A machine-learning photometric classifier for massive stars in nearby galaxies - I. The method

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Volume 666 (October 2022)

A&A, 666 (2022) A122

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Table 4.

Data availability per class and photometric band.

Class	Sources	[3.6]	σ_[3.6]	[4.5]	σ_[4.5]	[5.8]	σ_[5.8]	[8.0]	σ_[8.0]	[24]	σ_[24]
	(#)	(%)	(%)	(%)	(%)	(%)	(%)	(%)	(%)	(%)	(%)
BSG	261	100	100	100	100	100	80	100	70	100	41
YSG	103	100	100	100	100	100	92	100	78	99	30
RSG	512	100	100	100	100	99	99	100	93	99	41
BeBR	17	100	100	100	100	100	100	100	100	100	94
LBV	6	100	100	100	100	100	100	100	100	100	66
WR	53	100	100	100	100	100	94	100	86	100	43
GAL	24	100	100	100	100	100	100	100	100	100	100

Class	Sources	g	σ_g	r	σ_r	i	σ_i	z	σ_z	y	σ_y
	(#)	(%)	(%)	(%)	(%)	(%)	(%)	(%)	(%)	(%)	(%)

BSG	261	96	96	96	96	96	96	96	96	96	96
YSG	103	96	96	96	96	96	96	96	96	96	96
RSG	512	96	93	97	97	97	97	97	97	97	97
BeBR	17	100	100	100	100	100	100	100	100	94	94
LBV	6	100	100	100	100	100	100	100	100	100	100
WR	53	83	83	84	83	88	88	90	88	86	84
GAL	24	100	100	100	100	100	100	100	95	100	100

Class	Sources	J_UK	G	G_BP	G_RP

	(#)	(%)	(%)	(%)	(%)
BSG	261	81	90	87	87
YSG	103	82	96	95	95
RSG	512	84	96	94	94
BeBR	17	70	100	100	100
LBV	6	66	83	66	66
WR	53	75	71	50	50
GAL	24	83	95	83	83

Notes. The first column lists the classes used and the second one the corresponding number of sources in the sample. For each class, the subsequent columns provide the fractions of sources with secure measurements in the corresponding photometric bands and their errors (which do not include objects with problematic measurements and upper limits).

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