Machine learning for phenomenology

Name: Machine learning for phenomenology
Start: 2018-04-03T08:00:00+01:00
End: 2018-04-06T18:00:00+01:00
Location: IPPP Durham

3–6 Apr 2018

IPPP Durham

Europe/London timezone

Contribution List

1. Introduction

Ben Nachman (LBNL)

03/04/2018, 14:00

General overview

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

Bryan Ostdiek (University of Oregon)

03/04/2018, 15:30

General overview

16. Generative models introduction

Michela Paganini (Yale)

03/04/2018, 16:15

General overview

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

Nicolas Greiner (University of Zurich)

04/04/2018, 09:30

Jets

4. PDFs from NN to ML

Stefano Forte (Milan University)

04/04/2018, 10:00

Jets

14. Machine learnng: new ideas for PDF determination

Stefano Carrazza

04/04/2018, 10:30

Jets

5. Generative models

Michela Paganini (Yale)

04/04/2018, 11:30

Jets

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

Ece Akilli

04/04/2018, 14:00

b tagging

7. Mauro Verzetti

04/04/2018, 14:45

b tagging

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

Josh Bendavid (CERN)

04/04/2018, 16:00

b tagging

8. Zihao Jiang

05/04/2018, 09:30

Analysis-level ML

10. Kristian Gregeren

05/04/2018, 10:15

Analysis-level ML

12. Chase Shimmin

05/04/2018, 14:45

New ML methods

13. Clustering techniques for parameter space scanning in BSM

Dorigo Tommaso

05/04/2018, 16:00

New ML methods

11. Weak supervision

Bryan Ostdiek (University of Oregon)

05/04/2018, 16:45

New ML methods

9. David Wardrope

Analysis-level ML

15. Machine Learning for Boosted W-tagging in CMS

Thea Årrestad

New prospects