Danielle Burns Supervisors: Prof A. Anjum & Dr L. Barnby .

1. Alternative Convolutional Neural Networks for the analysis of High Energy Physics data from LHC Experiments Danielle Burns Supervisors: Prof A. Anjum & Dr L. Barnby.

2. Research Objectives Aim: To use Machine Learning to detect the charmed baryon, within ALICE, and thus create a framework for CNN design for alternative particle identification problems. Objectives: • Provide a new way of applying Supervised Machine Learning to HEP, specifically for Particle Identification. • Create a framework for development of the Convolutional Neural Networks for future use, via the GRID. • Identify a currently undetectable particle and show that Machine Learning can improve current detection systems.

3. Research Questions Is Machine Learning really worth it? • What Machine Learning method should we choose? How can we demonstrate this method will work? What can Machine Learning do for us? Will allow for faster and more effective detector response • Convolutional Neural Networks • CNNs will be adapted for HEP data and applied to aid the identification of the charmed baryon within the ALICE detector “Machine Learning is the next internet”. It allows us to improve predictions or behaviours from given data.

4. Machine Learning “A way of getting data to do the work itself” We consider two phases: Training - A model is learned from training data Application - A model makes decisions about new test data Split into two kinds: Supervised Learning - Learn to predict an output given an input Unsupervised Learning - Learn a good internal representation of the input

5. Related Work in HEP

6. Challenges Higher backgrounds Larger Data Volume Unknown New Physics

7. Supervised Learning for Charmed Baryon Detection. Function with changeable parameters F(x) Error Function ERROR π κ ᴘ Λc π κ ᴘ • Λc MC MC + 1 + +

8. Convolutional Neural Network

9. Expected Contributions • To create a framework for CNN development for HEP particle identification. • To enhance the detection capabilities of ALICE for the, currently undetectable, charmed baryon. • Not only should we be able to identify the presence of the particle but also the nature of the tracks directly related to the occurrence of the particle. Source: CERN https://home.cern

10. References • Boosted Decision Trees as an Alternative to Artificial Neural Networks for Particle Identification, B.P. Roe, H-J. Yang, J. Zhu et al. Nuclear Instruments and Methods in Physics Research A,543.2 (2005): 577-584. • Studies of Boosted Decision Trees for MiniBooNE Particle Identification, B.P. Roe, H-J. Yang, J. Zhu. Nuclear Instruments and Methods in Physics Research A,555.1 (2005): 370-385. • Higgs Boson Discovery with Boosted Trees. T. Chen, T. He, JMLR: Workshop and Conference Proceedings 43: 69-80, 2015. • Support vector regression as a signal discriminator in high energy physics, D.O. Whiteson ,N.A. Naumann, Neurocomputing, 55 (2003) 251-264. • Support vector machines in analysis of top quark production, A. Vaiciulis . Nuclear Instruments and Methods in Physics Research A,502 (2003): 492-494. • Application of neural networks to Higgs Boson Search, F. Hakl, M. Hlavacek, R. Kalous. Nuclear Instruments and Methods in Physics Research A, 502 (2003) 489-491. • Identification of events using artificial neural networks, P. Pavlopoulos, G. Polivka, S. Vlachos, H. Wendler. Nuclear Instruments and Methods in Physics Research A, 359 (1995) 566-579. • Particle identification in the NA48 experiment using neural networks, L. Litov, . Nuclear Instruments and Methods in Physics Research A,502 (2003) 495-499.

11. Questions?