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Deep Learning in High Energy Physics Minho Kim Based on 1904.08549 and upcoming articles Collaborated with M. Park, K. C. Kong, J. H. Kim, and K. T. Matchev 1

> v ] v P ] v , ] P Z v P Ç W Z Ç ]Title Microsoft PowerPoint - presentation.pptx Author kmhmo Created Date 8/20/2019 4:58:55 PM

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Page 1: > v ] v P ] v , ] P Z v P Ç W Z Ç ]Title Microsoft PowerPoint - presentation.pptx Author kmhmo Created Date 8/20/2019 4:58:55 PM

Deep Learning in High Energy Physics

Minho KimBased on 1904.08549 and upcoming articles

Collaborated with M. Park, K. C. Kong, J. H. Kim, and K. T. Matchev

1

Page 2: > v ] v P ] v , ] P Z v P Ç W Z Ç ]Title Microsoft PowerPoint - presentation.pptx Author kmhmo Created Date 8/20/2019 4:58:55 PM

Introduction

• In the high energy physics (HEP), signal is not aloneand has a lot of background around it. Thus it is very important to reduce background.

• There are several ways to reduce background. Most of them are based on cut analysis using physical variables.

• People are trying to use all information from data now.

• How can we handle all information?

2

Page 3: > v ] v P ] v , ] P Z v P Ç W Z Ç ]Title Microsoft PowerPoint - presentation.pptx Author kmhmo Created Date 8/20/2019 4:58:55 PM

Introduction

• In the high energy physics (HEP), signal is not aloneand has a lot of background around it. Thus it is very important to reduce background.

• There are several ways to reduce background. Most of them are based on cut analysis using physical variables.

• People are trying to use all information from data now.

• How can we handle all information?

Very small signal portion!

3

Page 4: > v ] v P ] v , ] P Z v P Ç W Z Ç ]Title Microsoft PowerPoint - presentation.pptx Author kmhmo Created Date 8/20/2019 4:58:55 PM

The Solution : Deep Learning

• Deep learning (DL) can be one of the best solution.• Main difference is use of feature extraction.

Deep Learning

Traditional Learningtowardsdatascience.com

4

Page 5: > v ] v P ] v , ] P Z v P Ç W Z Ç ]Title Microsoft PowerPoint - presentation.pptx Author kmhmo Created Date 8/20/2019 4:58:55 PM

The Solution : Deep Learning

• Deep learning (DL) can be one of the best solution.• Main difference is use of feature extraction.

Deep Learning

Traditional Learningtowardsdatascience.com

rsipvision.com

5

Page 6: > v ] v P ] v , ] P Z v P Ç W Z Ç ]Title Microsoft PowerPoint - presentation.pptx Author kmhmo Created Date 8/20/2019 4:58:55 PM

The Solution : Deep Learning

• Deep learning allows you to use all physics information from data.

• One of the successful approach is making LHC data as (jet) image and feed them to deep learning.

Experiment Data Jet ImageJHEP07(2016) 069L. De Oliveira, et al.

Deep Learningeta

phi

6

Page 7: > v ] v P ] v , ] P Z v P Ç W Z Ç ]Title Microsoft PowerPoint - presentation.pptx Author kmhmo Created Date 8/20/2019 4:58:55 PM

Applications on LHC : Higgs Pair Production• Higgs pair production is very important to

understand Higgs potential and electroweak symmetry breaking of SM.

• Because major decay processes of Higgs are b quarks pair and W boson pair, it suffers from enormous QCD background.

- Higgs Potential

7

Page 8: > v ] v P ] v , ] P Z v P Ç W Z Ç ]Title Microsoft PowerPoint - presentation.pptx Author kmhmo Created Date 8/20/2019 4:58:55 PM

Applications on LHC : Higgs Pair Production

- Higgs pair production(h h > b b~ w- w+)

- Top pair production(t t~ > b b~ w- w+)

1904.08549

Jet image of signal and background

8

Page 9: > v ] v P ] v , ] P Z v P Ç W Z Ç ]Title Microsoft PowerPoint - presentation.pptx Author kmhmo Created Date 8/20/2019 4:58:55 PM

Applications on LHC : Higgs Pair Production

1904.08549

Jet image of signal and background

- Higgs pair production(h h > b b~ w- w+)

- Top pair production(t t~ > b b~ w- w+)

Can deep learning distinguish this?

9

Page 10: > v ] v P ] v , ] P Z v P Ç W Z Ç ]Title Microsoft PowerPoint - presentation.pptx Author kmhmo Created Date 8/20/2019 4:58:55 PM

Note : Data Limitation

• Unfortunately, deep learning performance highly depends on the number of data to learn.

• In high energy physics, it is very difficult to generate a lot of data.

• We can enhance DL performance as follows:- By using featured variables,- By utilizing optimized DL architecture.

10

Page 11: > v ] v P ] v , ] P Z v P Ç W Z Ç ]Title Microsoft PowerPoint - presentation.pptx Author kmhmo Created Date 8/20/2019 4:58:55 PM

Use of Featured Variables

• We can extract good physical variables from data.

• Its effect is similar to reducing dimensions.

1904.08549

11

Page 12: > v ] v P ] v , ] P Z v P Ç W Z Ç ]Title Microsoft PowerPoint - presentation.pptx Author kmhmo Created Date 8/20/2019 4:58:55 PM

Use of Featured Variables

• We can use jet image and featured variables simultaneously :

Main Deep Learning Architecture

Charged Jet ImageNeutral Jet Image

Featured Variables

OutputsMergeSub DL

Sub DL

Sub DL

12

Page 13: > v ] v P ] v , ] P Z v P Ç W Z Ç ]Title Microsoft PowerPoint - presentation.pptx Author kmhmo Created Date 8/20/2019 4:58:55 PM

Use of Featured Variables

• We can use jet image and featured variables simultaneously :

Main Deep Learning Architecture

Charged Jet ImageNeutral Jet Image

Featured Variables

OutputsMergeSub DL

Sub DL

Sub DL

12

Page 14: > v ] v P ] v , ] P Z v P Ç W Z Ç ]Title Microsoft PowerPoint - presentation.pptx Author kmhmo Created Date 8/20/2019 4:58:55 PM

Use of Featured Variables

• We can use jet image and featured variables simultaneously :

Main Deep Learning Architecture

Charged Jet ImageNeutral Jet Image

Featured Variables

OutputsMergeSub DL

Sub DL

Sub DL

12

Page 15: > v ] v P ] v , ] P Z v P Ç W Z Ç ]Title Microsoft PowerPoint - presentation.pptx Author kmhmo Created Date 8/20/2019 4:58:55 PM

Use of Featured Variables

Signal Background

• The correlation between jet image only DL and featured variables only DL clearly shows that there is uncorrelated information!

1904.08549

Page 16: > v ] v P ] v , ] P Z v P Ç W Z Ç ]Title Microsoft PowerPoint - presentation.pptx Author kmhmo Created Date 8/20/2019 4:58:55 PM

Use of Featured Variables

• Then deep learning can distinguish signal and background with better performance.

1904.08549

- Signal and (all) background discrimination performance of Higgs pair production with tight cut

13

Page 17: > v ] v P ] v , ] P Z v P Ç W Z Ç ]Title Microsoft PowerPoint - presentation.pptx Author kmhmo Created Date 8/20/2019 4:58:55 PM

Optimized Deep Learning Architecture• Appropriate deep learning algorithm can also give

us overwhelming performance without featured variables.

- Preliminary, signal and background (ttbar only) discrimination performance with loose cut

Original results, CNN, image + variables

Image only, ResNet, trial1

Image only, ResNet, trial2

Image only, ResNet, trial3

Image only, ResNet, trial4

Main Deep Learning Architecture

Charged Jet ImageNeutral Jet ImageLepton Jet ImagePhoton Jet ImageNeutrino Jet Image

Outputs

14

Page 18: > v ] v P ] v , ] P Z v P Ç W Z Ç ]Title Microsoft PowerPoint - presentation.pptx Author kmhmo Created Date 8/20/2019 4:58:55 PM

Conclusion

• It is very hard to obtain best performance using traditional analysis method in the HEP.

• Deep learning can be the breakthrough.• It is showing outstanding performance in various

high energy physics problems.

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Page 19: > v ] v P ] v , ] P Z v P Ç W Z Ç ]Title Microsoft PowerPoint - presentation.pptx Author kmhmo Created Date 8/20/2019 4:58:55 PM

Thanks

Page 20: > v ] v P ] v , ] P Z v P Ç W Z Ç ]Title Microsoft PowerPoint - presentation.pptx Author kmhmo Created Date 8/20/2019 4:58:55 PM

Back Up

Page 21: > v ] v P ] v , ] P Z v P Ç W Z Ç ]Title Microsoft PowerPoint - presentation.pptx Author kmhmo Created Date 8/20/2019 4:58:55 PM

Higgsness, Topness

• Higgsness and Topness examine how many specific kinematic resembles Higgs or Top quarks.

• We can use momentum which minimize Higgsnessor Topness as neutrino momentum.

Page 22: > v ] v P ] v , ] P Z v P Ç W Z Ç ]Title Microsoft PowerPoint - presentation.pptx Author kmhmo Created Date 8/20/2019 4:58:55 PM

ResNet

• Residual network (ResNet) uses operation jumping which prevents gradient vanishing problem.


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