20 years since my first publication.

I'm old

Twenty years ago (and a couple of months because I've been slow with finishing this article), I was starting my master's degree at CU Boulder, and the research I did as an undergrad and my first paper had just been published. I was so excited! As a dyslexic, I never thought anything I had a hand in helping to write would be published… and those who knew me when I was younger would have said the same. During my postdoc, I decided to try to learn how to write better and literally would practice for a few hours each day… that's how my first blog on science started and why I keep this substack. But that story is a different post, and this one is about that first exciting paper.

This paper resulted from two or three years of (undergraduate) research. I don't remember when I started this project as it wasn't the first project I worked on - but close. I loved my time at Augsburg College (now Augsburg University) in the space physics lab. It was a welcoming and safe space to "grow up" as a scientist. As anyone who knows him would say, Mark Engebretson is a fantastic, kind, empathetic, supportive, and patient mentor. I was incredibly lucky to have him as a supervisor and mentor! He reminded me of what I thought Mister Rodgers would be like if I ever met him. Jennifer Posh, a researcher in the lab, was like the older sister helping guide us through, ensuring that our rough results, writing, and communication of our analysis were polished a bit so that Mark always thought we were better than we were. When she went on maternity leave, we found out just how much she did for us. Again, she was so kind, caring, and supportive - you always felt that you were a scientist - not only learning to be a scientist.

Another great thing about working with Mark was that he had a fairly constant set of international collaborators coming through Augsburg. At the time, I had no idea if this was normal, but I didn't think too much of it. I mean, it was terrific to meet all these people who had accomplished what I wanted to achieve - becoming a scientist who could travel the world to work with friends and collaborators to improve our understanding of the universe. I mean - who doesn't go to college wanting to do that!?!?!

Years later, I realized just how special this place was. Mark is an anomaly. The sheer number of people that came through his lab at Augsburg and stayed in Heliophysics and, more broadly, Aerospace and STEM is incredible! Almost everywhere I go in the US and many places abroad, people come up and state how they came through Augsburg as students, researchers, or collaborators. Others have tried - and done well - but have yet to succeed quite like him. And it was through him and others at Augsburg (like Dixie Shafer and Rebecca Dupont) that I was set on my current path, and had the support to stay on my current path. And it started with this paper…

This paper used data from some of the Antarctic magnetometer stations. These datasets are excellent in so many ways. Antarctica is unique in that it is very magnetically quiet (not too many people, electronics, or animals there to cause issues with the magnetometers), unlike the North Pole; has a lot of land mass covering a fascinating area of our Earth's magnetic field, unlike the North Pole; doesn't have the same geo-political issues as the North Pole; and doesn't have animals that eat through the wires or cause problems, unlike the North Pole. Being in the remote Antarctic outback also comes with some hardships, like consistent power through the winter or getting high-resolution data back to the university from the middle of nowhere. However, because of the efforts of the International Geophysical Year (1957), we have more stations than we would otherwise have there - the efforts from the Geophysical International Year have had a massively outsized positive impact on science over the last almost 70 decades. It would be amazing to see a similar effort today to deploy and maintain so many instruments to help create a consistent and cohesive dataset! But I digress…

While working on this paper, I remember having to go through many, many, many datasets by hand. To be honest, I loved it. It was meditative. After a stressful finals season, sitting in the lab when everyone else was gone, just going through slide after slide of data well into the night, was an excellent way to decompress. It also meant that I learned a lot about the data.

Many today state that we should use machine learning and artificial intelligence to do just this - why waste the brain power of minds to do this work? Okay - sometimes you need ML and AI tools to get through the vast quantity of data we have. But we also learn a lot when we do this by hand, eye, sound… by a person. I learned what was good data and what was bad. I learned how close an actual event could look like bad data and how bad data could look like an event in some instances. I learned what was odd and what seemed normal. I learned how tweaking the thresholds I used to define an event could change my statistics - and why that might be good or bad. I also learned how my assumptions about an event and how I defined "an event" would likely impact my results. This is not something I would have learned if I had just been given an ML algorithm to identify my events automatically. Sometimes, I worry that this is me just being "old." I mean, I must be old if this paper was written 20 years (plus a month or two) ago. But then I think about the conversations we had discussing edge cases, or even just the conversations I had when I had to train the next student who would take over the study. I'm not sure I would have had those conversations, had those lessons, and been as accurate if I had just started with an algorithm. The research would have gone faster for sure. However, those hours of humdrum were vital. Those hours spent with the data made me confident with my results, and those conversations allowed me to dive into deep discussions and accurately communicate what we had learned through this study at conferences.

And we learned a bit from this study. Was this a breakthrough study that changed the field? No - most science isn't. Was this a worthwhile study beyond training new scientists? Absolutely! It's not my most cited work, but it has a solid citation base. The positive citations of this work show that others have used what was learned, which is one of the proofs of Mark Engebretson's brilliance. Throughout his career, he has consistently found science projects accessible to undergraduate students AND that contribute to the broader research community.

Okay, so on to what we learned.

We looked at magnetometer data in the ELF and VLF range - so between the frequencies of about 0.5 - 4 kHz. Within this frequency range, we identified a few different types of pulsations by eye, their characteristics, and how they were modulated by Pc3 waves (even longer period waves between 10 - 45 seconds). We refer to them as quasi-periodic emissions (QP) and periodic emissions (PE).

We looked at all sorts of things and combinations of stuff. This may be where I learned that sometimes the brute-force method of trying everything and making lots of plots is the best way forward in science. Well, it is not always the best way, but it is a great way.

After my first steps into the research world, we found that QP events occurred more frequently in the auroral region, regardless of whether they occurred with or without PEs. PE events occurred more frequently as you moved to lower latitudes. QP and PE events also differed in the time of year they occurred. QPs occur more often in the Australian summer between November and February. PEs occur more frequently during the northern hemisphere summer, around June through August. And when you see them together? Those don't have a seasonal dependence.

There were many more results, but one that is interesting is that we discussed how we saw a lower fraction of QP events associated with PEs than our collaborator, Andy Smith. I mention this as this was a very memorable set of discussions. Andy is a fantastic person - once described to me as reminiscent of Santa Claus. He was one of our international collaborators Mark would have come to visit the lab at Augsburg College/University.

I remember Andy stopped by when I was finishing up the project, training the new student, and starting to put the paper together. We had a series of long conversations going through plot after plot. We argued over what is an event and what isn't an event. We discussed what thresholds we should use - as in how much higher than the background the event needed to be in order to be called an event. We talked about the shape and consistency of wave power. All of these aspects make small but noticeable differences to the event list.

I wonder if we would have had these conversations if we had used an AI/ML identification tool… I think not, as we often seem to use it unquestionably once someone else has used it. Instead, two people used their eyes to identify events. Then they had to dive deep into a discussion about what was right - which was captured in the literature. Of course, this can and should also happen with any ML/AI tool, and using one's eye can often lead to many other issues, including consistency, especially consistency between people.

Now, this result doesn't have a direct path toward societal impacts and regulations helping to mitigate space weather events. It did in the sense that these types of research projects are what many of us in the field learned how to conduct research. The value of these projects goes well beyond the scientific knowledge we gained. These projects are what develop new researchers and teach them, well, us, the skills that will become important for our future careers.

And if you had told me when this paper was first published that it was only the first of quite a few I've helped with or written- I wouldn't have believed you. If you told 2004 me where I would be today - I wouldn't have believed you. What can change in twenty years is amazing - I wonder what the next twenty will have in store.