论文标题

永恒通货膨胀中的贝叶斯推理:解决量度问题的解决方案

Bayesian Reasoning in Eternal Inflation: A Solution to the Measure Problem

论文作者

Khoury, Justin, Wong, Sam S. C.

论文摘要

传统上,永恒通货膨胀的概率被定义为有限的频率分布,但是独特而明确的概率度量仍然难以捉摸。在本文中,我们根据贝叶斯推理提出了一种不同的方法。我们的起点是管理真空动力学的主方程,它描述了真空网络上的随机步行。我们的概率需要两种先前的信息,这既与初始条件有关:成核时间的先前密度$ρ(t)$,以及祖先真空的先前概率$p_α$。对于祖先真空吸尘器,我们主张统一的先验作为保守的选择,尽管我们的结论对此选择不敏感。在成核时期,我们认为统一的先验与主方程的时间翻译不变性一致,代表最小信息的选择。由此产生的预测概率与Bousso的“全息”先前概率一致,并且与Garriga和Vilenkin的“共同”概率密切相关。尽管提供了最少的帮助先验,但这些概率具有令人惊讶的预测性。他们偏爱周围的景观地形的真空吸尘器,是一个深漏斗,类似于自然出现的蛋白质的折叠漏斗。他们预测,我们在接近平衡的方法中存在,比景观的混合时间早得多。我们还考虑了体积加权的$ρ(t)$,相当于按物理量称重真空。在这种情况下,预测概率与GSVW度量一致。贝叶斯框架使我们能够比较均匀时间和体积加权假设的合理性,以通过计算每个贝叶斯证据来解释我们的数据。我们认为,在一般和合理的假设下,后部赔率压倒性地偏向于统一的时间假设。

Probabilities in eternal inflation are traditionally defined as limiting frequency distributions, but a unique and unambiguous probability measure remains elusive. In this paper, we present a different approach, based on Bayesian reasoning. Our starting point is the master equation governing vacuum dynamics, which describes a random walk on the network of vacua. Our probabilities require two pieces of prior information, both pertaining to initial conditions: a prior density $ρ(t)$ for the time of nucleation, and a prior probability $p_α$ for the ancestral vacuum. For ancestral vacua, we advocate the uniform prior as a conservative choice, though our conclusions are fairly insensitive to this choice. For the time of nucleation, we argue that a uniform prior is consistent with the time-translational invariance of the master equation and represents the minimally-informative choice. The resulting predictive probabilities coincide with Bousso's "holographic" prior probabilities and are closely related to Garriga and Vilenkin's "comoving" probabilities. Despite making the least informative priors, these probabilities are surprisingly predictive. They favor vacua whose surrounding landscape topography is that of a deep funnel, akin to the folding funnels of naturally-occurring proteins. They predict that we exist during the approach to near-equilibrium, much earlier than the mixing time for the landscape. We also consider a volume-weighted $ρ(t)$, which amounts to weighing vacua by physical volume. The predictive probabilities in this case coincide with the GSVW measure. The Bayesian framework allows us to compare the plausibility of the uniform-time and volume-weighted hypotheses to explain our data by computing the Bayesian evidence for each. We argue, under general and plausible assumptions, that posterior odds overwhelmingly favor the uniform-time hypothesis.

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