Stellar Locus Regression: Accurate Color Calibration, and the Real-time Determination of Galaxy Cluster Photometric Redshifts
F. William High, Christopher W. Stubbs, Armin Rest, Brian Stalder,, Peter Challis

TL;DR
Stellar Locus Regression (SLR) is a real-time, calibration-free method that adjusts stellar colors to a universal locus, enabling accurate photometric redshifts and color calibration without traditional zeropoint calibration.
Contribution
This paper introduces SLR, a novel technique that calibrates colors directly to a universal stellar locus in real-time, improving accuracy and efficiency in astronomical photometry.
Findings
Achieves 14-18 mmag RMS uncertainties in Galactic reddening correction.
Returns precise colors over a wide range of airmass with 5-14 mmag residuals.
Enables galaxy cluster photometric redshifts with 0.6% uncertainty.
Abstract
We present Stellar Locus Regression (SLR), a method of directly adjusting the instrumental broadband optical colors of stars to bring them into accord with a universal stellar color-color locus, producing accurately calibrated colors for both stars and galaxies. This is achieved without first establishing individual zeropoints for each passband, and can be performed in real-time at the telescope. We demonstrate how SLR naturally makes one wholesale correction for differences in instrumental response, for atmospheric transparency, for atmospheric extinction, and for Galactic extinction. We perform an example SLR treatment of SDSS data over a wide range of Galactic dust values and independently recover the direction and magnitude of the canonical Galactic reddening vector with 14--18 mmag RMS uncertainties. We then isolate the effect of atmospheric extinction, showing that SLR accounts…
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