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Deep learning for imaging calorimeters - datase...

Mehdi
August 08, 2014
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Deep learning for imaging calorimeters - datasense talk

Mehdi

August 08, 2014
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Transcript

  1. Deep learning for imaging calorimeters Mehdi Cherti Supervisor : Balázs

    Kégl (Linear Accelerator Laboratory / AppStat group) 1 DigiCosme/Datasense, July 8th 2014
  2. Outline • Introduction • Calorimeters • Description of the project

    • Convolutional neural networks • Results and discussion • Perspectives 2
  3. Introduction (1/2) 3 Context : International Linear Accelerator (ILC) project

    Purpose : Measure empirically the efficiency of deep learning algorithms on raw calorimeter data
  4. Description of the project (1/4) • 4D data : For

    each position (x, y, z) we have an energy deposit • Input dimensionality : 9720 pixels 7 18 18 18 18 30 30 x y z
  5. Description of the project (2/4) • Baseline : we use

    manual extracted features and we train them with adaboost (multiboost software) • These features are : front image, layers difference and layers energy 8
  6. Description of the project (3/4) 9 x Front image :

    y z x y covariance matrix of the points (x, y)
  7. Convolutional neural networks • Supervised deep learning algorithm • State

    of the art results in object recognition • Detect features regardless of their position with convolution • Some translation invariance with pooling 11
  8. Results and discussion (1/3) • Pre-processing (standardization) and Rectified linear

    units (ReLU) made the optimization step faster • Pooling did not help much 12
  9. Results and discussion (3/3) • Convolutional networks did not help

    much but we should point out that the dataset is noisy • Convolutional networks are less prone to overfitting 14 Noisy instances:
  10. Perspectives • Separating tracks of two independent particles (mixture of

    particles) • Prediction of the energy and the number of secondary particles • Prediction of the energy of the incoming particle • Design a generative model for calorimeter data 15 Mixture of particles :