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 the regression of shower parameters, namely its core position and energy at the ground, using water Cherenkov detectors. We design our method using simulations. In this contribution, we explore the recovery of the shower’s center coordinates. We evaluate the limits of such estimation near the borders of the arrays, including the when the center is outside the detector’s range. We also address the feasibility of recovering other parameters, such as ground energy. We used Bayesian Neural Networks and derived and quantified systematic errors arising from Deep Learning models and optimized the network design. The method could be easily adapted to estimate other parameters.
Keywords
Machine Learning, Deep Learning
| Subcategory | Experimental Methods & Instrumentation |
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