论文标题
猫VS狗，光子vs Hadron
Cats vs Dogs, Photons vs Hadrons
论文作者
论文摘要
在带有Cherenkov望远镜的Gamma Ray天文学中，需要机器学习模型来猜测什么样的粒子产生了检测到的光以及它们的能量和方向。这项工作的重点是分类任务，训练一个适合二进制分类的简单卷积神经网络（因为这可能是猫与狗的分类问题），它使用了蒙特卡洛（Montecarlo Data）对单个Astri望远镜产生的输入未清洗的图像。结果表明，相对于经典的随机森林方法具有增强的判别能力。
In gamma ray astronomy with Cherenkov telescopes, machine learning models are needed to guess what kind of particles generated the detected light, and their energies and directions. The focus in this work is on the classification task, training a simple convolutional neural network suitable for binary classification (as it could be a cats vs dogs classification problem), using as input uncleaned images generated by Montecarlo data for a single ASTRI telescope. Results show an enhanced discriminant power with respect to classical random forest methods.
