Average Fe K

Context. One of the most important parameters in the XRB (X-ray background) synthesis models is the average efficiency of accretion onto SMBH (super-massive black holes). This can be inferred from the shape of broad relativistic Fe lines seen in X-ray spectra of AGN (active galactic nuclei). Several studies have tried to measure the mean Fe emission properties of AGN at different depths with very different results. Aims. We compute the mean Fe emission from a large and representative sample of AGN X-ray spectra up to redshift ~3.5. Methods. We developed a method of computing the rest-frame X-ray average spectrum and applied it to a large sample (more than 600 objects) of type 1 AGN from two complementary medium sensitivity surveys based on XMM-Newton data, the AXIS and XWAS samples. This method makes use of medium-to-low quality spectra without needing to fit complex models to the individual spectra but with computing a mean spectrum for the whole sample. Extensive quality tests were performed by comparing real to simulated data, and a significance for the detection of any feature over an underlying continuum was derived. Results. We detect with a 99.9% significance an unresolved Fe Kα emission line around 6.4 keV with an EW ~ 90 eV, but we find no compelling evidence of any significant broad relativistic emission line in the final average spectrum. Deviations from a power law around the narrow line are best represented by a reflection component arising from cold or low-ionization material. We estimate an upper limit for the EW of any relativistic line of 400 eV at a 3σ confidence level. We also marginally detect the so-called Iwasawa-Taniguchi effect on the EW for the unresolved emission line, which appears weaker for higher luminosity AGN. Conclusions. We computed an upper limit for the average relativistic Fe Kα line contribution that is significantly lower than previously reported values from similar analyses. Our results, however, are in excellent agreement with individual analyses of local AGN samples. We attribute this difference either to our more sophisticated method of modeling the underlying continuum, to intrinsic differences in source populations, and/or to the uneven data quality of the individual spectra of the various samples