BNL, Upton, New York, USA
ORNL, Oak Ridge, Tennessee, USA
ORNL RAD, Oak Ridge, Tennessee, USA
TimePix3 is a 65k hybrid pixel readout chip with simultaneous Time-of-Arrival (ToA) and Time-over-Threshold (ToT) recording in each pixel*. The chip operates without a trigger signal with a sparse readout where only pixels containing events are read out. The flexible architecture allows 40 MHits/s/cm2 readout throughput, using simultaneous readout and acquisition by sharing readout logic with transport logic of superpixel matrix formed using 2x4 structure. The chip ToA records 1.5625 ns time resolution. The X-ray and charged particle events are counted directly. However, indirect neutron counts use 6Li fission in a scintillator matrix, such as ZnS(Ag). The fission space-charge region is limited to 5-9 um. A photon from scintillator material excites a photocathode electron, which is further multiplied in dual-stack MCP. The neutron count event is a cluster of electron events at the chip. We report on the EPICS areaDetector** ADTimePix3 driver that controls Serval*** using json commands. The driver directs data to storage and to a real-time processing pipeline and configures the chip. The time-stamped data are stored in raw .tpx3 file format and passed through a socket where the clustering software identifies individual neutron events. The conventional 2D images are available as images for each exposure frame, and a preview is useful for sample alignment. The areaDetector driver allows integration of time-enhanced capabilities of this detector into SNS beamlines controls and unprecedented time resolution.
*T Poikela et al 2014 JINST 9 C05013.
**https://github.com/areaDetector
***Software provided by the vendor (ASI) that interfaces detector (10GE) and EPICS data acquisition ioc ADTimePix3
RadiaSoft LLC, Boulder, Colorado, USA
ORNL RAD, Oak Ridge, Tennessee, USA
ORNL, Oak Ridge, Tennessee, USA
RadiaSoft has been developing a flexible front-end framework, written in Python, for rapidly developing and testing automated sample alignment IOCs at Oak Ridge National Laboratory. We utilize YAML-formatted configuration files to construct a thin abstraction layer of custom classes which provide an internal representation of the external hardware within a controls system. The abstraction layer takes advantage of the PCASPy and PyEpics libraries in order to serve EPICS process variables & respond to read/write requests. Our framework allows users to build a new IOC that has access to information about the sample environment in addition to user-defined machine learning models. The IOC then monitors for user inputs, performs user-defined operations on the beamline, and reports on its status back to the control system. Our IOCs can be booted from the command line, and we have developed command line tools for rapidly running and testing alignment processes. These tools can also be accessed through an EPICS GUI or in separate Python scripts. This presentation provides an overview of our software structure and showcases its use at two beamlines at ORNL.
RadiaSoft LLC, Boulder, Colorado, USA
ORNL RAD, Oak Ridge, Tennessee, USA
ORNL, Oak Ridge, Tennessee, USA
Neutron cameras are utilized at the HB2A powder diffractometer to image the sample for alignment in the beam. Typically, neutron cameras are quite noisy as they are constantly being irradiated. Removal of this noise is challenging due to the irregular nature of the pixel intensity fluctuations and the tendency for it to change over time. RadiaSoft has developed a novel noise reduction method for neutron cameras that inscribes a lower envelope of the image signal. This process is then sped up using machine learning. Here we report on the results of our noise reduction method and describe our machine learning approach for speeding up the algorithm for use during operations.
RadiaSoft LLC, Boulder, Colorado, USA
ORNL RAD, Oak Ridge, Tennessee, USA
ORNL, Oak Ridge, Tennessee, USA
Neutron scattering experiments are a critical tool for the exploration of molecular structure in compounds. The TOPAZ single crystal diffractometer at the Spallation Neutron Source studies these samples by illuminating samples with different energy neutron beams and recording the scattered neutrons. During the experiments the user will change temperature and sample position in order to illuminate different crystal faces and to study the sample in different environments. Maintaining alignment of the sample during this process is key to ensuring high quality data are collected. At present this process is performed manually by beamline scientists. RadiaSoft in collaboration with the beamline scientists and engineers at ORNL has developed a new machine learning based alignment software automating this process. We utilize a fully-connected convolutional neural network configured in a U-net architecture to identify the sample center of mass. We then move the sample using a custom python-based EPICS IOC interfaced with the motors. In this talk we provide an overview of our machine learning tools and show our initial results aligning samples at ORNL.