Ryan Coffee - Computing for Light and Neutron Sources - Webinar Apr 1st - Merging data visualization with experimental design for the future of Free Electron Laser science

30 Mar 2015

Dear all,



the next CLNSF presentation will be by Ryan Coffee from SLAC/LCLS on "Merging data visualization with experimental design for the future of Free Electron Laser science".



The webinar will take place Wednesday Apr. 1st at 17:00 CEST (16:00BST, 08:00PDT(!), 10:00CDT). Please note the different time, due to daylight time changes!



To join the meeting just point your (flash-capable) browser to https://webconf.vc.dfn.de/clnsf/

No registration required, but please provide your name at guest-login.



For details, slides, recordings and upcoming talks please have a look at



https://confluence.slac.stanford.edu/display/CLNSF/Home

https://indico.desy.de/categoryDisplay.py?categId=333



Please forward the information to interested colleagues, the webinars are open to everyone.



Note: the previous webinar by Ryan on Mar 18th had to be aborted due to an outage at the German nren (DFN) disrupting the VideoConferencing (and other) services. Apologies to everyone joining last time.



Cheers, Frank.



Abstract:

In this webinar we will discuss how improved data visualization can facilitate experimental design for Free Electron Lasers (FELs). With the emergence of higher repetition-rate FELs, we must mature in our relationship to data acquisition, transfer, storage and analysis. By merging the best of x-ray science, ultrafast laser science, data science, and particle physics communities we can create a new breed of experimentalists who design the experimental hardware and software for flexibility and scalability. We will showcase some very simple examples where fluctuation analysis and redundancy can accommodate incomplete or corrupted data and detectors. We will find hidden uses for seemingly unrelated diagnostics. These examples will help motivate our need for improved real-time analysis to help us cope with the oncoming tide of data.