ML4Jets2022

Session

Interpretability

4 Nov 2022, 09:00

Multipurpose Room (aka Livingston Hall) (Rutgers University)

Conveners

Interpretability

• Savannah Jennifer Thais (Princeton University (US))
• Prasanth Shyamsundar (Fermi National Accelerator Laboratory)

Interpretability

• Abhijith Gandrakota (Fermi National Accelerator Lab. (US))
• Purvasha Chakravarti (University College London)

68. Infra-red and collinear safe Graph Neural Networks

Vishal Singh Ngairangbam

04/11/2022, 09:00

Zoom

Hadronic signals of new-physics origin at the Large Hadron Collider can remain hidden within the copiously produced hadronic jets. Unveiling such signatures require highly performant deep-learning algorithms. We construct a class of Graph Neural Networks (GNN) in the message-passing formalism that makes the network output infra-red and collinear (IRC) safe, an important criterion satisfied...

48. Resilience of Quark-Gluon Tagging

Lorenz Vogel (ITP, Heidelberg University)

04/11/2022, 09:20

Discriminating quark-initiated from gluon-initiated jets is an extremely challenging yet important task in high-energy physics. Recent studies have shown that the discriminating features between quark and gluon jets produced by the Monte Carlo generator Pythia differ significantly from the features produced by Herwig. To understand this simulation-dependent discrepancy, we propose a Bayesian...

1. Boost-Invariant Polynomials: an efficient and interpretable approach to jet tagging

Mr Jose Miguel Munoz Arias (EIA University)

04/11/2022, 09:40

Zoom

Besides modern architectures designed via geometric deep learning achieving high accuracies via Lorentz group invariance, this process involves high amounts of computation. Moreover, the framework is restricted to a particular classification scheme and lacks interpretability.
To tackle this issue, we present BIP, an efficient and computationally cheap framework to build rotational,...

8. Learning to Identify Semi-Visible Jets

Taylor James Faucett (University of California, Irvine)

04/11/2022, 10:00

Zoom

We train a network to identify jets with fractional dark decay (semi-visible jets) using the pattern of their low-level jet constituents, and explore the nature of the information used by the network by mapping it to a space of jet substructure observables. Semi-visible jets arise from dark matter particles which decay into a mixture of dark sector (invisible) and Standard Model (visible)...

39. Feature selection with Distance Correlation

RANIT DAS

04/11/2022, 10:20

Feature selection algorithms can be an important tool for AI explainability. If the performance of neural networks trained on low-level data can be reproduced by a small set of high-level features, we can hope to understand “what the machine learned”. We present a new algorithm that selects features by ranking their Distance Correlation (DisCo) values with truth labels. We apply this algorithm...

42. Jet tagging with deep sets of subjets

Dimitrios Athanasakos

04/11/2022, 11:10

We introduce a complete basis of subjets for machine learning-based jet tagging. The subjets are obtained with (i) a fixed radius or (ii) the clustering is performed until a fixed number of subjets is obtained.
For nonzero values of the subjet radius, the resulting classifier is Infrared-Collinear (IRC) safe. By lowering the subjet radius, we can increase the sensitivity to nonperturbative...

21. Blueprints for Training Information Bottlenecks for Collider Analyses

Prasanth Shyamsundar (Fermi National Accelerator Laboratory)

04/11/2022, 11:30

Dimensionality reduction is a crucial aspect of data analysis in high energy physics, even if accompanied by information loss. Several methods, including histogram- and kernel-based analyses, are only computationally feasible for low-dimensional data. Furthermore, simulation models used in HEP can often only be validated for low-dimensional data. We provide several blueprints for using machine...

51. Weakly Supervised Learning for Muon Discrimination in Unlabeled Collider Data

Edmund Witkowski (UCI)

04/11/2022, 11:50

Zoom

We use unlabeled collision data from CMS and weakly-supervised learning to train models which can distinguish prompt muons from non-prompt muons using patterns of low-level particle activity in vicinity of the muon, and interpret the models in the space of energy flow polynomials. Particle activity associated with muons is a valuable tool for identifying prompt muons, those due to heavy boson...

40. Can You Hear the Shape of a Jet?

Rikab Gambhir (MIT)

04/11/2022, 12:10

The identification of interesting substructures within jets is an important tool to search for new physics and probe the Standard Model. In this talk, we present 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 events...

35. Neural Estimation of Energy Movers Distance

Ouail Kitouni (Massachusetts Inst. of Technology (US))

04/11/2022, 12:30

We propose a novel neural architecture that enforces an exact upper bound on the Lipschitz constant of the model by constraining the norm of its weights. This architecture was useful in developing new algorithms for the LHCb trigger which have robustness guarantees as well as powerful inductive biases leveraging the neural network’s ability to be monotonic in any subset of features. A new and...