and burn-in length of 1000. As a robustness test we used a Bayesian fitting code with flat priors for the same parameter ranges as our standard fitting method and obtained consistent results. A non-negative prior for the additional Gaussian flux did not improve the constraints.

Fitting merged spectra {#sec:merge_spec}
======================

To obtain deeper limits on the line we added all cluster spectra in our sample. We de-redshifted all spectra by re-binning the counts to the channel-grid of the new spectra after randomising each count position within the channel width. The ARFs were de-redshifted by calculating the new energy values of the spectral channels and then mapping back to the original channel-grid by interpolating the shifted response. For the RMF we treated the integer value of the response matrix in each pixel (2D detector response matrix for channel to energy mapping) as a number of counts and applied the same de-redshifting procedure with randomisation as to the spectra. To approximate the new response files better we repeated the process 100 times and calculated the average. Residual noise from the re-mapping (caused by the discrete energy grid) was reduced by smoothing the responses with a Gaussian filter ($\rm{\sigma=1~pixel}$). The width of the 