Deep Regression Techniques for Decoding Dark Matter with Strong Gravitational Lensing

Description

Strong gravitational lensing is a promising probe of the substructure of dark matter to better understand its underlying nature. Deep learning methods have the potential to accurately identify images containing substructure, and differentiate WIMP particle dark matter from other well motivated models, including vortex substructure of dark matter condensates and superfluids.

This project will focus on further development of the DeepLense pipeline that combines state-of-the art of deep learning models with strong lensing simulations based on lenstronomy. The focus of this project is on using deep regression techniques for estimating dark matter properties, including population-level quantities and properties of dark matter particle candidates (e.g. CDM, WDM, axions, SIDM).

Duration

Total project length: 175 hours.

Task ideas

Expected results

Requirements

Python, PyTorch and relevant past experience in Machine Learning.

Test

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

Mentors

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

Participating Organizations