Unveiling the secrets of new physics through top quark tagging
R Sahu, S Ashanujjaman, K Ghosh - The European Physical Journal …, 2024 - Springer
The ubiquity of top-rich final states in the context of beyond the Standard Model (BSM)
searches has led to their status as extensively studied signatures at the LHC. Over the past …
searches has led to their status as extensively studied signatures at the LHC. Over the past …
Foundations of automatic feature extraction at LHC–point clouds and graphs
Deep learning algorithms will play a key role in the upcoming runs of the Large Hadron
Collider (LHC), hel** bolster various fronts ranging from fast and accurate detector …
Collider (LHC), hel** bolster various fronts ranging from fast and accurate detector …
Streamlined jet tagging network assisted by jet prong structure
A Hammad, MM Nojiri - Journal of High Energy Physics, 2024 - Springer
A bstract Attention-based transformer models have become increasingly prevalent in collider
analysis, offering enhanced performance for tasks such as jet tagging. However, they are …
analysis, offering enhanced performance for tasks such as jet tagging. However, they are …
A Lorentz-Equivariant Transformer for All of the LHC
We show that the Lorentz-Equivariant Geometric Algebra Transformer (L-GATr) yields state-
of-the-art performance for a wide range of machine learning tasks at the Large Hadron …
of-the-art performance for a wide range of machine learning tasks at the Large Hadron …
Equivariant, safe and sensitive—graph networks for new physics
A bstract This study introduces a novel Graph Neural Network (GNN) architecture that
leverages infrared and collinear (IRC) safety and equivariance to enhance the analysis of …
leverages infrared and collinear (IRC) safety and equivariance to enhance the analysis of …
A detailed study of interpretability of deep neural network based top taggers
Recent developments in the methods of explainable artificial intelligence (XAI) allow
researchers to explore the inner workings of deep neural networks (DNNs), revealing crucial …
researchers to explore the inner workings of deep neural networks (DNNs), revealing crucial …
Feature selection with distance correlation
R Das, G Kasieczka, D Shih - Physical Review D, 2024 - APS
Choosing which properties of the data to use as input to multivariate decision algorithms—
also known as feature selection—is an important step in solving any problem with machine …
also known as feature selection—is an important step in solving any problem with machine …
Streamlining latent spaces in machine learning using moment pooling
Many machine learning applications involve learning a latent representation of data, which
is often high-dimensional and difficult to directly interpret. In this work, we propose “moment …
is often high-dimensional and difficult to directly interpret. In this work, we propose “moment …
Enforcing exact permutation and rotational symmetries in the application of quantum neural networks on point cloud datasets
Z Li, L Nagano, K Terashi - Physical Review Research, 2024 - APS
Recent developments in the field of quantum machine learning have promoted the idea of
incorporating physical symmetries in the structure of quantum circuits. A crucial milestone in …
incorporating physical symmetries in the structure of quantum circuits. A crucial milestone in …
Accuracy versus precision in boosted top tagging with the ATLAS detector
G Aad, E Aakvaag, B Abbott… - Journal of …, 2024 - iopscience.iop.org
The identification of top quark decays where the top quark has a large momentum
transverse to the beam axis, known as top tagging, is a crucial component in many …
transverse to the beam axis, known as top tagging, is a crucial component in many …