Multispacecraft Study of the Spatio-Temporal Variability of Solar Energetic Particles (SEP) Profiles in the Inner Heliosphere
- Institution
- University of New Hampshire, NH
- Principal investigator
- Bala Poduval
- Funding
- $559K
- Duration
- 2023–2026
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Abstract
Solar energetic particles (SEPs) are particles that are accelerated from the Sun in energetic events, such as solar flares and coronal mass ejections. They can move at relativistic speeds and when directed to the Earth, can cause space weather-related issues for human technology as well as radiation risks to humans. This project aims to gain better insights on SEP characteristics – including intensity, flux, and spatio-temporal evolution in the inner heliosphere. The research products will be made publicly available and the work is led by women. Undergraduate students will be mentored. The project investigates how the near-Sun characteristics of solar energetic particles (SEPs) and their spatio-temporal variability help determine their origin, acceleration and propagation in the inner heliosphere, and constrains modeling of the seed population from which they arise. Data sets from NASA’s Parker Solar Probe and Solar Orbiter will be used. Additionally, the Energetic Particle Radiation Environment Model (EPREM) will be used for simulating the SEP events and the Current Sheet Source Surface (CSSS) model used for mapping the PSP locations back to the Sun for establishing the magnetic connectivity. The three objectives of the study are: Obj 1: Obtain SEP Time Profiles (intensity, integral flux, fluence, spectral index and composition) at multiple locations in the heliosphere and investigate their temporal variability. Obj 2: Model Comparison - use the knowledge gained in Obj 1 to constrain the parameters of EPREM simulations of SEP transport in the steady solar wind and heuristic models of shocks and switchbacks. Obj 3: Preparation of AI-ready Data and Data Reconstruction using algorithms developed using techniques of artificial intelligence and following the recommendations of Poduval et al. 2022 for AI-ready data. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
Research team
- Bala PoduvalPI
- Jonathan T Niehof
- Matthew A Young
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