An international team of scientists has devised a mathematical tool that can reduce timing uncertainties during changing events, improving accuracy by a factor of up to 300.
Led by University of Wisconsin-Milwaukee researchers Abbas Ourmazd and Russell Fung, the team built a machine-learning algorithm by extracting a weak "arrow of time" from noisy data with corrupt time stamps.
The tool can be viewed as restoring the initial sequence of a deck of cards after it has been shuffled.
The researchers tested the algorithm at the SLAC National Accelerator Laboratory using the world's brightest x-ray free electron laser (XFEL), which serves as a camera of matter at nanoscale. They used the mathematical tool to reconstruct a clear movie of molecules as the bonds holding their atoms together were torn apart.
The algorithm identifies internal correlations to make sense of the many snapshots. The vast volume of data the XFEL generates aids the algorithm in this task.
"One image viewed in conjunction with another gives you richer information than you would get by considering the two images separately," Ourmazd says.
The researchers note the tool has numerous applications, from dating past climate-change events with better precision to determining when molecular bonds form or break during chemical reactions lasting only a few quadrillionths of a second.
From University of Wisconsin-Milwaukee
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