A PSF-based approach to Kepler/K2 data. I. Variability within the K2 Campaign 0 star clusters M 35 and NGC 2158
M. Libralato, L. R. Bedin, D. Nardiello, G. Piotto

TL;DR
This paper introduces a PSF-based neighbor subtraction method for K2 data, significantly improving photometric precision in dense star clusters and enabling detailed variability studies.
Contribution
The authors develop a novel PSF neighbor-subtraction technique tailored for K2 super-stamp data, enhancing photometry in crowded regions beyond classical aperture methods.
Findings
Achieved magnitudes as faint as Kp~24 with 10% precision over 6.5 hours
Improved faint-end photometric accuracy by a factor of several in crowded regions
Extracted and analyzed light curves for ~60,000 stars, identifying 2133 variables.
Abstract
Kepler and K2 data analysis reported in the literature is mostly based on aperture photometry. Because of Kepler's large, undersampled pixels and the presence of nearby sources, aperture photometry is not always the ideal way to obtain high-precision photometry and, because of this, the data set has not been fully exploited so far. We present a new method that builds on our experience with undersampled HST images. The method involves a point-spread function (PSF) neighbour-subtraction and was specifically developed to exploit the huge potential offered by the K2 "super-stamps" covering the core of dense star clusters. Our test-bed targets were the NGC 2158 and M 35 regions observed during the K2 Campaign 0. We present our PSF modeling and demonstrate that, by using a high-angular-resolution input star list from the Asiago Schmidt telescope as the basis for PSF neighbour subtraction, we…
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