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

Example noisy input:

Example correct input:

Multilayer Perceptrons

Training loss	Validation loss	Training Time	Inference Time	Train/Val Loss Plot	Example Case
0.0018	0.0219	298	0.000182

Convolutional Networks

Epochs	Training loss	Validation loss	Training Time	Inference Time	Train/Val Loss Plot	Example Case
50	0.0034	0.0035	108	0.000165
50	0.0027	0.0026	222	0.000162

Examples

Noisy	Correct	MLP	CNN