Use AMPLFI posteriors to compute p_astro over population model range
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- Dominant language
- Python
- Stars
- 18
- Forks
- 28
- Avg merge
- 2d 2h
- Merged PRs (30d)
- 7
Description
The per-class signal rate for class $c$ (BBH, BNS, NSBH) is
$$
R_c = \int p(d|\theta) \pi_c(\theta) d\theta
$$
where $\pi_c$ is the class sub-population from #465. Rather than approximating this with a fixed mass fraction, estimate it via importance sampling over AMPLFI's posterior samples $\theta_i \sim q(\theta|d)$:
$$
R_c \cdot \frac{1}{N} \sum_i \frac{\pi_c(\theta_i)}{q(\theta_i)}
$$
The Monte Carlo variance of the importance weights gives error bars. Implement in libs/p_astro/ and wire into online/subprocesses/p_astro.py.
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reading the existing implementation in libs/p_astro/ and the integration point in online/subprocesses/p_astro.py, then trace how AMPLFI posterior samples and the population model are represented. Implement per-class rate estimation across the population model range with importance sampling, use the weight variance for error bars, and verify that the online subprocess uses the result.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- data, machine-learning
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Quiet
- Clarity
- Mostly clear
- Newbie friendliness
- 48/100