Machine learning for phenomenology

Contribution List

1. Introduction

Ben Nachman (LBNL)

4/3/18, 2:00 PM

General overview

2. How to figure out what information the machine is learning from

Bryan Ostdiek (University of Oregon)

4/3/18, 3:30 PM

General overview

16. Generative models introduction

Michela Paganini (Yale)

4/3/18, 4:15 PM

General overview

3. Discriminating gluon fusion and VBF in Higgs production with machine learning

Nicolas Greiner (University of Zurich)

4/4/18, 9:30 AM

Jets

4. PDFs from NN to ML

Stefano Forte (Milan University)

4/4/18, 10:00 AM

Jets

14. Machine learnng: new ideas for PDF determination

Stefano Carrazza

4/4/18, 10:30 AM

Jets

5. Generative models

Michela Paganini (Yale)

4/4/18, 11:30 AM

Jets

6. Identification of Hadronically Decaying W Bosons and Top Quarks Using Machine Learning in ATLAS

Ece Akilli

4/4/18, 2:00 PM

b tagging

7. Mauro Verzetti

4/4/18, 2:45 PM

b tagging

17. Use of Machine Learning Techniques for improved Monte Carlo Integration

Josh Bendavid (CERN)

4/4/18, 4:00 PM

b tagging

8. Zihao Jiang

4/5/18, 9:30 AM

Analysis-level ML

10. Kristian Gregeren

4/5/18, 10:15 AM

Analysis-level ML

12. Chase Shimmin

4/5/18, 2:45 PM

New ML methods

13. Clustering techniques for parameter space scanning in BSM

Dorigo Tommaso

4/5/18, 4:00 PM

New ML methods

11. Weak supervision

Bryan Ostdiek (University of Oregon)

4/5/18, 4:45 PM

New ML methods

9. David Wardrope

Analysis-level ML

15. Machine Learning for Boosted W-tagging in CMS

Thea Årrestad

New prospects