Light Curve Skill

To compute astronomical source variability metrics from time-series photometry, you can use this Skill to calculate Eta, V-index, and sigma-deviation for sources detected in your mosaic. It supports per-mosaic, source-specific, and full catalog processing modes.

ESE detection works by identifying potential Extreme Scattering Events based on defined criteria applied to your computed variability metrics. The Skill evaluates time-series photometry data to flag sources exhibiting significant brightness changes over time.

Yes, you can process a full catalog of astronomical sources for brightness changes using the full catalog processing mode. This allows you to automatically flag all sources in a new radio astronomy mosaic that exhibit significant variability.

The Eta variability metric is used in astronomical photometry to quantify the ratio of point-to-point scatter to overall brightness variation. This Skill calculates Eta alongside V-index and sigma-deviation to help classify source behavior and identify transient events.

Yes, the DSA-110 pipeline supports storing individual photometry measurements in database tables. This Skill integrates directly with those database tables to store and query both variability statistics and the underlying photometry data.

Variability metrics for transient event identification rely on sufficient time-series photometry data points to calculate accurate Eta, V-index, and sigma-deviation. Incomplete or noisy photometric data within your mosaic can impact the reliability of ESE detection.