From Star Light to Planet Cores: A Classroom Module on Inferring Exoplanet Composition

If you want students to understand exoplanet composition, one of the best entry points is not the planet itself, but the star it formed around. That may sound counterintuitive at first, yet it reflects a central idea in modern astronomy: stars and their planets are chemically connected. In this undergraduate lab module, students use stellar spectroscopy to measure the abundances of key elements in a host star, then connect those measurements to plausible planet interiors. The result is a hands-on, data-rich experience that sits squarely inside a modern curriculum and reinforces the kind of real analysis emphasized in astronomy education reforms like SURGE.

This module is designed for undergraduates who have seen a little spectroscopy, a little coding, and a little planetary science, but it also works well in upper-level high school classrooms or teacher training workshops. It blends authentic data, stepwise analysis, and a research-style narrative: observe a star, measure the chemistry, infer the likely building blocks available in the protoplanetary disk, and then reason about what a planet made from that material might look like inside. Along the way, students practice critical thinking when tools are confidently wrong, a skill that matters just as much in science as in the age of AI-generated shortcuts.

Because this lesson is meant to be a true lab module rather than a decorative classroom activity, it includes real spectra, a table of common mineralogical implications, sample learning outcomes, assessment ideas, and advice for adapting the activity to different levels of preparation. It also reflects the research direction of scientists like Johanna Teske, whose work focuses on quantifying the diversity of exoplanet compositions and linking that diversity to the chemistry of host stars, planet formation environments, and observational constraints from surveys such as Carnegie Science and SURGE.

Why Host-Star Chemistry Is a Powerful Proxy for Planet Interiors

The basic scientific logic

The solar system taught astronomers that composition matters. Mercury, Earth, and Mars formed from the same broad nebular material, but their interiors and surfaces diverged because of temperature, chemistry, collision history, and volatile loss. Exoplanet systems are even more diverse, and we usually cannot directly sample a planet’s interior. Instead, we use the star as a chemical anchor. Because a star and its planets formed from the same giant cloud of gas and dust, the star’s elemental ratios provide a first-order clue to what solids were available in the disk.

For students, the crucial point is that this does not mean the star “determines” the planet in a simple one-to-one way. A host star is not a direct blueprint. It is more like a recipe card showing the pantry items available before cooking began. Disk temperature gradients, migration, giant impacts, atmospheric escape, and water delivery all change the final dish. Still, stellar abundances are among the most useful constraints we have for estimating whether a planet is likely iron-rich, silicate-dominated, or volatile-rich.

This is exactly the kind of reasoning used in observational exoplanet science today, especially in work that combines stellar characterization with planet mass and radius measurements from missions like TESS. TESS identifies transiting planets, while spectroscopy and radial velocities help measure host stars and planetary masses. That combination gives students a realistic taste of how multiple data streams are fused into one scientific inference.

What students can infer, and what they cannot

Students can infer likely bulk building blocks, such as whether a system is enriched in refractory elements like magnesium, silicon, and iron, or whether it may be relatively depleted in one of those elements. They can estimate ratios such as Mg/Si and Fe/Si, then interpret what those ratios might imply for mantle mineralogy and core fraction. Those calculations are not the same as knowing the full interior structure, but they are enough to support meaningful scientific claims.

Students cannot infer exact mantle layering, core temperature, or the presence of a liquid outer core from host-star spectroscopy alone. Those require mass, radius, density, thermal models, and sometimes atmospheric data. This distinction is pedagogically useful because it trains students to think in terms of evidence and uncertainty rather than “answer hunting.” It also supports discussions of scientific limits, much like the way modern data teams think carefully about confidence, uncertainty, and scope in projects such as serverless cost modeling for data workloads or curated data pipelines: the method matters as much as the result.

Why this is an ideal undergraduate lab

An undergraduate lab works best when it teaches process, not just answer retrieval. This module hits several goals at once: students practice reading spectra, identify absorption lines, use abundance ratios, interpret geochemical consequences, and communicate results in scientific language. It is also naturally modular, so instructors can scale it up for a major-level spectroscopy course or simplify it for an introductory astronomy class.

The lesson also aligns with the SURGE report’s broader message that astronomy programs are expanding rapidly and need more structured, research-like experiences. SURGE’s findings emphasize the diversity of degree structures and the importance of preparing students for varied careers through authentic coursework. A lab like this fits that need beautifully, especially when paired with a focused discussion of the hidden ROI of college majors: students see that astronomy skills transfer into data science, instrumentation, education, and scientific communication.

Learning Outcomes and SURGE-Aligned Assessment

Core learning outcomes

By the end of the module, students should be able to explain how stellar composition is measured, calculate basic abundance ratios from spectroscopic data, and interpret those ratios in the context of rocky planet building blocks. They should also be able to distinguish between direct observations and model-based inference. Finally, students should be able to present a concise, evidence-based conclusion about what kind of planet interior is most plausible for a chosen system.

To make those outcomes explicit, I recommend stating them in the assignment handout using action verbs. For example: “Measure,” “compare,” “calculate,” “justify,” and “evaluate.” That makes the module easier to grade and easier for students to navigate. It also helps with course mapping, accreditation-style documentation, and departmental planning, which matters in the increasingly varied landscape of undergraduate astronomy education described in SURGE analyses.

Assessment aligned to student growth

A strong assessment plan should capture both technical work and scientific reasoning. One portion can be a worksheet with line-identification and abundance calculations. Another can be a short interpretation memo where students describe whether the host star suggests an iron-rich, Earth-like, or potentially volatile-enriched planet population. A final reflection prompt can ask students to identify one source of uncertainty and one way additional data would improve the inference.

For instructors building broader program-level assessment, this lab can support learning outcomes related to data literacy, scientific communication, teamwork, and ethical interpretation of evidence. If you are already thinking about how to modernize departmental assessment, it may help to borrow from resource planning approaches used in other sectors, such as practical playbooks for workflow design and accessibility-first review templates. The educational lesson is the same: define the goal first, then choose the tool.

SURGE-style scaffolding

SURGE’s findings suggest that undergraduate astronomy education benefits when courses intentionally build toward higher-order skills instead of assuming students will absorb them incidentally. This lab does that by scaffolding from observation to inference. Students first identify spectral features, then compute ratios, then interpret the ratios, and only then connect to planet interiors. That ladder is what transforms a worksheet into a genuine research experience. It is also a strong fit for both BA and BS programs, since it supports conceptual understanding and quantitative analysis at the same time.

What Materials You Need for the Lab

Recommended data sources and software

The good news is that this module does not require a major observatory. Instructors can use archived high-resolution stellar spectra, published line lists, and common analysis tools such as Python, spreadsheet software, or browser-based notebook environments. If students are more advanced, you can include open-source spectral-fitting packages or a simplified equivalent. The most important requirement is that the data be clean enough for students to measure equivalent widths or line depths without drowning in instrumental complexity.

For the planet side of the exercise, use publicly available stellar and planetary parameters from TESS-discovered systems. Many TESS planets have enough literature follow-up to support an instructor-curated case study. The pairing is ideal: TESS gives a transiting planet candidate and its radius, while spectroscopy gives the host-star chemistry. In a teaching context, that creates a compelling chain of evidence from light curve to star to planet interior.

If your department is trying to balance sophistication with practicality, think like an educator planning equipment access rather than a consumer browsing upgrades. The right choice is not always the fanciest. It is the one that maximizes learning per hour. That philosophy is similar to the decision-making logic used in other technical guides, like tooling decision frameworks and value-first alternatives.

Minimum instructor preparation

Before class, the instructor should prepare one host-star spectrum, a line list for iron, magnesium, silicon, and calcium, and a short background sheet on how these elements relate to rocky planet mineralogy. It helps to precompute a few checkpoints so students can verify whether their measurements are reasonable without giving away the final interpretation. Instructors should also prepare a short methods appendix explaining any approximations, such as using abundance ratios instead of a full stellar-atmosphere solution.

For a more polished experience, you can prepare two versions of the dataset: one “clean” set for first-time students and one “messier” set with noise, continuum-placement challenges, or blended lines for an advanced section. This allows the same lab to serve multiple skill levels. If you want to refine your instructional materials further, the process can borrow from strategies in rapid testing and iteration, where small changes are evaluated before scaling.

Step-by-Step Lab Workflow: From Spectrum to Interior Constraint

Step 1: Orient students to the science question

Begin with the question: “What kind of planet is likely to form around this star?” That framing is powerful because it gives the spectroscopy a purpose. Students should understand that they are not just measuring lines; they are reconstructing the chemical environment of planet formation. A short pre-lab discussion can contrast stars with planets, explain elemental abundances, and introduce the idea of condensation temperatures in a protoplanetary disk.

A useful analogy is baking. If you know a kitchen has lots of flour, sugar, and butter, you can predict something about the kinds of desserts that might be made, but not the exact recipe. The star is the pantry. The planet is the finished dish. And the disk’s thermal history is the baker.

Step 2: Identify and measure spectral lines

Students inspect a normalized optical spectrum and locate absorption features associated with Fe I, Mg I, Si I, and Ca I. Depending on the level of the class, they may use manual line identification or a guided template overlay. The next step is to estimate equivalent widths, line depths, or another abundance-sensitive metric. The key is to preserve the relationship between the data and the inference, rather than providing a black-box output.

At this stage, students will likely encounter the reality that spectroscopic measurements are imperfect. Continuum placement can shift a result. Noise can obscure weak lines. Blends can complicate the interpretation. That difficulty is educationally valuable because it mirrors real research practice, including the kind of careful instrument handling that scientists like Johanna Teske pursue with high-resolution optical and near-infrared spectroscopy in programs tied to planet-finding spectrographs.

Step 3: Convert measurements into abundance ratios

Once line measurements are in hand, students convert them into relative abundances using a simplified calibration table or a provided curve-of-growth approximation. The pedagogical goal is not to reproduce a full stellar abundance analysis pipeline. Instead, students should see how spectroscopy turns light into numbers and numbers into chemical ratios. The important ratios for rocky planet inference are often Mg/Si and Fe/Si, because they influence silicate mineralogy and the relative size of metallic cores.

Encourage students to compare their values to solar ratios or to a known benchmark star. Then ask them what the ratios imply qualitatively. Does the system appear more iron-rich than the Sun? Does it favor olivine-rich or pyroxene-rich silicate mantles? Such questions move the lab from mechanics to reasoning.

Step 4: Connect chemistry to planet interiors

This is the conceptual centerpiece of the module. Students use the host-star composition to estimate the types of solids likely to form in the disk, then infer broad interior consequences for terrestrial planets. Higher Fe/Si can suggest larger metallic cores relative to silicate mantles. Mg/Si near or above unity can favor mineral assemblages like olivine and pyroxene. A lower Mg/Si can change the balance toward silica-rich phases, which can alter mantle structure and density.

You can keep the modeling qualitative, or add a quantitative extension using simplified mass-balance calculations. For example, students can estimate the fraction of iron available for core formation after accounting for oxidized iron in silicates. This extension works especially well in advanced labs where students have already seen stoichiometry or geochemical partitioning.

Step 5: Compare with known exoplanet properties

To ground the lab in actual astronomy, provide the radius of a TESS planet and, if available, a measured mass from the literature. Students can calculate bulk density and then compare their chemically inferred interior expectations with the observed density. If the planet is unusually dense, they might expect a substantial iron core. If it is low density, they must ask whether the planet likely contains volatiles, an atmosphere, or a water-rich layer.

This comparison is where students learn the important difference between “composition from chemistry” and “composition from density.” Both are useful, and both are incomplete on their own. This is a great moment to discuss how astronomers integrate multiple data sources, much like how modern analysts combine observations, models, and uncertainty estimates in fields as different as real-time geospatial pipelines and hardware decision frameworks.

Real-Spectrum Example: A Simplified Classroom Case Study

Example host star

Below is a simplified teaching case for a Sun-like star observed at moderate-to-high spectral resolution. In the actual lab, the instructor can provide the spectrum in a FITS file, CSV, or interactive notebook format. For clarity, the class should focus on a handful of clean lines rather than an overwhelming forest of features. The goal is to develop confidence in the method, not to reproduce an observatory pipeline from scratch.

Suppose the measured abundance pattern relative to solar is: [Fe/H] slightly above solar, Mg/Fe near solar, and Si/Fe slightly below solar. Students can immediately ask whether this combination suggests a somewhat iron-rich rocky planet population. If the host star belongs to a TESS-identified multi-planet system, the implication becomes even more interesting, because multiple planets may share a common chemical birthplace but diverge in size and density due to later evolution.

Example reasoning chain

First, students note that iron is a major core-forming element. If the star is enriched in iron relative to silicon, the available material may support planets with large metallic cores. Second, they compare magnesium and silicon to estimate dominant mantle minerals. Third, they consider the planet’s observed radius and available mass estimate. If the planet is unusually compact for its size, that strengthens the case for a dense interior. If it is puffier than expected, then volatiles or an atmosphere may be important despite the chemistry.

One of the most valuable classroom outcomes is that students learn to speak in probability, not certainty. They should not say, “This planet has an iron core of exactly 33 percent.” Instead, they might say, “The host-star chemistry is consistent with a rocky planet that could have a relatively large iron fraction, but the observed density and possible volatile content prevent a precise interior model.” That is the language of real science.

What to tell students about uncertainty

Uncertainty is not a flaw in the lab; it is the lab. Students should estimate measurement uncertainty for line strength and propagate that uncertainty into abundance ratios if the class is ready. Even a simplified uncertainty discussion teaches an essential lesson: small errors in elemental ratios can matter when interpreting mineralogy. This is one reason exoplanet composition is such a rich teaching topic—it sits right at the intersection of observation, chemistry, and modeling.

Pro Tip: Ask students to write one sentence in the form “If the host star is X relative to solar, then the planet interior may be Y because...” This sentence structure keeps the reasoning explicit and makes assessment much easier.

Data Analysis, Coding, and Classroom Workflow Options

Low-code version for introductory students

If your students are early in their astronomy training, a spreadsheet-based or guided notebook version works well. Provide preselected line measurements and ask students to calculate ratios, compare to benchmark values, and make a short written inference. This version still teaches the central idea without requiring students to debug code. It is especially useful in large enrollment courses or labs with uneven prior preparation.

To keep the activity active, ask students to annotate the spectrum by hand or use digital markup tools. This gives them a tactile sense of data interpretation that supports retention. It also mirrors how students build observational literacy in other technical subjects, much like structured approaches to exam preparation or guided practice in problem-solving.

Python or notebook version for intermediate students

For a more advanced lab, students can work in Python and measure lines from a normalized spectrum using basic plotting tools. They can compute abundance proxies from a provided calibration, generate a simple bar chart of elemental ratios, and then compare several stars or systems. This version improves scientific fluency and can be adapted to include error propagation, data cleaning, or a small reproducibility component.

Students who want additional challenge can compare their findings to published stellar parameters or use a small grid of spectra for multiple systems. That turns the lab into a mini-research project. It also trains them to work with open data in the way professional astronomers do, which is one reason the lesson pairs so well with program goals around hands-on undergraduate astronomy education.

Group roles and collaborative structure

One of the best ways to improve the lab is to assign roles: data analyst, line identifier, discussion leader, and recorder. That keeps everyone engaged and reduces the risk that one student dominates while others watch. It also reflects real scientific teamwork, where specialists contribute different strengths. For instructors interested in classroom management, this structure is as important as the scientific content.

As a teaching strategy, clear roles also support inclusion. Students who are less comfortable with math may still contribute meaningfully through pattern recognition and interpretation, while stronger coders can focus on the analysis notebook. This kind of collaborative design echoes broader institutional efforts to create more inclusive and effective learning environments, which are increasingly central to astronomy education culture.

Comparison Table: What Different Inference Paths Tell You

The table below helps students see why host-star spectroscopy is powerful but incomplete, and why combining multiple measurements leads to better planetary characterization.

Assessment Ideas, Rubrics, and Deliverables

Short lab report structure

A compact lab report can be surprisingly effective if it asks for the right things. Require an introduction with the scientific question, a methods section describing how spectra were analyzed, a results section containing the calculated abundance ratios, and a discussion section connecting those ratios to planet interiors. Add one paragraph on uncertainty and one sentence on what future observations would improve the inference.

This format teaches students that science writing is a structured argument. It also gives instructors an easy grading rubric. You can score for data accuracy, reasoning, clarity, and use of uncertainty language. If you want students to connect the exercise to careers, ask them to reflect on how similar workflows appear in research, engineering, data science, and education.

Rubric dimensions

Grade the assignment across four dimensions: technical analysis, scientific interpretation, communication, and reflection on limitations. For technical analysis, did students correctly identify lines and compute ratios? For interpretation, did they use those ratios to infer plausible interior composition? For communication, was the explanation clear and well-organized? For reflection, did they acknowledge uncertainty and the need for additional data?

That framework helps students understand that the point is not to produce the “right answer” in a vacuum. It is to build a defensible argument from observations. This is a teaching pattern that also shows up in high-quality professional decision-making guides, including articles about quantitative metrics and live-coverage tactics, where the workflow is as important as the headline.

Extension and honors options

For students ready for more challenge, add a second star with a different abundance pattern and ask them to compare likely planet interiors. Another extension is to provide a simplified planetary density estimate and ask students to evaluate whether the chemistry and density tell the same story. A third option is to require a one-slide presentation to the class, mimicking a research group meeting.

If your program values interdisciplinary communication, this module can also be used in teacher preparation or outreach workshops. The same science can be translated for non-majors, school groups, or public programs with only slight adjustment. That makes it a strong investment for departments seeking both depth and flexibility in their teaching portfolio.

Common Pitfalls and How to Avoid Them

Overpromising certainty

The biggest teaching pitfall is implying that host-star composition directly reveals a planet’s exact interior. It does not. Students should learn to say “constrains,” “suggests,” or “is consistent with,” rather than “proves.” This helps them adopt scientific language that is accurate and mature. It also protects against a common beginner’s mistake: treating a model output as if it were an observed fact.

Use examples to show how different formation histories can lead to similar bulk compositions or how similar stars can still host very different planets. That makes the lesson more realistic and more intellectually honest. It also reinforces the core educational value of astronomy: learning to think with partial information.

Using too much technical machinery

Another pitfall is turning the lab into a software tutorial with no scientific payoff. Keep the coding or spreadsheet work tightly linked to the scientific question. If a step does not help students reason about composition, remove or simplify it. The best labs are the ones where every action supports a claim.

Instructors can avoid overload by offering a “core path” and an “extension path.” The core path should be enough for all students to complete the essential analysis. The extension path can include curve-of-growth concepts, uncertainty propagation, or multi-star comparisons for students who want a deeper challenge.

Ignoring the observational context

Finally, don’t forget to situate the lab in the broader exoplanet discovery ecosystem. Students should know that TESS finds transits, radial velocity instruments measure masses, and stellar spectroscopy supplies the chemistry. When the pieces are presented together, the module becomes more than a chemistry exercise; it becomes a miniature version of the exoplanet discovery pipeline. That is especially compelling for students who enjoy the observational side of astronomy and may want to pursue instrumentation or research.

Pro Tip: End the lab by asking students what observation they would request next if they had telescope time. The answers reveal whether they truly understand the limitations of their evidence.

Teacher Notes for Adapting the Module to Different Courses

Introductory astronomy course

For intro courses, focus on the narrative arc and the interpretation. Keep the math light, provide pre-measured lines, and emphasize conceptual understanding. Students should leave with a grasp of why stars matter for planet formation and why exoplanet interiors are inferred rather than directly seen. This version is ideal when the goal is literacy, not advanced measurement technique.

To boost engagement, pair the lab with a short reading on a real exoplanet researcher and a current mission result. A short profile of an observer like Johanna Teske can help students see that this work is active, collaborative, and impactful. It also makes the research pathway feel tangible.

Upper-level undergraduate course

For upper-level students, add raw spectra, a line list, uncertainty estimates, and a short literature comparison. Have them critique their own assumptions. Ask whether the star’s abundance pattern alone is enough to infer a rocky planet composition, or whether other information is needed. This pushes students toward the kind of analytical flexibility expected in research settings.

You can also tie the module to broader professional development. Students may explore how astronomy skills connect to data analysis, modeling, coding, and science communication, which broadens their view of the field. That broader perspective is useful in a time when undergraduate astronomy programs are growing rapidly and need to show students the many routes astronomy training can take.

Teacher-training or outreach version

For educators, the module works as a model lesson in scientific reasoning with authentic data. Teachers can observe how the lesson guides students from observation to interpretation and how that structure can be adapted to other sciences. It is also an excellent example of how to bring current astronomy research into the classroom without overwhelming learners.

If you teach in a department interested in curriculum development, the module can be paired with department-wide conversations about sequencing, scaffolding, and undergraduate research exposure. That makes it useful beyond a single lab period. It becomes part of a larger teaching strategy.

Frequently Asked Questions

Conclusion: Turning Spectra Into Planet Stories

A good astronomy lab does more than confirm a formula. It gives students a story they can trace from raw evidence to scientific interpretation. This module does exactly that. Starting with starlight, students measure chemical fingerprints, reason about what materials were available in a planet-forming disk, and infer what a planet’s interior might plausibly look like. That progression is powerful because it mirrors how exoplanet scientists actually work.

It also teaches an enduring lesson about science: the universe is often understood indirectly, through careful inference rather than direct inspection. The host star may be distant and the planet hidden, but together they can still reveal a coherent picture. That makes this a memorable and intellectually honest undergraduate lab, one that serves the goals of modern astronomy education and prepares students for deeper work in observation, data analysis, and exoplanet composition research.

For instructors looking to expand the lesson into a broader unit, the next steps could include atmosphere comparisons, planet formation simulations, or a class discussion of how this chemistry-based approach complements other methods in exoplanet science. And if you want to keep students connected to the broader landscape of undergraduate astronomy, the SURGE report is a valuable reminder that thoughtful curriculum design matters as much as cutting-edge data. In a field growing as quickly as astronomy, labs like this help ensure that students do not just learn facts — they learn how to think.

Related Reading

• The Landscape of Undergraduate Astronomy and Astrophysics Degree Requirements - A useful overview of how astronomy curricula are evolving across institutions.
• Dr. Johanna Teske - Carnegie Science - A researcher profile centered on exoplanet composition and host-star chemistry.
• A Week-by-Week Approach to AP and University Exam Prep - Helpful for structuring student pacing and review in quantitative courses.
• Classroom Lessons to Teach Students When an AI Is Confidently Wrong - A smart companion piece on evidence checking and critical thinking.
• The Hidden ROI of College Majors - A broader look at why practical skills in astronomy can pay off across careers.