The Magnetospheric Multiscale Mission (MMS) contains on each of its constellation spacecraft a mass spectrometer instrument: the Hot Plasma Composition Analyzer (HPCA). HPCA collects particle count rates for 63 energy channels along with multiple polar and azimuthal angular bins to achieve both energetic and spatial distribution of ions in the interplanetary medium. Ions known as pick up ions (PUI) can originate from a number of sources in the heliosphere: the solar wind, the inner source region, the Earth’s geocorona, and the interstellar neutral (ISN) population. The overall goal is to obtain more complete description of the spatial and energetic pick up ion distribution in the local interplanetary medium at or near 1 AU.
This internship project would primarily focus on the generation of pick up ion energy distributions by species and location using time of flight (TOF) analysis of selected intervals of HPCA burst and survey data. As the project progresses, the intent is to convert the TOF data into a mass per charge energy distribution to more clearly investigate the spatial and energetic distributions of heavy element pick up ions. Due to the motion of the Sun relative to the interstellar medium, PUIs from various species are likely to exhibit spatial focusing cones at or near 1 AU. Observations of the helium focusing cone have been made and its spatial extent has been mapped. HPCA’s TOF measurements provide an avenue to use existing data to refine and predict the potential focusing cones of other heavy elements while providing insight into the energetics and spatial distribution of PUIs in and around Earth’s magnetosphere. Connected to the TOF data analysis is a theoretical modeling component of the pick-up ion composition of the Earth’s interplanetary environment. This model will be developed building off of the Warsaw Test Particle Model of Dr. Justyna Sokol and others. Initially Dr. Sokol’s code was run in Mathematica and updates are needed to improve processing time and refine the particle generation functions for 1 AU. The goal of this project is to develop a functional modeling code which adapts the WTPM to a Python coding environment and refines the necessary functions to improve predictive pick-up ion source functions to better map the distributions at 1 AU. Our modeling efforts have the following objectives: Objective 1: Develop and test computational models using inputs of neutral atom densities and local solar conditions (solar wind density, and velocity), to develop physically consistent physical models for the inner source, geocoronal, and interstellar H+ densities and compare with existing models. Objective 2: Use current insitu and remote observations to refine the models to more accurately reflect the observations made using MMS-HPCA and other observations Objective 3: Expand the developed IDL models to obtain density distribution predictions for heavier ions typical to interstellar PUI observations.
The scope of this project would be to generate and analyze TOF spectra for selected date and time interval coordinated with our SWRI colleagues Dr. Roman Gomez and Dr. Justyna Sokol. HPCA provide a wealth of data and intervals that will provide insight into the PUI population but additional researchers are needed to conduct the data reduction and analysis for the individual intervals. After initial TOF analysis, the student would collaborate on converting the TOF distribution to a mass per charge (m/q) distribution to more easily identify the presence of species of interest. A second student would lead student work on the Python modeling efforts of pick-up ion sources and distributions at 1 AU. This student should have at least a basic understanding of programming, and experience with Python would be a plus, but training would be available. In Oregon, this project would be led by Dr. Aaron Coyner; however, both the PI and student intern would work in close collaboration with the HPCA team at SWRI to provide needed data analysis to support their ongoing research mission. Students will be expected to commit 10 hours per week to this effort through background research, coding and analysis of MMS data.
Students in any STEM major are welcome to apply. Physics, engineering, and mathematics students will potentially have more background. Understanding of data processing with Excel, python, C++ or IDL will be beneficial.