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Ideas tagged with dslhc
Supervised and unsupervised machine learning approach to the CMS data quality monitoring
The CMS experiment at the LHC is one of the biggest and most complex general purpose detectors ever built. The constant monitoring of the data quality is vital to guarantee a proper and efficient operation of the detector and reliable physics results. The choice of the key variables to be monito...
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
Data Science and High Energy Physics collaboration enforcement by Higher Education Institutions
Data Science @ LHC workshops have been intensifying cooperation in the Data Science (DS) and High Energy Physics (HEP) professional communities. The need for such cooperation risen substantially in recent years, since it requires combining competences in HEP of everyone who are working in differ...
Create standalone simulation tools to facilitate collaboration between HEP and machine learning community
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 [HiggsML](http://doi.org/10.7483/OPENDATA.ATLAS.ZBP2.M5T8) and ...
By Kyle Cranmer, Tim Head, jean-roch vlimant, Vladimir Gligorov, Maurizio Pierini, Gilles Louppe, Andrey Ustyuzhanin, Balázs Kégl, Peter Elmer, Juan Pavez, Amir Farbin, Sergei Gleyzer, Steven Schramm, Lukas Heinrich, Michael Williams, Christian Lorenz Müller, Daniel Whiteson, Peter Sadowski, Pierre Baldi
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...
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