Alleviating the transit timing variation bias in transit surveys. I. RIVERS: Method and detection of a pair of resonant super-Earths around Kepler-1705

11/12/2021
by   A. Leleu, et al.
7

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 tracks of low-signal-to-noise-ratio(S/N) perturbed planets in river diagrams. We recover the transit parameters of these candidates by fitting the light curve. The individual transit S/N of Kepler-1705b and c are about three times lower than all the previously known planets with TTVs of 3 hours or more, pushing the boundaries in the recovery of these small, dynamically active planets. Recovering this type of object is essential for obtaining a complete picture of the observed planetary systems, and solving for a bias not often taken into account in statistical studies of exoplanet populations. In addition, TTVs are a means of obtaining mass estimates which can be essential for studying the internal structure of planets discovered by transit surveys. Finally, we show that due to the strong orbital perturbations, it is possible that the spin of the outer resonant planet of Kepler-1705 is trapped in a sub- or super-synchronous spin-orbit resonance.

READ FULL TEXT

page 2

page 3

page 5

page 7

page 14

page 15

page 17

page 18

research
01/12/2021

New Bias Calibration for Robust Estimation in Small Areas

Using sample surveys as a cost effective tool to provide estimates for c...
research
10/16/2014

Super-resolution method using sparse regularization for point-spread function recovery

In large-scale spatial surveys, such as the forthcoming ESA Euclid missi...
research
11/04/2022

Towards Asteroid Detection in Microlensing Surveys with Deep Learning

Asteroids are an indelible part of most astronomical surveys though only...
research
06/29/2019

High Sensitivity Snapshot Spectrometer Based on Deep Network Unmixing

In this paper, we present a convolution neural network based method to r...
research
08/29/2022

Inferring subhalo effective density slopes from strong lensing observations with neural likelihood-ratio estimation

Strong gravitational lensing has emerged as a promising approach for pro...

Please sign up or login with your details

Forgot password? Click here to reset