Higgs Boson Discovery with Boosted Trees

Tianqi Chen, Tong He
Proceedings of the NIPS 2014 Workshop on High-energy Physics and Machine Learning, PMLR 42:69-80, 2015.

Abstract

The discovery of the Higgs boson is remarkable for its importance in modern Physics research. The next step for physicists is to discover more about the Higgs boson from the data of the Large Hadron Collider (LHC). A fundamental and challenging task is to extract the signal of Higgs boson from background noises. The machine learning technique is one important component in solving this problem. In this paper, we propose to solve the Higgs boson classification problem with a gradient boosting approach. Our model learns ensemble of boosted trees that makes careful tradeoff between classification error and model complexity. Physical meaningful features are further extracted to improve the classification accuracy. Our final solution obtained an \emphAMS of 3.71885 on the private leaderboard, making us the top 2% in the Higgs boson challenge.

Cite this Paper

BibTeX
@InProceedings{pmlr-v42-chen14, title = {Higgs {B}oson {D}iscovery with {B}oosted {T}rees}, author = {Chen, Tianqi and He, Tong}, booktitle = {Proceedings of the NIPS 2014 Workshop on High-energy Physics and Machine Learning}, pages = {69--80}, year = {2015}, editor = {Cowan, Glen and Germain, Cécile and Guyon, Isabelle and Kégl, Balázs and Rousseau, David}, volume = {42}, series = {Proceedings of Machine Learning Research}, address = {Montreal, Canada}, month = {13 Dec}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v42/chen14.pdf}, url = {https://proceedings.mlr.press/v42/chen14.html}, abstract = {The discovery of the Higgs boson is remarkable for its importance in modern Physics research. The next step for physicists is to discover more about the Higgs boson from the data of the Large Hadron Collider (LHC). A fundamental and challenging task is to extract the signal of Higgs boson from background noises. The machine learning technique is one important component in solving this problem. In this paper, we propose to solve the Higgs boson classification problem with a gradient boosting approach. Our model learns ensemble of boosted trees that makes careful tradeoff between classification error and model complexity. Physical meaningful features are further extracted to improve the classification accuracy. Our final solution obtained an \emphAMS of 3.71885 on the private leaderboard, making us the top 2% in the Higgs boson challenge.} }
Endnote
%0 Conference Paper %T Higgs Boson Discovery with Boosted Trees %A Tianqi Chen %A Tong He %B Proceedings of the NIPS 2014 Workshop on High-energy Physics and Machine Learning %C Proceedings of Machine Learning Research %D 2015 %E Glen Cowan %E Cécile Germain %E Isabelle Guyon %E Balázs Kégl %E David Rousseau %F pmlr-v42-chen14 %I PMLR %P 69--80 %U https://proceedings.mlr.press/v42/chen14.html %V 42 %X The discovery of the Higgs boson is remarkable for its importance in modern Physics research. The next step for physicists is to discover more about the Higgs boson from the data of the Large Hadron Collider (LHC). A fundamental and challenging task is to extract the signal of Higgs boson from background noises. The machine learning technique is one important component in solving this problem. In this paper, we propose to solve the Higgs boson classification problem with a gradient boosting approach. Our model learns ensemble of boosted trees that makes careful tradeoff between classification error and model complexity. Physical meaningful features are further extracted to improve the classification accuracy. Our final solution obtained an \emphAMS of 3.71885 on the private leaderboard, making us the top 2% in the Higgs boson challenge.
RIS
TY - CPAPER TI - Higgs Boson Discovery with Boosted Trees AU - Tianqi Chen AU - Tong He BT - Proceedings of the NIPS 2014 Workshop on High-energy Physics and Machine Learning DA - 2015/08/27 ED - Glen Cowan ED - Cécile Germain ED - Isabelle Guyon ED - Balázs Kégl ED - David Rousseau ID - pmlr-v42-chen14 PB - PMLR DP - Proceedings of Machine Learning Research VL - 42 SP - 69 EP - 80 L1 - http://proceedings.mlr.press/v42/chen14.pdf UR - https://proceedings.mlr.press/v42/chen14.html AB - The discovery of the Higgs boson is remarkable for its importance in modern Physics research. The next step for physicists is to discover more about the Higgs boson from the data of the Large Hadron Collider (LHC). A fundamental and challenging task is to extract the signal of Higgs boson from background noises. The machine learning technique is one important component in solving this problem. In this paper, we propose to solve the Higgs boson classification problem with a gradient boosting approach. Our model learns ensemble of boosted trees that makes careful tradeoff between classification error and model complexity. Physical meaningful features are further extracted to improve the classification accuracy. Our final solution obtained an \emphAMS of 3.71885 on the private leaderboard, making us the top 2% in the Higgs boson challenge. ER -
APA
Chen, T. & He, T.. (2015). Higgs Boson Discovery with Boosted Trees. Proceedings of the NIPS 2014 Workshop on High-energy Physics and Machine Learning, in Proceedings of Machine Learning Research 42:69-80 Available from https://proceedings.mlr.press/v42/chen14.html.

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