PHYSTAT-Anomalies

Contribution List

1. Introduction

Louis Lyons (Imperial College (GB)), Olaf Behnke (Deutsches Elektronen-Synchrotron (DE))

24/05/2022, 17:00

Session 1a. Chair: Mikael Kuusela

2. Landscape of model independent searches

Ben Nachman (Lawrence Berkeley National Lab. (US))

24/05/2022, 17:10

Session 1a. Chair: Mikael Kuusela

ABSTRACT:
This talk will briefly define and motivate anomaly detection at the Large Hadron Collider and will then give an overview of various method classifications based on the underlying physics goals and assumptions (and how these translate to statistical concepts). No one method will be able to cover all possibilities and it is essential to have a spectrum of techniques to achieve broad...

3. Learning New Physics from a machine

Andrea Wulzer (Padova)

24/05/2022, 17:55

Session 1a. Chair: Mikael Kuusela

ABSTRACT:
Strategies to detect data departures from a given reference model, with no prior bias on the nature of the new physical model responsible for the discrepancy might play a vital role in experimental programs where, like at the LHC, increasingly rich experimental data are accompanied by an increasingly blurred theoretical guidance in their interpretation. I will describe one such...

11. Discussion

24/05/2022, 18:40

Session 1a. Chair: Mikael Kuusela

4. Relation of model independent searches with Goodness of Fit, 2-sample tests, etc.

Larry Wasserman (Carnegie Mellon University), Larry Wasserman (Carnegie Mellon University)

24/05/2022, 19:10

Session 1b. Chair Tom Junk

ABSTRACT:
I will review goodness of fit testing and two sample testing in the
context of trying to test for a new signal. My goal is to point to
results in the statistics literature that might be unfamiliar in the
physics community. Topics will include: optimal tests,
classifier-based tests, reproducing kernel Hilbert space tests, level set tests, bump tests and robustness.

5. Discussion

Robert Cousins Jr (University of California Los Angeles (US)), Robert Cousins Jr (University of California Los Angeles (US))

24/05/2022, 19:55

Session 1b. Chair Tom Junk

6. Challenges of anomaly detection with LHC data

Ines Ochoa (LIP Laboratorio de Instrumentacao e Fisica Experimental de Particulas (PT))

25/05/2022, 17:00

Session 2a: Chair: Ann Lee

ABSTRACT:
In recent years, there have been many proposed methodologies for machine learning anomaly detection at the LHC, such as those reported in the LHC Olympics and Dark Machines community reports.  The first search using machine-learning anomaly detection was performed by ATLAS in the dijet final state, a fully data-driven analysis that uses the Classification Without Labels method and...

7. LHC Olympics

Gregor Kasieczka (Hamburg University)

25/05/2022, 17:45

Session 2a: Chair: Ann Lee

ABSTRACT:
We are at the beginning of a new era of data-driven, model-agnostic new physics searches at colliders that combine recent breakthroughs in anomaly detection and machine learning. This contribution will report on the LHC Olympics 2020, a community challenge accompanied by a set of simulated collider events. Participants in these Olympics have developed their methods using an R&D...

12. Discussion

25/05/2022, 18:30

Session 2a: Chair: Ann Lee

8. Supervised, unsupervised and data-derived signal regions

Sascha Caron (Nikhef National institute for subatomic physics (NL))

25/05/2022, 19:00

Session 2b. Chair: David van Dyk

ABSTRACT:
Data-driven methods are becoming increasingly popular and could give us new insights when searching for signals from new physics. On the other hand, theoretical models and supervised learning approaches should not be neglected.
In this talk we present and compare different ways of defining "signal regions" at the Large Hadron Collider that are of interest for a "goodness-of-fit"...

9. Discussion

Richard Lockhart (SFU)

25/05/2022, 19:45

Session 2b. Chair: David van Dyk

10. Closing remarks

Olaf Behnke (Deutsches Elektronen-Synchrotron (DE))

25/05/2022, 20:15

Session 2b. Chair: David van Dyk