Granulation signatures as seen by Kepler short-cadence data

II. A hierarchical route to inferring stellar radii from granulation

¹ Stellar Astrophysics Centre (SAC), Department of Physics and Astronomy, Aarhus University, Ny Munkegade 120, 8000 Aarhus C, Denmark
² School of Physics and Astronomy, University of Birmingham, Edgbaston B15 2TT, UK

^★ Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.

Received: 27 March 2026
Accepted: 25 May 2026

Abstract

Context. Stellar granulation arises from near-surface convection and is imprinted in stellar photometric time series. Yet the links between granulation observables and fundamental stellar properties remain underexploited.

Aims. We aim to establish a statistically robust framework for inferring stellar radii directly from granulation signals in long-duration space-based photometry with aid from stellar atmospheric parameters.

Methods. We constructed a Bayesian hierarchical model to connect stellar radius and granulation, relating radius through regression to the total granulation amplitude, primary characteristic frequency of the granulation, stellar effective temperature, and surface metallicity. The derivation was separately performed for three granulation models, propagating the complete marginal posteriors of the granulation parameters to account for intrinsic dispersion of the derived relations. Each background model yields a unique radius posterior, subsequently combined using Bayesian evidence as weights. This produces radius posteriors that best represent the given star by marginalising over the different background models.

Results. The granulation–radius relations were derived from a heterogeneous sample of 363 stars, combining seismic and interferometric targets from multiple sources with their associated systematics. Application to an independent sample of 367 stars recovers the reference radii within 1σ in ≈73% of cases. The distribution of residuals is consistent with a well-calibrated and unbiased inference. Across applications, the granulation-inferred radii achieve a precision of ≈10%. The agreement with seismic and interferometric benchmarks demonstrates that granulation carries predictive information on stellar radii at a level comparable to several established techniques.

Conclusions. This work enables the inference of stellar radii from granulation signals; directly applicable to data from Kepler, TESS, and the upcoming ESA PLATO mission. Using granulation as a structural diagnostic enables radius estimation for stars, which may otherwise be challenging to characterise, and thereby provide a complementary approach to stellar characterisation across diverse populations.