相关论文: Emulating Recombination with Neural Networks using…

The CMB is a powerful probe of early-universe physics but is only observed after passing through large-scale structure, which changes the observed spectra in important model-dependent ways. This is of particular concern given recent claims…

When CMB data are used to derive cosmological parameters, their very choice does matter: some parameter values can be biased if the parameter space does not cover the "true" model. This is a problem, because of the difficulty to parametrize…

The Cosmic Microwave Background (CMB), which permeates the entire Universe, is the radiation left over from just 380,000 years after the Big Bang. On very large scales, the CMB radiation field is smooth and isotropic, but the existence of…

Machine learning (ML) algorithms have revolutionized the way we interpret data in astronomy, particle physics, biology and even economics, since they can remove biases due to a priori chosen models. Here we apply a particular ML method, the…

In this paper, analogies between multiparticle production in high-energy collisions and the time evolution of the early universe are discussed. A common explanation is put forward under the assumption of an unconventional early state: a…

We describe a new map-making code for cosmic microwave background (CMB) observations. It implements fast algorithms for convolution and transpose convolution of two functions on the sphere (Wandelt & G\'{o}rski 2001). Our code can account…

Early cancer detection remains one of the most critical challenges in modern healthcare, where delayed diagnosis significantly reduces survival outcomes. Recent advancements in artificial intelligence, particularly deep learning, have…

Cosmological models exhibiting crossing of the phantom divide improve the fit to current data, suggesting late-time dark energy (DE) dynamics at $\sim3\sigma$ CL. However, they favor low values of $H_0$, in tension with SH0ES. This may…

Neural ordinary differential equations (Neural ODEs) propose the idea that a sequence of layers in a neural network is just a discretisation of an ODE, and thus can instead be directly modelled by a parameterised ODE. This idea has had…

Some cosmic microwave background (CMB) data allow a cosmological scenario in which the free streaming of neutrinos is delayed until close to matter-radiation equality. Interestingly, recent analyses have revealed that large-scale structure…

Spectral distortions of the Cosmic Microwave Background (CMB) offer the possibility of probing processes which occurred during the evolution of our Universe going back up to Z$\simeq 10^7$. Unfortunately all the attempts so far carried out…

Spectral features in the CMB energy spectrum contain a wealth of information about the physical processes in the early Universe, z < 2 x 10^6. The CMB spectral distortions are complementary to all other probes of cosmology. In fact, most of…

We present a deep learning model trained to emulate the radiative transfer during the epoch of cosmological reionization. CRADLE (Cosmological Reionization And Deep LEarning) is an autoencoder convolutional neural network that uses…

Deep learning, as a highly efficient method for metasurface inverse design, commonly use simulation data to train deep neural networks (DNNs) that can map desired functionalities to proper metasurface designs. However, the assumptions and…

The relationship between observed tracers such as galaxies and the underlying dark matter distribution is crucial in extracting cosmological information. As the linear bias model breaks down at quasi-linear scales, the standard perturbative…

We show that the $H_0$ tension can be resolved by making recombination earlier, keeping the fit to cosmic microwave background (CMB) data almost intact. We provide a suite of general necessary conditions to give a good fit to CMB data while…

The advancement of human healthspan and bioengineering relies heavily on predicting the behavior of complex biological systems. While high-throughput multiomics data is becoming increasingly abundant, converting this data into actionable…

This paper presents a systematic literature review focusing on the application of machine learning techniques for deriving observational constraints in cosmology. The goal is to evaluate and synthesize existing research to identify…

Continued improvements on existing reconstruction methods are vital to the success of high-energy physics experiments, such as the IceCube Neutrino Observatory. In IceCube, further challenges arise as the detector is situated at the…

Primordial magnetic fields (PMFs) may explain observations of magnetic fields on extragalactic scales. They are most cleanly constrained by measurements of cosmic microwave background radiation (CMB) anisotropies. Their effects on cosmic…