Self-Supervised Learning for End-to-End Particle Reconstruction for the CMS Experiment
3podi
The End-to-End Deep Learning project within the CMS experiment at the Large Hadron Collider plays a critical role in identifying and reconstructing...
Implementation of Quantum Generative Adversarial Networks to Perform HEP Analysis at LHC
Adithya Penagonda
The project aims to implement different GAN architectures ranging from classical models to fully quantum models, including hybrid models as well. And...
Quantum transformer for High Energy Physics Analysis at the LHC
Alessandro Tesi
This proposal sets out to implement several kinds of Quantum Vision Transformers (QViT) for High Energy Physics (HEP) analysis at the Large Hadron...
Equivariant Vision Networks for Predicting Planetary Systems' Architectures
Alexandra Murariu
Understanding the architecture of planetary systems is crucial for insights into their formation and evolution. This project aims to leverage the...
Learning quantum representations of classical high energy physics data with contrastive learning
Amey Bhatuse
In this project, we introduce quantum contrastive learning, a quantum machine learning technique to enhance performance of models involving high...
Resilient Physics-Informed Anomaly Detection and Inference of Lensing Images on Sparse Datasets
Anirudh Shankar
Inferences using machine learning methods are becoming increasingly necessary in probing dynamical systems with complex and imperfect datasets. This...
Evolutionary and Transformer Models for Symbolic Regression
Aryamaan Thakur
Symbolic Regression refers to discovering a function that accurately fits a given dataset. Evolutionary/genetic algorithms have been dominating this...
Physics-Guided Machine Learning
Ashutosh Ojha
The project aims to develop a physics-informed neural network framework in order to do the inference for the properties and distribution of dark...
Superresolution for Strong Gravitational Lensing
Atal
The "Superresolution for Strong Gravitational Lensing" project aims to enhance the resolution of astronomical images through advanced deep learning...
QMLHEP3: Learning quantum representations of classical HEP data with contrastive learning
duydl
QMLHEP3, or "Learning quantum representations of classical high energy physics data with contrastive learning," aims to study an intersection of...
Exoplanet Atmosphere Characterization
Gaurav Shukla
This project intends to develop cutting-edge machine-learning tools for spectral analysis to characterize the atmospheres of exoplanets. The project...
Quantum Graph Neural Networks for High Energy Physics Analysis at the LHC
Haemanth V
The Large Hadron Collider (LHC) built by CERN is the world’s largest and the most powerful particle accelerator, which generates about 1 billion...
Exoplanet Atmosphere Characterization
Helen Tan
This project seeks to improve the characterization of exoplanet atmospheres by employing machine learning models to analyze transmission spectrum...
Diffusion Models for Gravitational Lensing Simulation
J Rishi
My project focuses on utilizing Diffusion Models for Gravitational Lensing Simulation, encompassing two primary tasks. The first task involves...
Quantum Graph Neural Networks for High Energy Physics Analysis at the LHC
Jogi Suda Neto
Discovering new phenomena, like the Higgs boson, involves the identification of rare signals that could shed light into unanswered questions about...
Equivariant quantum neural networks for High Energy Physics Analysis at the LHC
Lázaro Díaz
The investigation of symmetries has long been a cornerstone in the analysis of physical systems. As formalized by Noether’s theorem, conserved...
Masked Auto-Encoders for Efficient E2E Particle Reconstruction & Compression for CMS Experiment
Lokesh Badisa
The goal of this project is to develop Masked Vision Transformer for reconstruction and compression of CMS data. It is much easy to procure...
Quantum Generative Adversarial Networks for Monte Carlo Simulations
Luis Rey
This project aims to improve the efficiency and accuracy of Monte Carlo simulations by leveraging Quantum Generative Adversarial Networks (QGANs)....
Quantum Diffusion Model for High Energy Physics
Masha
Classical diffusion models (DMs) have experienced rapid growth in usability, availability, and research. The classical DM algorithm consists of two...
Transformer Models for Symbolic Calculations of Squared Amplitudes in HEP
Ritesh Bhalerao
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...
Evolutionary and Transformer Models for Symbolic Regression
Samyak Jha
Symbolic Regression serves as a powerful tool for uncovering symbolic expressions that encapsulate data patterns, such as physical laws. This project...
Learning quantum representations of classical high energy physics data with contrastive learning
Sanya Nanda
This project investigates the fusion of classical data encoding onto quantum models using contrastive learning techniques. The objective is to...
Masked Auto-Encoders for End-to-End Particle Reconstruction and Compression for the CMS Experiment
Shashank Shekhar Shukla
Proposal Summary: The project aims to achieve two primary objectives: efficient and precise particle identification and the development of effective...
Learning Representation Through Self-Supervised Learning on Real Gravitational Lensing Images
Sreehari Iyer
Strong gravitational lensing provides a means to probe dark matter substructure. In recent years, machine learning techniques, particularly...
Non-local GNNs for Jet Classification
Tanmay Bakshi
The quest for new physics has encompassed and intrigued physicists for decades. At CERN LHC, high-energy proton-proton collisions can create new,...
Graph Neural Networks for Particle Momentum Estimation in the CMS Trigger System
Vishak K Bhat
This project aims to apply Deep Learning algorithms specifically Graph Neural Networks (GNN) for momentum regression in the trigger system....
Learning Representation Through Self-Supervised Learning on Real Gravitational Lensing Images
yaashwardhan
Deep learning has transformed the analysis of supervised lensing data, utilizing feature spaces to uncover latent variables related to dark matter....
