Data reduction and processing for photon science detectors

Abstract

New detectors in photon science experiments produce rapidly-growing volumes of data. For detector developers, this poses two challenges; firstly, raw data streams from detectors must be converted to meaningful images at ever-higher rates, and secondly, there is an increasing need for data reduction relatively early in the data processing chain. An overview of data correction and reduction is presented, with an emphasis on how different data reduction methods apply to different experiments in photon science. These methods can be implemented in different hardware (e.g., CPU, GPU or FPGA) and in different stages of a detector’s data acquisition chain; the strengths and weaknesses of these different approaches are discussed.