This is a reflective, end-of-year historical deviation. From wandering stars and stubborn philosophers to lost planets, national rivalries, and Pluto’s demotion, this episode explores how we learned what the planets are and why the process matters as much as the answer.

You see every once in a while, Whimsical Wavelengths takes a historical deviation. This is one of those episodes.

Instead of cutting-edge research, this solo episode steps back to ask a bigger question: how did we actually figure out what the planets are, and what does that process tell us about how science works?

From the wandering lights tracked by Babylonian astronomers over 3,000 years ago, to Greek ideas of moving stars, to the long-lived geocentric universe of Ptolemy, we trace how humanity slowly built models of the solar system. Along the way, we meet Copernicus, Galileo, Kepler, and Newton, and see how new tools, better measurements, and sometimes better messengers reshaped our understanding of the cosmos.

This episode also digs into one of science’s most fascinating detective ensure stories: the discovery of Neptune. Using nothing but mathematics and Newton’s laws, astronomers predicted the existence of a new planet before anyone had ever seen it. The result was a mix of brilliance, nationalism, bruised egos, and a controversy that still makes historians uncomfortable.

From there, we follow the trail to Pluto, Planet X, and the lingering idea that the solar system might still be hiding something. We look at how bad data can lead to compelling but wrong conclusions, why Pluto never solved the problem it was meant to, and how modern observations have resurrected the question in the form of Planet Nine.

Along the way, this episode touches on:

• Why ancient astronomers called planets “wandering stars”
• How telescopes changed everything, and why early ones were still not enough
• Why stellar parallax took centuries to measure
• How people, politics, and pride shape scientific progress
• Why Pluto was discovered, celebrated, and eventually reclassified
• And why the idea of a missing planet refuses to die

This is not just a story about planets. It’s a story about how science moves forward: imperfectly, collaboratively, and sometimes reluctantly, as better data forces us to let go of comfortable ideas.

If we want a thriving future scientific community, we first need to understand why people choose — or don’t choose — careers in STEM.

This week on Whimsical Wavelengths, we turn the telescope around and look not at stars, but at the people who choose to study them. What shapes a scientist’s identity? Why do some students pursue STEM — and astronomy in particular — while others drift away? And how do mentorship, representation, and community determine who sees themselves as “belonging” in science?

To explore these questions, I’m joined by Dr. Zachary Richards: a researcher whose path moved from physics into science education, now a Research Associate at the American Museum of Natural History and a faculty member at York College, City University of New York. His recent work examines how scientists form professional identity, how students imagine themselves in scientific roles, and how educational environments influence those choices.

In this episode, we discuss:

• how early experiences shape STEM career decisions
• the role of mentorship and representation in building scientific identity
• why astronomy offers a unique lens for studying belonging in STEM
• parallels between challenges in astronomy and fields like geoscience, where enrollment and workforce sustainability are pressing issues
• what institutions can do to attract and support the next generation of scientists

Whether you’re a scientist, an educator, or simply curious about how people end up devoting their lives to understanding the universe, this episode offers a thoughtful look at the human side of STEM.

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In this episode of Whimsical Wavelengths, we explore how machine learning is opening new possibilities in geophysical imaging and inversion workflows. Like image segmentation! We look at how modern computational tools can help interpret what we cannot observe directly beneath the surface.

Our guest, Johnathan Kutti, joins us to break down how machine learning approaches can assist with geophysical inversion, improve subsurface models, and support decision-making in exploration and environmental studies. With experience both in the field and in building mathematical tools, he brings a grounded perspective on how these methods work in practice.

We start by outlining what geophysics actually is—using physics to study the Earth’s structure and processes—and why inversion methods are so central to the field. Because we cannot directly measure physical properties everywhere inside the Earth, geophysical inversion works backward from measurable data such as magnetics, gravity, or electromagnetic responses to estimate what the subsurface must look like.

The conversation then moves into:

• Why geophysical inversions have infinite possible solutions
• How physical assumptions and constraints narrow those solutions
• Where machine learning and image segmentation can help
• Examples of integrating AI into geoscience workflows
• Practical realities from years spent collecting data across remote terrain

If you've ever wondered how AI and scientific modeling intersect—or how we “illuminate the void” geophysically—this episode offers both clarity and depth.

UBC Geophysical Inversion Facility: https://gif.eos.ubc.ca/

Whimsical Wavelengths Links

Facebook:www.facebook.com/WhimsicalWavelengths

Instagram: @whimsical.wavelengths

Bluesky: @whimsicallambda.bsky.social

Email: whimsical.wavelengths@gmail.com

Patreon: patreon.com/WhimsicalWavelengths