Baryon Challenge
Setting up the last details for the DESC Baryon Challenge!
Contacts: @chrgeorgiou, @elisachisari
Day/Time: Wed - Fri
Main communication channel: #desc-baryon-challenge
GitHub repo: github.com/LSSTDESC/baryon-challenge
Zoom room (if applicable):
Goals and deliverable
The aim of the sprint is to plan the development for missing details regarding the baryon challenge, and get started on their development. These are:
- Generation of mock data.
- Metrics for evaulating models.
- Documentation and github pages for the challenge.
- Infrastructure update.
Resources and skills needed
Knowledge of CCL, Firecrown, and/or modelling baryonic feedback is appreciated.
Detailed description
The DESC Baryon Challenge pits different models of baryonic feedback against each other with the goal of informing analysis choices for LSST Y1 cosmology. The challenge will focus on cosmic shear data with 3x2-pt priors. Synthetic mock data will be generated and the challenge entrants will submit their model as a function that can be interfaced with the likelihood code Firecrown. The models will then be used in a MCMC analysis and evaluated in terms of performance (accuracy and precision).
Baryon Challenge
Setting up the last details for the DESC Baryon Challenge!
Contacts: @chrgeorgiou, @elisachisari
Day/Time: Wed - Fri
Main communication channel: #desc-baryon-challenge
GitHub repo: github.com/LSSTDESC/baryon-challenge
Zoom room (if applicable):
Goals and deliverable
The aim of the sprint is to plan the development for missing details regarding the baryon challenge, and get started on their development. These are:
Resources and skills needed
Knowledge of CCL, Firecrown, and/or modelling baryonic feedback is appreciated.
Detailed description
The DESC Baryon Challenge pits different models of baryonic feedback against each other with the goal of informing analysis choices for LSST Y1 cosmology. The challenge will focus on cosmic shear data with 3x2-pt priors. Synthetic mock data will be generated and the challenge entrants will submit their model as a function that can be interfaced with the likelihood code Firecrown. The models will then be used in a MCMC analysis and evaluated in terms of performance (accuracy and precision).