Machine learning reveals hidden components of X-ray pulses

Ultrafast pulses from X-ray lasers reveal how atoms move at timescales of a femtosecond. However, measuring the properties of the pulses themselves is challenging. While determining a pulse's maximum strength, or'amplitude,' is straightforward, the time at which the pulse reaches the maximum, or'phase,' is often hidden. A new study trains neural networks to analyze the pulse to reveal these hidden sub-components. Physicists also call these sub-components'real' and'imaginary.' Starting from low-resolution measurements, the neural networks reveal finer details with each pulse, and they can analyze pulses millions of times faster than previous methods.