Learning objectives
- predict thermal rms, estimate system temperature from a measured rms, and convert Jy per beam into Rayleigh-Jeans brightness temperature.
- Propagate a statistical or calibration uncertainty and report a limitation.
- Preserve a reproducible record of inputs, transformations, and outputs.
Preparation, materials, and setup
Use Python 3 with the standard library (or a spreadsheet capable of regression and plotting), the supplied ../data/radiometer_trials.csv, and the relevant lecture notes. Record file name, row count, columns, units, and provenance level before editing. No facility observing time is assumed.
Dataset status: the supplied compact record is synthetic/instructor-provided (level 3), not an archived observation. Compare its analysis workflow with the real survey or observatory source cited in ../reference-log.md.
Computed check: T_b = 307.9 K.
Apparatus and procedure
- Reproduce the radiometer prediction for every bandwidth and integration-time row.
- Use the measured rms columns to estimate eta_s or identify a systematic floor; do not fit below the supplied floor.
- Convert the listed 1.4-GHz peak flux density per beam to T_b using the Gaussian beam solid angle.
- Repeat the T_b calculation after doubling both beam axes and explain the factor-of-four beam-area change.
- Propagate SEFD, bandwidth, integration-time, and beam uncertainties to the reported derived quantities.
Measurement record and uncertainty
| Record | Required entry |
|---|---|
| Input | File, release/source, units, masks or flags, and provenance level. |
| Model | Equation, parameter values, convention, and approximation. |
| Statistics | Fit covariance or counting error plus one calibration/selection/systematic term. |
| Validation | Independent recomputation, residual plot, and limiting-case check. |
Deliver an rms-versus-integration plot, one brightness-temperature calculation, and a table separating thermal and systematic terms.
Deliverables and criteria
- Reproducible code or spreadsheet with inputs and units documented.
- One publication-quality labeled plot and a short interpretation (400-700 words).
- Uncertainty budget that separates random, common-mode, and model terms where relevant.
- Credit requires correct method, units, interpretation, provenance, and a limitation; a copied numeric result without an audit trail earns at most half credit.
Interpretive boundary
At fixed SEFD and bandwidth, ideal rms follows tau^-1/2; excess long-time scatter is evidence against pure radiometer noise. State explicitly which parts of your result are facts about this prepared teaching record and which are transferable inference methods.
References and provenance
Course teaching record: ../data/radiometer_trials.csv, level 3. Real facility/survey context: reference log. No external image is used.