A k-NN Method to Classify Rare Astronomical Sources: Photometric Search of Brown Dwarfs with Spitzer/IRAC
Massimo Marengo, Mayly C. Sanchez

TL;DR
This paper introduces a weighted k-NN statistical method for photometrically identifying rare astronomical objects like brown dwarfs, avoiding biases from traditional cut-based searches, and demonstrates its effectiveness on Spitzer survey data.
Contribution
The paper presents a novel k-NN based metric for unbiased photometric search of rare sources, optimized for efficiency and completeness, applied to brown dwarf detection.
Findings
Confirmed the absence of late-T dwarfs in the surveyed fields.
Detected a few L/early-T candidates undergoing follow-up.
Method shows high completeness and potential for future surveys.
Abstract
We present a statistical method for the photometric search of rare astronomical sources based on the weighted k-NN method. A metric is defined in a multi-dimensional color-magnitude space based only on the photometric properties of template sources and the photometric uncertainties of both templates and data, without the need to define ad-hoc color and magnitude cuts which could bias the search. The metric is defined as a function of two parameters, the number of neighbors k and a threshold distance D_th that can be optimized for maximum selection efficiency and completeness. We apply the method to the search of L and T dwarfs in the Spitzer Extragalactic First Look Survey and the Bootes field of the Spitzer Shallow Survey, as well as to the search of sub-stellar mass companions around nearby stars. With high level of completeness, we confirm the absence of late-T dwarfs detected in at…
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