Nikolaus Howe

nhh1@williams.edu

CERN, Travel dates: ?? -> June 30

Project: Photon identification and calorimeter imaging with deep learning

Supervisors: Maurizio Pierini, Jean-Roch Vlimant

Aytaj Aghabayli

agaytac14509@sabah.edu.az

CERN, Travel dates:

Project: Optimizing data Quality Monitoring with Machine Learning algorithms

Supervisors: Jean-Roch Vlimant, Federico

Partner: Yandex/EP-CMG-CO

The Data quality monitoring system of the CMS experiment is a software infrastructure producing in real time histograms of sensitive quantities, associated to specific detector components or high-level physics objects (e.g. jet spectra, etc). These histograms are compared to reference plots and the outcome of the comparison is used by dedicated online and offline shifters to judge the quality of the collected data and identify transient problems with the detector. This kind of activity is the textbook case for the application of advanced Machine Learning techniques. Not only one could expand the number of monitored quantities beyond the limit of what is humanly possible. ML algorithms are also extremely good in identify correlations and patterns between features, allowing for the possibility of defining a system capable to predict problems. The student will investigate the possibility of building specific applications for this.

Jayesh Mahapatra

jayeshmahapatra@gmail.com

CERN, Travel dates: 20 June → 20 August

Project: Jet Identification with imaging algorithms

Supervisors: Maurizio Pierini, Jean-Roch Vlimant

Jet tagging, i.e. the identification of the nature of the particle starting a jet showering, is one of the most important tools to perform data analyses at the LHC. Traditionally, Machine Learning (ML) techniques have been exploited to b-jet tagging. Recently, new kind of jet taggers were introduced, to extend the physics reach of the LHC experiments: charm tagging, top tagging, H tagging, W/Z tagging. The use of modern ML techniques could boost the performances of jet tagging algorithms and improve the quality of CMS physics analyses. The candidate will optimise some of the tagging algorithms, investigating two research lines: (i) find the optimal set of variables to be used in the algorithm; (ii) find the best algorithms among those available in recent computing-science literature.

Kaustuv Datta

Supervisors: Maurizio Pierini, Jean-Roch Vlimant

We propose to train a Deep Neural Network on a dataset of two photon candidates. The DNN should try to cluster the samples in two categories (real photons and fake photons), using as input features the cluster hope variables normally employed for photon IDs. The DNN will learn how to optimally cluster the events maximizing the likelihood ratio L(S+B)/ L(B), where L(B) is the likelihood computed under the hypothesis that the diphoton distribution is described by a background falling function, while the L(S+B) is the probability that a peak of some kind (e.g., Higgs boson at 125 GeV, or some other new- physics resonance at higher values). The ratio L(S+B)/L(B) is intended to be profiled over the signal and background yield.

Supervisors: Maurizio Pierini, Dustin Andersen

Partners: Intel/Cloudera/EP-UCM

Project: Discovering the top quark at the LHC with unsupervised Machine Learning algorithms

Supervisors: Maurizio Pierini, Jean-Roch Vlimant

The study of top quark at the LHC makes use of so-called supervised machine learning (ML) algorithms (e.g., the Neural Network used to train the jet b tagging). Unlike other particles decaying to jets (e.g. W, Z, or H), the top quark is abundantly produced at the LHC. The large signal-to-background ratio makes it an ideal case to test the discovery potential of the LHC. We propose to define a search for top quarks in 1l+jets events, based on unsupervised ML algorithms. Without teaching the algorithm what a top quark is and what it looks like, we propose to test the possibility of highlighting the existence of the top quark by clustering similar events into classes, that could then be compared to the known background. The top signal should emerge as an unassociated event category. This would be the first application of unsupervised ML algorithms at the LHC.

Olga Lyudchik

<a target="_blank" href="mailto: helga.lyudchik@gmail.com"> helga.lyudchik@gmail.com</a>

The study of top quark at the LHC makes use of so-called supervised machine learning (ML) algorithms (e.g., the Neural Network used to train the jet b tagging). Unlike other particles decaying to jets (e.g. W, Z, or H), the top quark is abundantly produced at the LHC. The large signal-to-background ratio makes it an ideal case to test the discovery potential of the LHC. We propose to define a search for top quarks in 1l+jets events, based on unsupervised ML algorithms. Without teaching the algorithm what a top quark is and what it looks like, we propose to test the possibility of highlighting the existence of the top quark by clustering similar events into classes, that could then be compared to the known background. The top signal should emerge as an unassociated event category. This would be the first application of unsupervised ML algorithms at the LHC.