Agentic AI for Autonomous Gravitational Lensing Simulation Workflows
Contributor: Aatmaj28
DeepLenseSim (built on lenstronomy) requires substantial manual intervention for configuring parameters, managing outputs, and iterating on failures...
Science and medicine
Machine Learning for Science (ML4SCI)
Machine learning applications in science
Technologies
AIC++data analysismachine learningPython
Topics
algorithmsastronomymachine learningphysicsscience and medicine
- Total projects
- 152
- Years participating
- 6
- Most recent
- 2026
Participation history
Historical projects
Radiomics Feature Extraction and Calcium Phenotype Discovery
Contributor: AdityaParashar
A preliminary pipeline on 30 COCA patients achieves Spearman rho = 0.986 between extracted calcium volume and Agatston score, with 11/15 features...
Exoplanet Atmosphere Characterization
Contributor: Anushree Upasham
Exoplanet Atmosphere Characterisation plays a vital role in understanding chemical compositions, weather patterns and habitability of the exoplanet....
[DeepLense] Foundation Model for Gravitational Lensing - WaveLens-JEPA
Contributor: Arnesh2212
Strong gravitational lensing is a powerful probe of dark matter structure and cosmology, yet no foundation model exists that is purpose-built for...
Quantum Resource Analysis and Benchmarking
Contributor: ashutoshm1771
Frameworks like PennyLane and Qiskit handle circuit construction and simulation well but provide almost no tools for measuring the quantum resources...
Event Classification With Masked Transformer Autoencoders
Contributor: Avishikta Bhattacharjee.
The proposal titled "Event Classification With Masked Transformer Autoencoders" aims to enhance particle jet tagging by developing a...
Using Next-Gen Transformers to Seed Generative Models for Symbolic Regression
Contributor: Dhruv1108
Symbolic regression (SR) aims to discover human-interpretable mathematical expressions from numerical data. While recent transformer models achieve...
DeepLense: Lens Finding for LSST Images
Contributor: DhruvSrivastava
In GSoC 2025, I prepared a novel physics-informed Swin Transformer to classify between lens models in simulated gravitational lensing images. The...
Physics-Informed Neural Network Shape Optimization
Contributor: George Liao
Vanilla MLP PINNs suffer from spectral bias and slow convergence, which directly limits the quality of shapes produced by PINN-based shape...
Brain-to-Brain Decoder: Leakage-Aware Validation and Interpretable CEBRA Mapping for Dyadic EEG
Contributor: Hubert Huang
This proposal is for the ML4SCI GSoC 2026 project “Brain-to-Brain Decoder – Validating Neural Synchrony Patterns in Human Conversation.” The project...
Hybrid Quantum-Classical Representation Learning for Dark Matter Substructure Classification
Contributor: Jen-Yu Chang
This proposal presents a plan to develop hybrid quantum-classical models for classifying dark matter substructure from strong gravitational lensing...
Quantum Sinusoidal Kolmogorov Arnold Networks for High Energy Physics
Contributor: Jorge Toral
The High Luminosity LHC program requires novel computational approaches to process massive datasets and identify rare signals. This project...
Agentic Lagrangian Extraction from the Literature ML4SCI – HEPSIM5
Contributor: kenwu
Hundreds of BSM Lagrangians have been proposed in the literature, but translating them into validated FeynRules .fr model files is still a manual,...
Linear attention vision transformers for end to end mass regression and classification
Contributor: Krish Malik
This project addresses a key challenge in High Energy Physics: efficient and accurate end-to-end reconstruction of particle properties from detector...
EXXA - Denoising Astronomical Observations of Protoplanetary Disks
Contributor: Krishan Yadav
Astronomical observations of protoplanetary disks often contain noise that obscures important structures such as rings and gaps that may indicate...
Machine Learning for Gravitational Lens Finding
Contributor: MichaelJiao
Strong gravitational lenses are rare and scientifically valuable, but finding them in large imaging surveys requires automated methods. Furthermore,...
Quantum Latent Diffusion Models for High-Resolution Simulation
Contributor: Miguel Pámanes
Accurate simulation of particle interactions within detectors is one of the most computationally expensive tasks in High Energy Physics (HEP). While...
Linear Attention Vision Transformers for CMS End-to-End Jet Classification and Mass Regression
Contributor: Naresh Meena
This project develops linear-scale attention vision transformers for CMS End-to-End jet classification and proxy mass regression on 8-channel 99.1%...
Neural Operators for Fast Simulation of Strong Gravitational Lensing
Contributor: PARAS BALANI
Strong gravitational lensing is a key observational probe for dark matter and cosmology, but traditional ray-tracing simulations are computationally...
Unsupervised Super-Resolution and Analysis of Real Lensing Images
Contributor: Prajwal Uday
Strong gravitational lensing is among the most powerful observational probes of dark matter substructure. High-resolution lensing images encode the...
Hybrid 3D CNN with Deformable Attention and FNO for CAC Segmentation
Contributor: Rajat Nandkumar Shedshyal
This project develops a high-precision, clinically viable pipeline for segmenting Coronary Artery Calcium (CAC) from non contrast cardiac CT scans...
Physics-Aware Super-Resolution of CMS Jet Images Using Transformer - Diffusion Architectures
Contributor: rajveer43
At the CMS detector, jet images are stored at reduced resolution, discarding ~75% of total deposited energy. Standard super-resolution fails here...
Physics-Informed Models for Squared Amplitude Calculation
Contributor: Ria Khatoniar
This project aims to advance the SYMBA framework for calculating squared scattering amplitudes in high-energy physics by incorporating...
Graph Representation Learning for Fast Detector Simulation
Contributor: Rushil Singha
This project addresses the challenge of fast and accurate particle detector simulation in high-energy physics. Traditional Monte Carlo methods are...
Data Augmentation Using Physics-Informed Plaque Growth Simulation
Contributor: Shriyam Baloni
This project addresses the challenge of limited and imbalanced datasets in coronary artery calcium (CAC) analysis, particularly for rare high-risk...
Physics Informed Neural Network Diffusion Equation (PINNDE)
Contributor: Sijil
Generating simulations of calorimeter showers of interacting particles are a crucial step in analyzing the results of large scale experiments at LHC....
Building and Comparing Segmentation Strategies for Coronary Artery Calcium CAC
Contributor: Soham Jadhav
CAC segmentation is harder than it looks. Calcium deposits are tiny, sparse, and look identical to nearby bone a small miss doesn't just drop your...
Foundation models for End-to-End event reconstruction
Contributor: Suryansh Mishra
This project aims to develop a multi-modal foundation model training pipeline for end-to-end particle reconstruction in the CMS experiment. Building...
Physics Guided Machine Learning on Real Gravitational Lensing Images
Contributor: Susmit
Strong gravitational lensing is a powerful tool for studying dark matter, but machine learning models trained on simulated data often fail to...
Foundation Models for Exoplanet Characterization
Contributor: Tanmay Singhal
This project aims to build a multimodal foundation model for exoplanet characterization by addressing the challenge of heterogeneous astronomical...
Physics-Informed Neural Network Diffusion Equation (PINNDE)
Contributor: Tina Shen
Building on the PINNDE proof-of-concept from GSoC 2025, I propose two directions: (1) systematic improvements to the PINN architecture and training —...
Quantum Circuit Design with LLMs
Contributor: Tomoya Hatanaka
Variational quantum circuits are essential for near term quantum algorithms but designing them involves complex manual trial and error. This project...
Deep Graph Anomaly Detection with Contrastive Learning for New Physics Searches
Contributor: Yeyang
This project proposed to develop a graph-based unsupervised Anomaly Detection model for identifying new physics at the LHC, combining contrastive...
Foundation models for End-to-End event reconstruction
Contributor: Aaditya Porwal
Current deep learning models for CMS particle reconstruction are often task-specific. This project aims to develop versatile foundation models by...
Super-Resolution and Analysis of Gravitational Lensing Images
Contributor: Aleksandr Duplinskii
This project develops an open-source, diffusion-based super-resolution (SR) pipeline for enhancing the resolution of gravitational lensing images....
Quantum Particle transformer for High Energy Physics Analysis at the LHC
Contributor: Alessandro Tesi
This proposal aims to build upon and expand the progress made in recent years on Quantum Vision Transformers (QViTs) for High Energy Physics (HEP)...
Discovering Hidden Symmetries in CMS Calorimetric Data via Semi-Supervised Learning
Contributor: Alexandra Murariu
This project tackles the challenge of learning hidden symmetries in complex, high-dimensional physics data using modern machine learning techniques....
Unsupervised super-resolution and analysis of observed lensing images
Contributor: Anirudh Shankar
Machine learning based analyses are proving to be very potent in understanding complex physical systems. This project aims to develop a reinforcement...
Q-MAML for Variational Quantum Algorithms for High Energy Physics Analysis at the LHC
Contributor: arnavsinghal09
This project focuses on developing a new machine learning approach called Quantum Model-Agnostic Meta-Learning (Q-MAML) specifically for high-energy...
Physics Guided Machine Learning on Real Lensing Images
Contributor: Ashutosh Ojha
The project aims to develop a physics-informed neural network framework to infer the properties and distribution of dark matter in the lensing galaxy...
Discovery of hidden symmetries and conservation laws
Contributor: Avishikta_Bhattacharjee
Because of its cylindrical shape, the Compact Muon Solenoid (CMS) detector has intrinsic rotational symmetries. Data analysis can be greatly improved...
Physics-Informed Performer for Symbolic Squared Amplitudes in HEP
Contributor: Ayush Mishra A
In high-energy physics (HEP), automating the symbolic computation of squared amplitudes is essential for predicting cross-sections and validating...
A Diffusion-Based Deep Learning Framework for Denoising Protoplanetary Disk Observations
Contributor: Chukwunwogor Faithful
This project proposes ProtoDiff, a diffusion-based deep learning model designed to denoise astronomical observations of protoplanetary disks from...
Continual learning for data quality monitoring
Contributor: Daksh Mor
This project addresses the challenge of data drift in high-energy physics, where evolving detector conditions cause AI models for data quality...
DeepLense: Gravitational Lens Finding Project
Contributor: DhruvSrivastava
This project aims to develop and optimize deep learning algorithms for identifying strong gravitational lenses within wide-field surveys such as the...
Exoplanet Atmosphere Characterization
Contributor: Gaurav Shukla
This project intends to develop cutting-edge machine-learning tools for spectral analysis to characterize the atmospheres of exoplanets. The project...
Discovery of hidden symmetries and conservation laws
Contributor: Green Kedia
This project aims to uncover fundamental symmetries and conserved laws hidden within complex high-energy physics data (specifically CMS datasets). It...
Diffusion Models for Gravitational Lensing Simulation
Contributor: hamees-sayed
My project focuses on developing diffusion models for gravitational lensing simulations. I will implement and evaluate various diffusion-based...
Foundation Model for Gravitational Lensing
Contributor: J Rishi
This project aims to develop a vision foundation model for strong gravitational lensing by comparing various self-supervised learning techniques,...
Latent Neural Signatures in Clinical vs. Neurotypical Dyads: A CEBRA Pipeline
Contributor: jeffreyhuang1009
This project aims to develop a cutting-edge computational pipeline—"Latent Neural Signatures in Clinical vs. Neurotypical Dyads: A CEBRA Pipeline"—to...
Data Processing Pipeline for the LSST
Contributor: Kartik Mandar
The project, "Data Processing Pipeline for the LSST," is designed to bridge the gap between the LSST data ecosystem and DeepLense's deep learning...
Foundation models for symbolic regression tasks
Contributor: Krish Malik
Problem: Symbolic regression is crucial for discovering underlying physical laws from data, but traditional methods are often computationally...
Neural Harmony – Decoding Social Interactions with CEBRA-based framework for analysing EEG data
Contributor: Maria Glushanina
This project aims to decode the neural dynamics of social interactions by adapting the CEBRA framework to analyze dyadic EEG data. Focusing on...
Quantum Diffusion Model for HEP
Contributor: Masha
Diffusion models have experienced rapid growth in usability, availability, and research. The classical algorithm consists of two main parts: a...
Implementation of Quantum Generative Adversarial Networks to Perform HEP Analysis at the LHC
Contributor: Maya Ma
This project aims to implement a Quantum Generative Adversarial Network (QGAN) using the Pennylane framework to explore the advantages of quantum...
Building a Foundational Model for Symbolic Regression in High Energy Physics
Contributor: Miche
This project proposes the development of a foundational model for symbolic regression tailored to high energy physics (HEP). Symbolic regression can...
State-space models for squared amplitude calculation in high-energy physics
Contributor: Prasanth Naidu
One of the most important physical quantities in particle physics is the cross section, or a probability that a particular process takes place in the...
Quantum Kolmogorov-Arnold Networks for High Energy Physics Analysis at the LHC
Contributor: Ria Khatoniar
This project explores Quantum Kolmogorov–Arnold Networks (QKANs) as a novel and interpretable architecture for analyzing collider data from the...
Next-Generation Transformer Models for Symbolic Calculations of Squared Amplitudes in HEP
Contributor: 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...
Graph Representation Learning for Fast Detector Simulation
Contributor: Rushil Singha
High-fidelity detector simulations are critical for accurate analysis in particle physics, but traditional Monte Carlo-based methods are...
Physics informed neural network diffusion equation
Contributor: Sijil
This project aims at incorporating Physics informed neural network (PINN) based ODE solvers into the Diffusion probabilistic models(DPM) , to build a...
Quantum Machine Learning For Exoplanet Characterization
Contributor: Sourish Phate
This project investigates the potential of quantum machine learning methods, such as quantum feature encoding and quantum neural networks (QNNs), for...
Foundation Models for Exoplanet Characterization
Contributor: Tanmay Singhal
The project aims at building foundational models suitable for characterizing the vast astronomical data and emphasizing their use case for various...
A Self-Supervised, Physics-Informed Hybrid Transformer Framework for Multi-Tasks in HEP
Contributor: Thanh P Nguyen
Accurately classifying particle collisions—whether distinguishing quark- from gluon-initiated jets or isolating Higgs events from complex...
Learning quantum representations of classical high energy physics data with contrastive learning
Contributor: Sanya Nanda
This project investigates the fusion of classical data encoding onto quantum models using contrastive learning techniques. The objective is to...
Non-local GNNs for Jet Classification
Contributor: 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,...
Quantum transformer for High Energy Physics Analysis at the LHC
Contributor: 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...
Superresolution for Strong Gravitational Lensing
Contributor: Atal
The "Superresolution for Strong Gravitational Lensing" project aims to enhance the resolution of astronomical images through advanced deep learning...
Graph Neural Networks for Particle Momentum Estimation in the CMS Trigger System
Contributor: 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
Contributor: yaashwardhan
Deep learning has transformed the analysis of supervised lensing data, utilizing feature spaces to uncover latent variables related to dark matter....
Equivariant quantum neural networks for High Energy Physics Analysis at the LHC
Contributor: Lazaro Raul Diaz Lievano
The investigation of symmetries has long been a cornerstone in analyzing physical systems. As formalized by Noether’s theorem, conserved quantities...
Masked Auto-Encoders for Efficient E2E Particle Reconstruction & Compression for CMS Experiment
Contributor: 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...
Resilient Physics-Informed Anomaly Detection and Inference of Lensing Images on Sparse Datasets
Contributor: Anirudh Shankar
Inferences using machine learning methods are becoming increasingly necessary in probing dynamical systems with complex and imperfect datasets. This...
Quantum Graph Neural Networks for High Energy Physics Analysis at the LHC
Contributor: Jogi Suda Neto
Discovering new phenomena, like the Higgs boson, involves the identification of rare signals that could shed light into unanswered questions about...
QMLHEP3: Learning quantum representations of classical HEP data with contrastive learning
Contributor: duydl
QMLHEP3, or "Learning quantum representations of classical high energy physics data with contrastive learning," aims to study an intersection of...
Equivariant Vision Networks for Predicting Planetary Systems' Architectures
Contributor: Alexandra Murariu
Understanding the architecture of planetary systems is crucial for insights into their formation and evolution. This project aims to leverage the...
Physics-Guided Machine Learning
Contributor: 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...
Evolutionary and Transformer Models for Symbolic Regression
Contributor: Aryamaan Thakur
Symbolic Regression refers to discovering a function that accurately fits a given dataset. Evolutionary/genetic algorithms have been dominating this...
Quantum Generative Adversarial Networks for Monte Carlo Simulations
Contributor: Luis Rey
This project aims to improve the efficiency and accuracy of Monte Carlo simulations by leveraging Quantum Generative Adversarial Networks (QGANs)....
Diffusion Models for Gravitational Lensing Simulation
Contributor: J Rishi
My project focuses on utilizing Diffusion Models for Gravitational Lensing Simulation, encompassing two primary tasks. The first task involves...
Learning quantum representations of classical high energy physics data with contrastive learning
Contributor: Amey Bhatuse
In this project, we introduce quantum contrastive learning, a quantum machine learning technique to enhance performance of models involving high...
Implementation of Quantum Generative Adversarial Networks to Perform HEP Analysis at LHC
Contributor: Adithya Penagonda
The project aims to implement different GAN architectures ranging from classical models to fully quantum models, including hybrid models as well. And...
Exoplanet Atmosphere Characterization
Contributor: Gaurav Shukla
This project intends to develop cutting-edge machine-learning tools for spectral analysis to characterize the atmospheres of exoplanets. The project...
Quantum Diffusion Model for High Energy Physics
Contributor: Masha
Classical diffusion models (DMs) have experienced rapid growth in usability, availability, and research. The classical DM algorithm consists of two...
Learning Representation Through Self-Supervised Learning on Real Gravitational Lensing Images
Contributor: Sreehari Iyer
Strong gravitational lensing provides a means to probe dark matter substructure. In recent years, machine learning techniques, particularly...
Masked Auto-Encoders for End-to-End Particle Reconstruction and Compression for the CMS Experiment
Contributor: Shashank Shekhar Shukla
Proposal Summary: The project aims to achieve two primary objectives: efficient and precise particle identification and the development of effective...
Self-Supervised Learning for End-to-End Particle Reconstruction for the CMS Experiment
Contributor: 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...
Evolutionary and Transformer Models for Symbolic Regression
Contributor: Samyak Jha
Symbolic Regression serves as a powerful tool for uncovering symbolic expressions that encapsulate data patterns, such as physical laws. This project...
Transformer Models for Symbolic Calculations of Squared Amplitudes in HEP
Contributor: 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...
Quantum Graph Neural Networks for High Energy Physics Analysis at the LHC
Contributor: 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...
Diffusion Models for Fast Detector Simulation
Contributor: Akshit Choudhari
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
Contributor: 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
Contributor: 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
Contributor: 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
Contributor: 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
Contributor: 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
Contributor: 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
Contributor: 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)
Contributor: 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
Contributor: 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
Contributor: 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
Contributor: 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
Contributor: 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
Contributor: 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
Contributor: 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
Contributor: 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
Contributor: 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
Contributor: 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
Contributor: 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
Contributor: 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
Contributor: 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
Contributor: 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
Contributor: yaashwardhan
Problem Statement: An unprecedented amount of lensing data is available, however manually labeling it is unsustainable. Hence an approach must be...
Finding Exoplanets with Astronomical Observations
Contributor: Jack Mcnish
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
Contributor: 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
Contributor: 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
Contributor: 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
Contributor: 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
Contributor: 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
Contributor: 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
Contributor: 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
Contributor: 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
Contributor: 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
Contributor: 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
Contributor: 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
Contributor: 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
Contributor: 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
Contributor: 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
Contributor: 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
Contributor: 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
Contributor: 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
Contributor: 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
Contributor: 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...
Machine Learning Model for the Planetary Albedo
Contributor: Sofia Strukova
The goal of the project is to use ML techniques to identify relationships between planetary mapped datasets, with the goal of providing deeper...
Equivariant Neural Networks for Dark Matter Morphology with Strong Gravitational Lensing
Contributor: Apoorva Vikram Singh
The study of substructures in the dark matter has shown signs of promise to deliver on the open-ended and long-standing problem of the identity of...
Graph Neural Networks for Particle Momentum Estimation in the CMS Trigger System
Contributor: Emre Kurtoglu
The Compact Muon Solenoid (CMS) is a detector at the Large Hadron Collider (LHC) located near Geneva, Switzerland. The CMS experiment detects the...
On the potential of graph-based models in High Energy Physics
Contributor: Ali A. Hariri
The Large Hadron Collider (LHC) at CERN is the world's highest energy particle accelerator, delivering the highest energy proton-proton collisions...
Machine Learning Model for the Albedo of Mercury
Contributor: Georgios Pipilis
Using Deep Learning techniques in order to model the relationship between the planetary albedo and chemical composition of Mercury.
Direct Objective Function for Anomaly Detection
Contributor: Yurii Halychanskyi
Currently, DeepLense supports the following models for unsupervised dark matter classification: - Adversarial Autoencoder - Convolutional...
Background Estimation with Neural AutoRegressive Flows
Contributor: Muhammad Ehsan ul Haq
Neural AutoRegressive Flows are one of the most recent addition to the family of autoregressive flows. By using NAFs, probability density estimation...
End-to-End Deep Learning Reconstruction for CMS Experiment
Contributor: 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...
Graph Neural Networks for End-to-End Particle Identification with the CMS Experiment
Contributor: Shravan Chaudhari
This project focuses on the study and implementation of Graph Neural Networks (GNNs) for low-momentum Tau Particle Identification using the CMS Open...
Normalizing Flows for Fast Detector Simulation
Contributor: Aditya Ahuja
DeepFalcon is an ultra-fast non-parametric detector simulation package. This project aims to extend DeepFalcon by adding functionality for Graph...
Quple - Quantum GAN
Contributor: Chi Lung Cheng
The proposed project "Quple - Quantum GAN" serves as an extension to the 2020 GSoC project "Quple" with a major focus on the implementation of...
Dimensionality Reduction for Studying Diffuse Circumgalactic Medium
Contributor: Jakub Rybak
This project will seek to identify dimensionality-reduction methods that achieve a reduction in the number of features while maintaining predictive...
Quantum Convolutional Neural Networks for High-Energy Physics Analysis at the LHC
Contributor: Eraraya Ricardo Muten
One of the challenges in High-Energy Physics (HEP) is events classification, which is to predict whether an image of particle jets belongs to events...
Background Estimation with Neural Autoregressive Flows Proposal
Contributor: Sinan Gençoğlu
Data-driven background estimation is crucial for many scientific searches, including searches for new phenomena in experimental datasets. Neural...
Machine Learning for Turbulent Fluid Dynamics
Contributor: Amey Varhade
Our understanding of Turbulence is still not very clear, studying fluid transitions to turbulence still poses challenging problems. The Navier-Stokes...
End-to-End Deep Learning Regression for Measurements with the CMS Experiment
Contributor: Anis Ismail
Experiments conducted at the Large Hadron Collider (LHC) are the source of the most important discoveries in new physics. One of the most prominent...
Decoding quantum states through Nuclear Magnetic Resonance
Contributor: Anantha Rao
At low temperatures, many materials transition into an electronic phase which cannot be classified as a simple metal or insulator, and quantum phases...
Domain Adaptation for Decoding Dark Matter with Strong Gravitational Lensing
Contributor: Marcos Tidball
Dark matter is one of the biggest questions in current cosmology, and many different theories were created to try to explain it. One of the...
Uncovering the Enigma of Type-Ia Supernovae: Thermonuclear Supernova Classification via their Nuclear Signatures
Contributor: Sophia He
Fundamental questions about Thermonuclear Supernovae (Type-Ia or SNeIa), the beacons visible across the universe, remain unanswered. Using the...