Foundation Model for Gravitational Lensing

Description

Strong gravitational lensing is a powerful tool for studying dark matter and the large-scale structure of the universe. This project focuses on developing a vision foundation model specifically designed for lensing data, which can be fine-tuned for a variety of downstream tasks, including classification, super-resolution, regression, and lens finding.

This project will explore different training strategies such as self-supervised learning, contrastive learning, or transformer-based models to learn meaningful representations of lensing images. By leveraging diverse datasets and training methodologies, the model will serve as a general-purpose backbone that can adapt to different astrophysical tasks while improving generalization across various observational conditions.

Duration

Total project length: 350 hours.

Difficulty level

Advanced

Task ideas

Develop a pre-training strategy for learning robust representations of gravitational lensing data.
Fine-tune the foundation model for multiple tasks such as classification, super-resolution, and regression.
Evaluate the model’s performance on different astrophysical datasets and benchmark against traditional methods.

Expected results

A vision foundation model for gravitational lensing capable of being fine-tuned for various astrophysical tasks.
Improved generalization and adaptability across different lensing datasets and observational setups.

Requirements

Python, PyTorch, experience with machine learning, and familiarity with computer vision techniques.
Understanding of self-supervised learning, representation learning, and deep learning architectures.

Test

Please use this link to access the test for this project.

Mentors

Michael Toomey (Massachusetts Institute of Technology)
Sergei Gleyzer (University of Alabama)
Pranath Reddy (University of Florida)
Anna Parul (Observatoire de Paris)

Please DO NOT contact mentors directly by email. Instead, please email ml4-sci@cern.ch with Project Title and include your CV and test results. The relevant mentors will then get in touch with you.

Corresponding Project

DEEPLENSE