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Title:
Ghost in the time series: no planet for Alpha Cen B
Authors:
Rajpaul, V.; Aigrain, S.; Roberts, S.
Affiliation:
AA(Sub-department of Astrophysics, Department of Physics, University of Oxford, Oxford OX1 3RH, UK ), AB(Sub-department of Astrophysics, Department of Physics, University of Oxford, Oxford OX1 3RH, UK), AC(Pattern Recognition and Machine Learning Group, Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK)
Publication:
Monthly Notices of the Royal Astronomical Society: Letters, Volume 456, Issue 1, p.L6-L10 (MNRAS Homepage)
Publication Date:
02/2016
Origin:
OUP
Astronomy Keywords:
methods: data analysis, techniques: radial velocities, stars: activity, stars: individual: Alpha Cen B, planetary systems
Abstract Copyright:
2015 The Authors Published by Oxford University Press on behalf of the Royal Astronomical Society
DOI:
10.1093/mnrasl/slv164
Bibliographic Code:
2016MNRAS.456L...6R

Abstract

We re-analyse the publicly available radial velocity (RV) measurements for Alpha Cen B, a star hosting an Earth-mass planet candidate, Alpha Cen Bb, with 3.24 d orbital period. We demonstrate that the 3.24 d signal observed in the Alpha Cen B data almost certainly arises from the window function (time sampling) of the original data. We show that when stellar activity signals are removed from the RV variations, other significant peaks in the power spectrum of the window function are coincidentally suppressed, leaving behind a spurious yet apparently significant `ghost' of a signal that was present in the window function's power spectrum ab initio. Even when fitting synthetic data with time sampling identical to the original data, but devoid of any genuine periodicities close to that of the planet candidate, the original model used to infer the presence of Alpha Cen Bb leads to identical conclusions: viz., the 3σ detection of a half-a-metre-per-second signal with 3.236 d period. Our analysis underscores the difficulty of detecting weak planetary signals in RV data, and the importance of understanding in detail how every component of an RV data set, including its time sampling, influences final statistical inference.
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