Fastest or Significant: A Systematic Framework for Validating Global Minimum Variability Timescale Measurements of Gamma-ray Bursts

TL;DR

This paper develops a systematic framework to validate gamma-ray burst variability timescale measurements, addressing biases and noise effects, and provides practical guidelines for interpreting MVT data reliably.

Contribution

It introduces an empirical validation curve and workflow for robust MVT measurement classification, improving the reliability of physical inferences from gamma-ray burst data.

Findings

Shorter intrinsic timescales require higher SNR for detection.

Many published MVT values are upper limits, not precise measurements.

The framework helps distinguish genuine variability from noise artifacts.

Abstract

The minimum variability timescale (MVT) is a key observable used to probe the central engines of Gamma-Ray Bursts (GRBs) by constraining the emission region size and the outflow Lorentz factor. However, its interpretation is often ambiguous: statistical noise and analysis choices can bias measurements, making it difficult to distinguish genuine source variability from artifacts. Here we perform a comprehensive suite of simulations to establish a quantitative framework for validating Haar-based MVT measurements. We show that in multi--component light curves, the MVT returns the most statistically significant structure in the interval, which is not necessarily the fastest intrinsic timescale, and can therefore converge to intermediate values. Reliability is found to depend jointly on the MVT value and its signal-to-noise ratio ($\mathrm{SNR}_{\mathrm{MVT}}$), with shorter intrinsic timescales requiring proportionally higher $\mathrm{SNR}_{\mathrm{MVT}}$ to be resolved. We use this relation to define an empirical MVT Validation Curve, and provide a practical workflow to classify measurements as robust detections or upper limits. Applying this procedure to a sample of Fermi-GBM bursts shows that several published MVT values are better interpreted as upper limits. These results provide a path toward standardizing MVT analyses and highlight the caution required when inferring physical constraints from a single MVT measurement in complex events.