Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2106.00972

Predicting exoplanet mass from radius and incident flux: A Bayesian mixture model

Qi Ma·Sujit K Ghosh
View original source ↗Read source document ↗Export metadata ↓

Abstract

The relationship between mass and radius (M-R relation) is the key for inferring the planetary compositions and thus valuable for the studies of formation and migration models. However, the M-R relation alone is not enough for planetary characterization due to the dependence of it on other confounding variables. This paper provides a non-trivial extension of the M-R relation by including the incident flux as an additional variable. By using Bayesian hierarchical modeling (BHM) that leverages the flexibility of finite mixture models, a probabilistic mass-radius-flux relationship (M-R-F relation) is obtained based on a sample of 319 exoplanets. We find that the flux has nonnegligible impact on the M-R relation, while such impact is strongest for hot-Jupiters. On the population level, the planets with higher level of flux tend to be denser, and high flux could trigger significant mass loss for plants with radii larger than $13R_{\oplus}$. As a result, failing to account for the flux in mass prediction would cause systematic over or under-estimation. With the recent advent of computing power, although a lot of complex statistical models can be fitted using Monte Carlo methods, it has largely remain illusive how to validate these complex models when the data are observed with large measurement errors. We present two novel methods to examine model assumptions, which can be used not only for the models we present in this paper but can also be adapted for other statistical models.

Explore related subjects

astro-ph.EPastro-ph.IM

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Qi Ma, Sujit K Ghosh. 2021-06-02. Predicting exoplanet mass from radius and incident flux: A Bayesian mixture model. https://doi.org/10.1093/mnras%2Fstab1584

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Bridging magnetothermal winds and photoevaporation to model discs dispersal

Protoplanetary disc dispersal is driven by two processes usually modelled separately: photoevaporative and magnetohydrodynamic (MHD) disc winds. Global simulations indicate that in the inner disc these are not distinct outflows but a single magnetothermal wind. We assemble a closed-form, two-phase model that respects it. A single-field-line wind, whose base is fixed by the irradiated temperature and penetration column, supplies the launch and feeds a secular evolution, with photoevaporation convolved on as a sink. The flux closure $B_z\proptoΣ^q$ is self-limiting: for $q\le1/2$ depletion alone cannot demagnetise the disc, so dispersal requires independent flux loss, parameterised by the magnetic Reynolds number $\mathcal{R}_m$. Integrating the coupled system yields two regimes. Efficient flux loss ($\mathcal{R}_m\lesssim1$) lets the magnetisation front recede by over an order of magnitude and opens a photoevaporative gap. Flux retention ($\mathcal{R}_m\gg1$) drives the front outward, sustains accretion, and defers dispersal by $\approx2.7$~Myr. Deriving the base from stellar irradiation instead of prescribing it, we find that the cold-launch approximation is valid during the early stages of disc evolution: anchoring the base at plasma equipartition ($β_{\rm base} \sim 1$) confines irradiation's influence on the magnetic lever arm to the magnetothermal annulus, decoupling the peak accretion rate from the incident flux. Both regimes clear the disc inside-out, through either a photoevaporatively amplified cavity wall or an expanding magnetothermal front.

astro-ph.EP↗
arXiv2026

Faithful Neural Embeddings for 3D Exoplanet Climate Modeling

With the rapid advancement of telescopes like JWST and Ariel, there is an urgent need for efficient 3D climate models to interpret observations of exoplanet atmospheres. Traditional 3D general circulation models (GCMs) are computationally intensive, prompting the development of machine learning (ML) emulators to accelerate simulations. Recent work, such as that by Plaschzug et al. 2026 \cite{plaschzug2026accelerating}, uses a dense neural network (DNN) to predict local gas temperatures and winds from input parameters, including local gas pressure, spatial coordinates (longitude and latitude), and global temperature. However, this model relies on predicting individual temperature values (points) at specific grid points, which can be limited by the resolution and constraints of the training grid. In this work, we investigate a couple of alternative frameworks based on latent-space representations of local gas temperature ($\text{T}_{\text{gas}}$) to obtain a faithful, low-dimensional representation of these profiles. This represents the first step toward developing a latent space regression model, offering a structurally cohesive alternative to the existing point-wise prediction method \cite{plaschzug2026accelerating}. By capturing the optimal embedding space of atmospheric data, our proposed framework can produce simulated profiles while maintaining computational efficiency, making it suitable for large-scale exoplanet ensemble studies.

astro-ph.EP↗
arXiv2026

Some challenges for the long-term survival of Naiad, Neptune's innermost moon

The Naiad-Thalassa 73:69 mean-motion resonance implies these moons have co-existed for $\gtrsim$1 Gyr, raising the question of how they survived to the present day. We examine three challenges to Naiad's long-term survival: tidal disruption, heliocentric bombardment, and runaway collisional erosion by planetocentric debris. We constrain Naiad's internal strength requirements from its nominal density and shape, compute present-day impact rates on Neptune's inner moons from heliocentric bombardment, and use $N$-body simulations to model the fate of ejecta produced by non-disruptive impacts. First, Naiad's nominal density of ${\sim}0.8\text{ g cm}^{-3}$ and elongated shape suggest it cannot be held together by self-gravity alone, implying a cohesive strength of $\gtrsim10$ kPa to avoid tidal disruption, although this constraint is relaxed if Naiad has a higher density of ${\gtrsim}1.3\text{ g cm}^{-3}$. Second, we show that Naiad may have been disrupted in the last 1 Gyr by heliocentric bombardment, although this depends sensitively on the size-frequency distribution of Kuiper Belt objects at small sizes and on Naiad's catastrophic disruption threshold, both of which are poorly constrained. Third, and most notably, we find that even small, non-disruptive impacts can trigger runaway collisional erosion by planetocentric debris on extremely short timescales. Avoiding this ``sesquinary catastrophe'' requires that Naiad has a collisional strength of at least several MPa, which is difficult to reconcile with being a reaccumulated ``rubble pile'', leftover from the capture of Triton and the subsequent cataclysm of Neptune's primordial satellite system. Together, these results suggest that Naiad may be a physically unusual object among small ring-moons -- possibly a largely coherent, monolithic fragment -- and that our understanding of Neptune's inner satellite system leaves much to be explained.

astro-ph.EP↗