• HOME
  • NEWS
  • EXPLORE
    • CAREER
      • Companies
      • Jobs
    • EVENTS
    • iGEM
      • News
      • Team
    • PHOTOS
    • VIDEO
    • WIKI
  • BLOG
  • COMMUNITY
    • FACEBOOK
    • INSTAGRAM
    • TWITTER
Sunday, August 3, 2025
BIOENGINEER.ORG
No Result
View All Result
  • Login
  • HOME
  • NEWS
  • EXPLORE
    • CAREER
      • Companies
      • Jobs
        • Lecturer
        • PhD Studentship
        • Postdoc
        • Research Assistant
    • EVENTS
    • iGEM
      • News
      • Team
    • PHOTOS
    • VIDEO
    • WIKI
  • BLOG
  • COMMUNITY
    • FACEBOOK
    • INSTAGRAM
    • TWITTER
  • HOME
  • NEWS
  • EXPLORE
    • CAREER
      • Companies
      • Jobs
        • Lecturer
        • PhD Studentship
        • Postdoc
        • Research Assistant
    • EVENTS
    • iGEM
      • News
      • Team
    • PHOTOS
    • VIDEO
    • WIKI
  • BLOG
  • COMMUNITY
    • FACEBOOK
    • INSTAGRAM
    • TWITTER
No Result
View All Result
Bioengineer.org
No Result
View All Result
Home NEWS Science News Chemistry

New machine learning tool diagnoses electron beams in an efficient, non-invasive way

Bioengineer by Bioengineer
March 24, 2021
in Chemistry
Reading Time: 3 mins read
0
Share on FacebookShare on TwitterShare on LinkedinShare on RedditShare on Telegram

It can help operators optimize the performance of X-ray lasers, electron microscopes, medical accelerators and other devices that depend on high-quality beams

IMAGE

Credit: Adi Hanuka/SLAC National Accelerator Laboratory

Beams of accelerated electrons power electron microscopes, X-ray lasers, medical accelerators and other devices. To optimize the performance of these applications, operators must be able to analyze the quality of the beams and adjust them as needed.

For the past few years, researchers at the Department of Energy’s SLAC National Accelerator Laboratory have been developing “virtual diagnostics” that use machine learning to obtain crucial information about beam quality in an efficient, non-invasive way. Now, a new virtual diagnostic approach, published in Scientific Reports, incorporates additional information about the beam that allows the method to work in situations where conventional diagnostics have failed.

“Our method can be used to diagnose virtually any machine that uses electron beams, whether it’s an electron microscope for imaging of ultrasmall objects or a medical accelerator used in cancer therapy,” said SLAC research associate Adi Hanuka, who led the study.

Conventional beam diagnostics are physical devices that need to interact with the beam to measure its properties, such as intensity and shape. This interaction often destroys or alters the beam or requires its deflection, so it cannot be used at the same time for the actual application. Technical limitations also prevent accurate measurements in some cases, for instance when the beam’s electron pulses are fired at a very high rate or are very intense.

The new method has none of these limitations because it is not a physical device. Instead, it uses a neural network – a machine learning algorithm inspired by the neural network of the brain. Once the SLAC team had trained the neural network on data taken with the lab’s particle accelerators, the algorithm was able to accurately predict beam properties for experimental situations.

The researchers demonstrated the method by comparing its predictions with experimental and simulated data for the electron beams of the Linac Coherent Light Source (LCLS) X-ray laser, its future upgrade LCLS-II, and the recently upgraded Facility for Advanced Accelerator Experimental Tests (FACET-II), three DOE Office of Science user facilities at SLAC.

In particular, the results show that the machine learning approach helps in situations that are beyond the capabilities of conventional tools. In the case of LCLS-II, for example, the neural network can provide detailed information about each of the million electron pulses per second the machine will produce – an unprecedented pulse rate that exceeds the limits of present diagnostic technology. Virtual diagnostics can also provide accurate information about FACET-II’s high-intensity beam, which is challenging to analyze with physical devices.

###

This research was funded by the DOE Office of Science and the Laboratory Directed Research and Development (LDRD) program at SLAC.

SLAC is a vibrant multiprogram laboratory that explores how the universe works at the biggest, smallest and fastest scales and invents powerful tools used by scientists around the globe. With research spanning particle physics, astrophysics and cosmology, materials, chemistry, bio- and energy sciences and scientific computing, we help solve real-world problems and advance the interests of the nation.

SLAC is operated by Stanford University for the U.S. Department of Energy’s Office of Science. The Office of Science is the single largest supporter of basic research in the physical sciences in the United States and is working to address some of the most pressing challenges of our time.

Media Contact
Manuel Gnida
[email protected]

Original Source

https://www6.slac.stanford.edu/news/2021-03-24-new-machine-learning-tool-diagnoses-electron-beams-efficient-non-invasive-way.aspx

Related Journal Article

http://dx.doi.org/10.1038/s41598-021-82473-0

Tags: Chemistry/Physics/Materials SciencesComputer ScienceResearch/DevelopmentRobotry/Artificial IntelligenceSoftware EngineeringTechnology/Engineering/Computer Science
Share13Tweet8Share2ShareShareShare2

Related Posts

Bright Excitons Enable Optical Spin State Control

Bright Excitons Enable Optical Spin State Control

August 3, 2025
blank

Flame Synthesis Creates Custom High-Entropy Metal Nanomaterials

August 2, 2025

Innovative Acid-Base Bifunctional Catalyst Enhances Production of Essential Lithium-Ion Battery Material

August 1, 2025

Oven-Temperature Treatment (~300℃) Enhances Catalyst Performance by Six Times

August 1, 2025
Please login to join discussion

POPULAR NEWS

  • Blind to the Burn

    Overlooked Dangers: Debunking Common Myths About Skin Cancer Risk in the U.S.

    60 shares
    Share 24 Tweet 15
  • Neuropsychiatric Risks Linked to COVID-19 Revealed

    49 shares
    Share 20 Tweet 12
  • Dr. Miriam Merad Honored with French Knighthood for Groundbreaking Contributions to Science and Medicine

    46 shares
    Share 18 Tweet 12
  • Study Reveals Beta-HPV Directly Causes Skin Cancer in Immunocompromised Individuals

    38 shares
    Share 15 Tweet 10

About

We bring you the latest biotechnology news from best research centers and universities around the world. Check our website.

Follow us

Recent News

Institutional Factors Impacting Cervical Cancer Guideline Compliance

Bright Hybrid Excitons Boost Scalable X-ray Scintillators

Tau PET Positivity Varies by Age, Genetics, and Sex

  • Contact Us

Bioengineer.org © Copyright 2023 All Rights Reserved.

Welcome Back!

Login to your account below

Forgotten Password?

Retrieve your password

Please enter your username or email address to reset your password.

Log In
No Result
View All Result
  • Homepages
    • Home Page 1
    • Home Page 2
  • News
  • National
  • Business
  • Health
  • Lifestyle
  • Science

Bioengineer.org © Copyright 2023 All Rights Reserved.