Topic: Accelerator science | SLAC National Accelerator Laboratory

Feature

The algorithm pairs machine-learning techniques with classical beam physics equations to avoid massive data crunching.

This is a representation of a particle beam traveling through an accelerator.

Illustration

Perovskites’ unusual response to light could explain the high efficiency of these next-generation solar cell materials.

Illustration

Scientists use a series of magnets to transform an electron bunch into a narrow current spike which then produces a very intense attosecond X-ray...

Photograph

SLAC is a world leader in X-ray and ultrafast science. We make our X-ray laser and synchrotron facilities available to researchers from around the...

Person standing in the Linac Coherent Light Source (LCLS) Undulator Hall

Feature

SLAC works with two small businesses to make its ACE3P software easier to use in supercomputer simulations for optimizing the shapes of accelerator structures.

A large, complex shape is seen against a blue background crisscrossed with white lines. The shape is dark blue and resembles a brick partially topped with a thick shark’s fin. Three areas of bright red, orange and green, are on the shape’s bottom edge.

Illustration

With help from two small businesses, SLAC’s vintage ACE3P software is spreading its wings to make supercomputing easier and faster.

Feature

An extension of the Stanford Research Computing Facility will host several data centers to handle the unprecedented data streams that will be produced by...

News Brief

Knowing a magnet’s past will allow scientists to customize particle beams more precisely in the future. As accelerators stretch for higher levels of performance...

A magnet on a test stand inside SLAC National Accelerator Laboratory.

Feature

Edelen draws on machine learning to fine tune particle accelerators, while Kurinsky develops dark matter detectors informed by quantum information science.

Side by side photographs of a woman and a man.

News Release

The facility, LCLS-II, will soon sharpen our view of how nature works on ultrasmall, ultrafast scales, impacting everything from quantum devices to clean energy.

Animation

Now that the cavities have been cooled, the next step is to pump them with more than a megawatt of...

The algorithm pairs machine-learning techniques with classical beam physics equations to avoid massive data crunching.

Perovskites’ unusual response to light could explain the high efficiency of these next-generation solar cell materials.

Scientists use a series of magnets to transform an electron bunch into a narrow current spike which then produces a very intense attosecond X-ray flash.

SLAC is a world leader in X-ray and ultrafast science. We make our X-ray laser and synchrotron facilities available to researchers from around the world.

SLAC works with two small businesses to make its ACE3P software easier to use in supercomputer simulations for optimizing the shapes of accelerator structures.

With help from two small businesses, SLAC’s vintage ACE3P software is spreading its wings to make supercomputing easier and faster.

An extension of the Stanford Research Computing Facility will host several data centers to handle the unprecedented data streams that will be produced by a new generation of scientific projects.

Knowing a magnet’s past will allow scientists to customize particle beams more precisely in the future. As accelerators stretch for higher levels of performance, understanding subtle effects, such as those introduced by magnetic history, is becoming more critical.

Edelen draws on machine learning to fine tune particle accelerators, while Kurinsky develops dark matter detectors informed by quantum information science.

The facility, LCLS-II, will soon sharpen our view of how nature works on ultrasmall, ultrafast scales, impacting everything from quantum devices to clean energy.

Now that the cavities have been cooled, the next step is to pump them with more than a megawatt of microwave power to accelerate the electron beam from the new source. Electrons passing through the cavities will draw energy from...

Accelerator science

Researchers develop clever algorithm to improve our understanding of particle beams in accelerators

Particle beam traveling through an accelerator

Perovskites

XLEAP illustration

Linac Coherent Light Source (LCLS) Undulator Hall

Vintage SLAC accelerator software spreads its wings

SLAC’s vintage ACE3P software

SLAC expands and centralizes computing infrastructure to prepare for data challenges of the future

Researchers model accelerator magnets' history using machine learning approach

SLAC’s Auralee Edelen and Noah Kurinsky receive prestigious DOE Early Career Awards

SLAC’s superconducting X-ray laser reaches operating temperature colder than outer space

Cryomodule cavity

Researchers develop clever algorithm to improve our understanding of particle beams in accelerators

Particle beam traveling through an accelerator

Perovskites

XLEAP illustration

Linac Coherent Light Source (LCLS) Undulator Hall

Vintage SLAC accelerator software spreads its wings

SLAC’s vintage ACE3P software

SLAC expands and centralizes computing infrastructure to prepare for data challenges of the future

Researchers model accelerator magnets' history using machine learning approach

SLAC’s Auralee Edelen and Noah Kurinsky receive prestigious DOE Early Career Awards

SLAC’s superconducting X-ray laser reaches operating temperature colder than outer space

Cryomodule cavity