Photometric Redshift Uncertainty Guide
Why This Calculator Helps
Photometric redshift work often starts with broad image data, not spectra. Each filter records part of a galaxy signal. The final redshift estimate is useful, but it is never exact. A clear uncertainty value helps analysts decide whether an object is ready for science, follow up, or review.
What The Inputs Mean
This calculator brings several checks into one workflow. It uses the central photometric redshift, the lower and upper credible interval limits, optional spectroscopic redshift, magnitude error, filter count, and prior width. The tool converts interval widths into an estimated sigma. It then adds practical noise terms through quadrature. This creates a combined uncertainty for quick reporting.
Why Normalized Values Matter
The normalized uncertainty is important. A redshift error of 0.05 can be small near high redshift and large near low redshift. Dividing by one plus redshift makes the result easier to compare between objects. The same idea is used for the optional bias check against a reference redshift.
Bias And Outlier Review
The calculator also reports pull and outlier status. Pull compares the absolute redshift difference with the combined uncertainty. A high pull suggests that the interval may be too narrow, the photometry may be noisy, or the template fit may need inspection. The outlier flag uses a common normalized threshold of 0.15 for fast screening.
Reading The Quality Label
Use the quality label as a guide, not a final scientific verdict. Excellent results usually have narrow intervals, many useful filters, and low magnitude error. Broad results may still be valid when the object is faint, blended, or affected by missing bands. The calculator shows the numbers so the decision stays transparent.
Exporting Results
The exports are useful for repeatable review. The CSV file stores the main calculated values for spreadsheets. The PDF file gives a simple record for notes, class work, or observation logs. Keep the original photometric catalog and method notes with any exported report.
Batch Review Tip
For batch projects, calculate one source at a time, then compare exported rows later. Look for repeated bias by field, magnitude bin, color range, or template family. Those patterns can reveal calibration issues. They can also show where more filters or deeper imaging would improve the redshift estimate. When uncertainty grows, mark the object for cautious interpretation before using derived distance, luminosity, or environment measurements in analysis.