arXiv subjects

Steven Vance

Publications and source records attributed to Steven Vance.

3 recordsLinked to original sources

arXiv2026

Joint dynamical-geophysical evidence for a limit cycle in the Galilean moons

Io, Europa, and Ganymede orbit in the Laplace mean-motion resonance, where their orbital and thermochemical evolution are strongly coupled [1-5]. Forced eccentricities sustain tidal dissipation, powering Io's volcanism and maintaining Europa's subsurface ocean [6-8]. Tidal heating depends on the moons' eccentricities, semimajor axes, and interior properties. Orbital evolution depends on how satellite dissipation affects the resonant dynamics [4, 8]. This coupled evolution has not been treated in a self-consistent framework constrained by modern observations. Here, we combine modern astrometric and geophysical measurements of migration rates, surface heat fluxes, tidal response, and moment of inertia [9-13] with an orbital-thermochemical evolution model to constrain the long-term evolution and present state of the moons. We show that the joint observations select an ongoing late-time limit cycle, likely established $\sim$0.8-2.0 Gyr ago through feedback between thermal and orbital evolution. The present-day state recurs within this oscillatory branch, whose cycles repeat every $\sim$95-150 Myr. Io presently experiences high dissipation and migrates inward, whereas Europa and Ganymede continue to migrate outward. Europa is predicted to undergo episodes of inward migration during the cycle. During the cycles, Io's mean melt fraction varies substantially ($\sim$10-30%), while Europa's ice-shell thickness varies by $\sim$3-15 km. The predicted structures of Europa and Io will be assessed by future missions including Europa Clipper, JUICE, and IVO [14-17].

astro-ph.EP↗

arXiv2026

Follow Your Heart: Landmark-Guided Transducer Pose Scoring for Point-of-Care Echocardiography

Point-of-care transthoracic echocardiography (TTE) makes it possible to assess a patient's cardiac function in almost any setting. A critical step in the TTE exam is acquisition of the apical 4-chamber (A4CH) view, which is used to evaluate clinically impactful measurements such as left ventricular ejection fraction (LVEF). However, optimizing transducer pose for high-quality image acquisition and subsequent measurement is a challenging task, particularly for novice users. In this work, we present a multi-task network that provides feedback cues for A4CH view acquisition and automatically estimates LVEF in high-quality A4CH images. The network cascades a transducer pose scoring module and an uncertainty-aware LV landmark detector with automated LVEF estimation. A strength is that network training and inference do not require cumbersome or costly setups for transducer position tracking. We evaluate performance on point-of-care TTE data acquired with a spatially dense "sweep" protocol around the optimal A4CH view. The results demonstrate the network's ability to determine when the transducer pose is on target, close to target, or far from target based on the images alone, while generating visual landmark cues that guide anatomical interpretation and orientation. In conclusion, we demonstrate a promising strategy to provide guidance for A4CH view acquisition, which may be useful when deploying point-of-care TTE in limited resource settings.

cs.CV↗

arXiv2021

DEWPython: A Python Implementation of the Deep Earth Water Model and Application to Ocean Worlds

There are two main methods of calculating the thermodynamic properties of water and solutes: mass action (including the Helgeson-Kirkham-Flowers (HKF) equations of state and model) and Gibbs free energy minimization (e.g. Leal et al., 2016). However, in certain regions of pressure and temperature the HKF model inaccurately predicts the speciation and concentration of solutes (e.g. Miron et al., 2019). The Deep Earth Water (DEW) model uses a series of HKF-type equations to calculate the properties of water and solute concentrations at high temperatures (373 - 1473 K) and pressures (0.1 - 6 GPa) (e.g. Huang and Sverjensky, 2019; Pan et al., 2013; Sverjensky et al., 2014). The DEW model is synthesized in an Excel spreadsheet and calculates Gibbs energies of formation, equilibrium constants, and standard volume changes for reactions. Here we present an object-oriented Python implementation of the DEW model, called DEWPython. Our model expands on DEW by increasing model efficiency, streamlining the input process, and incorporating SUPCRT in-line. Additionally, our model builds in minerals and aqueous species from the thermodynamic database slop16.dat (Boyer, 2019) which would normally be calculated separately. We also present a set of reactions relevant to icy ocean world interiors calculated with the DEWPython. The favorability of these reactions indicates likely formation of certain organic species under extreme pressures relevant to ocean worlds.

physics.geo-ph↗