Speaker
Description
Nuclear Track Detectors (NTDs) have been in use for decades,
mainly as detectors of heavily ionizing particles. Existence of natural
thresholds of detection makes them an ideal choice as detectors in the
search for rare, heavily ionizing hypothesized particles (e.g. Monopoles,
Strangelets etc.) against a large low-Z background in cosmic rays as well
as particle accelerators. But identification of particle tracks in
NTDs presents a significant challenge, with conventional image analysis
software coming up short, requiring the intervention of human experts.
This makes the job of scanning NTDs a painstakingly slow process, prone
to human errors. In recent years, the use of Machine Learning techniques
has opened up the possibilities of new advances in image analysis. In this
work, we have taken a technique combining sequential application of
convolution and de-convolution previously developed by us and further
upgraded it with the use of Artificial Neural Network. This has further
reduced the need for manual intervention, is producing better
results than commercially available software and is promising to dramatically
speed up the scanning process, thereby facilitating the more widespread
adaptation of NTDs.
