Solar Flare Analysis
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Figure 3 (Mason et al., 8)
Background
In the fall of 2021, I enrolled in a Course-based Undergraduate Research Experience (CURE) overseen by the Colorado Physics Laboratory Research Effort (C-PhLARE). The goal of this program was to determine the mechanism that counterintuitively heats coronae to temperatures that are orders of magnitude hotter than the corresponding photospheres (Mason et al., 4). The two competing mechanisms are nanoflares and Alfvén waves. This research indicated that nanoflares were not the responsible mechanism, suggesting that Alfvén waves are the dominant mechanism of this heating.
A notable aspect of this research was the massive number of co-authors involved. Over 1,400 undergraduate students collaborated in small groups of 3-4 to gather and analyze data, which underwent several rounds of peer review. This data was eventually published in The Astrophysical Journal in the article titled “Coronal Heating as Determined by the Solar Flare Frequency Distribution Obtained by Aggregating Case Studies” by Mason et al. on May 10, 2023. Students, like myself, who gave their consent were recognized as co-authors.
Throughout this project, we were only permitted to use Python. Although I had never used Python before, I quickly learned the necessary aspects of Python and how to effectively write a reusable coding framework. This streamlined our analysis process and saved our team both time and effort.
Teamwork & Leadership
Over a semester, I led a team of four undergraduate students who were all STEM majors. Since we shared similar knowledge bases, there weren't many teaching opportunities. However, one arose when we realized that no one on the team had experience with Python or coding, except for me and one other member. To enhance the team's effectiveness and skills, I took the initiative to explain how to develop pseudo-code, teach team members how to read error messages, introduce useful debugging methods, and provide helpful resources.
Motivating a team is one of the most important aspects of leadership. I found that the most effective way to motivate our group was to make every member equally responsible for our shared grades. To foster this sense of responsibility, I adopted a democratic approach, encouraging discussion and deliberation on all aspects of our work to arrive at group decisions. This method ensured that no team member felt left out or overlooked.
Since this was a fully online course, we encountered several issues with Google Colab. The most significant problem was team members accidentally overwriting each other's code, resulting in conflicts, frustration, and unusable code. To resolve this, I appointed one person to take the lead in dictating the code while sharing their screen. Eventually, each team member took turns leading dictation, which allowed for collaborative input, debugging, and verification without the risk of overwriting each other's work.
Analysis
Figure 2 (Mason et al., 7)
We retrieved our data from the GOES-15/XRS satellite. Each team member identified a flare suitable for analysis. The criteria for suitable flares were no overlapping flares, flares that were closer to the solar maximum, and flatter preflare irradiation. We then compared our selected flares to determine the best flare which provided an initial form of peer review.
Next, we recorded the time window of the flare to ensure we could locate the data if it was damaged or lost. We established the preflare baseline irradiance and subtracted it from the curve to “zero" the flare. This baseline was derived using averages of the light line shortly before and after the flare. We then defined the integration margins to isolate the flare. After integrating the flare and converting it into energy units, we obtained the flare's total energy. All calculations and graphs were created using Python.
Finally, we constructed a presentation outlining all our data and methods, which we presented presented to the class. Our calculations and presentations were cross-validated by other teams. We submitted documentation, files, and raw code to the administration for peer review and future research use.