SearcharxivSearch

arXiv subjects

Lidiya Ahmed

Publications and source records attributed to Lidiya Ahmed.

2 recordsLinked to original sources

Direct Measurement of Polar Coronal Hole-like Solar Wind in its Acceleration Phase

The early evolution of fast polar coronal hole (PCH) solar wind remains largely unconstrained by in situ measurements. In March 2025, Parker Solar Probe (Parker) at its closest approach of 9.86 Solar Radii ($R_\odot$) measured outflow from a large equatorial coronal hole (ECH) which was also measured at 1\,au and at intermediate distances by Solar Orbiter (also near its perihelion). At 1\,au the stream properties are consistent with PCH properties established by Ulysses. The stream was measured by Parker substantially below the Alfvén surface, with proton temperatures in excess of 2\,MK and a speed at $\sim$10\,$R_\odot$ which was only $\sim$60\% of its asymptotic value. The Solar Orbiter data indicates that the acceleration is largely complete by 60~$R_{\odot}$. Spherically-polarized fluctuations in the stream are observed to develop from near-transverse and small-angle at Parker to full reversal ``switchbacks'' at Solar Orbiter. Comparison of the implied acceleration profile to historical doppler-dimming measurements suggests that the stream's low coronal acceleration is similar to that of PCH flows. Consistent with previous work, this acceleration requires significantly more energy than can be provided by the observed thermal pressure gradients, with a significant contribution likely from the abundant Alfvénic fluctuation energy observed at Parker. These observations provide unique constraints on models of the radial evolution of the fastest solar wind, and indicate that these wind streams experience gradual, steady acceleration over their first few tens of solar radii of evolution.

astro-ph.SR

Bayesian and Deterministic Neural Network approaches to Faraday Cup calibration and plasma parameter estimation

We describe a novel scheme for analyzing particle detector measurements when a well-calibrated, similarly instrumented spacecraft is present in a similar orbit. To prepare ground truth from measurements provided by a reference spacecraft, the method uses dynamic time warping (DTW)--a technique often used for pattern-matching in time series data. An artificial neural network (ANN) is created and trained to reproduce this ground truth from measurements at the target spacecraft. Unlike previous approaches, this procedure is insensitive to calibration errors in the target data stream, as the neural network may be trained from poorly calibrated particle spectra or even directly from low-level data in engineering units. We demonstrate a proof-of-concept by training an ANN to estimate solar wind proton densities, temperatures, and speeds from the DSCOVR PlasMag Faraday Cup, using the \textit{Wind} Solar Wind Experiment as a reference. We present both deterministic and Bayesian neural network approaches. Applications for Parker Solar Probe, HelioSwarm, and other missions are discussed.

astro-ph.SR