First off, a disclaimer: I'm not sure if this is the right Stack Exchange for this question, but I'm not aware of a machine learning specific SE.
I am doing research into characterising particle jets in high-energy physics. I am trying to use image recognition techniques, in particular convolutional neural networks to characterize jets into two classes.
These classes can be distinguished by the following features:
- Sudden 'jump' in the number of hits between layers of a detector
- Radius of concentration of hits
- Energy deposited in each layer
I am using 123x123x4 images. Each pixel in each channel represents a level of energy deposited in a layer of the detector. I am concerned that it may even be impossible to do this in a deep-learning approach, as there are typically only 150-300 pixels filled in each image.
I would like to use a ConvNet to classify the two different types of jet. However, I am not sure what architecture to use.
There are other variables that might be of importance in classification, and I would like to be able to include these also (probably in the dense layer immediately before the output).
I tried the following architecture, and trained with Ada, Adamax and Adadelta with no convergence:
___________ _________ _________ _________ ________ ______
| Conv | | Max | | Conv | | Max | | | | |
Image --> | Layer 1 | --> | Pool 1 | --> | Layer 2 | --> | Pool 2 | -->| | | |
|_________| |________| |_________| |________| | Dense | | Out |
| Layer |-->|_____|
Other ------------------------------------------------------------>| |
Data | |
|_______|
Are there any suggestions for architectures I should try?