  The Spallation Neutron Source (SNS) at Oak Ridge National Laboratory (ORNL)
operates in the event mode. Time-of-flight (TOF) information about each
detected neutron is collected separately and saved as a descriptive entry in a
database enabling unprecedented accuracy of the collected experimental data.
Nevertheless, the common data processing pipeline still involves the binning of
data to perform analysis and feature extraction. For weak reflections, improper
binning leads to sparse histograms with low signal-to-noise ratios, rendering
them uninformative. In this study, we propose the Bayesian approach for the
identification of Bragg peaks in TOF diffraction data. The method is capable of
adaptively handling the varying sampling rates found in different regions of
the reciprocal space. Unlike histogram fitting methods, our approach focuses on
estimating the true neutron flux function. We accomplish this by employing a
profile fitting algorithm based on the event-level likelihood, along with a
multiresolution histogram-level prior. By using this approach, we ensure that
there is no loss of information due to data reduction in strong reflections and
that the search space is appropriately restricted for weak reflections. To
demonstrate the effectiveness of our proposed model, we apply it to real
experimental data collected at the TOPAZ single crystal diffractometer at SNS.
