ML4GW / ML4GW/aframe

Update background online

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online-overhaul
Dominant language
Python
Stars
18
Forks
28
Avg merge
2d 2h
Merged PRs (30d)
7

Description

The online pipeline loads a fixed background model at startup, but the background needs to be updated periodically from live quiet-time data. Because the background informs both the FAR estimate and the p_astro model, the detection threshold should update alongside it. The update strategy also needs to be determined; e.g., whether to use a rolling window of recent samples or accumulate all data since deployment.

Contributor guide

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First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

Start by examining the online pipeline's startup loading of the fixed background model and how that background feeds the FAR estimate, p_astro model, and detection threshold. Determine whether updates should use a rolling window of recent quiet-time samples or accumulate data since deployment, then define how the threshold updates with the background.

Written by the indexing model from the issue text.

Assessment

Tech stack
python
Domain
machine-learning
Issue type
Feature
Difficulty
5/5
Estimated time
Over a week
Activity status
Quiet
Clarity
Needs clarification
Newbie friendliness
35/100

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