UT laser design powers new ultrafast X-ray facility

For 20 years, physicists at The University of Texas at Austin have refined a compact laser-plasma accelerator, and that technology will now power LaNeXT, a new facility to reveal nature’s fastest processes in detail. Louisiana State University will host the facility, built in partnership with UT Austin, allowing researchers to capture motion at the atomic and molecular level in billionths and quadrillionths of a second.

“We have been developing these compact laser accelerators at UT for the last 20 years,” said Bjorn “Manuel” Hegelich, associate professor of physics at UT, “And now we are finally at the point where someone else can build one and use it as a tool.” LaNeXT promises to visualize the hidden steps of chemical reactions, material responses and biological processes previously too swift to observe directly.

UT Laser-Plasma Accelerator Drives LaNeXT Facility

LaNeXT, the new Laboratory for X-ray Science and Technology, will generate X-ray pulses as short as 30 femtoseconds, a duration where light travels only approximately the width of a small bacterium. The accelerator’s design allows it to fire 100 times per second, effectively increasing the average power output by a factor of 100 and accelerating the pace of potential experiments. This increased speed is critical for observing processes previously too rapid for direct observation, filling a long-standing gap in scientific methodology.

The unique combination of technologies at LaNeXT, a synchrotron, the UT-designed laser accelerator and advanced sample fabrication, is not found at any other facility globally. Researchers plan to initially expose semiconductor samples to the synchrotron’s beam and then use the ultrafast laser-based X-rays from LaNeXT to measure the resulting changes in the material.

Gerald Schneider, chemistry professor and leader of the LaNeXT team at LSU, said, “For a long time, scientists have had to infer what happens in the middle of a reaction by looking at the starting point and the final result. LaNeXT will let us watch those hidden steps unfold.” Schneider compares the challenge to the wagon-wheel illusion in old Western movies, where a wheel appears to spin backward because the camera isn’t recording fast enough.

Schneider said, “The motion is real, but what we see depends on how fast we take the pictures. LaNeXT gives us a much faster camera for the molecular world.” The facility will also be a testing ground for this new, more energy-efficient particle accelerator design before wider implementation.

We have been developing these compact laser accelerators at UT for the last 20 years.

Bjorn “Manuel” Hegelich, associate professor of physics at UT

LaNeXT Combines Synchrotron and Ultrafast X-ray Probing

Understanding these rapid transformations is particularly relevant to the ongoing effort to revitalize semiconductor manufacturing within the United States, a sector vital to both the tech economy and national security. Beyond semiconductor research, LaNeXT’s technology has the potential to substantially reduce operating costs for existing large-scale synchrotron facilities, offering a path toward more sustainable scientific research.

One of the biggest challenges in modern science is that the most important changes often happen too fast to see directly.

Gerald Schneider, chemistry professor and leader of the LaNeXT team at LSU
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Ivy Delaney

Ivy Delaney has been working with neural networks and machine learning since the mid-nineties, back when a couple of hidden layers and a long afternoon of training counted as ambitious. She has watched the field go from academic curiosity to the thing quietly running underneath everything, and she brings that long view to quantum computing. For Quantum Zeitgeist she covers the ground where the two fields meet. That means quantum machine learning and the variational algorithms it leans on, and it also means the less glamorous but more interesting story of classical machine learning already doing real work inside quantum machines, decoding error-correcting codes, calibrating noisy hardware and learning the error models that simulators depend on. She writes about the hardware those algorithms have to run on too, and about the post-quantum cryptography scramble that the same hardware has set off. Her stories typically start with the paper, whether that is peer-reviewed work, conference proceedings or an arXiv preprint, with the source linked so you can hold a claim up against the research it came from. She is unimpressed by benchmarks that will not say what they beat, and by demonstrations that only work in the press release.

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