Coding interface/calls in CCL for direct isolation and removal of intrinsic alignment including linear and non-linear regimes

[mishakb]

[Coding interface/calls in CCL to serve direct isolation and removal of intrinsic alignment including linear and non-linear regimes]

[We will work on code in CCL and its interfacing with FastPT to add components that will allow direct isolation and removal of IA correlations using self-calibrating techniques at linear and non-linear levels. This will include non-linear bias and non-linear orders for IA. We developed analytical expressions for these non-linear terms and in this sprint, we will work on implementing how to use them in CCL via FastPT. The work on FastPT is completed and under testing but making is work with CCL and validating it and applying it to an example are things we aim to do at the Sprint week.]

Contacts: Mustapha Ishak @mishakb, Avijit Bera, Vinu Soraya Day/Time: TBD Main communication channel: sprint slack-channel. ia-channel (TBC) GitHub repo:
Zoom room (if applicable): To be added

Goals and deliverable

1) CCL code working to call updated FastPT with the new terms added for direct detection self-calibration techniques 2) a working toy or basic example

Resources and skills needed

Python, some knowledge of CCL, FastPT, [describe the resources (software, skills, data, or just enthusiasm) needed for this project]

Detailed description

TBAdded [add additional details about the project]

[Vinukcs]

Initial goals for the week:

• Assess the sensitivity of the the SC scaling relation to model parameters
• This SC uses FastPT to calculate new terms. Integrate the method to CCL and other DESC tools.

Achievements of this week:

• Evaluated the changes in the ratio P_gl/P_ml as we change model parameters in the TATT by 5% or 10% → less than 1 percent change!

Plans for follow up work:

• Further integration with DESC tools such as TXPipe and CCL