An adaptive-binning method for generating constant-uncertainty/constant-significance light curves with Fermi-LAT data

TL;DR

This paper introduces an adaptive-binning technique for Fermi-LAT data that produces light curves with constant uncertainty or significance, improving information content over fixed-binning methods, especially for blazar studies.

Contribution

The paper presents a novel adaptive-binning method for Fermi-LAT light curves, allowing dynamic interval determination and enhanced data analysis capabilities.

Findings

Method effectively captures source variability.

Improves duty cycle and power-density spectrum analysis.

Validated through Monte-Carlo simulations.

Abstract

We present a method enabling the creation of constant-uncertainty/constant-significance light curves with the data of the Fermi-Large Area Telescope (LAT). The adaptive-binning method enables more information to be encapsulated within the light curve than with the fixed-binning method. Although primarily developed for blazar studies, it can be applied to any sources. This method allows the starting and ending times of each interval to be calculated in a simple and quick way during a first step. The reported mean flux and spectral index (assuming the spectrum is a power-law distribution) in the interval are calculated via the standard LAT analysis during a second step. The absence of major caveats associated with this method has been established by means of Monte-Carlo simulations. We present the performance of this method in determining duty cycles as well as power-density spectra relative to the traditional fixed-binning method.