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)
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...
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...
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,...
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)...
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...
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...
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...
