Finding Exoplanets with Astronomical Observations. The aim of this of this project would be to apply machine learning / deep learning methods on...
Implementation of QGANs to Perform High Energy Physics Analysis at the LHC
Abhay Kamble
One of the challenges in High-Energy Physics(HEP) is fast simulation of particle transport, and hence various deep neural network methods have been...
Anomalies Detection
Amal Saif
Anomaly detection is the process of identifying data points, events, and observations that differ from a dataset's expected behavior, called...
Finding Exoplanets with Astronomical Observations
Mahdi Boulila
The purpose of this project is to use publicly available data from astronomical observations intended to identify exoplanets in order to determine...
Gravitational Lens Finding for Dark Matter Substructure Pipeline
Mriganka Nath
Machine learning techniques are regarded to have the potential to help researchers better comprehend dark matter. Convolutional Neural Networks...
Quantum Generative Adversarial Neural Networks for High Energy Physics Analysis at the LHC
Amey Bhatuse
One of the problems in High Energy Physics experiments is that particle collisions give rise to novel subatomic particles which need to be detected...
Updating the DeepLense Pipeline
Saranga Kingkor Mahanta
The study of dark matter substructures has shown promise in addressing the open-ended and long-standing challenge of identifying the true nature of...
Vision Transformers for End-to-End Particle Reconstruction for the CMS Experiment
Diptarko Choudhury
The project aims to use Vision Transformer-based architectures to classify high-energy particles. The data consists of multi-channel simulated images...
Transformers for Dark Matter Morphology with Strong Gravitational Lensing
Kartik Sachdev
Strong gravitational lensing is a phenomenon where the light of distant galaxies is bent and distorted by the gravity of massive galaxy clusters,...
Finding Exoplanets with Astronomical Observations
Jason Terry
The ultimate purpose of this project is to train image-based deep learning models to detect planets embedded in protoplanetary disks. This will be...
Symbolic empirical representation of squared amplitudes in high-energy physics
Marco Knipfer
Calculating squared amplitudes and cross sections of a Feynman Diagram in high-energy physics is a tedious task. While software like MARTY (A Modern...
Quantum Variational Autoencoders for HEP Analysis at the LHC
ToMago
In the search for physics beyond the standard model physics, the growing amount of data and the evasive of the signals that are being searched for,...
Graph Neural Networks for End-to-End Particle Identification with the CMS Experiment
Jai Bardhan
Graph Neural Networks for End-to-End Particle Identification with the CMS Experiment: The goal of the project would be to develop, train, test, and...
End-to-End Deep Learning Reconstruction for CMS Experiment
Purva Chaudhari
One of the important aspects of searches for new physics at the Large Hadron Collider (LHC) involves the identification and reconstruction of single...
Transformers for Dark Matter Morphology with Strong Gravitational Lensing
Archil Srivastava
Since Dark Matter was discovered, physicists have been trying to understand its composition. In practice, the best method to detect substructure is...
Graph Neural Networks for End-to-End Particle Identification with the CMS Experiment
Xin Yi
In recent years, the convolutional neural network has been successfully applied in particle physics identification tasks. Despite its great success,...
Equivariant Transformers for Decoding Dark Matter with Strong Gravitational Lensing
Yurii Claus
Convolutional Neural Networks require large receptive fields in order to track long-range dependencies within an image, which in practice involves...
Quantum Convolutional Neural Networks for High Energy Physics Analysis at the LHC
Gopal Ramesh Dahale
This study aims to show the capabilities of QML especially QCNN for classifying the HEP image datasets. QCNN can be completely quantum or can be a...
Fast Accurate Symbolic Empirical Representation of Histograms
Aaron Mayer
This proposal regards mapping histogram data to its underlying function; both of which can be represented as sequences and hence a transformer can...
Deep Regression Exploration
Zhongchao Guan
Using Deep Regression techniques to decode Dark Matter with Strong Gravitational Lensing. Try to use the SOTA deep model (such as transformers) to do...
