Objectives
- Apply the galactic archaeology and the milky way in time and a working synthesis: what makes a galaxy? toolkit to a traceable dataset.
- Quantify uncertainty or model sensitivity rather than reporting a bare fit.
- Communicate a result with a figure, units, and provenance.
Preparation and materials
Use Python 3, NumPy, Matplotlib, Astropy, and the supplied CSV in data/. Read the paired lecture notes and inspect the CSV header before analysis. No live observing is required; the activity is reproducible on a laptop.
Dataset: the complete course data bundle, with one independent cross-check
Setup and procedure
- Copy the CSV into a working directory and record its filename, row count, columns, and units.
- Plot the raw observables before applying a model. Label axes and preserve the supplied uncertainties.
- Fit or transform the data using the equation from Lectures 13–14; show one intermediate calculation by hand and reproduce it in code.
- Repeat the result after a stated perturbation: remove one outer-radius point, vary the distance by 10%, or change the model family as appropriate.
- Write a short interpretation separating measured values, adopted literature values, and derived quantities.
Measurement and analysis record
| Item | Student record |
|---|---|
| Input file and provenance | Filename, source, access date, license/status |
| Calibration or cuts | Exact criteria and number of retained rows |
| Primary result | Value, units, uncertainty or sensitivity |
| Model check | Residual plot or comparison statistic |
Uncertainty and limitations
Use the tabulated uncertainty where available and propagate it through the stated formula. If the dataset lacks per-row errors, report sensitivity to a defensible perturbation and label the result model-dependent. Do not treat a standard published value as a new measurement.
Deliverables and assessment
- Reproducible notebook or script with environment and input filename.
- One labeled figure and one table of derived values.
- 600–900 word interpretation with an uncertainty statement and provenance paragraph.
- Short limitations section naming at least two threats to inference.
Grading: analysis correctness 35%, uncertainty/model critique 25%, reproducibility 20%, figure and scientific communication 20%. Revisions are encouraged under the course resubmission policy.
Data provenance
the complete course data bundle, with one independent cross-check The reference log identifies whether each value is level 1 live-verified, level 2 standard published and flagged for spot-check, or level 3 synthetic/instructor-provided.