BEGIN:VCALENDAR VERSION:2.0 PRODID:Data::ICal 0.22 BEGIN:VEVENT DESCRIPTION:Dr. Jim Pivarski \, Princeton University\n\n
Experimental particle physics is an intensely computational field of science. In fact\, particle physicists were arguabl y the first non-secret (non-cryptography) users of digital computers\, and have been pushing the boundaries of pattern recognition and throughput ev er since. For decades\, our unique needs justified custom software at all levels of the stack\, maintained "\;in-house"\; by physicists\, bu t the situation changed in the 21st century. Machine learning and analysis of web-scale datasets (i.e. "\;Big Data"\;) has become an industr y on its own\, under the catch-all name "\;data science."\; Physic ists are responding by adopting data science toolsets and methodologies\, integrating them with traditional physics software\, though the process is ongoing and differs in degree across physics groups. \;
\n\nThis talk will pre sent a big picture of how experimental particle physicists have used data analysis software in the past 75 years\, how our needs have dictated a cho ice of programming languages and toolkits\, and how those choices are chan ging. We'\;ll see how pattern recognition evolved from semi-automated t o algorithmic to machine learning\, how programming languages transitioned from Fortran to C++ to include a significant mix of Python\, and how soft ware was organized from site-custom solutions to standard packages like CE RNLIB and ROOT to also include a mix of data science tools. Finally\, thes e choices are not purely technical: communities form around software tools \, and integrating toolsets integrates physicists with the larger world. < /span>
\n DTSTART:20211206T210000Z LOCATION:Online\, Room via Zoom SUMMARY:Programming languages\, toolkits\, and communities in particle phys ics data analysis END:VEVENT END:VCALENDAR