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

File URLs

Questions#

Please refer to the corresponding Project 01 notebook for background questions related to this project. In this project, you are to focus on machine learning application(s).

Question 01#

First 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 on a CPU, like one of the shallow networks with hyperparameters shown in Table 2 of ref [1] or even a smaller network. Next train a fully-connected deep neural network (DNN) using a GPU by increasing the number of hidden layers and neuron within the layers. The exact DNN setup is up to you, as long as it has meaningfully more trainable parameters than the shallow NN from the first part. Show the classification output for signal and background for both networks, as well as the ROC curve and AUC metric.

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]

Acknowledgements#

  • Initial version: Mark Neubauer

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