1. Machine Learning for Data Certification at CMS
by Humza Khan
Mentor: Dr. Nural Akchurin, Federico De Guio

2. Overview Compact Muon Solenoid (CMS) Data Certification
Machine Learning Introduction
Preprocessing
Supervised Learning
Support Vector Machines, Boosted Decision Trees, Stochastic Gradient Descent
Unsupervised Learning
One-class SVM, Isolation Forest, Autoencoders
Further Steps

3. Compact Muon Solenoid (CMS)
Major experiment, along with ATLAS
LHC smashes particles together
CMS is like a giant camera for collisions
Multiple layers for tracking different particles

4. Compact Muon Solenoid (CMS) continued

5. Data Certification
LHC produces approximately 25 petabytes of data per year
Not all of it is interesting for physics
“good” vs. “bad” data
Preliminary filters get sort a lot of the data
Still leaves a large amount of unclassified data
Needs to be manually checked by detector experts

6. Machine Learning Introduction
Make computers learn a problem without being explicitly programmed
Clever uses of statistics and computer science to analyze patterns in data
Classification vs. Regression
Both assign numerical values to data samples
Regression assigns from a continuous set
Classification assigns from a discrete set
Given 𝑛 data samples
Each has 𝑞 features (values that describe the object)
Each sample (usually) has a label

7. Machine Learning Introduction
Split data into training and testing sets
Train model with training set
Feed testing set to model, then see how accurate predictions are
Supervised learning has labels attached to samples
Unsupervised learning does not have labels attached to samples

8. Preprocessing Feature scaling Feature selection
Dimensionality reduction
Given 43 features
Represent Pt, Eta, Phi, MetPt MetPhi, Vertices, Cross Section
Mean, RMS, Q1, Q2, Q3, Q4, Q5

9. Preprocessing

10. Support Vector Machines (SVM)
Data with 𝑛 features is in 𝑛-dimensional space
Find 𝑛−1 dimensional hyperplane to divide data
Maximize distance between hyperplane and points
Not all data is linearly separable

11. Support Vector Machines (SVM)

12. Stochastic Gradient Descent (SGD)
Finds greatest derivative at point and moves that way
Good at finding minima quickly
Can get stuck at local minimum instead of global
SGD only updates based on one sample instead of all

13. Stochastic Gradient Descent (SGD)

14. One-class SVM Novelty detection
Train only on good data,
Test on both
Useful when classes are extremely different in size
Only have 5% background

15. One-class SVM

16. Autoencoders Neural network Dimensionality reducer
Computational representation of human neurons
Dimensionality reducer
Finds non-linear correlations within features

17. Further Steps Inspect labels Further explore deep learning
Image recognition

18. Sources
A4-at jpg?itok=NaAYCj1Z
svm-plot-oneclass-py

20. Thanks! Jean Kirsch, Steven Goldfarb, and Thomas Schwarz
Lounsberry Foundation
Federico de Guio
Nural Akchurin
Giovanni Franconi
Filip Sîroky