Operational since 2002 on-board the INTEGRAL observatory, the SPI spectrometer can be used to perform polarization measurements in the hard Xray/softγ-ray domain (∼130 keV -8 MeV). However, this phenomenon is complex to measure at high energy and requires high fluxes. Cyg X-1 appears as the best candidate amongst the X-ray binaries since it is one of the brightest persistent sources in this energy domain. Furthermore, a polarized component has recently been reported above 400 keV from IBIS data. We have therefore dedicated our efforts to develop the required tools to study the polarization in the INTEGRAL SPI data and have first applied them to 2.6 Ms of Cyg X-1 observations, covering 6.5 years of the INTEGRAL mission.We have found that the high energy emission of Cyg X-1 is indeed polarized, with a mean polarization fraction of 76% ± 15% at a position angle estimated to 42 • ± 3 • , for energies above 230 keV. The polarization fraction clearly increases with energy. In the 130-230 keV band, the polarization fraction is lower than 20 %, but exceeds 75 % between 370 and 850 keV, with the (total) emission vanishing above this energy. This result strongly suggests that the emission originates from the jet structure known to emit in the radio domain. The same synchrotron process could be responsible for the emission from radio to MeV, implying the presence of high energy electrons. This illustrates why the polarization of the high energy emission in compact objects is an increasingly important observational objective.

Built on top of the Geant4 toolkit, GATE is collaboratively developed for more than 15 years to design Monte Carlo simulations of nuclear-based imaging systems. It is, in particular, used by researchers and industrials to design, optimize, understand and create innovative emission tomography systems. In this paper, we reviewed the recent developments that have been proposed to simulate modern detectors and provide a comprehensive report on imaging systems that have been simulated and evaluated in GATE. Additionally, some methodological developments that are not specific for imaging but that can improve detector modeling and provide computation time gains, such as Variance Reduction Techniques and Artificial Intelligence integration, are described and discussed.