Speaker
Description
Deep Learning methods are among the state-of-art of several computer vision tasks, intelligent control systems, fast and reliable signal processing and inference in big data regimes. It is also a promising tool for scientific analysis, such as gamma/hadron discrimination.
We present an approach based on Deep Learning for shower parameter regression of water Cherenkov detectors. We design our method using simulations. In this contribution, we explore the recovery of the shower’s center coordinates and its ground energy. We evaluate the limits of such estimation near the borders of the arrays, including when the center is outside the detector’s range. We made use of several Deep Learning architectures to select the most promising and optimized the network design. The method could be easily adapted to estimate other parameters of interest.
| Key Words | Machine Learning, Deep Learning, water Cherenkov detectors |
|---|
