
<script type="text/javascript">
<!--
document.write('<div id="oa_widget"></div>');
document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=undefined&type=result"></script>');
-->
</script>
The Large Hadron Collider will commence Run 4 in 2026, collecting over 100 times more data than was used to discover the Higgs. This unprecedented dataset, 3 ab-1, will enable precision measurements and searches for new physics and rare processes in ways not done before. To analyze this large dataset the field of particle physics will have to adopt new techniques. These include using big-data techniques from industry, pushing the frontiers of statistical modeling, and adapting machine learning techniques to physics problems. Several large collaborations have been built to organize this effort, including IRIS-HEP, by the NSF. This talk will explore the motivations and some of these techniques as well as how the field is trying to systematically attack the problem. Colloquia given at University of California, Santa Cruz
Physics, CERN, Computing, LHC, IRIS-HEP, Particle Physics, HL-LHC
Physics, CERN, Computing, LHC, IRIS-HEP, Particle Physics, HL-LHC
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
views | 4 | |
downloads | 2 |