CNF:ML Data Denoise
This page will be updated about the machine learning data de-noising effort.
Problem
Traditional tracking algorithms are computationally intensive, especially for high luminosity experiments with multi-track final states where all combinations of segments in drift chambers have to be considered for firing best track candidates. At high luminosity the number of random segments (unrelated to the tracks) are increasing and as a result the number of possible combinations also increases, making the whole process longer.
Goal
Use Deep Learning Autoencoders that are trained on noisy tracks to produce the original track without the noise.
Results
Hardware specs:
Tesla V100 GPU
Example noisy input:
Example correct input:
Both the MLP and CNN networks used relu activation for all hidden layers and sigmoid for the output. Binary cross-entropy was used as the loss function and Adam as the optimizer.
Multilayer Perceptrons
| Training loss | Validation loss | Training Time | Inference Time | Train/Val Loss Plot | Example Case |
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| 0.0018 | 0.0219 | 298 | 0.000182 | 
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Convolutional Networks
Train and test on single track
| Epochs | Training loss | Validation loss | Training Time | Inference Time | Train/Val Loss Plot | Example Case | Valid Hits Histogram | Noise Added Histogram |
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| 50 | 0.0024 | 0.0024 | 238 | 0.000123 | 
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| 100 | 0.0023 | 0.0025 | 471.2 | 0.0001 | 
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Train on single and test on multiple tracks
| Epochs | Validation loss | Inference Time | Example Case | Valid Hits Histogram | Noise Added Histogram |
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| 50 | 0.027 | 0.00012 | 
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| 100 | 0.024 | 0.000125 | 
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Train and test on multiple tracks
| Epochs | Training loss | Validation loss | Training Time | Inference Time | Train/Val Loss Plot | Example Case | Valid Hits Histogram | Noise Added Histogram |
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| 50 | 0.00537 | 0.00505 | 0.0001 | 
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File:Cnf ml cnn denoised mult track.png | 
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Train on multiple and test on 2-3 tracks
Correct Track:
Noisy Input Track:
| Epochs | Validation loss | Inference Time | Example Case | Valid Hits Histogram | Noise Added Histogram |
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| 50 | 0.0078 | 
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Examples
| Noisy | Correct | MLP | CNN |
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File:Ae mlp denoised 1.png | 
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File:Ae mlp denoised 2.png | 
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File:Ae mlp denoised 3.png | 
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File:Ae mlp denoised 1m.png | 
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File:Ae mlp denoised 2m.png | 
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File:Ae mlp denoised 3m.png | 
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Work Plan
- create a histogram comparing how many points in the reconstructed image match points in the ground truth
- create a histogram comparing how many points in reconstructed image are noise (are not in the original image)
- try network (trained with one track) with new data set that contains more than one track, and provide example plots for presentation
- create the efficiency and noise plots for the multi-track data sample reconstruction.
- train network with multi-track data and evaluate performance on multi-track testing sample.
- put on the web site architecture of the network and try with a little deeper architecture (4032->2016->1008->336 -> 224 -> 336 -> 1008 -> 2016 -> 4032)
Resources
https://userweb.jlab.org/~gavalian/Documents/NN_Vary_Width_Depth%20(Nguyen).pdf