MAISTEP -- a new grid-based machine learning tool for inferring stellar parameters I. Ages of giant-planet host stars

TL;DR

This paper introduces MAISTEP, a grid-based machine learning tool that accurately infers stellar parameters like age, radius, and mass from atmospheric data, and applies it to study the ages of giant-planet host stars.

Contribution

We developed a novel machine learning method combining four algorithms to estimate stellar parameters solely from atmospheric constraints, validated against seismic data, and applied it to analyze giant-planet host star ages.

Findings

Machine learning inferences closely match seismic data with minimal bias.

Stars hosting Hot Jupiters are statistically younger than those hosting Warm and Cold Jupiters.

The tool provides reliable stellar ages, aiding exoplanet demographic studies.

Abstract

Our understanding of exoplanet demographics partly depends on their corresponding host star parameters. With the majority of exoplanet-host stars having only atmospheric constraints available, robust inference of their parameters is susceptible to the approach used. The goal of this work is to develop a grid-based machine learning tool capable of determining the stellar radius, mass, and age using only atmospheric constraints and to analyse the age distribution of stars hosting giant planets. Our machine learning approach involves combining four tree-based machine learning algorithms (Random Forest, Extra Trees, Extreme Gradient Boosting, and CatBoost) trained on a grid of stellar models to infer stellar radius, mass, and age using Teff, [Fe/H], and luminosities. We perform a detailed statistical analysis to compare the inferences of our tool with those based on seismic data from the APOKASC and LEGACY samples. Finally, we apply our tool to determine the ages of stars hosting giant planets. Comparing the stellar parameter inferences from our machine learning tool with those from the APOKASC and LEGACY, we find a bias (and a scatter) of -0.5\% (5\%) and -0.2\% (2\%) in radius, 6\% (5\%) per cent and -2\% (3\%) in mass, and -9\% (16\%) and 7\% (23\%) in age, respectively. Therefore, our machine learning predictions are commensurate with seismic inferences. When applying our model to a sample of stars hosting Jupiter-mass planets, we find the average age estimates for the hosts of Hot Jupiters, Warm Jupiters, and Cold Jupiters to be 1.98, 2.98, and 3.51 Gyr, respectively. These statistical ages of the host stars confirm previous predictions - based on stellar model ages for a relatively small number of hosts, as well as on the average age-velocity dispersion relation - that stars hosting Hot Jupiters are statistically younger than those hosting Warm and Cold Jupiters.