Learning computationally expensive functions

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 production of simulated data is the single largest consu...

By Tim Head

Brief Ideas for the Data Science at LHC Workshop 2015

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 something new: we encourage you to submit contribution...

By Kyle Cranmer, Tim Head, jean-roch vlimant, Vladimir Gligorov, Andrew Lowe, Maurizio Pierini, Gilles Louppe, David Rousseau, Maria Spiropulu

Kickstarting research into end-to-end trigger systems

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 features take more CPU time to construct, therefor...

By Tim Head, Vladimir Gligorov

When to worry about negative weights in MVA training for HEP analysis

A discussion at DS@LHC2015 concerned the treatment of negative weights in MVA training. This is relevant for HEP since the advent of MC generators at NLO in QCD, as [the matching of Matrix-Element to Parton-Shower generators](http://dx.doi.org/10.1088/1126-6708/2002/06/029) assigns negative weig...

By Andrea Giammanco

Recasting through reweighting

*Recasting* refers to reinterpreting the results of searches for new particles or standard model measurements in the context of different theoretical models \[[1](http://inspirehep.net/record/872781)\]. The fundamental task is to replace the original hypothesis $p_0(x)$ with a new hypothesi...

By Kyle Cranmer, Lukas Heinrich