[SPRINT] BLISS and JIF Notebook Walkthrough

[ismael-mendoza]

Bayesian Light Source Separator (BLISS) and Joint Image Framework (JIF) Notebook Walkthrough

This sprint will be a walkthrough of two existing DESC Tools, BLISS and JIF, that will be part of the planned pixel-to-shear posterior pipeline of the Bayesian Pipelines Topical Team.

Contacts: @ismael-mendoza, @mdschneider Time: Morning 10a-11a Main communication channel: #desc-bayesian-pipelines-tt GitHub repo: https://github.com/LSSTDESC/bayesian-pipelines-pixels In-person/Virtual/Hybrid: In-person Zoom room (if applicable): N/A

Goals and deliverable

• Walkthrough attendants through the notebooks so they come out with a sense of how to use these tools.
• Participants will analyze a set of images from a simple parametric galaxy model and draw posterior samples of their galaxy parameters (with either BLISS or JIF).

Resources and skills needed

• Some experience with python would be helpful
• Interest in weak lensing or predicting shear from galaxy images

Detailed description

Notebooks will be provided in a google colab format or binder, so no installation will be required.

More details on tools provided below:

BLISS - Is a fully probabilistic deblender that can output probabilistic catalogs of counts, locations, fluxes given an image. It can also output samples of deblended images that capture uncertainty in location and degree of blending. BLISS will be used to posterior samples of counts, locations, and deblended images to the shear inference algorithms.

JIF - This algorithm fits a parametric model to a single galaxy and MCMC the parameter space, propagating to shear. The project will test this as a viable tool for shear inference when coupled with BLISS, which is expected to provide JIF with posterior samples of counts and locations.

[ismael-mendoza]

Due to unavailability of leads, we will move this to the sprint week