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Register a new userTesting the Multiverse: Bayes, Fine-Tuning and Typicality
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Luke A. Barnes
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Theory testing in the physical sciences has been revolutionized in recent decades by Bayesian approaches to probability theory. Here, I will consider Bayesian approaches to theory extensions, that is, theories like inflation which aim to provide a deeper explanation for some aspect of our models (in this case, the standard model of cosmology) that seem unnatural or fine-tuned. In particular, I will consider how cosmologists can test the multiverse using observations of this universe.
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The physical processes that determine the properties of our everyday world, and of the wider cosmos, are determined by some key numbers: the constants of micro-physics and the parameters that describe the expanding universe in which we have emerged. We identify various steps in the emergence of stars, planets and life that are dependent on these fundamental numbers, and explore how these steps might have been changed, or completely prevented, if the numbers were different. We then outline some cosmological models where physical reality is vastly more extensive than the universe that astronomers observe (perhaps even involving many big bangs), which could perhaps encompass domains governed by different physics. Although the concept of a multiverse is still speculative, we argue that attempts to determine whether it exists constitute a genuinely scientific endeavor. If we indeed inhabit a multiverse, then we may have to accept that there can be no explanation other than anthropic reasoning for some features our world.
Fine-tuning in physics and cosmology is often used as evidence that a theory is incomplete. For example, the parameters of the standard model of particle physics are unnaturally small (in various technical senses), which has driven much of the search for physics beyond the standard model. Of particular interest is the fine-tuning of the universe for life, which suggests that our universes ability to create physical life forms is improbable and in need of explanation, perhaps by a multiverse. This claim has been challenged on the grounds that the relevant probability measure cannot be justified because it cannot be normalized, and so small probabilities cannot be inferred. We show how fine-tuning can be formulated within the context of Bayesian theory testing (or emph{model selection}) in the physical sciences. The normalizability problem is seen to be a general problem for testing any theory with free parameters, and not a unique problem for fine-tuning. Physical theories in fact avoid such problems in one of two ways. Dimensional parameters are bounded by the Planck scale, avoiding troublesome infinities, and we are not compelled to assume that dimensionless parameters are distributed uniformly, which avoids non-normalizability.
Current theories of the origin of the Universe, including string theory, predict the existence of a multiverse containing many bubble universes. These bubble universes will generically collide, and collisions with ours produce cosmic wakes that enter our Hubble volume, appear as unusually symmetric disks in the cosmic microwave background (CMB) and disturb large scale structure (LSS). There is preliminary observational evidence consistent with one or more of these disturbances on our sky. However, other sources can produce similar features in the CMB temperature map and so additional signals are needed to verify their extra-universal origin. Here we find, for the first time, the detailed three-dimensional shape and CMB temperature and polarization signals of the cosmic wake of a bubble collision in the early universe consistent with current observations. The predicted polarization pattern has distinctive features that when correlated with the corresponding temperature pattern are a unique and striking signal of a bubble collision. These features represent the first verifiable prediction of the multiverse paradigm and might be detected by current experiments such as Planck and future CMB polarization missions. A detection of a bubble collision would confirm the existence of the Multiverse, provide compelling evidence for the string theory landscape, and sharpen our picture of the Universe and its origins.
Recently, the formation of primordial black holes (PBHs) from the collapse of primordial fluctuations has received much attention. The abundance of PBHs formed during radiation domination is sensitive to the tail of the probability distribution of primordial fluctuations. We quantify the level of fine-tuning due to this sensitivity. For example, if the main source of dark matter is PBHs with mass $10^{-12}M_odot$, then anthropic reasoning suggests that the dark matter to baryon ratio should range between 1 and 300. For this to happen, the root-mean-square amplitude of the curvature perturbation has to be fine-tuned within a $7.1%$ range. As another example, if the recently detected gravitational-wave events are to be explained by PBHs, the corresponding degree of fine-tuning is $3.8%$. We also find, however, that these fine-tunings can be relaxed if the primordial fluctuations are highly non-Gaussian, or if the PBHs are formed during an early-matter-dominated phase. We also note that no fine-tuning is needed for the scenario of a reheating of the universe by evaporated PBHs with Planck-mass relics left to serve as dark matter.
In this paper, we develop Bayes and maximum a posteriori probability (MAP) approaches to monotonicity testing. In order to simplify this problem, we consider a simple white Gaussian noise model and with the help of the Haar transform we reduce it to the equivalent problem of testing positivity of the Haar coefficients. This approach permits, in particular, to understand links between monotonicity testing and sparse vectors detection, to construct new tests, and to prove their optimality without supplementary assumptions. The main idea in our construction of multi-level tests is based on some invariance properties of specific probability distributions. Along with Bayes and MAP tests, we construct also adaptive multi-level tests that are free from the prior information about the sizes of non-monotonicity segments of the function.
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