
Machine Learning Reveals Hidden Components of X-ray Pulses
Neural networks determine the amplitude and phase of X-ray pulses, enabling new, high-resolution quantum studies.
Neural networks determine the amplitude and phase of X-ray pulses, enabling new, high-resolution quantum studies.
Scientists capture the short-lived hydroxyl-hydronium pair and the induced dynamic response in ionized liquid water in unprecedented detail.
Discovery of a short-lived state could lead to faster and more energy-efficient computing devices.
Ultrafast electrons shed light on the web of hydrogen bonds that gives water its strange properties, vital for many chemical and biological processes.
A newly designed X-ray oscillator may enable atomic level precision with intense X-ray pulses.
Scientists use a machine learning algorithm to reduce tuning time of a dozen instruments at once.
An X-ray image taken with a novel X-ray wavefront imager results in high precision measurements of intensity and direction of the X-ray beam.
Observation of impulsive stimulated X-ray Raman scattering with attosecond soft X-ray pulses.
Harnessing the intensity of a terahertz laser pulse brings the resolution of electron scattering closer to the scale of electron and proton motion.
X-ray scattering measures the positions of atoms as they vibrate in a two-dimensional cover sheet.
Ultrafast X-rays track how associated pairs of atoms find new locations when triggered by light.
New method could enable studying the fastest interactions of ultrabright X-rays with matter, a vital way of learning about chemical reactions.