14th International Workshop on Boosted Object Phenomenology, Reconstruction, Measurements and Searches in HEP

Session

ML

15 Aug 2022, 16:00

Auditorium VMP8 (University of Hamburg)

Conveners

ML: 1

• Gregor Kasieczka (Hamburg University (DE))

4. Bridge between Classical & Quantum Machine Learning

Jack Araz (IPPP - Durham University)

15/08/2022, 16:00

Presentation

Tensor Networks (TN) are approximations of high-dimensional tensors designed to represent locally entangled quantum many-body systems efficiently. In this talk, we will discuss how to use TN to connect quantum mechanical concepts to machine learning techniques, thereby facilitating the improved interpretability of neural networks. As an application, we will use top jet classification against...

13. Symmetries, Safety, and Self-Supervision

Lorenz Vogel (Heidelberg University)

15/08/2022, 16:20

Presentation

Collider searches face the challenge of defining a representation of high-dimensional data such that (i) physical symmetries are manifest, (ii) the discriminating features are retained, and (iii) the choice of representation is data-driven and new-physics agnostic. We introduce JetCLR (Contrastive Learning of Jet Representations) to solve the mapping from low-level jet constituent data to...

67. Embedding quarks and gluons in a lower dimensional space for learning the latent structure

Sang Eon Park (Massachusetts Inst. of Technology (US))

15/08/2022, 16:40

Presentation

There is a growing recent interest in endowing the space of collider events with a metric structure calculated directly in the space of its inputs. For quarks and gluons, the recently developed energy mover's distance has allowed for a quantification of what is different between physical events. However, the large number of particles within jets makes using metrics and interpreting these...

45. Can You Hear the Shape of a Jet?

Rikab Gambhir (MIT)

15/08/2022, 17:00

Presentation

The identification of interesting substructures within jets is an important tool to search for new physics and probe the Standard Model. In this paper, we present \textsc{SHAPER}, a general framework for defining computing shape-based observables, which generalizes the $N$-jettiness from point clusters to any extended shape. This is accomplished by minimizing the $p$-Wasserstein metric between...

23. NAE bother, anomalies

Luigi Favaro

15/08/2022, 17:20

Presentation

The main goal for the upcoming LHC runs is still to discover BSM physics. It will require analyses able to probe regions not linked to specific models but generally identified as beyond the Standard Model. Autoencoders are the ideal analysis tool for this type of search. Energy-based machine learning models have been shown to be flexible and powerful models to describe high-dimensional feature...