QBDT, a new boosting decision tree method with systematical uncertainties into training for High Energy Physics-Reference-Cited by-同舟云学术

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QBDT, a new boosting decision tree method with systematical uncertainties into training for High Energy Physics

Published:2019-06 Issue: Volume:930 Page:15-26
ISSN:0168-9002
Container-title:Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment
language:en
Short-container-title:Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment

Author:

Xia Li-Gang

Publisher

Elsevier BV

Subject

Instrumentation,Nuclear and High Energy Physics

Reference40 articles.

1. ATLAS Collaboration, ATLAS-CONF-2017-029, http://cds.cern.ch/record/2261772.

2. Measurement of the photon identification efficiencies with the ATLAS detector using LHC Run-1 data

3. CMS Collaboration, CMS-PAS-TAU-16-002, http://cds.cern.ch/record/2196972.

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1. Holistic approach to predicting top quark kinematic properties with the covariant particle transformer;Physical Review D;2023-06-22

2. Simulation-based anomaly detection for multileptons at the LHC;Journal of High Energy Physics;2023-01-13

3. Bias and priors in machine learning calibrations for high energy physics;Physical Review D;2022-08-15

4. Machine learning in the search for new fundamental physics;Nature Reviews Physics;2022-05-19

5. Online-compatible unsupervised nonresonant anomaly detection;Physical Review D;2022-03-08

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