arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2005.00721cond-mat.dis-nncond-mat.str-el
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学习机器在多体局域化转换中学习什么

Learning What a Machine Learns in a Many-Body Localization Transition

Rubah Kausar, Wen-Jia Rao, Xin Wan

摘要:

我们采用卷积神经网络来探索随机自旋系统中的不同相位,目的是了解神经网络选择识别相位的具体特征。以归一化到带宽的能谱作为输入数据,我们证明了最小非平凡核宽度的网络选择能级间距作为签名来区分多体局域相和热相。我们还研究了具有增加的内核宽度的神经网络的性能,基于此,我们找到了一种替代的诊断方法来从这种无序相互作用系统的原始能量谱中检测相位。

英文摘要:

We employ a convolutional neural network to explore the distinct phases in random spin systems with the aim to understand the specific features that the neural network chooses to identify the phases. With the energy spectrum normalized to the bandwidth as the input data, we demonstrate that a network of the smallest nontrivial kernel width selects level spacing as the signature to distinguish the many-body localized phase from the thermal phase. We also study the performance of the neural network with an increased kernel width, based on which we find an alternative diagnostic to detect phases from the raw energy spectrum of such a disordered interacting system.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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