  {"id":33067,"date":"2016-04-27T13:28:50","date_gmt":"2016-04-27T18:28:50","guid":{"rendered":"https:\/\/uwm.edu\/news\/?p=33067"},"modified":"2016-05-06T10:55:02","modified_gmt":"2016-05-06T15:55:02","slug":"uwm-researchers-create-a-better-way-to-find-out-when","status":"publish","type":"post","link":"https:\/\/uwm.edu\/news\/uwm-researchers-create-a-better-way-to-find-out-when\/","title":{"rendered":"51ÁÔÆæ researchers create a better way to find out &#8216;when&#8217;"},"content":{"rendered":"<p>Let\u2019s say you\u2019re trying to pinpoint when a particular past event occurred, but your best possible estimate puts it only within a span of 10,000 years. Now imagine if something could shrink that window of \u201cwhen\u201d to just 30 years.<\/p>\n<p>That\u2019s the power of a new mathematical tool devised and tested by an international team of scientists, led by two from the University of Wisconsin-Milwaukee.<\/p>\n<p>The tool, a machine-learning algorithm honed by Abbas Ourmazd and Russell Fung, reduces timing uncertainties during changing events, improving accuracy by a factor of up to 300.<\/p>\n<figure id=\"attachment_33095\" aria-describedby=\"caption-attachment-33095\" style=\"width: 300px\" class=\"wp-caption alignleft\"><img loading=\"lazy\" decoding=\"async\" class=\"size-full wp-image-33095\" src=\"https:\/\/uwm.edu\/news\/wp-content\/uploads\/sites\/41\/2016\/04\/algorithmProfs_TF.jpg\" alt=\"Distinguished Professor Abbas Ourmazd and Senior Scientist Russell Fung. \" width=\"300\" height=\"200\" \/><figcaption id=\"caption-attachment-33095\" class=\"wp-caption-text\">51ÁÔÆæ Distinguished Professor Abbas Ourmazd (left) and Senior Scientist Russell Fung. (51ÁÔÆæ Photo\/Troye Fox)<\/figcaption><\/figure>\n<p>It could have 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.<\/p>\n<p>\u201cTiming uncertainty has been a bane in many areas of science for a long time,\u201d said Ourmazd, 51ÁÔÆæ distinguished professor of physics. \u201cYou often have data, but no exact time stamps on that data.\u201d<\/p>\n<p>Joining 51ÁÔÆæ\u2019s researchers in the discovery were scientists from the Center for Free-Electron Laser Science at DESY Research Center in Hamburg, Germany; the University of Hamburg; and Northwestern University.<\/p>\n<p><a href=\"http:\/\/www.nature.com\/nature\/journal\/v532\/n7600\/full\/nature17627.html\">The work appears in the April 27 online issue of the journal Nature<\/a>.<\/p>\n<p>Ourmazd and Fung built the algorithm by extracting a weak \u201carrow of time\u201d from noisy data with corrupt time stamps. Think of it as restoring the initial sequence of a deck of cards after it has been shuffled.<\/p>\n<p>\u201cThere are some remnants of the original sequence information in the shuffled deck,\u201d said Fung, a 51ÁÔÆæ senior scientist. \u201cThere\u2019s a weak whisper of time, like a faint voice in a loud party.\u201d<\/p>\n<p>The researchers conceived the algorithm while working with data from a project tracking the movement of molecules using an X-ray free electron laser. Called an XFEL, this equipment at the SLAC National Accelerator Laboratory is the world\u2019s brightest X-ray laser and it serves as a camera of matter at nanoscale.<\/p>\n<p>The XFEL uses an ultraquick flash of light to grab a \u201csnapshot\u201d with such speed that the sequence of snapshots yields a slow-motion movie of extremely rapid events.<\/p>\n<p>Making such a movie requires not only hundreds of thousands of snapshots, but also knowledge of the precise time when each snapshot was taken. Despite the unparalleled speed of the XFEL, much of the action is smeared, because the sequence of events is scrambled.<\/p>\n<p>The algorithm Ourmazd and Fung devised resolves this problem. In an experiment at the Linac Coherent Light Source, the XFEL at SLAC, the researchers used the mathematical tool to reconstruct a clear movie of molecules as the bonds holding their atoms together were torn apart.<\/p>\n<p>The algorithm identifies internal correlations to make sense of the ocean of snapshots. The sheer amount of data the XFEL generates aids the algorithm in this task.<\/p>\n<p>\u201cOne image viewed in conjunction with another gives you richer information than you would get by considering the two images separately,\u201d Ourmazd said.<\/p>\n<p>The research team sees applications in other sciences involving dynamical histories that are imprecisely known, such as geology, metrology, chemistry, biology and astronomy.<\/p>\n<p>\u201cThere\u2019s a treasure trove of information to be had,\u201d Ourmazd said, \u201cand we\u2019ve pried the door open.\u201d<\/p>\n","protected":false},"excerpt":{"rendered":"<p>51ÁÔÆæ physicists have created a machine-learning algorithm that improves the accuracy of timing estimates by a factor of up to 300.<\/p>\n","protected":false},"author":836,"featured_media":33099,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":"","uwm_wg_additional_authors":[]},"categories":[174,175],"tags":[],"section":[140,139],"display_categories":[115,116],"related-coverage":[],"uwmnews-feed":[158,161],"class_list":["post-33067","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news","category-research","section-science","section-science-technology","display_categories-top-story-secondary","display_categories-top-story-section","uwmnews-feed-letters-science","uwmnews-feed-hard-science"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.3 (Yoast SEO v27.3) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>51ÁÔÆæ researchers create a better way to find out &#039;when&#039;<\/title>\n<meta name=\"description\" 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