Analysing data from an experiment at the Large Hadron Collider at CERN requires large amounts of computing power.
In addition to large amounts of experimental data, each individual analysis requires large amounts of simulated data. This... continue reading
Science makes progress by reusing results and building on them. For research software this is pretty hard (the people writing it often do not have the time to make slick installers like big libraries do). As a result there is not as much reuse as... continue reading
The Data Science @ LHC workshop was a resounding success. We do not plan to have traditional proceedings tied to individual talks, but we do want to capture the ideas that were generated during the workshop. With that in mind, we want to try... continue reading
Discussions at recent workshops have made it clear that one of the key barriers to collaboration between high energy physics and the machine learning community is access to training data. Recent successes in data sharing through the... continue reading
The data volumes (O(TB/s)) created at the Large Hadron Collider (LHC) are too large to record. Typical rejection factors are O(100-1000), and using as little CPU time as possible to reject an event is the goal. More powerful decision... continue reading
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