KOBEsim: A Bayesian observing strategy algorithm for planet detection in radial velocity blind-search surveys^★

¹ Centro de Astrobiología (CAB), CSIC-INTA, Camino Bajo del Castillo s/n, 28692, Villanueva de la Cañada, Madrid, Spain
e-mail: obalsalobre@cab.inta-csic.es
² Depto. Estadística e Investigación Operativa, Universidad de Cádiz, Avda. República Saharaui s/n, 11510 Puerto Real, Cádiz, Spain
³ Instituto de Astrofísica e Ciências do Espaço, Universidade do Porto, CAUP, Rua das Estrelas, 4150-762 Porto, Portugal
⁴ Departamento de Física e Astronomia, Faculdade de Ciências, Universidade do Porto, Rua do Campo Alegre, 4169-007 Porto, Portugal
⁵ Centro Astronómico Hispano en Andalucía, Observatorio de Calar Alto, Sierra de los Filabres, 04550 Gérgal, Almería, Spain
⁶ Instituto de Astrofísica de Andalucia, Glorieta de la Astronomia s/n, Granada, Spain
⁷ Aix-Marseille Univ, CNRS, CNES, LAM, Marseille, France

Received: 3 May 2022
Accepted: 20 October 2022

Abstract

Context. Ground-based observing time is precious in the era of exoplanet follow-up and characterization, especially in high-precision radial velocity instruments. Blind-search radial velocity surveys thus require a dedicated observational strategy in order to optimize the observing time, which is particularly crucial for the detection of small rocky worlds at large orbital periods.

Aims. We developed an algorithm with the purpose of improving the efficiency of radial velocity observations in the context of exoplanet searches, and we applied it to the K-dwarfs Orbited By habitable Exoplanets experiment. Our aim is to accelerate exoplanet confirmations or, alternatively, reject false signals as early as possible in order to save telescope time and increase the efficiency of both blind-search surveys and follow-up of transiting candidates.

Methods. Once a minimum initial number of radial velocity datapoints is reached in such a way that a periodicity starts to emerge according to generalized Lomb-Scargle periodograms, that period is targeted with the proposed algorithm, named KOBEsim. The algorithm selects the next observing date that maximizes the Bayesian evidence for this periodicity in comparison with a model with no Keplerian orbits.

Results. By means of simulated data, we proved that the algorithm accelerates the exoplanet detection, needing 29-33% fewer observations and a 41–47% smaller time span of the full dataset for low-mass planets (m_p < 10 M_⊕) in comparison with a conventional monotonic cadence strategy. For 20 M_⊕ planets we found a 16% enhancement in the number of datapoints. We also tested KOBEsim with real data for a particular KOBE target and for the confirmed planet HD 102365 b. These two tests demonstrate that the strategy is capable of speeding up the detection by up to a factor of 2 (i.e., reducing both the time span and number of observations by half).