Detecting Secular Perturbations in Kepler Planetary Systems Using Simultaneous Impact Parameter Variation Analysis (SIPVA)

TL;DR

The paper introduces SIPVA, a new Bayesian method for detecting impact parameter variations in multi-planet systems, which improves accuracy over traditional methods without heavy computational costs.

Contribution

SIPVA is a novel linear model integrated into MCMC that outperforms existing impact parameter analysis methods in efficiency and accuracy.

Findings

SIPVA outperforms the Individual Fit in artificial system tests.

SIPVA detects impact parameter trends in more Kepler candidates.

Theoretical distribution of impact parameter variations aligns with observed data.

Abstract

Recovering impact parameter variations in multi-planet systems is an effective approach for detecting non-transiting planets and refining planetary mass estimates. Traditionally, two methodologies have been employed: the Individual Fit, which fits each transit independently to analyze impact parameter changes, and the Dynamical Fit, which simulates planetary dynamics to match transit light curves. We introduce a new fitting method, Simultaneous Impact Parameter Variation Analysis (SIPVA), which demonstrates advantages over the Individual Fit and avoids the computational cost of N-body integrations required by the Dynamical Fit. SIPVA directly incorporates a linear time-dependent model for impact parameters into the Markov Chain Monte Carlo (MCMC) framework by fitting all transits simultaneously. We evaluate SIPVA and the Individual Fit on artificial systems with varying log-likelihood ratios and find that SIPVA consistently outperforms the Individual Fit in recovery rates and accuracy. When applied to selected Kepler planetary candidates exhibiting significant transit duration variations (TDVs), SIPVA identifies significant impact parameter trends in 10 out of 16 planets, whereas the Individual Fit does so in only 4. We also employ probabilistic modeling to simulate the theoretical distribution of planets with significant impact parameter variations across all observed Kepler systems and compare the distribution of recovered candidates by the Individual Fit and Dynamical Fit from previous work with our theoretical distribution. Our findings offer an alternative framework for analyzing planetary transits, relying solely on Bayesian inference without requiring prior assumptions about the planetary system's dynamical architecture.