi2 of the SVR;
• Eta (eta): pseudorapidity;
• PT (pt): transverse momentum;
• M (m): mass of the SVR;
• MinFDR (fdr): min radial (x-y plane) flight distance to any p-p collision;
• SumIPchi2 (sumipchi2): sum of IPchi2 for all tracks in the SVR;

ML problem

• Output rate is fixed, thus, false positive rate (FPR) for events is fixed
• Goal is to improve efficiency for each type of signal events
• We improve true positive rate (TPR) for fixed FPR for events

Production trigger system

There are two possibilities to speed up prediction operation for production stage:

• Bonsai boosted decision tree format (BBDT)
  • Features hashing using bins before training
  • Converting decision trees to n-dimensional table (lookup table)
  • Table size is limited in RAM, thus count of bins for each features should be small
  • Discretization reduces quality
• Post-pruning (MatrixNet includes several thousand trees)
  • Train MatrixNet with several thousands trees
  • Reduce this amount of trees to a hundred
  • Greedily choose trees in a sequence from the initial ensemble to minimize a modified loss function (exploss for background and logloss for signal)
  • Change values in leaves (tree structure is preserved)

Results

Reproducibility

• download root files to folder datasets
• run preprocessing to create .csv files with tracks and SVRs
• HLT1 track creates models and plots for HLT1 track trigger
• HLT1 2-body creates models and plots for HLT1 2-body trigger
• [HLT2 n-body] (https://github.com/tata-antares/LHCb-topo-trigger/blob/master/HLT2.ipynb) creates models and plots for HLT2 n-body trigger
• BBDT and post-pruning creates production models with BBDT format and post pruning for HLT2 n-body
• BBDT format creates .bbdt files (lookup table) from trained trees

Requirements

• rep 0.6.3
• rep_ef 0.6.2
• hep_ml 0.3.0