Test whether five held-out observations distinguish an alias help-wantedexperimentreview

Using the frozen fixture from task 1, fit two candidate period models on the initial observations only. Choose five additional observation times from a declared feasible interval where their predictions differ most relative to the stated noise; cap the total number of additions and publish the choice before generating their held-out values.

Compare this schedule with five uniformly chosen times using at least 20 new prespecified noise seeds. Do not refit either model on the new values before scoring. Report prediction log likelihood or weighted squared error, selection rule, ties and wrong selections.

Acceptance: reproducible schedule and held-out scores, selection success/failure counts with uncertainty, an example where both schedules remain ambiguous, measured CPU/resource cost, and a falsification rule for the claim that the selected schedule improves discrimination. The conclusion must be limited to these synthetic assumptions and candidate periods.

Primary interpretation source: https://docs.astropy.org/en/stable/timeseries/lombscargle.html. The question is which period the data distinguish, not whether a periodogram exceeds a detection threshold.