(Fast) Machine Learning at the LHC · 09/10/19 Fast ML, ML at the LHC, J.-R. Vlimant 31 ML in...
Transcript of (Fast) Machine Learning at the LHC · 09/10/19 Fast ML, ML at the LHC, J.-R. Vlimant 31 ML in...
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(Fast) Machine Learning at the LHC
(or rather at the experiments at the LHC)
Jean-Roch [email protected] @vlimant
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Outline
I.Physics at the LHCII.The Case for Machine LearningIII.Applying ML at the LHCIV.Fast ML at the LHC
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09/10/19 Fast ML, ML at the LHC, J.-R. Vlimant 3
High Energy PhysicsEndeavor
In a nutshell
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09/10/19 Fast ML, ML at the LHC, J.-R. Vlimant 4
Big Science Pipeline
LHC Computing Grid 200k cores pledge toCMS over ~100 sites
CMS Detector1PB/s
CMS L1 & High-Level Triggers
50k cores, 1kHz
Large Hadron Collider40 MHz of collision
CERN Tier-0 Computing Center20k cores dedicated
CERN Tier-0/Tier-1 Tape Storage
200PB total LHC Grid Remote Access to 100PB of data
Rare SignalMeasurement~1 out of 106
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09/10/19 Fast ML, ML at the LHC, J.-R. Vlimant 5
The Large Hadron Collider
8.5 kilometers
26.7 km accelerator colliding 6.5 TeV protons beams.Beams prepared by LINAC2, PSB, PS, SPS
Geneva, Switezrland
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09/10/19 Fast ML, ML at the LHC, J.-R. Vlimant 6
Colliding Hadrons
Probing fundamental laws of physics as large spectrum ofparticles (known and unknown) can be produced
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09/10/19 Fast ML, ML at the LHC, J.-R. Vlimant 7
The Standard Model
Well demonstrated effective model. We can predict most of the observations.We can use a large amount of simulation.
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09/10/19 Fast ML, ML at the LHC, J.-R. Vlimant 8
Simulating Hadron Collisions
Event Generator: compute predictions of the standard models toseveral orders of expansion in coupling constants (LO, NLO,NNLO, ...) using proton density functions.
Hadronization: phenomenological model of the evolution ofhadrons under the effect of QCD.
Material simulator: transports all particles throughout meters ofdetector, using high resolution geometrical description of thematerials.
Electronic emulator: converts simulated energy deposits insensitive material, into the expected electronic signal, includingnoise from the detector.
Madgraph,Pythia,Sherpa, ...
Pythia, ...
GEANT 4,GEANT V
Homegrownsoftware
Non-differentiable sequence of complex simulators ofthe signal expected from the detectors.
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09/10/19 Fast ML, ML at the LHC, J.-R. Vlimant 9
Size Of The Challenge
1 eventevery
500.000 proton collision
Low probability of producing exotic and interesting signals.Observe rare events from a large amount of data.
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09/10/19 Fast ML, ML at the LHC, J.-R. Vlimant 10
Size Of The Challenge
Low probability of producing exotic and interesting signals.Observe rare events from a large amount of data.
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09/10/19 Fast ML, ML at the LHC, J.-R. Vlimant 11
CMS Detector
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09/10/19 Fast ML, ML at the LHC, J.-R. Vlimant 12
CMS 100 Megapixel Camera
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CMS Readout
Highly heterogeneous system Raw data is 100M channelssampled every 25 ns : 1Pb/s50EB per day in readout and
online processing.
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Event Filtering
Ultra fast decision to keep the relevant data.In hardware and software.
1000 Gb/s1000
Gb
/s
105 H
z
40 M
Hz
1-3
kHz
L1 HLT
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09/10/19 Fast ML, ML at the LHC, J.-R. Vlimant 15
Looking for the Unkown
Searches for new Physics(SUSY, BSM, ...) scan over alarge number of potentialsignal models
➢ We don't know exactly whatto expect
➢ Tedious search procedure➢ How to trigger on the
unknown
https://twiki.cern.ch/twiki/bin/view/CMSPublic/PhysicsResultsSUS
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09/10/19 Fast ML, ML at the LHC, J.-R. Vlimant 16
Computing GridTier0
CERNComputer Center
IN2P3 T1 INFN T1FNAL T1... ...
10 – 40 to 100 Gb/s
300-1500MB/s
T2 T2
T2 T2
T2 T2T2 T2
T2 T2T2 T2
● Hundreds of computer centers (100-10k cores per site)● Increased use as a cloud resources (any job anywhere)● Increasing use of additional cloud and HPC resource● Real time data processing at Tier0● Data and Simulation production at Tier1 and Tier2● High bandwidth networks between disk storage
10 – 40 to 100 Gb/s 10 – 40 to 100 Gb/s
T3
T3 T3
T3
10 to Nx10 Gb/s
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09/10/19 Fast ML, ML at the LHC, J.-R. Vlimant 17
Take home message :
Measure rare and exotic, unknownprocesses out of orders of magnitude larger
backgrounds.
The Standard Model predicts with precisionwhat to expect from many processes.
Reconstruct, identify and reject large amountof event within resource constraints.
Operate a complex ensemble of complexsystems from LHC to analysis.
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09/10/19 Fast ML, ML at the LHC, J.-R. Vlimant 18
High Energy Physics Data Representation
With bias on CMS
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09/10/19 Fast ML, ML at the LHC, J.-R. Vlimant 19
A Journey Through Matter
Particles leave hints of their passage in sub-detectors.Specific (but overlapping) pattern for each particle type.
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09/10/19 Fast ML, ML at the LHC, J.-R. Vlimant 20
What is a Jet
Quark&gluons hadronize as they propagate.Any particle decaying in quark/gluons will result in a “jet” of particles
in the direction of the original particle.Ambiguities on the original particle gets worse in boosted systems.
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09/10/19 Fast ML, ML at the LHC, J.-R. Vlimant 21
From RAW to High Level dataDetector
DataDetector Data Local
reconstructionJet ClusteringParticle
representationHigh levelfeatures
The reconstruction of an event goes from the digitalsignal of the individual sub-detector to a sequence of
particles, jets, and high-level features
Event Processing
Dimensionality reduction
Globalization of information
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What is an Event
One collision-event every 25 ns / 40MHzAdd 40 such on top of each other currently. Up to 200 such overlay in the horizon 2025.
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09/10/19 Fast ML, ML at the LHC, J.-R. Vlimant 23
Take home message :
Complex geometry of detectors, whereparticle leaves significative pattern of energy.
Up to 200 (averaged) overlay of collisions ina single event snapshot.
Multiple level of data representation.
Event reconstruction is mostly patternrecognition tasks.
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09/10/19 Fast ML, ML at the LHC, J.-R. Vlimant 24
The Case for MachineLearning
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09/10/19 Fast ML, ML at the LHC, J.-R. Vlimant 25
Operation Vectorization
ANN ≡ matrix operations ≡ parallelizable
Computation of prediction from artificial neural networkmodel can be vectorized to a large extend.
Multiple computing architecture well suited for inference(GPU, TPU, FPGA, ...)
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09/10/19 Fast ML, ML at the LHC, J.-R. Vlimant 26
Learning from Complexity
“Simple” machine learning model can extract informationfrom complex dataset.
More classical algorithm counter part may take years of development.
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09/10/19 Fast ML, ML at the LHC, J.-R. Vlimant 27
Physics Knowledge
Machine Learning can help understand Physics.We can make better models with Physics.
P. Komiske, E. Metodiev, J. Thaler, https://arxiv.org/abs/1810.05165
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09/10/19 Fast ML, ML at the LHC, J.-R. Vlimant 28
Possible Utilizations
Accuracy Speed
Interpretable
➔ Fast surrogate models (trigger, simulation, ...) for computingrestricted algorithms.
➔ Model more accurate than existing algorithms (tagging, ...)➔ Model performing otherwise impossible tasks (operations, ...)
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09/10/19 Fast ML, ML at the LHC, J.-R. Vlimant 29
Take home message :
Machine learning offers the possibility forbetter Physics-per-dollar ratio.
Increased sensitivity of algorithms byharnessing the complexity in the data.
Extracting Physics knowledge from data.
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09/10/19 Fast ML, ML at the LHC, J.-R. Vlimant 30
Machine Learning atthe LHC
Only a selected few examples flashed, much more on-going effort not shown here
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09/10/19 Fast ML, ML at the LHC, J.-R. Vlimant 31
ML in Simulation
● Non-differentiable simulators have large number ofparameters
➢ Machine learning is used for tuning➢ Machine learning can be used to infer posteriors over
parameters
● Simulation is the second most computing intensive taskat the LHC experiments
➢ Lots of development towards using generativemodels as fast surrogate
➢ Potential for extreme speed ups (>1000x) for part ofthe simulation
➢ Wide range of simulated products: analysis levelfeatures, particle set, calorimeter, ...
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09/10/19 Fast ML, ML at the LHC, J.-R. Vlimant 32
Generative models
● Use of adversarial generative network, variational auto-encoders to simulate EM calorimeter showers
● On the verge of reaching acceptable fidelity
https://indico.cern.ch/event/708041/contributions/3270775/
One of many ongoing worksin simulation
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09/10/19 Fast ML, ML at the LHC, J.-R. Vlimant 33
Trigger
● Machine learning models used to classify/triggerevents in real-time
➢ LHCb turbo-stream➢ ML for event reconstruction
● Lots of potential applications of ML➢ Improved background rejection➢ Surrogate reconstruction algorithms➢ New signal detection➢ Decision control➢ ...
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09/10/19 Fast ML, ML at the LHC, J.-R. Vlimant 34
New Physics Mining
● Variational Auto-Encoder learns arepresentation of the standard modelprocesses
● Can potentially help identify and recordunexpected events
Unexpected!
https://arxiv.org/abs/1811.10276
One of many ongoing worksin trigger
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09/10/19 Fast ML, ML at the LHC, J.-R. Vlimant 35
Reconstruction
● Machine learning used in energy regression andclassification at many levels
➢ Jet tagging as a showcase of deep learning.➢ Local reconstruction, particle id, ...
● Development of deep learning applications, takingadvantage of rawest complex data
➢ Local reconstruction (calorimeter, ...)➢ Object identification (including jet tagging)➢ Energy regression➢ Tracking, vertexing, ...➢ Energy clustering, particle flow, ...➢ Noise cancelling, pileup mitigation, ...➢ ...
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09/10/19 Fast ML, ML at the LHC, J.-R. Vlimant 36
Charged Particle Tracking
● Tracker hits form graph, using simple geometrical constraints● Graph neural network and message passing network achieve
classification of good edges● Promising approach on TrackML dataset at 200PU
https://arxiv.org/abs/1810.06111
One of many ongoing works
in reconstruction
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09/10/19 Fast ML, ML at the LHC, J.-R. Vlimant 37
Analysis
● Machine learning long been used in signal-background classification
➢ Increased sensitivity of many analysis, betterPhysics-per-dollar
● Continuous development of new methods usingmachine learning for analysis
➢ Multi-signal, multi-background categorisations➢ Background modeling➢ New physics searches➢ Inverse problem➢ ...
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09/10/19 Fast ML, ML at the LHC, J.-R. Vlimant 38
Likelihood-Free Inference
Slide K. Cranmer
One of many ongoing works in analysis
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09/10/19 Fast ML, ML at the LHC, J.-R. Vlimant 39
Operation
● “Operation” covers a wide range of environments:detector control, data acquisition systems, onlinemonitoring, computing centers, networking, storage,transfers, ...
● Potential for more efficient operation and automatemanpower intensive tasks
● Large amount of mostly untouched analytics fromlots of appliance. Sometimes difficulty withunstructured data and/or lack of clear objectives, ...
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09/10/19 Fast ML, ML at the LHC, J.-R. Vlimant 40
Online Anomaly Detection
https://arxiv.org/abs/1808.00911
Unsupervised and supervised methods to identifyalarming patterns in the muon drift tubes chambers.
One of many ongoing worksin operation
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09/10/19 Fast ML, ML at the LHC, J.-R. Vlimant 41
Take home message :
Machine learning has long been used inseveral tasks (classification, regression, ...)
Many promising R&D projects using deeplearning on more complex tasks.
With aim at better accuracy, or better resourceefficiency (or both ...).
Fast inference engine is a must have.
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09/10/19 Fast ML, ML at the LHC, J.-R. Vlimant 42
Fast Machine Learning
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09/10/19 Fast ML, ML at the LHC, J.-R. Vlimant 43
Inference
➢ Adding more machine learning models in eventreconstruction may increase the computationrequirements for the experiments. Fast inference engine on standard CPU
➢ Heterogenous computing facilities (HPC, HLT, ...) arebeing integrated to the LHC workflows” : Fast inferenceengine on hosted accelerators (GPU, FPGA, ...)
➢ Cloud/Edge resources might be easier to integrate inlarge facilities : offloading computation to remoteaccelerators (GPU, TPU, FPGA, ...) ,
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09/10/19 Fast ML, ML at the LHC, J.-R. Vlimant 44
Training
➢ Data locality, resource access, training recipes, ... aremaking training models sometimes complicated andlimit the development of machine learning in the field :training as a service on dedicated resources.
➢ Complex models needing ever growing datasets maytake days to week to converge. Hinder turnaround timeand fast development : distributed training &optimization to increase productivity.
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09/10/19 Fast ML, ML at the LHC, J.-R. Vlimant 45
Summary
● The LHC is a large play ground for advancedmachine learning
● Specific challenges related to data representation,detector complexity, computation restrictions, ...
● Fast inference is mandatory for triggering systems.● Affordable inference needed in order to deploy
machine learning in event reconstruction.● Fast training desirable to speed up development
cycles.