相关论文: Can we learn from matter creation to solve the $H_…

Soft biological tissues exhibit a tendency to maintain a preferred state of tensile stress, known as tensional homeostasis, which is restored even after external mechanical stimuli. This macroscopic behavior can be described using the…

Di-Higgs production at the LHC associated with missing transverse energy is explored in the context of simplified models that generically parameterize a large class of models with heavy scalars and dark matter candidates. Our aim is to…

\noindent Hyper-parameter selection is a central practical problem in modern machine learning, governing regularization strength, model capacity, and robustness choices. Cross-validation is often computationally prohibitive at scale, while…

Can new cosmic physics be uncovered through tensions amongst datasets? Tensions in parameter determinations amongst different types of cosmological observation, especially the `Hubble tension' between probes of the expansion rate, have been…

We study the possibility of accommodating both early and late-time tensions using a novel reinforcement learning technique. By applying this technique, we aim to optimize the evolution of the Hubble parameter from recombination to the…

Understanding the connections between galaxy stellar mass, star formation rate, and dark matter halo mass represents a key goal of the theory of galaxy formation. Cosmological simulations that include hydrodynamics, physical treatments of…

Many real-world optimisation problems such as hyperparameter tuning in machine learning or simulation-based optimisation can be formulated as expensive-to-evaluate black-box functions. A popular approach to tackle such problems is Bayesian…

The Hubble constant ($H_0$) tension is one of the major open problems in modern cosmology. This tension is the discrepancy, ranging from 4 to 6 $\sigma$, between the $H_0$ value estimated locally with the combination of Supernovae Ia (SNe…

The problem of statistical inference for open chaotic systems measured with error is complicated by the interaction of the uncertainty introduced by chaos, and the various sources of random or external variation. Here a method of…

In this paper we develop a dynamic form of Bayesian optimization for machine learning models with the goal of rapidly finding good hyperparameter settings. Our method uses the partial information gained during the training of a machine…

The explanation of the accelerated expansion of the Universe poses one of the most fundamental questions in physics and cosmology today. If the acceleration is driven by some form of dark energy, one can try to constrain the parameters…

Many machine learning models require a training procedure based on running stochastic gradient descent. A key element for the efficiency of those algorithms is the choice of the learning rate schedule. While finding good learning rates…

As an explanation for the acceleration of the universe, we propose dark matter with self-interactions characterized by a negative pressure; there is no vacuum energy whatsoever in this Cardassian model. These self-interactions may arise due…

We investigate deviations from the cosmic distance duality relation adopting model-dependent and -independent approaches using i) a Taylor expansion, ii) a power-law parameterization, iii) a logarithmic correction, iv) a (2;1) Pad\'e…

Model retraining is usually treated as an ongoing maintenance task. But as Harrison Katz now argues, retraining can be better understood as approximate Bayesian inference under computational constraints. The gap between a continuously…

The Bayesian learning rule is a natural-gradient variational inference method, which not only contains many existing learning algorithms as special cases but also enables the design of new algorithms. Unfortunately, when variational…

The simplest cosmological model ($\Lambda$CDM) is well-known to suffer from the Hubble tension, namely an almost $5 \sigma$ discrepancy between the (model-based) early-time determination of the Hubble constant $H_0$ and its late-time (and…

With the emersion of precise cosmology and the emergence of cosmic tensions, we are faced with the question of whether the simple model of cold dark matter needs to be extended and whether doing so can alleviate the tensions and improve our…

The growth index of matter fluctuations is computed for ten distinct accelerating cosmological models and confronted to the latest growth rate data via a two-step process. First, we implement a joint statistical analysis in order to place…

We show that the $H_0$ tension can be alleviated in the framework of Horndeski/generalized galileon gravity. In particular, since the terms depending on $G_5$ control the friction in the Friedmann equation, we construct specific sub-classes…