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Визуальный образ Yandex Data Factory

Role of Machine Learning in High Energy physics research at LHC

Nikita Kazeev 10.04.2015, AIST, Ekaterinburg

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What is physics?

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What is physics?Empirical study of objective

Universe

Experiment is everything

〉Aristotle “proved” heavier objects fall faster.

〉His proof was elaborate and well-thought-of

〉It was undisputed until Galileo

〉Is refuted by a simple experiment

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Image credit: http://www.liveyourmagic.com/

Unsolved problems in Physics

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Inside (and around) a black hole. Quantum gravity.

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Unsolved problems in Physics

Image credit: NASA

We made a golf ball with holes (empirical), but still can’t calculate the airflow

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Turbulence

Image credit: CD-adapco

History of Physics — Galilei, XVII

〉Disproved Aristotle’s theory of gravity

〉Dropped balls of different weight

〉Took paper notes

9Image credit: heritage-history.com

History of Physics — LHCb, 2015

〉LHCb registers 10 mln. collisions per second

〉Extensive computer system is used for acquisition, filtering, storage and analytics

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〉Results of interactions, including proton collisions, are fundamentally random. The higher is energy, the more variants are possible.

〉Unfortunately, standard model (SM) explains all the available experiments — except gravity. We continue trying — Beyond Standard model (BSM), supersymmetry (SUSY).

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Two words on state-of-the art in particle physics

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The LHCb detector

〉The unit of data

〉Results from a collision of two proton bunches

〉In the beginning has raw detector readings

〉Has reconstructed information on the detected product particles — angles, type, impulses

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Event

〉Formulate prediction. Usually, probability of a specific decay channel.

〉Test it.

〉Events we are looking for are very (orders of magnitude) rare.

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Analysis — from data to article

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Analysis — data processing pipeline

Detector Triggers GRID

Event Index107 events/s

Offline analysis

103 events/s

Publication

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Analysis — data processing pipeline

Detector Triggers GRID

Event Index107 events/s

Offline analysis

103 events/sM

L

ML

Publication

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How to apply Machine Learning?

〉Remember Standard Model?

〉Use Monte-Carlo (MC) to generate samples of signal (interesting) and background (not interesting) events.

〉Train classifiers.

〉Voilà! Aller à Opéra.

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How to apply Machine Learning?

〉Remember Standard Model?

〉Use Monte-Carlo (MC) to generate samples of signal (interesting) and background (not interesting) events.

〉Train classifiers.

〉Voilà! Aller à Opéra.

〉Fail. Background is too diverse for generation. Combinatorial. Computationally expensive, wasteful - we’ll throw away the majority generated events. Plus some complicated stuff called “calibration”.

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How to apply Machine Learning?

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How to test a hypothesis?

〉Define signal decay

〉Calculate expected event count in its mass window in background-only assumption

〉Generate signal MC

〉Use the mass cut to select real background from sidebands

〉Fit the classifier on MC signal and real background

〉Apply the classifier to the target region, find the actual event count

〉If it’s inconsistent with background-only hypothesis, you have a discovery.

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Higgs discovery