arXiv subjects

D. da Silva

Publications and source records attributed to D. da Silva.

3 recordsLinked to original sources

arXiv2026

Advancing Heliophysics and Space Weather Modeling through Open Science

We present a community-wide effort to develop a strategy and action plan to advance heliophysics and space weather modeling through open science. While open science has the potential to enhance the quality and pace of scientific discovery, its application to scientific modeling requires more careful consideration regarding open data and open software guidelines, as scientific models differ significantly from data analysis software. We gathered feedback from modeling teams worldwide through a living survey and discussion sessions at the 2024 Open Science Workshop in College Park, USA, and at the 2025 COSPAR ISWAT Working Meeting in Cape Canaveral, USA. We complement these findings with lessons learned from almost 25 years of experience at the Community Coordinated Modeling Center in enabling open use of models. We identify key roadblocks in current open science practices and guidelines and offer recommendations for future progress across four overlapping themes: open use of models and simulation results, open validation, open development, and open collaboration. An essential outcome of the discussion is the need for model developers and model users to speak with a united voice and promote the role of models in future open science efforts. We introduce a new cross-domain community initiative called the Heliophysics Open Modeling Environment (HOME), which will be integrated as an overarching activity within COSPAR ISWAT. HOME will serve as a platform for modelers and model users to work together, facilitate community modeling, improve the scientific return on modeling investment, and advance understanding, modeling, and forecasting in heliophysics and space weather.

physics.space-ph↗

arXiv2023

The 17 April 2021 widespread solar energetic particle event

Context. A solar eruption on 17 April 2021 produced a widespread Solar Energetic Particle (SEP) event that was observed by five longitudinally well-separated observers in the inner heliosphere at heliocentric distances of 0.42 to 1 au: BepiColombo, Parker Solar Probe, Solar Orbiter, STEREO A, and near-Earth spacecraft. The event produced relativistic electrons and protons. It was associated with a long-lasting solar hard X-ray flare and a medium fast Coronal Mass Ejection (CME) with a speed of 880 km/s driving a shock, an EUV wave as well as long-lasting radio burst activity showing four distinct type III burst. Methods. A multi-spacecraft analysis of remote-sensing and in-situ observations is applied to attribute the SEP observations at the different locations to the various potential source regions at the Sun. An ENLIL simulation is used to characterize the interplanetary state and its role for the energetic particle transport. The magnetic connection between each spacecraft and the Sun is determined. Based on a reconstruction of the coronal shock front we determine the times when the shock establishes magnetic connections with the different observers. Radio observations are used to characterize the directivity of the four main injection episodes, which are then employed in a 2D SEP transport simulation. Results. Timing analysis of the inferred SEP solar injection suggests different source processes being important for the electron and the proton event. Comparison among the characteristics and timing of the potential particle sources, such as the CME-driven shock or the flare, suggests a stronger shock contribution for the proton event and a more likely flare-related source of the electron event. Conclusions. We find that in this event an important ingredient for the wide SEP spread was the wide longitudinal range of about 110 degrees covered by distinct SEP injections.

astro-ph.SR↗

arXiv2020

Automatic Region Identification over the MMS Orbit by Partitioning n-T space

Space plasma data analysis and mission operations are aided by the categorization of plasma data between different regions of the magnetosphere and identification of the boundary regions between them. Without computerized automation this means sorting large amounts of data to hand-pick regions. Using hand-labeled data created to support calibration of the Fast Plasma Instrument, this task was automated for the MMS mission with 99.9% accuracy. The method partitions the number density and ion temperature plane into sub-planes for each region, fitting boundaries between the sub-planes using a machine learning technique known as the support vector machine. This method presented in this paper is novel because it offers both statistical automation power and interpretability that yields scientific insight into how the task is performed.

physics.space-ph↗