Business and scientific objective
Frame: reduce a large astronomical object table into a prioritized review list.
Identify high-priority variable star candidates
Large sky-survey datasets contain many sources with limited variability signal. The task is to rank objects by evidence of non-constant brightness so analysts can focus follow-up work on the strongest candidates.
Ranked candidate catalog
The workflow produces a candidate table with object identifiers, diagnostic variability metrics, and a composite score that can feed later validation, visualization, or classification work.
Data foundation
Rubin Observatory DiaObject summary metrics, sampled for reproducibility.
The notebook queries object-level g-band variability features from the Rubin Observatory dp1.DiaObject table, then exports a 5,000-row sample to keep the demo portable. The selected fields include coordinates, flux summary statistics, observation counts, uncertainty estimates, and variability metrics.
g_psfFluxMAD, g_psfFluxMax, g_psfFluxMean, g_psfFluxMeanErr,
g_psfFluxMin, g_psfFluxNdata, g_psfFluxSigma, g_psfFluxStetsonJ, g_psfFluxChi2
FROM dp1.DiaObject
For a public portfolio version, the authenticated Rubin query is treated as the source extraction step, while the sampled CSV is the reproducible demo input.
Workflow
A simple pipeline from raw metrics to candidate selection.
Load
Read the sampled g-band DiaObject dataset and inspect shape, columns, null values, and feature ranges.
Explore
Visualize variability distributions and the relationship between flux dispersion and Stetson J.
Filter
Retain records with positive g_psfFluxSigma and g_psfFluxStetsonJ so log-scale diagnostics are interpretable.
Score
Standardize complementary variability metrics and combine them into a single ranking index.
Select
Sort objects by the composite score and select the top 5% as candidate variable stars.
Export
Save the ranked candidate set for downstream review, validation, and modeling.
Candidate scoring method
A transparent ranking score designed for interpretability.
Reduced chi-square: g_psfFluxChi2
Measures deviation from constant brightness relative to photometric uncertainty. Higher values suggest stronger evidence against a constant-flux source.
Stetson J: g_psfFluxStetsonJ
Captures correlated variability structure across observations. Higher positive values are treated as stronger variability evidence.
Composite score
Because the two metrics live on different scales, each is standardized before summation.
Objects with higher VarIndex values exhibit stronger and more consistent variability signals across both metrics.
Results and diagnostics
The high-variability tail becomes the candidate review set.
Distribution of g-band flux sigma. The right-skewed tail motivates ranked candidate selection.
Log-log relationship between flux sigma and Stetson J. Sparse upper-right regions indicate stronger variability signatures.
Top-scoring candidates cluster in the upper-right region of variability space, consistent with stronger non-constant brightness behavior.
Top ranked candidates
First ten objects after sorting by composite variability score.
| diaObjectId | g_psfFluxChi2 | g_psfFluxStetsonJ | VarIndex |
|---|---|---|---|
| 579576630817594587 | 482,363.000 | 339.038544 | 21.804609 |
| 628762458852426088 | 761,798.000 | 103.974905 | 18.049122 |
| 614436165939888139 | 85,812.100 | 262.254544 | 11.318546 |
| 579575325147529607 | 5,817.990 | 41.463971 | 1.290933 |
| 628769055922193502 | 6,432.190 | 40.320410 | 1.258518 |
| 630407465686532255 | 5,544.980 | 37.792791 | 1.144264 |
| 614437059293090505 | 6,958.570 | 32.252146 | 0.957558 |
| 579578623682412873 | 1,250.080 | 33.242482 | 0.887375 |
| 579578005207122886 | 1,736.320 | 28.177015 | 0.701381 |
| 628767200496320531 | 812.243 | 27.823323 | 0.670210 |
The highest ranked objects are not asserted as confirmed variable stars. They are prioritized candidates for downstream validation.
Limitations and next steps
What this demo does and does not claim.
- Candidate ranking only; no confirmed classification labels are used.
- Uses g-band summary metrics rather than full light-curve time-series modeling.
- Threshold selection is intentionally simple and should be calibrated for production use.
- Authentication-dependent query execution is separated from the public reproducible sample.
- Add multi-band variability features and color information.
- Engineer period, amplitude, and light-curve morphology features.
- Validate against labeled variable-star catalogs.
- Compare threshold ranking with clustering or supervised classification models.