179 papers
Space physics explores the dynamic environment surrounding our planet and the wider solar system, focusing on how charged particles, magnetic fields, and solar winds interact with celestial bodies. This field helps us understand phenomena like auroras, space weather that can disrupt satellites, and the fundamental behavior of plasma in the vacuum of space. It bridges the gap between astronomy and particle physics, revealing the invisible forces that shape our cosmic neighborhood.
At Gist.Science, we process every new preprint in this category as it appears on arXiv, ensuring you get immediate access to the latest research. For each paper, we provide both a detailed technical summary for experts and a plain-language explanation that makes complex concepts understandable for everyone. Below are the latest space physics papers from arXiv, curated and simplified for your reading.
This study compares high-Mach-number interplanetary and terrestrial quasi-parallel shocks to reveal that while Foreshock Compressive Structures initiate under similar upstream conditions, the interplanetary shock fails to develop fully evolved nonlinear structures due to a spatially limited growth zone and a lack of global curvature that inhibits the lateral supply of energetic ions.
This study utilizes the uncoupled Energetic Particle Radiation Environment Model (EPREM) to systematically analyze how variations in physical parameters, such as diffusion and shock profile, influence the morphology and longitudinal spread of simulated widespread solar energetic particle events, revealing conditions under which flux may diminish at large longitudinal distances from the shock origin.
Using automatic detection of discrete features alongside Juno-UVS and in-situ data, this study demonstrates that magnetodisc scattering primarily drives electron precipitation in Jupiter's ultraviolet aurora and classifies injection signatures into dawn-storm and non-dawn-storm types, while suggesting that outer emission arcs are broadened sequences of these signatures.
This study utilizes a data-constrained, resistive magnetohydrodynamic simulation to demonstrate that a non-force-free initial magnetic field, characterized by a Lorentz force imbalance, can spontaneously trigger the formation and subsequent eruption of a flux rope in active region NOAA 12241 without requiring pre-existing structures or photospheric driving motions.
This paper presents a time-dependent, data-driven model of the heliosphere that reveals how solar-cycle variations generate recurring fast magnetosonic waves, which drive a highly oscillatory termination shock, transmit into the interstellar medium to explain Voyager pressure observations, and highlight the insufficiency of time-dependent effects alone to account for the sub-Alfvénic region ahead of the heliopause.
This study presents a fully automated deep learning pipeline utilizing YOLOv8 and BoT-SORT to characterize the propagation parameters of mid-latitude ionospheric plasma structures from all-sky airglow images, demonstrating superior efficiency and reliability compared to previous semi-automatic methods for handling large datasets.
This paper proposes an angular-momentum preserving dissipative model for the N-body problem that reduces to homographic equations for central configurations, analyzes the resulting two-body dynamics via Poincaré compactification, and demonstrates that the dissipation does not affect periapsis precession.
The paper introduces CLARE, a classification-based regression machine learning model that significantly improves electron temperature prediction accuracy and provides uncertainty estimates by transforming the continuous output space into discrete intervals using AKEBONO satellite data.
This paper presents a comprehensive cold-fluid analysis of the Weibel instability across non-relativistic and relativistic, single- and multi-species regimes, deriving scaling laws that accurately predict filament spacing and magnetic field saturation in both laboratory laser experiments and MMS spacecraft observations of astrophysical shocks.
This paper presents a novel data mining method that generates calibrated probabilistic forecasts of solar wind speed by comparing baseline model predictions and recent observations against historical analogs, thereby improving uncertainty quantification and reducing root-mean-square error compared to traditional deterministic models.