Artificial Intelligence Applied to Photon and Neutron Science

Session

Afternoon 2

12 Nov 2019, 16:00

102. DOE's Center for Advanced Mathematics for Energy Research Applications (CAMERA): Artificial Intelligence, Machine Learning, and Experimental Facilities: Present and Future

Dr Jamie Sethian (LBNL/UC Berkeley)

12/11/2019, 16:00

The Center for Advanced Mathematics for Energy Research Applications (CAMERA) is a US Department of
Energy-wide institute focused on building and deploying mathematical algorithms to accelerate our
ability to understand data coming out of synchrotron light sources. CAMERA, consisting of interdisciplinary
teams of applied mathematicians, statisticians, signal processors, computer scientists,...

42. Machine Learning at ILL

Dr Paolo Mutti

12/11/2019, 16:50

Recently, by using deep learning methods, computers are able to surpass or come close to matching human performance on image analysis and pattern recognition. This advanced method could also help interpreting data from neutron scattering experiments. Those data contain rich scientific information about structure and dynamics of materials under investigation, and deep learning could help...

43. Machine learning algorithms for image processing in CryoEM

Dr Carlos Oscar Sorzano (CNB Madrid)

12/11/2019, 17:15

Single particle analysis by Electron Microscopy is a well established technique to analyze the three-dimensional structure of biological macromolecules. The acquired images have a signal-to-noise ratio between 0.1 and 0.01 so that all the image processing steps require to be very robust to extremely high levels of noise. Machine and deep learning algorithms have such characteristics when...

122. Machine learning and artificial intelligence in MX

Dr Melanie Vollmar (Diamond Light Source)

12/11/2019, 17:40

In recent years, large-scale facilities such as synchrotrons have encountered a steep increase in demand for computational resources. This is mainly due to very large data rates and volumes and a growing interest by users in real-time feedback during an experiment. Additionally, using X-ray crystallography has gradually become a standard laboratory method rather than a scientific discipline...

123. Convolutional neural networks for DESY photon science

Dr Philipp Heuser (DESY)

12/11/2019, 18:05

We are exploiting possible applications of artificial intelligence at the German electron synchrotron (DESY) in Hamburg, in particular in the field of photon science. Our current focus is on the use of convolutional neural networks applied to 2D and 3D image analysis for life science.

We will present successful applied semantic segmentation of volumetric 3D synchrotron radiation...

77. Machine Learning for accelerating understanding from Neutron Scattering Data

Dr Garrett E. Granroth (ORNL)

13/11/2019, 16:00

In many cases obtaining results from a neutron scattering measurement is solving an inverse problem. Often this problem is solved by time consuming random guessing. However there may be hidden relationships between the model and the data that can be identified by machine learning to accelerate the solution process. A particularly powerful case has been demonstrated for systems with...

78. Machine Learning at Diamond Light Source: Past, Present and Future

Dr Mark Basham (DLS)

13/11/2019, 16:25

Around 8 years ago, the B24 beamline at Diamond Light Source identified an issue with its potential data collections, that we didn’t have an immediate answer for. The issue was that although the planned beamline could collect a Cryo Soft X-ray Tomography image of sub cellular structure with amazing resolution in a matter of minutes, due to the complexities of radiation damage and acquisition...

79. Big Data Science Center at the Shanghai Synchrotron Radiation Facility - The first Superfacility Platform in China

Dr Alessandro Sepe (SSRF)

13/11/2019, 16:50

The rapid development of synchrotron facilities has massively increased the speed with which experiments can be performed, while new methods and techniques have increased the amount of raw data collected during each experiment. Traditionally, users collect data during their assigned and limited beamtime and then spend many months analysing them. With the huge increase in data volume, this is...

80. Deep learning for small angle scattering under grazing incidence

Dr Marina Ganeva (JCNS / MLZ)

13/11/2019, 17:15

Grazing-incidence small-angle scattering (GISAS) is a well established technique to analyze thin multilayered films containing nano-sized objects. It offers a lot of benefits, however the data analysis is challenging and time-consuming.

Nowadays deep learning is widely applied in various areas of our all-day life. In many image analysis applications it achieves already a human-level...

124. Towards automated analysis for neutron reflectivity

Dr Daniil Mironov (STFC)

13/11/2019, 17:40

The current workflow of neutron (and x-ray) reflectivity requires human intervention at almost every stage, from data capture, through data reduction, and into data analysis. While some of these steps necessitate expert knowledge and judgement, being able to remove the ones which do not remains a goal, enabling on experiment feedback for the system being measured, and lowering the barrier to...