19–24 May 2024
Music City Center
US/Central timezone

Machine learning assisted control and data analysis for an MeV ultrafast electron diffraction beamline and photocathode laser system

TUPS76
21 May 2024, 16:00
2h
Blues (MCC Exhibit Hall A)

Blues

MCC Exhibit Hall A

Poster Presentation MC6.D13 Machine Learning Tuesday Poster Session

Speaker

Trudy Bolin (University of New Mexico)

Description

An MeV ultrafast electron diffraction (MUED) instrument system is a unique characterization technique used to study ultrafast processes in a variety of materials by a pump-probe method. This technology can be advanced further into a turnkey instrument by using data science and artificial intelligence (AI) mechanisms in conjunction with high-performance computing. This can facilitate automated operation, data acquisition, and real-time or near-real-time data processing with minimal intervention by a beamline scientist. Real-time optimization and virtual beamline diagnostics combined with deep learning can be applied to the MUED diffraction patterns to recover valuable information on subtle lattice variations that can lead to a greater understanding of a wide range of material systems. Additionally, understanding the laser beam that drives photocathode electron production helps to further optimize the system. A data-science-enabled MUED facility will open this technique to a wider user base and provide a state-of-the-art instrument. Updates on research and development efforts for the MUED instrument in the Accelerator Test Facility of Brookhaven National Laboratory are presented.

Region represented North America
Paper preparation format Word

Primary author

Trudy Bolin (University of New Mexico)

Co-authors

Aasma Aslam (University of New Mexico) Sandra Biedron (University of New Mexico)

Presentation materials

There are no materials yet.