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

3D Input

Example noisy input:

Example correct output:

4x6 Kernels

Epochs	Training loss	Validation loss	Training Time	Inference Time	Train/Val Loss Plot	Example Case
20	0.000064	0.00007	7822	0.00087

Different Noise

Example noisy input:

Example correct output:

Noisy	Correct	Denoised
					} 2D Input 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 Valid Hits Histogram Noise Added Histogram 0.0018 0.0219 298 0.000182 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 50 0.0024 0.0024 238 0.000123 100 0.0023 0.0025 471.2 0.0001 Train and test on single track V2 4x6 Kernels Epochs Training loss Validation loss Training Time Inference Time Train/Val Loss Plot Example Case Valid Hits Histogram Noise Added Histogram 12 0.00048 0.00085 204 0.00024 Per segment histograms: Train and test on multiple tracks V2 Epochs Training loss Validation loss Training Time Inference Time Train/Val Loss Plot Example Case Valid Hits Histogram Noise Added Histogram 20 0.0029 0.0030 683 0.0002 File:Cnf ml cnn denoised mult track v2.png Train on single and test on multiple tracks Epochs Validation loss Inference Time Example Case Valid Hits Histogram Noise Added Histogram 50 0.027 0.00012 100 0.024 0.000125 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 50 0.00537 0.00505 0.0001 File:Cnf ml cnn denoised mult track.png Train and test on multiple tracks with more kernels Epochs Training loss Validation loss Training Time Inference Time Train/Val Loss Plot Example Case Valid Hits Histogram Noise Added Histogram 50 0.0041 0.0040 943 0.0001 File:Cnf ml cnn denoised mult track more.png 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 50 0.0078 Train on multiple and test on 2-3 tracks V2 Epochs Validation loss Inference Time Example Case Valid Hits Histogram Noise Added Histogram 20 0.0010 0.0007 Train on new 2-3 tracks and test on multitrack V2 Epochs Training loss Validation loss Training Time Inference Time Train/Val Loss Plot Example Case Valid Hits Histogram Noise Added Histogram 20 0.0043 0.003 517.2 0.0003 File:Cnn ae train val mult loss more V2 new multitrack.png File:Cnf ml cnn denoised mult2 3 track V2 new multitrack.png Train on new 2-3 tracks and test on old 2-3 tracks V2 Epochs Training loss Validation loss Training Time Inference Time Train/Val Loss Plot Example Case Valid Hits Histogram Noise Added Histogram 20 0.0043 0.0045 517.2 0.0003 Train on multiple and test on 2-3 tracks with more kernels Epochs Validation loss Inference Time Example Case Valid Hits Histogram Noise Added Histogram 50 0.0075 0.0008 Examples Noisy Correct MLP CNN CNN V2 CNN V2 new File:Ae mlp denoised 1.png File:Ae mlp denoised 2.png File:Ae mlp denoised 3.png File:Ae mlp denoised 1m.png File:Ae mlp denoised 2m.png File:Ae mlp denoised 3m.png File:Ae mlp denoised 4m.png 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) Work Plan (November 17 Provide workflow for testing, given input image write out the truth and AI inferred image Write software to create an image from AI inferred image and Truth image to reconstruct better truth, by filling in missing pieces in AI produced image from truth image when the points are close Evaluate efficiency of reconstructing the truth image and noisiness of resulting image. Also make graph now much noise was removed from original image. Start working with Hall-C data. much bigger input and evaluate efficiency of cleaning noise at the first stage noise has to be added to the Hall-C data for training, it can start with one track sample, then 250 track sample. Resources https://userweb.jlab.org/~gavalian/Documents/NN_Vary_Width_Depth%20(Nguyen).pdf https://userweb.jlab.org/~gavalian/ML/2020/AutoEncoder/