Description

This project’s aims are twofold: developing an unsupervised super-resolution architecture to upscale the quality of lensing images constructed using real galaxy sources, and to obtain insight about the lenses themselves. An unsupervised super-resolution technique could be very valuable for lensing studies as access to high resolution lensing images for training and study can be limited, especially given potential lensing data from upcoming surveys such as Euclid and LSST. The overall goal of this project is to develop an architecture that can better study the characteristics of the gravitational lenses and their substructure.

Duration

Total project length: 175/350 hours.

Difficulty level

Intermediate/Advanced

Task ideas

• Start with unsupervised SR of simulated images and think of ways to bridge the gap to real images.
• Try then integrating into the pipeline modules that study characteristics of the lenses.

Expected results

• A more capable architecture that can operate on a wider variety of lensing images, including lensing images created with real galaxy datasets.
• Insight into the lensing systems, and their sub-structures

Requirements

• Python, PyTorch and relevant past experience in Machine Learning.
• Familiarity with astrophysics and gravitational lensing is preferred but not required.

Test

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

Mentors

• Michael Toomey (Massachusetts Institute of Technology)
• Pranath Reddy (Independent Researcher)
• Sergei Gleyzer (University of Alabama)
• Hamees Sayed (IIT Madras)

Please DO NOT contact mentors directly by email. General questions can be directed to ml4-sci@cern.ch. To submit your proposal, CV, and test task solutions, please use this Google form.

Links

• Paper 1
• Paper 2
• Paper 3

Corresponding Project

• DEEPLENSE

Participating Organizations

• Alabama
• MIT
• IIT Madras

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