Usage of SVM for a Triggering Mechanism for Higgs Boson Detection

author: Klemen Kenda, Artificial Intelligence Laboratory, Jožef Stefan Institute
published: Dec. 8, 2017,   recorded: October 2017,   views: 880


Related Open Educational Resources

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.


Real-time classification of events in high energy physics is essential to deal with huge amounts of data, produced by proton-proton collisions in ATLAS detector at Large Hadron Collider in CERN. With this work we have implemented a triggering mechanism method for saving relevant data, based on machine learning. In comparison with the state of the art machine learning methods (gradient boosting and deep neural networks) shortcomings of Support Vector Machines (SVM) have been compensated with extensive feature engineering. Method has been evaluated with special metrics (average median significance) suggested by the domain experts. Our method achieves significantly higher precision and 8% lower average median significance than the current state of the art method used at ATLAS detector (XGBoost).

See Also:

Download slides icon Download slides: sikdd2017_kenda_higgs_boson_01.pdf (4.0 MB)

Help icon Streaming Video Help

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: