Updates to fitting for faint traces. #35
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jchate6
approved these changes
Dec 16, 2024
25 is small enough of step to sample the dispersion direction well, but large enough to boost the signal-to-noise be a factor of ~5. |
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I've updated how we fit the width of the profile. It is much closer to how we fit the centroid now. We bin the data down by again a factor of 25, interpolating onto a common grid and then fit a simple polynomial + gaussian model. |
jchate6
approved these changes
Jan 8, 2025
| profile_sigmas.append(Legendre(best_fit, domain=domain)) | ||
| return profile_sigmas | ||
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| # Group the data in bins of 25 columns (25 is big enough to increase the s/n by a factor of 5 but small enough |
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This PR updates our trace fitting algorithm to better deal with faint (read low signal-to-noise) traces. To fit the center, we take 25 pixel steps and fit the entire 25-pixel wide block using a match filter to get the trace center. We have also included error bars in the centroids estimated from this. These steps are fit to a Legendre polynomial to get initial coefficient guesses. We then fit the entire trace simultaneously (with a fixed width) to get the centers of the trace. Once we have the centers, we fix them and allow the width to vary. We include a background term that is a Legendre polynomial but the coefficients vary smoothly across the image.