In 2015, I was assigned to develop an online planetary astronomy course at the University of Saskatchewan, which provided an opportunity to correct an issue that had been nagging at me for some years.

The issue I saw is that the basic problem of figuring out what the planets are — and that Earth is itself one of the planets orbiting the Sun — is not merely a historical curiosity. It is the foundational problem from which modern science itself was born.

This was the first time in human history that careful observation, mathematical modelling, and conceptual reasoning were brought together to distinguish appearance from reality: to explain why the heavens look the way they do from Earth, and to determine which description of the cosmos actually corresponds to the structure of the world.

Yet despite this central role in the birth of science, standard introductory astronomy textbooks typically provide only superficial glosses of the problem. The Scientific Revolution is often presented as a simple replacement of one picture with another — geocentric replaced by heliocentric — with little attention paid to the profound epistemic challenge that made the problem so difficult in the first place.

In doing so, many of the most important details are collapsed into a handful of declarative statements: that the Earth moves, that the Sun is at the centre, that retrograde motion is an illusion — without confronting why these conclusions were so hard to reach, or how one could possibly know them to be true while standing on a moving Earth.

Instead, students are often left with the false impression that religious dogma was the primary obstacle to discovering that Earth is a planet. In reality, it is not at all obvious that the Earth is spinning at hundreds of metres per second while orbiting the Sun at 30 kilometres per second. Prior to the development of inertial reasoning, an explanation that placed the Earth at rest at the centre of the cosmos connected far more directly with everyday experience and observation.

By the late Middle Ages, astronomers had developed an extraordinarily successful geocentric model that accounted for even the most complex motions observed in the sky — including those of the planets themselves.

Galileo’s greatest opponents were not the Church as an institution, but his scientific peers. Unable to defeat his arguments, many instead appealed to authority, using institutional influence to suppress what they could not refute. At the same time, Galileo was also significantly wrong on several critical points — for example, he believed uniform circular motion to be inertial, and argued incorrectly that the tides were caused by Earth’s motion as the oceans sloshed back and forth.

Science is messy. The simplified narratives commonly presented in introductory textbooks — which flatten the story of ancient astronomy and the Scientific Revolution — miss what is, in my view, the most important lesson astronomy has to teach: how science actually works, in all its uncertainty, revision, and struggle toward coherence.

The first part of this course therefore represents my attempt to rectify that omission — to explain how science truly emerged from the problem of the planets, and to teach students how we infer the structure of reality from limited, perspective-bound observations, and how easily misleading appearances can be mistaken for truth.

The course then moves on to explain how astronomers use the interaction of light with quantised matter to decode astronomical observations and determine the physical natures of distant worlds. Finally, it works through the Solar System itself, describing both historical observations and the discoveries that have emerged from modern space missions.

The original overview videos show a much younger version of me, from my early years of teaching. The course content was last updated in 2025.

Star clusters are among the most useful objects in the sky — not just because they’re beautiful, but because they’re natural laboratories. Every star in a cluster formed from the same cloud of gas at roughly the same time, which means that when you look at a cluster, the differences you see between stars are real, physical differences that can be measured, modelled, and understood.

This activity takes you from that simple observation all the way through to a complete, research-grade analysis of real star clusters using professional astronomical tools and data. Along the way, you’ll learn essentially everything an introductory astronomy student needs to know about how stars work, how they age, and how they die — not through passive reading, but by actually doing the analysis yourself.

By the time you’ve worked through all the examples, you will know how to:

• process raw telescope images into calibrated, aligned, colour-composite photographs
• measure the brightnesses and colours of thousands of stars simultaneously
• use proper motion and distance data from the Gaia space mission to distinguish cluster members from unrelated foreground and background stars
• fit theoretical stellar evolution models to your data to determine a cluster’s age, metallicity, distance, and interstellar reddening
• recognise and interpret the full range of features visible in cluster HR diagrams — from the main sequence and turnoff point through red giants, horizontal branch stars, asymptotic giant branch stars, white dwarfs, and blue stragglers
• understand the physical processes responsible for each of these features in terms of the fundamental physics of stellar structure and evolution

The activity is built around six guided examples, each analysing a different cluster chosen to introduce the next layer of complexity: a young open cluster still dominated by hydrogen-fusing main sequence stars; a young star-forming region with variable extinction and ongoing star formation; a moderately old open cluster showing a well-developed red giant population and an emerging group of white dwarfs; and finally an ancient globular cluster whose HR diagram is completely dominated by evolved giant stars. Two optional examples round out the complete introduction to star cluster photometry research — covering image alignment and stacking, and the addition of infrared survey data to create multi-wavelength colour composites.

The analysis tools used throughout — Afterglow for image processing and photometry, and Clustermancer for field star removal and isochrone fitting — were developed by the Skynet Robotic Telescope Network. Clustermancer requires no login. Afterglow and the sample image data currently require a Skynet account, which for now means a quick email to me at daryl[dot]janzen[at]usask[dot]ca — account creation directly through the site is coming in a future version of Skynet.

Whether you’re a student working through this as part of a course, or someone who stumbled across it and wants to actually do astronomy rather than just read about it — welcome. This is the real thing.