Background modelling for $\gamma$-ray spectroscopy with INTEGRAL/SPI

• Main Authors: Siegert, Thomas, Diehl, Roland, Weinberger, Christoph, Pleintinger, Moritz M. M, Greiner, Jochen, Zhang, Xiaoling
• Format: Journal Article
• Language: English
• Published: 04.03.2019
• Subjects: Physics - High Energy Astrophysical Phenomena; Physics - Instrumentation and Methods for Astrophysics
• DOI: 10.48550/arxiv.1903.01096

A&A 626, A73 (2019) The coded-mask spectrometer-telescope SPI on board the INTEGRAL observatory records photons in the energy range between 20 and 8000 keV. A robust and versatile method to model the dominating instrumental background (BG) radiation is difficult to establish for such a telescope in the rapidly changing space environment. From long-term monitoring of SPI's Germanium detectors, we built up a spectral parameter data base, which characterises the instrument response as well as the BG behaviour. We aim to build a self-consistent and broadly applicable BG model for typical science cases of INTEGRAL/SPI, based on this data base. The general analysis method for SPI relies on distinguishing between illumination patterns on the 19-element Germanium detector array from BG and sky in a maximum likelihood framework. We illustrate how the complete set of measurements, even including the exposures of the sources of interest, can be used to define a BG model. We apply our method to different science cases, including point-like, diffuse, continuum, and line emission, and evaluate the adequacy in each case. From likelihood values and the number of fitted parameters, we determine how strong the impact of the unknown BG variability is. We find that the number of fitted parameters, i.e. how often the BG has to be re-normalised, depends on the emission type (diffuse with many observations over a large sky region, or point-like with concentrated exposure around one source), and the spectral energy range and bandwidth. A unique time scale, valid for all analysis issues, is not applicable for INTEGRAL/SPI, but must and can be inferred from the chosen data set. We conclude that our BG modelling method is usable in a large variety of INTEGRAL/SPI science cases, and provides nearly systematics-free and robust results.