Photometric brown-dwarf classification

Abstract

Aims. We present a method, named photo-type, to identify and accurately classify L and T dwarfs onto the standard spectral classification system using photometry alone. This enables the creation of large and deep homogeneous samples of these objects efficiently, without the need for spectroscopy. Methods. We created a catalogue of point sources with photometry in 8 bands, ranging from 0.75 to 4.6 μm, selected from an area of 3344 deg 2 , by combining SDSS, UKIDSS LAS, and WISE data. Sources with 13.0 < J < 17.5, and Y − J > 0.8, were then classified by comparison against template colours of quasars, stars, and brown dwarfs. The L and T templates, spectral types L0 to T8, were created by identifying previously known sources with spectroscopic classifications, and fitting polynomial relations between colour and spectral type. Results. Of the 192 known L and T dwarfs with reliable photometry in the surveyed area and magnitude range, 189 are recovered by our selection and classification method. We have quantified the accuracy of the classification method both externally, with spectroscopy, and internally, by creating synthetic catalogues and accounting for the uncertainties. We find that, brighter than J = 17.5, photo-type classifications are accurate to one spectral sub-type, and are therefore competitive with spectroscopic classifications. The resultant catalogue of 1157 L and T dwarfs will be presented in a companion paper.

Eight new luminous z ≥ 6 quasars discovered via SED model fitting of VISTA, WISE and Dark Energy Survey Year 1 observations

“…They were calculated for each individual DES image tile and applied separately for each tile. The objects were also compared to the derived brown dwarf colours from Skrzypek et al (2015). As these colours were given in the UKIRT Infrared Deep Sky Survey (UKIDSS) Large Area Survey (LAS) and SDSS pass bands, colour terms (these are given in A) were calculated between the surveys using the overlap between DES, UKIDSS, VHS and SDSS in Stripe 82.…”

“…Following this ranking, we visually inspected the candidates in ranked order to remove artefacts and junk sources, and also compared the quasar reduced χ 2 values to those obtained from a brown dwarf fit to the photometry. The likelihood of being a brown dwarf was calculated from the polynomial fits in Skrzypek et al (2015). Objects where the reduced χ 2 to be a brown dwarf was comparable to or higher than that to be a quasar were removed.…”

A Proper Motion Survey Using the First Sky Pass of Neowise-Reactivation Data

“…While previous studies have attempted photometric typing, mainly using color-spectral type polynomial relations (e.g., Sheppard 2014 andSkrzypek et al 2015), machine learning algorithms are an alternative tool to use to accomplish this type of classification. For this work, we utilized k-NN algorithm using code available from the scikit-learn project (Pedregosa et al 2011).…”

Photometric brown-dwarf classification

“…In a previous paper, Skrzypek et al (2015), hereafter Paper I, we presented a method, named photo-type, to identify and accurately classify samples of L and T dwarfs from multi-band photometry alone, without the need for spectroscopy. The motivation for developing the method was the need for a much larger homogeneous sample of L and T dwarfs, spanning the full range of spectral types, from L0 to T8, in order to characterise the LT population more precisely, by reducing the statistical errors on the measurements of properties of interest.…”