INTEGRAL/SPI data segmentation to retrieve source intensity variations

TL;DR

This paper introduces new segmentation techniques for INTEGRAL/SPI data to better model source intensity variations, improving sky models and source signal detection without relying solely on prior information.

Contribution

The paper presents two novel algorithms for segmenting source intensity variations in SPI data, one using external light curves and another relying solely on raw data.

Findings

Enhanced source modeling accuracy

Improved signal-to-noise ratios

Better understanding of source behavior

Abstract

The INTEGRAL/SPI, X-gamma-ray spectrometer (20 keV - 8 MeV) is an instrument for which recovering source intensity variations is not straightforward and can constitute a difficulty for data analysis. In most cases, determining the source intensity changes between exposures is largely based on a priori information. We propose techniques that help to overcome the difficulty related to source intensity variations, which make this step more rational. In addition, the constructed "synthetic" light curves should permit us to obtain a sky model that describes the data better and optimizes the source signal-to-noise ratios. For this purpose, the time intensity variation of each source was modeled as a combination of piecewise segments of time during which a given source exhibits a constant intensity. To optimize the signal-to-noise ratios, the number of segments was minimized. We present a first method that takes advantage of previous time series that can be obtained from another instrument on-board the INTEGRAL observatory. A data segmentation algorithm was then used to synthesize the time series into segments. The second method no longer needs external light curves, but solely SPI raw data. For this, we developed a specific algorithm that involves the SPI transfer function. The time segmentation algorithms that were developed solve a difficulty inherent to the SPI instrument, which is the intensity variations of sources between exposures, and it allows us to obtain more information about the sources' behavior.