Synchrotron light source facilities worldwide are evolving into the fourth generation, equipped with diffraction-limited storage rings. These machines generate high quality X-rays with intense brightness, low emittance, ultrafast pulse, and highly coherent beams, offering extreme spatial and temporal resolving power that enables multiscale and ultra-fast characterizations. Consequently, the...
We investigate new concepts for enhancing the data acquisition efficiency of scanning type instruments exploring a multidimensional feature space. We test machine-learning algorithms and probabilistic methods in order to minimize the number of experimental data points, which are required to determine models and model parameters down to precisions defined by the scientists. Data acquisition,...
Autonomous experimentation (AE) holds enormous promise for accelerating scientific discovery, by leveraging machine-learning to drive experimental loops where the machine selects and conducts experiments. This talk will discuss AE at synchrotron x-ray scattering beamlines. Deep learning is used to classify x-ray detector images, with performance improving when domain-specific data...
Neutron time-of-flight (TOF) data at the ORNL Spallation Neutron Source (SNS) contains multidimensional temporal information in diffraction and parameter spaces. The field's current state relies on sequential data reduction and analysis steps, often involving data transfer between different platforms and tools which introduces inefficiencies and hinders the seamless integration of different...
Autonomous experiments rely on the seamless integration of control systems, data acquisition, data processing, and optimization frameworks. However, the inherent variability in facility- or beamline-specific infrastructure components poses a challenge for developing more generalizable setups and presents an obstacle for replication studies and cross-facility experiments.
This project focuses...
