Machine learning in particle identification

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来自 National Research University Higher School of Economics 的课程

Addressing Large Hadron Collider Challenges by Machine Learning

12 个评分

National Research University Higher School of Economics

Addressing Large Hadron Collider Challenges by Machine Learning

12 个评分

课程 7（共 7 门，Specialization Advanced Machine Learning）

The Large Hadron Collider (LHC) is the largest data generation machine for the time being. It doesn’t produce the big data, the data is gigantic. Just one of the four experiments generates thousands gigabytes per second. The intensity of data flow is only going to be increased over the time. So the data processing techniques have to be quite sophisticated and unique. In this course we’ll introduce students into the main concepts of the Physics behind those data flow so the main puzzles of the Universe Physicists are seeking answers for will be much more transparent. Of course we will scrutinize the major stages of the data processing pipelines, and focus on the role of the Machine Learning techniques for such tasks as track pattern recognition, particle identification, online real-time processing (triggers) and search for very rare decays. The assignments of this course will give you opportunity to apply your skills in the search for the New Physics using advanced data analysis techniques. Upon the completion of the course you will understand both the principles of the Experimental Physics and Machine Learning much better.

从本节课中

Particle identification

This module is about detectors in high energy physics. It describes several detector designs, different detector systems, how they work and what particle parameters they measure. Several cases in high energy physics where machine learning can be successfully applied are demonstrated.

Machine learning in particle identification6:52

Uniform classifiers12:09

与讲师见面

Andrei Ustyuzhanin
Head of Laboratory for Methods of Big Data Analysis
HSE Faculty of Computer Science
Mikhail Hushchyn
Researcher at Laboratory for Methods of Big Data Analysis
HSE Faculty of Computer Science

In this video, we will talk

about how to use a particle responses in

the detector systems for the particle type identification.

The goal of the particle identification is to identify a type of

a particle associated with a track using responses from different detector systems.

There are five particle types:

Electron, Proton, Kaon, Pion and Muon.

There are five different detector systems: Tracking system,

Ring Imaging Cherenkov detector,

Electromagnetic calorimeter and Hadron calorimeter and finally Muon Chambers.

The particle identification problem can be considered

as multi-classification problem in machine learning.

A particle track responses in the detector systems are used as inputs of a classifier.

For an example, tracking systems provide information about particle track parameters,

track fit quality, particle momentum and charge,

and also decay vertex coordinates. RICH detector gives a particle emission angle

or delta likelihood for

a different particle type hypotheses or quality of circles fit in the detector.

Calorimeters measure a particle energy and

number of responses for these particle for an example,

Muon system tells is a track has hits inside the system or not,

and how much active chambers correspond to the track.

There are many other features.

Outputs of the classifier are six labels.

Five of them correspond to five different particle types.

Muon, Kaon, Pion, Proton and Electron.

The last one corresponds to all other particle types,

and just noisy tracks that are called Ghosts.

Ghost is a track that was recognized by mistake by a track pattern recognition method,

and doesn't correspond to any real particle in the detector.

So, for each track recognized in the detector,

the classifier gives probabilities to belong to each of these particle types.

This problem can be solved in different modes.

In the multi-classification mode,

in one particle versus rest particles mode or in one particle versus one particle mode.

For an example, one particle versus rest mode means that you

train a classifier to separate one particle from all other particles.

Modern detectors in high energy physics provide high quality of particle identification.

In terms of the area under the ROC curve,

it corresponds to values in the range from 0.9 to 0.995 depending on the particle type.

In your programming assignment for this week,

you will be able to train your own classifier for

the particle identification using toy Monte Carlo sample,

you will estimate the quality of the identification for each particle type.

In the machine learning community,

ROC curve is plotted as a dependence of

the true positive rate from the false positive rate.

However, in the high energy physics,

ROC curve is plotted as the dependence of

the one minus false positive rate from true positive rate.

One minus false positive rate is called

Background Rejection and true positive rate is called Signal Efficiency.

Quality of the particle identification

depends on particle parameters such as its momentum,

transverse momentum or energy.

The figure on the slide demonstrates strong dependency of the area under the ROC curve

for a particle type separated from all other types from the particle momentum.

The quality significantly drops in the low and high momentum regions.

Moreover, it's preferable to have no such dependencies.

In other words, we would like to have

flat or uniform dependency of

the particle identification quality on the different particle parameters.

Consider one more example.

Consider a classifier that separates one particle type as a signal versus

the rest particle types as background.

Output of this classifier is shown at the bottom of the slide.

Let's select the classifier output threshold value that

corresponds to the 60 percent of the global signal efficiency.

It means that this threshold selects 60 percent of all signal particles in the sample.

However, in different particle transverse momentum regions,

the signal efficiency differs from the global one as it's demonstrated in the top figure.

In several regions, the efficiency is higher than 60 percent.

But also, there are a lot of regions where

the signal efficiency is much lower than 60 percent.

Such effects bring systematic uncertainties to physics analysis.

And it's preferable when the classifier selects the same ratio of

signal particles on each momentum or transverse momentum regions for example.

Similar dependencies are present is modern high energy physics experiments.

For an example, the figure shown on the slide is provided by the LHCb experiment.

The figure demonstrates dependencies of the power and

efficiency from its transverse momentum for two different classifiers,

and for three different global efficiencies:

60 percent, 80 percent and 90 percent.

In the next video,

we will consider several approaches how to train

a classifier to avoid such dependencies.

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