Clouds and Ammonia in the Atmospheres of Jupiter and Saturn Determined From a Band‐Depth Analysis of VLT/MUSE Observations
Journal of Geophysical Research E: Planets American Geophysical Union 130:1 (2025)
Lunar thermal mapper ground testing calibration data
University of Oxford (2025)
Abstract:
Ground test data from the Lunar Thermal Mapper instrument. Described in Bowles et al. 2025 submitted to JGR Planets.archNEMESIS: An Open-Source Python Package for Analysis of Planetary Atmospheric Spectra
Journal of Open Research Software Ubiquity Press 13:1 (2025) 10
Abstract:
ArchNEMESIS is an open-source Python package developed for the analysis of remote sensing spectroscopic observations of planetary atmospheres. It is based on the widely used NEMESIS radiative transfer and retrieval tool, which has been extensively used for the investigation of a wide variety of planetary environments. The main goal of archNEMESIS is to provide the capabilities of its Fortran-based predecessor, keeping or exceeding the efficiency in the calculations, and benefitting from the advantages Python tools provide in terms of usability and portability. ArchNEMESIS enables users to compute synthetic spectra for a wide variety of planetary atmospheres, supporting multiple spectral ranges, viewing geometries (e.g., nadir, limb, and solar occultation), and radiative transfer scenarios, including multiple scattering. Furthermore, it provides tools to fit observed spectra and retrieve atmospheric and surface parameters using both optimal estimation and nested sampling retrieval schemes. The code, stored in a public GitHub repository under a GPL-v3.0 license, is accompanied by detailed documentation available at https://archnemesis.readthedocs.io/.
What We Don't C: Representations for scientific discovery beyond VAEs
Machine Learning and the Physical Sciences workshop at NeurIPS 2025
Abstract:
Accessing information in learned representations is critical for scientific discovery in high-dimensional domains. We introduce a novel method based on latent flow matching with classifier-free guidance that disentangles latent subspaces by explicitly separating information included in conditioning from information that remains in the residual representation. Across three experiments -- a synthetic 2D Gaussian toy problem, colored MNIST, and the Galaxy10 astronomy dataset -- we show that our method enables access to meaningful features of high dimensional data. Our results highlight a simple yet powerful mechanism for analyzing, controlling, and repurposing latent representations, providing a pathway toward using generative models for scientific exploration of what we don't capture, consider, or catalog.
Magma Ocean Evolution at Arbitrary Redox State
Journal of Geophysical Research: Planets American Geophysical Union 129:12 (2024) e2024JE008576