Particle colliders such as the Large Hadron Collider (LHC) play a crucial role in advancing our understanding of fundamental particles and their...
Graph Neural Networks for End-to-End Particle Identification with the CMS Experiment
Anthony Song
This project focuses on developing and evaluating end-to-end Graph Neural Network (GNN) models for low-momentum tau identification in the context of...
Equivariant Quantum Neural Networks for Continuous Symmetry in High Energy Physics
Cosmos Dong
This project aims to explore the development and application of equivariant quantum neural networks (EQNNs) for continuous symmetry in high-energy...
Exploring the underlying symmetries in particle physics with equivariant neural networks
Diptarko Choudhury
Symmetry is one of the most beautiful and interesting phenomena in physics. Particle Physics is dominated by Lorentz symmetry. It is seen that...
Prediction of High Energy Particle Kinematics via Masked Autoencoding
Eric Reinhardt
In high energy physics, much research revolves around the study of particles produced by colliding protons at near the speed of light. The Higgs...
Quantum transformer for High Energy Physics Analysis at the LHC
Eyup
Transformer-based models are gaining more and more traction in many fields, including physics. However, they are particularly known to require...
Equivariant Neural Networks for Dark Matter Morphology with Strong Gravitational Lensing
Geo Jolly
Strong gravitational lensing is a promising probe of the substructure of dark matter to better understand its underlying nature. Deep learning...
Quantum Graph Neural Networks for High Energy Physics Analysis at the LHC
Gopal Ramesh Dahale
The LHC at CERN contains large detectors which are made up of numerous small detectors that capture the hundreds of particles produced during...
Identifying the Physical Process of Planet Formation (EXXA)
Jason Terry
Planets form in complex, dynamic environments. The physics behind the process is not well-understood, and the limited high-quality observational data...
Super-Resolution for Strong Gravitational Lensing
K Pranath Reddy
Strong gravitational lensing is a promising probe of the substructure of dark matter to better understand its underlying nature. Deep learning...
Vision Transformers for End-to-End Particle Reconstruction for the CMS Experiment
Ka Wa Ho
The goal of the project is to apply and develop “end-to-end” vision transformer (ViT)-based networks for jet-flavor identification with the CMS open...
Self-Supervised Learning for Strong Gravitational Lensing
Kartik Sachdev
Supervised learning might be challenging in cases where there are extremely few known instances in a given category. This is a common occurrence in...
Lensiformer: A Physics-Informed Vision Transformer Architecture for Dark Matter Morphology
Lucas José
We introduce Lensiformer, a state-of-the-art transformer architecture that incorporates the principles of relativistic physics for the classification...
Quantum Transformers for HEP Analysis at the LHC
Marçal Comajoan Cara
This project aims to develop quantum transformer architectures for high energy physics (HEP) analysis at the Large Hadron Collider (LHC). The focus...
Finding Exoplanets with Astronomical Observations
Mihir Tripathi
Protoplanetary disks are birthplaces of planetary systems. Newly forming planets inside a protoplanetary disk interact with gas and dust in the disks...
Symbolic empirical representation of squared amplitudes in high-energy physics
Neeraj Anand
In particle physics, a cross section is a measure of the likelihood that particles will interact or scatter with one another when they collide. It is...
FASEROH : Building seq2seq model for mapping histograms to empirical symbolic representations
Pushpdeep Singh
The problem involves creating a seq2seq model for mapping histograms to empirical function sequences. I propose to tackle this in two steps. Step 1...
Invariant and Equivariant Quantum Graph Attention Transformers for HEP Analysis at the LHC
Roy T. Forestano
Machine learning algorithms are heavily relied on to understand the data generated at the European Council for Nuclear Research's (CERN) Large Hadron...
Deriving planetary surface composition from orbiting observations from spacecraft
Sandeepan Dhoundiyal
Multiple robotic spacecraft have been sent by NASA to collect orbital remote sensing data, which is used to analyze surface composition. Gamma-ray...
Updating the DeepLense Pipeline
Saranga Kingkor Mahanta
Studying the substructures of dark matter holds promise in solving the longstanding problem of determining the true nature of dark matter. By using...
Quantum Generative Adversarial Networks for HEP event generation the LHC
ToMago
An important part of the analysis pipeline of high energy physics experiments is the generation of expected data from first principles. For decades,...
SYMBA - Symbolic empirical representation of squared amplitudes in high-energy physics
VBaules
The interaction cross-section is an important quantity in high-energy physics, serving as a bridge between abstract theory and experiment. Is is also...
Self-Supervised Learning for Strong Gravitational Lensing
yaashwardhan
Problem Statement: An unprecedented amount of lensing data is available, however manually labeling it is unsustainable. Hence an approach must be...
