Constraining exoplanet population parameters with Kepler and simulation-based inference
Abstract
In the context of the rapidly growing field of galactic exoplanet population studies, we present an exoplanet simulator capable of generating synthetic populations for millions of stars in a few seconds (Padois et al., 2025). Our framework combines observed exoplanet detections with the latest planetary formation models, incorporating dependencies between host star properties and planet occurrence rates, orbital period distributions, and other key demographic features. However, many model parameters remain uncertain and model-dependent. To constrain some of the most critical ones, we couple our simulator with simulation-based inference (SBI): we define a set of 44 free parameters describing the population of different planet types (occurrence rates, mass-period distribution, mass-radius relation, etc.), run millions of simulations sampling from broad prior distributions, simulate their detectability by Kepler, and train a machine-learning algorithm to learn the relationships between input parameters and the resulting "observed" synthetic populations. The best-fit parameters are then inferred by comparing model outputs to real observations, for which we adopt the Kepler confirmed planet catalogue as our reference dataset, since it provides the largest homogeneous sample from a single facility. We present preliminary results for the planet occurrence rate as a function of stellar mass and metallicity, across different planet types, along with constraints on several key simulation parameters such as the orbital period distribution and the inclination dispersion within planetary systems. These refined parameters enable us to refine our yield predictions for upcoming exoplanet detection missions, including PLATO, Roman, and HAYDN.
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BibTeX
@misc{padois2026constraining,
title = {{Constraining exoplanet population parameters with Kepler and simulation-based inference}},
author = {Chloé Padois and Friedrich Anders and Daniel del Ser},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.37960},
eprint = {2609.37960},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.37960},
}
RIS
TY - UNPB
TI - Constraining exoplanet population parameters with Kepler and simulation-based inference
AU - Chloé Padois
AU - Friedrich Anders
AU - Daniel del Ser
PY - 2026
DA - 2026/09/29
PB - arXiv
DO - 10.48550/arxiv.2609.37960
UR - https://arxiv.org/abs/2609.37960
AB - In the context of the rapidly growing field of galactic exoplanet population studies, we present an exoplanet simulator capable of generating synthetic populations for millions of stars in a few seconds (Padois et al., 2025). Our framework combines observed exoplanet detections with the latest planetary formation models, incorporating dependencies between host star properties and planet occurrence rates, orbital period distributions, and other key demographic features. However, many model parameters remain uncertain and model-dependent. To constrain some of the most critical ones, we couple our simulator with simulation-based inference (SBI): we define a set of 44 free parameters describing the population of different planet types (occurrence rates, mass-period distribution, mass-radius relation, etc.), run millions of simulations sampling from broad prior distributions, simulate their detectability by Kepler, and train a machine-learning algorithm to learn the relationships between input parameters and the resulting "observed" synthetic populations. The best-fit parameters are then inferred by comparing model outputs to real observations, for which we adopt the Kepler confirmed planet catalogue as our reference dataset, since it provides the largest homogeneous sample from a single facility. We present preliminary results for the planet occurrence rate as a function of stellar mass and metallicity, across different planet types, along with constraints on several key simulation parameters such as the orbital period distribution and the inclination dispersion within planetary systems. These refined parameters enable us to refine our yield predictions for upcoming exoplanet detection missions, including PLATO, Roman, and HAYDN.
ER -