Fix maximize_likelihood in HeterodynedTransientLikelihoodFD

[thomasckng]

This PR depends on kazewong/flowMC#229.

Summary by CodeRabbit

• Bug Fixes
  • Improved selection of best-fit parameters to ensure results correspond to the highest likelihood.

[coderabbitai]

Walkthrough

The change updates the maximize_likelihood method in the HeterodynedTransientLikelihoodFD class to handle an additional return value (log_prob) from the optimizer.optimize call. It now selects the best-fit parameters based on the minimum log_prob, ensuring the returned parameters correspond to the highest likelihood.

Changes

File(s)	Change Summary
src/jimgw/single_event/likelihood.py	Modified maximize_likelihood to unpack a third return value (log_prob) from optimizer.optimize, and updated the selection of best-fit parameters to use the minimum log_prob index.

Poem

In the land of likelihood, numbers dance and play,
Now with log_prob guiding, we find the brightest ray.
The optimizer whispers, “Choose the best you see!”
With three gifts returned, the answer’s clear to me.
🥕 Hopping to the minimum, our rabbit finds the peak,
Where the fit is strongest, and the result unique!

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📥 Commits

Reviewing files that changed from the base of the PR and between 05143c1 and d03e70b.

📒 Files selected for processing (1)

• src/jimgw/single_event/likelihood.py (1 hunks)

🔇 Additional comments (1)

src/jimgw/single_event/likelihood.py (1)

524-528: Improved parameter selection criteria - Good enhancement!

The change properly handles the additional return value (log_prob) from the optimizer and selects the best parameter set by choosing the one with the minimum log probability (maximum likelihood). This is a solid improvement over the previous implementation where parameter selection criteria wasn't clear.

The code now:

1. Unpacks three values from the optimizer instead of two
2. Uses jnp.argmin(log_prob) to identify the best parameter set
3. Returns parameters associated with the highest likelihood

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[thomasckng]

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