Conveners
Equivariance and New Architectures
- Petar Maksimovic (Johns Hopkins University (US))
- Darius Faroughy (University of Zurich)
Equivariance and New Architectures
- Tobias Golling (Universite de Geneve (CH))
- Chase Owen Shimmin (Yale University (US))
In the deep learning era, improving the neural network performance in jet physics is a rewarding task as it directly contributes to more accurate physics measurements at the LHC. Recent research has proposed various network designs in consideration of the full Lorentz symmetry, but its benefit is still not systematically asserted, given that there remain many successful networks without taking...
During Run2 of the Large Hadron Collider (LHC), deep-learning-based algorithms were established and led to a significantly improved heavy flavor (b and c) jet tagging performance. In the scope of large-radius boosted jets like top-quark jets, Graph Neural Network (GNN) based models, e.g. ParticleNet, have reached state-of-the-art performance. As a step further, we present Particle Transformer...
Precise reconstruction of top quark properties is a challenging task at the Large Hadron Collider due to combinatorial backgrounds and missing information. We introduce a physics-informed neural network architecture called the Covariant Particle Transformer (CPT) for directly predicting the top quark kinematic properties from reconstructed final state objects. This approach is permutation...
A lot of attention has been paid to the applications of common machine learning methods in physics experiments and theory. However, much less attention is paid to the methods themselves and their viability as physics modeling tools. One of the most fundamental aspects of modeling physical phenomena is the identification of the symmetries that govern them. Incorporating symmetries into a model...
Collider searches face the challenge of defining a representation of high-dimensional data such that physical symmetries are manifest, the discriminating features are retained, and the choice of representation is new-physics agnostic. We introduce JetCLR to solve the mapping from low-level data to optimized observables though self-supervised contrastive learning. As an example, we construct a...
In high-energy heavy-ion collisions, the unconfined state of partons known as the Quark Gluon Plasma (QGP), is known to suppress the yield of jets with respect to proton-proton collision, as well as modify the structure of jets that transverse it. Nonetheless, samples of heavy-ion jets, even at the highest centralities, will contain a significant fraction of jets that, for one reason or the...
We introduce a novel framework to capture the inherent topological structure of collider events. Using persistence homology, the evolution of various topological features across scales is recorded graphically in a persistence diagram, and further encoded as scalars and vectors amenable to machine learning classifiers, showing excellent performance on both jet tagging and event classification...
High-multiplicity signatures at particle colliders can arise in Standard Model processes and beyond. With such signatures, difficulties often arise from the large dimensionality of the kinematic space. For final states containing a single type of particle signature, this results in a combinatorial problem that hides underlying kinematic information. We explore using a neural network that...
With current and future high-energy collider experiments' vast data collecting capabilities comes an increasing demand for computationally efficient simulations. Generative machine learning models allow fast event generation, yet so far are largely constrained to fixed data and detector geometries.
We introduce a Deep Sets based permutation equivariant generative adversarial network (GAN)...
