MIDAS : a quantitative framework for high-energy diffraction microscopy. Part II: accuracy, robustness and best practices

The increasing complexity of in situ high-energy diffraction microscopy (HEDM) experiments demands a quantitative understanding of the data analysis pipeline to ensure reproducible science. However, the influence of key analysis parameters on the accuracy and precision of microstructural reconstructions is often not well quantified, creating a barrier to progress. This paper addresses this critical gap by presenting a rigorous, systematic validation of the HEDM data reduction methodology as implemented in the MIDAS software suite. Using a new, dedicated Ti-7 Al dataset, we investigate both far-field (FF) and near-field (NF) HEDM. Our results reveal critical sensitivities, demonstrating that grain position accuracy in FF-HEDM is highly dependent on the diversity of sampled diffraction vectors, while orientation precision in NF-HEDM improves dramatically with increased detector separation. We demonstrate the methodology's robustness against common experimental challenges, such as severe diffraction peak overlap, which is effectively filtered by requiring crystallographic consistency. Based on these quantitative findings, we establish a framework of best practices for HEDM data acquisition and analysis to guide the community towards more accurate and reliable results.