Alleviating the transit timing variation bias in transit surveys. I. RIVERS: Method and detection of a pair of resonant super-Earths around Kepler-1705
A. Leleu, G. Chatel, S. Udry, Y. Alibert, J.-B. Delisle, R., Mardling

TL;DR
This paper introduces a neural network-based detection method for resonant super-Earths with large transit timing variations, improving detection sensitivity and addressing biases in transit surveys like Kepler.
Contribution
The authors develop a TTV-robust detection method using neural networks and demonstrate its effectiveness by recovering a pair of resonant super-Earths around Kepler-1705.
Findings
Successfully recovered a pair of resonant super-Earths with low S/N TTVs.
The method extends detection capabilities to smaller, dynamically active planets.
Highlights the importance of accounting for TTV bias in exoplanet population studies.
Abstract
Transit timing variations (TTVs) can provide useful information for systems observed by transit, as they allow us to put constraints on the masses and eccentricities of the observed planets, or even to constrain the existence of non-transiting companions. However, TTVs can also act as a detection bias that can prevent the detection of small planets in transit surveys that would otherwise be detected by standard algorithms such as the Boxed Least Square algorithm (BLS) if their orbit was not perturbed. This bias is especially present for surveys with a long baseline, such as Kepler, some of the TESS sectors, and the upcoming PLATO mission. Here we introduce a detection method that is robust to large TTVs, and illustrate its use by recovering and confirming a pair of resonant super-Earths with ten-hour TTVs around Kepler-1705. The method is based on a neural network trained to recover the…
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