Description

The Rubin Observatory will provide an unprecedented volume of astronomical data, accessible via a dedicated open-source software suite consisting of data reduction pipelines and tools for interacting with calibrated images and catalogs. In order to prepare for the application of DeepLense methods on upcoming LSST data, this project focuses on developing a complementary pipeline that integrates LSST’s data access tools with DeepLense workflows. This pipeline will enable efficient data retrieval, preprocessing, and adaptation for various DeepLense applications such as lens finding, super-resolution, and classification.

Duration

Total project length: 175/350 hours.

Difficulty level

Intermediate/Advanced

Task ideas

• Explore the existing LSST data access tools and design the workflow to provide the data for the DeepLense tasks.
• Test the workflow on the mock surveys provided by The Rubin Observatory.

Expected results

• A functional pipeline capable of interfacing LSST data with DeepLense applications.

Requirements

Python, familiarity with astronomical data processing, and understanding of data access APIs and pipelines.

Test

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

Mentors

• Michael Toomey (Massachusetts Institute of Technology)
• Sergei Gleyzer (University of Alabama)
• Anna Parul (Observatoire de Paris)
• Lucca Paris (Brown University)

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.

Links

• LSST Pipeline Docs
• Paper 1
• Paper 2

Corresponding Project

• DEEPLENSE

Participating Organizations

• Alabama
• Brown
• MIT
• PSL

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