A machine learning approach for correcting radial velocities using physical observables

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Volume 672 (April 2023)

A&A, 672 (2023) A118

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Table 2

Time-series data characteristics of 66 nightly binned data points of ε Eri using HARPS (top) and of 37 nightly binned data points from AUMic using CARMENES VIS (bottom), both extracted by raccoon.

Data	Unit	Mean (unit)	RMS (unit)	Uncertainty (unit)	RRE (%)	Significant periods (days)
RV	m s⁻¹	16453.68	4.36	1.19	39	12.44;5.56
BIS	m s⁻¹	32.48	5.56	2.44	17	12.06; 5.56; 10.60
FWHM	m s⁻¹	6463.83	12.01	5.72	23	11.81; 5.54; 175.43; 12.81; 10.32
CON	%	40.739	0.058	0.029	25	12.18; 5.54; 175.43; 10.63; 12.62

RV	m s⁻¹	−5064.84	132.26	6.76	25	4.90;2.59
BIS	m s⁻¹	−61.75	172.04	17.11	28	4.90; 37.72; 2.59; 5.74; 2.45
FWHM	m s⁻¹	12380.18	202.05	54.23	58	4.89; 4.19
CON^(*)	%	10.013	0.204	0.035	71	2.58

Notes. The RRE is the ratio between the original RMS versus the resulting one obtained after applying pre-whitening (i.e. until no signal with FAP < 1% is present in the data), ^(*): statistics without outlier.

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