Searching for Exotic Particles#
Overview#
A number of theories that propose to explain what happened in the very early universe (the first small fraction of a second) and link elementary particle physics and cosmology predict the existence of exotic particles that have yet to be discovered. IF these particles exist, they could contribute significantly to the dark matter in the universe and/or explain other puzzles in particle physics.
Data Sources#
Original Source
https://archive.ics.uci.edu/ml/datasets/HEPMASS (top-level description)
File URLs
Questions#
Please refer to the corresponding Project 01 notebook for background questions related to this project. In this project, you are to focused on machine learning application(s).
Question 01#
Implement and train a shallow neural network (NN) described in ref [1] for one of the exotic particle hypotheses. You should implement a NN one that makes the training time manageable, like one of the shallow networks with hyperparameters shown in Table 2 of ref [1] or even a smaller network. Can you show the NN classification output?
References#
[1] P.J. Sadowski, D. Whiteson, P. Baldi, “Searching for Exotic Particles in High-Energy Physics with Deep Learning”, Nature Commun. 5 (2014) 4308, e-Print: 1402.4735 [hep-ph]
[2] P. Baldi, K. Cranmer, T. Faucett, P. Sadowski, D. Whiteson, “Parameterized Machine Learning for High-Energy Physics”, Eur.Phys.J.C 76 (2016) 5, 235, e-Print: 1601.07913 [hep-ex]
Acknowledgements#
Initial version: Mark Neubauer
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