DOE Review Jean-Roch Vlimant

2011 CMS Achievement Award

For “excellent work in the Reconstruction project as convener as well as in the daily offline operation”.

2014 CMS Young Researcher Prize

For “his sustained and critical contributions to the development of software for the calorimeter and tracking triggers at HLT ; data quality monitoring, detector simulation

and reconstruction software”.

Roles in CMS Convener of the Physics Data Monte-Carlo Validation group

(2012/13). Expediting central production of data and simulation samples. Design and development of validation book-keeping tools and procedures. Design and development of production preparation, submission and book-keeping software.

2011 CMS Achievement Award Convener of the Offline Reconstruction group (2010/2011).

Development, tutoring, maintenance and managment of the offline reconstruction software. Contributions to high level trigger software. Sole developer of the cms sofware configuration builder.

Coordinator of the muon high level trigger (2009/2008). Development of the muon trigger and monitoring. Contributions to online Ecal and tracker local reconstruction.

Coordinator of tracking software group (2009). Development, maintenance and management of tracking software.

Recent Activities

2014 CMS Young Researcher Prize Maintenance of Monte-Carlo Management

platform (McM). Development and commissioning of new procedures to expedite production.

Analysis of jet substructure in search for “X to ZH” signatures.

Analysis of razor variables with 13 TeV simulation. Development of book-keeping service for

production of analysis samples. Automation of computing operation for central

production service resulting in shorter delays in preparation of samples for analysis

Research Machine Learning Techniques for applications to CMS challenges.

X to ZH with Jet Substructure

Search for heavy exotic particle X→ZH→2 lepton, 4 quarks where quarks hadronize in highly collimated jets. The four-pole structure of the resulting fat-jet is discriminated from background jets using n-subjetiness (tauN) and multivariate analysis techniques.

Cut on discriminant

Sep

arat

ion

sign

ifica

nce

Discrimination method– τ1– τ2– τ3– τ2/τ1– τ3/τ2– τ3/τ1– MLPBNN– LPCA

MLPBNN : neurla network with BFGS training method and bayesian regulator LPCA : 1-dimensional likelihood with PCA-transformed input variables

MLPBNN value

Arb

itrar

y un

it

Hashed≡

background

Colored≡

signal

Machine Learning Signal Extraction

Unsupervised clustering (self-organizing map : SOM, kMean, principal component analysis, correlation explanation, ... ) of pseudo-data towards supervised categorization of event populations and identification/discovery of unknown signal.

INPUT pseudo-datawith signal injected

OUTPUTcategorizationexhibiting unknown events

log(MR) log(MR)Self-Organizing-Map trained on pseudo-data and interpreted with known backgrounds

Complex Model Deep Learning R&D

Jet substructure : Apply image recognition technique to classification of energy-flow particle 4-momentum patterns.

Tracking : Rely on ability to learn complex models. Learn from hit pattern of charged particle for track reconstruction.

Data Science Workshop Organization

Http://cern.ch/DataScienceLHC2015Hands-on oriented workshop with presentation of contemporary machine learning techniques to foster.

Computing Operation Automation

Fully automatize handling of production requests Pre-defined simple rules of placement Automation of sanity check and final delivery Amount of operator work reduced Possible to handle smoothly more resource

Computing Operation Performance

Jean-Roch'sautomation

Great current total through-put

~250M/week ~500M/week

Delays of delivery to analysis much reduced

+

Computing Optimization R&D

Learn complex models using deep learning from monitoring and metric. Use models in intensive simulation within application of game theory techniques or reinforcement learning method. Steering computing, storage and network elements like robot arms.

AppliedTime to

completionModify

assignmentGlobal

monitoring

Computing WW grid