Differential Photometry works best when the target and reference star have similar spectra. But in vast majority of cases, spectra won’t be available. We look at machine learning techniques to see ways inwhich stellar magnitudes can be used as proxies to predict how well spectra wil correlate.
We look at the spectra of pairs of stars and how the correlation between them can be predicted based on their SDSS magnitude values. This is important for differential photometry, where we try and account for the effects of the atmosphere on stellar lightcurves by examining the optical behaviour of neighbouring stars. The benefit here is that atmospheric effects are wavelength dependent and choosing star pairs of similar spectra gives better results. Seeing as we only have spectra for a limited number of stars, using photometric magnitude values is important. This publication makes the bridge from these magnitudes to predicting the spectral matches.
The publication can be accessed from this link.
If you see mistakes or want to suggest changes, please create an issue on the source repository.
Text and figures are licensed under Creative Commons Attribution CC BY 4.0. Source code is available at https://github.com/eugene100hickey/fizzics, unless otherwise noted. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".
For attribution, please cite this work as
Hickey (2023, Jan. 8). Euge: Using machine learning to predict the correlation of spectra using SDSS magnitudes as an improvement to the Locus Algorithm. Retrieved from https://www.fizzics.ie/publications/2023-01-08-Locus_ML/
BibTeX citation
@misc{hickey2023using,
author = {Hickey, Eugene},
title = {Euge: Using machine learning to predict the correlation of spectra using SDSS magnitudes as an improvement to the Locus Algorithm},
url = {https://www.fizzics.ie/publications/2023-01-08-Locus_ML/},
year = {2023}
}