Ultrafast pulses from X-ray lasers reveal how atoms move at timescales of a femtosecond. That’s a quadrillionth of a second. 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.
Click here for original story, Machine learning reveals hidden components of X-ray pulses
Source: Phys.org