arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2006.07290hep-th
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用机器学习测试Swampland猜想

Testing Swampland Conjectures with Machine Learning

Nana Cabo Bizet, Cesar Damian, Oscar Loaiza-Brito, Damián Kaloni Mayorga Peña, J. A. Montañez-Barrera

摘要:

我们考虑由几何通量和非几何通量贯穿的各向同性环面$T^6$上的IIB型紧化。对于这个特定的设置,我们应用监督机器学习技术,即人工神经网络耦合到遗传算法,以获得超过六万通量配置产生的标量势至少有一个临界点。我们观察到,既有大的模质量和小的真空能量的稳定的AdS真空,以及不稳定的dS真空小快子质量和大的能量是不存在的,根据精炼德西特猜想。此外,通过考虑通量之间的层次结构,我们观察到,微扰的解决方案与真空能量和模量质量的小值是有利的,以及最轻的模量质量的情况下,远远大于相应的AdS真空尺度。最后,我们应用随机矩阵理论的一些结果得出结论,从这个字符串设置导出的最可能的质谱是,满足精炼de Sitter和AdS标度结构。

英文摘要:

We consider Type IIB compactifications on an isotropic torus $T^6$ threaded by geometric and non geometric fluxes. For this particular setup we apply supervised machine learning techniques, namely an artificial neural network coupled to a genetic algorithm, in order to obtain more than sixty thousand flux configurations yielding to a scalar potential with at least one critical point. We observe that both stable AdS vacua with large moduli masses and small vacuum energy as well as unstable dS vacua with small tachyonic mass and large energy are absent, in accordance to the Refined de Sitter Conjecture. Moreover, by considering a hierarchy among fluxes, we observe that perturbative solutions with small values for the vacuum energy and moduli masses are favored, as well as scenarios in which the lightest modulus mass is much greater than the corresponding AdS vacuum scale. Finally we apply some results on Random Matrix Theory to conclude that the most probable mass spectrum derived from this string setup is that satisfying the Refined de Sitter and AdS scale conjectures.

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