SBIR/STTR Research Award

11.a. X-ray Beamline Control with an Online Model for Automated Tuning and Reconfiguration

Award identifier 4ac50d54fa3a414b9c21b85328305c9c5ca58cbb101a7f48092aa99064651e89Awarding agency Department of Energy

Award amount

$1,149,521

Program and phase

SBIR · Phase II

Award year

2021

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Award details

Awarding agency
Department of Energy
Branch
Not reported
Recipient company
RADIASOFT LLC
Proposal award date
2021-05-03
Contract end date
2023-05-02
UEI
LKPJVNM8BMS5
Solicitation number
Not reported
Topic code
11a

Research abstract

X-ray beamlines comprise crucial components of the four synchrotron light sources (ALS, NSLS-II, APS, and SSRL) operated by the US Department of Energy. These high precision instruments require careful alignment and calibration, which is a slow, error prone process that wastes precious time and beam availability resources. The goal of this project is to create rapid beamline modeling tools that may be combined with the diagnostics data in real time to construct an “online model” that will improve the beamline operator’s knowledge of the photon beam and enable automated alignment and reconfiguration. The recently developing Bluesky software used to run beamline experiments will be combined with rapidly executing beamline models and diagnostics data to build the continually updated online model. During Phase I of this project, we constructed a variety of rapid x-ray transport models using both first principles and machine learning. The physics models adapted transfer matrix methods in conjunction with Wigner function methods to represent partially coherent radiation, an important property of the x-ray beams in these light source facilities. We also integrated these tools with our existing web-based Graphical User Interfaces for SHADOW and SRW. In Phase II, we will complete the construction of the rapid x-ray transport and machine learning models and apply this to operating beamlines at a synchrotron light source. In collaboration with our subcontract, we will expand the Bluesky software that already operates on these beamlines to include the rapid optics models and our machine learning methods. This technology will dramatically increase the efficiency of x-ray beamlines, reducing the time needed to set up new experiments, and to optimize beamline performance. It will also provide improved metadata associated with synchrotron experiments to create more repeatability, and improve the data analysis potential over a wide array of photon science techniques.

Technology keywords

Not reported

Other awards by this recipient

Verified by the same UEI. These are other public SBIR/STTR award pages, not a merged company record.

Awards with the same topic code

Verified by an exact topic code match. This is not a text-similarity match.

Research funding history

63 SBIR/STTR records share this UEI (2014–2025). Reported award amounts total $31,709,348. Reported amounts are not federal obligations.

Phases: Phase I, Phase II

Agencies: Department of Energy, Department of Health and Human Services, Department of Defense

Agency intelligence

Department of Energy

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