Constraints on Lorentz Invariance Violation from GRB 221009A Using the DisCan Method

TL;DR

This study uses the DisCan method with Shannon entropy to analyze LHAASO observations of GRB 221009A, setting new lower limits on the energy scale of Lorentz invariance violation at 95% confidence.

Contribution

It introduces the application of the DisCan method with Shannon entropy to constrain Lorentz violation using gamma-ray burst data, demonstrating stability and robustness of the approach.

Findings

Lower limits on linear Lorentz violation energy scale: >5.4×10^{19} GeV (subluminal) and >2.7×10^{19} GeV (superluminal).

Lower limits on quadratic Lorentz violation energy scale: >10.0×10^{12} GeV (subluminal) and >2.4×10^{12} GeV (superluminal).

Employing different entropy measures yields consistent constraints.

Abstract

Lorentz symmetry is a cornerstone of modern physics, and testing its validity remains a critical endeavor. In this work, we analyze the photon time-of-flight and time-shift data from LHAASO observations of Gamma-Ray Burst GRB 221009A to search for signatures of Lorentz violation. We employed the DisCan (dispersion cancellation) method with various information entropies as cost functions, designating the results obtained with Shannon entropy as our representative outcome. This choice is attributed to the parameter-free statistical properties of Shannon entropy, which has demonstrated remarkable stability as we continually refine and enhance our methodology. In the absence of more detailed data and physical context, it provides more stable and reliable results. We constrain the energy scale associated with Lorentz invariance violation. Our results yield 95\% confidence level lower limits of $E_{\text{QG},1} > 5.4 \times 10^{19} \, \text{GeV}$ (subluminal) and $E_{\text{QG},1} > 2.7 \times 10^{19} \, \text{GeV}$ (superluminal) for the linear case ($n$=1), and $E_{\text{QG},2} > 10.0 \times 10^{12} \, \text{GeV}$ (subluminal) and $E_{\text{QG},2} > 2.4 \times 10^{12} \, \text{GeV}$ (superluminal) for the quadratic case ($n$=2). Subsequently, we incorporated WCDA photons and the Knuth binning method to further optimize and complement our approach, while also performing filter using information entropies. Furthermore, we demonstrate that employing different information entropy measures as cost functions does not alter the order of magnitude of these constraints.