Applications of Machine Learning to the Monopole & Exotics Detector at the Large Hadron Collider

MoEDAL is the Monopole and Exotics Detector at the Large Hadron Collider. The Moedal Experiment uses Passive Nuclear Track Detector foils (NTDs) to look for magnetic monopoles, and other heavily ionising exotic particles at the Large Hadron Collider (LHC). Heavy particle radiation backgrounds at the Large Hadron Collider make image analysis of these NTD foils non-trivial compared to NTD image analysis under lower background conditions such as medical ion beam calibration or nuclear dosimetry. This thesis looks at multichannel and multidimensional Convolutional Neural Network (CNN) and Fully Convolutional Neural Network (FCN) based image recognition for identifying anomalous heavily ionising particle (HIP) etch pits within calibration NTD foils that have been exposed to both a calibration signal (heavy ion beam), and real LHC background exposure, serving as detector research and development for future MoEDAL NTD analyses. Image data was collected with Directed-Bright/Dark-Field illumination, parametrised at multiple off-axis illumination angles. Angular control of the light intensity distri- bution was achieved via a paired Fresnel lens and LED array. Information about the 3D structure of the etch pits is contained in these parametrised images which may as- sist in their identification and classification beyond what is possible in a simple 2D image. Convolutional Neural Network etch pit classifiers were trained using Xe, and Pb ion data with differing levels of LHC background exposure. An ensemble approach of combining classifiers trained on different objects, and data-channels is shown to improve classification performance. Transfer learning was used to generate Fully Convolutional Neural Networks for identifying HIP etch-pit candidates from wide area foil scan images. The performance of the FCN algorithm is evaluated using a novel MoEDAL R&D foil stack, in order to obtain blinded estimates of the signal acceptance and false prediction rate of an ML based NTD analysis. Additionally a method for pixel to pixel alignment of NTD foil scans is demonstrated that can be used for the training of U-Net FCN architectures

Remote measurements of heart valve sounds for health assessment and biometric identification

Heart failure will contribute to the death of one in three people who read this thesis; and one in three of those who don't. Although in order to diagnose patients’ heart condition cardiologists have access to electrocardiograms, chest X-rays, ultrasound imaging, MRI, Doppler techniques, angiography, and transesophageal echocardiography, these diagnostic techniques require a cardiologist’s visit, are expensive, the examination time is long and so are the waiting lists. Furthermore abnormal events might be sporadic and thus constant monitoring would be needed to avoid fatalities. Therefore in this thesis we propose a cost effective device which can constantly monitor the heart condition based on the principles of phonocardiography, which is a cost-effective method which records heart sounds. Manual auscultation is not widely used to diagnose because it requires considerable training, it relies on the hearing abilities of the clinician and specificity and sensitivity for manual auscultation are low since results are qualitative and not reproducible. However we propose a cheap laser-based device which is contactless and can constantly monitor patients’ heart sounds with a better SNR than the digital stethoscope. We also propose a Machine Learning (ML) aided software trained on data acquired with our device which can classify healthy from unhealthy heart sounds and can perform biometric authentication. This device might allow development of gadgets for remote monitoring of cardiovascular health in different settings