The authors provide a thorough study of popular scenario (early dark energy EDE) to alleviate the tension in measurements Hubble constant ${H}_{0}$, which either rely on early Universe probes and cosmological standard model ($\mathrm{\ensuremath{\Lambda}}$CDM) or late through direct, local distant measurements. show that inclusion numerous large-scale structure data is conflict with parameter space lifts ${H}_{0}$ severely limits existence EDE and, thus, makes it very unlikely resolve tension.

An axion-like field comprising $\sim 10\%$ of the energy density universe near matter-radiation equality is a candidate to resolve Hubble tension; this "early dark energy" (EDE) model. However, as shown in Hill et al. (2020), model fails simultaneously tension and maintain good fit both cosmic microwave background (CMB) large-scale structure (LSS) data. Here, we use redshift-space galaxy clustering data sharpen constraints on EDE We perform first analysis using full-shape power spectrum...

We present an effective field theory (EFT) approach to extract fundamental cosmological parameters from the Lyman-alpha forest flux fluctuations as alternative standard simulation-based techniques. As a first application, we reanalyze publicly available one-dimensional power spectrum data Sloan Digital Sky Survey. Our analysis relies on informative priors EFT that combination of public hydrodynamic simulation and emulator data. Assuming concordance model, our one-parameter yields 2%...

In this paper, we review and update constraints on the Early Dark Energy (EDE) model from cosmological data sets, in particular Planck PR3 PR4 cosmic microwave background (CMB) large-scale structure (LSS) sets including galaxy clustering weak lensing Survey, Subaru Hyper Suprime-Cam KiDS+VIKING-450, as well BOSS/eBOSS Lyman-[Formula: see text] forest data. We detail fit to CMB data, perform first analyses of EDE using CAMSPEC Hillipop likelihoods for rather than Plik, both which yield a...

Abstract The recent detection of gravitational waves and electromagnetic counterparts from the double neutron star merger event GW+EM170817 supports standard paradigm short gamma-ray bursts (SGRBs) kilonovae/macronovae. It is important to reveal nature compact remnant left after merger, either a black hole or star, their physical link origin long-lasting emission observed in SGRBs. diversity remnants may also lead different kinds transients that can be detected future. Here we study...

Abstract We present a kinetically mixed dark sector (KMIX) model to address the Hubble and S 8 tensions. Inspired from string theory, our includes two fields: an axion, which plays role similar scalar field in early energy models, dilaton. This theory differs other axio-dilaton models aimed at tension that there is necessarily kinetic mixing between fields allows for efficient transfer axion into dilaton has w ≈ 1. As direct consequence of these dynamics, we find does not need resort...

Strong gravitational lensing is a promising probe of the substructure dark matter halos. Deep learning methods have potential to accurately identify images containing substructure, and differentiate WIMP from other well motivated models, including vortex condensates superfluids. This crucial in future efforts true nature matter. We implement, for first time, classification approach identifying based on simulated strong with different substructure. Utilizing convolutional neural networks...

We explore full-shape analysis with simulation-based priors, which is the simplest approach to galaxy clustering data that combines effective field theory (EFT) on large scales and numerical simulations small scales. The core ingredient of our prior density EFT parameters we extract from a suite 10500 based halo occupation distribution (HOD) model. measure field-level forward model, enables us cancel cosmic variance. On side, develop new efficient calculate transfer functions using...

We present a targeted search for blazar flux-correlated high-energy ($\varepsilon_\nu > 1$ TeV) neutrinos from six bright northern blazars, using the public database of northern-hemisphere detected during "IC40" 40-string operations IceCube neutrino observatory (April 2008 to May 2009). Our blazars are subjects long-term monitoring campaigns by VERITAS TeV gamma-ray observatory. use publicly-available lightcurves identify periods excess and flaring emission. These predefined intervals serve...

The identity of dark matter remains one the most pressing questions in physics today. While many promising candidates have been put forth over last half-century, to date true elusive. it is possible that proposed may turn out be matter, at least equally likely correct physical description has yet proposed. To address this challenge, novel applications machine learning can help physicists gain insight into sector from a theory agnostic perspective. In work we demonstrate use unsupervised...

We review and update constraints on the Early Dark Energy (EDE) model from cosmological data sets, in particular Planck PR3 PR4 cosmic microwave background (CMB) large-scale structure (LSS) sets including galaxy clustering weak lensing Survey, Subaru Hyper Suprime-Cam, KiDS+VIKING-450, as well BOSS/eBOSS Lyman-$\alpha$ forest data. detail fit to CMB data, perform first analyses of EDE using CAMSPEC Hillipop likelihoods for rather than Plik, both which yield a tighter upper bound allowed...

We study the application of machine learning techniques for detection astrometric signature dark matter substructure. In this proof principle, a population subhalos in Milky Way will act as lenses sources extragalactic origin such quasars. train resnet-18, state-of-the-art convolutional neural network to classify angular velocity maps quasars into lensed and nonlensed classes. show that an SKA-like survey with extended operational baseline can be used probe substructure content demonstrate...

We present an approach to cosmology in which the Universe learns its own physical laws. It does so by exploring a landscape of possible laws, we express as certain class matrix models. discover maps that put each these models correspondence with both gauge/gravity theory and mathematical model learning machine, such deep recurrent, cyclic neural network. This establishes between solution run is not equivalence, partly because gauge theories emerge from $N \rightarrow \infty $ limits models,...

We present an efficient approach to set informative physically motivated priors for EFT-based full-shape analyses of galaxy survey data. extract these from simulated catalogs based on halo occupation distribution (HOD) models. As a first step, we build joint between EFT bias and HOD parameters 10,500 mock catalogs. make use the field level technique that allows cosmic variance cancellation, enabling precision calibration computationally inexpensive small-volume simulations. second neural...

Abstract Gravitational lensing data is frequently collected at low resolution due to instrumental limitations and observing conditions. Machine learning-based super-resolution techniques offer a method enhance the of these images, enabling more precise measurements effects better understanding matter distribution in system. This enhancement can significantly improve our knowledge mass within galaxy its environment, as well properties background source being lensed. Traditional typically...

Abstract The identity of dark matter has remained surprisingly elusive. While terrestrial experiments may be able to nail down a model, an alternative method is identify based on astrophysical or cosmological signatures. A particularly sensitive approach the unique signature substructure in galaxy–galaxy strong lensing images. Machine-learning applications have been explored for extracting this signal. Because limited availability high-quality images, these approaches exclusively relied...

We present a search for high-energy $\gamma$-ray emission from 566 Active Galactic Nuclei at redshift $z > 0.2$, the 2WHSP catalog of high-synchrotron peaked BL Lac objects with eight years Fermi-LAT data. focus on range where electromagnetic cascade induced by ultra-high-energy cosmic rays can be distinguished leptonic based spectral properties sources. Our analysis leads to detection 160 sources above $\approx$ $5\sigma$ (TS $\geq 25$) in 1 - 300 GeV energy range. By discriminating...

We study a minimal extension of recently proposed modification general relativity that draws on concepts from topological field theory to resolve aspects the cosmological constant problem. In original model, content was augmented include gauge and an adjoint-valued two-form without modifying classical gravitational dynamics. Here we kinetic term for which sources fluctuations constant. then spherically symmetric black holes simple homogeneous, isotropic model predicted by extended theory....

We present a kinetically mixed dark sector (KMIX) model to address the Hubble and $S_8$ tensions. Inspired from string theory, our includes two fields: an axion, which plays role similar scalar field in early energy models, dilaton. This theory differs other axio-dilaton models aimed at tension that there is necessarily kinetic mixing between fields allows for efficient transfer axion into dilaton has $w\approx1$. As direct consequence of these dynamics, we find does not need resort...

We present an effective field theory (EFT) approach to extract fundamental cosmological parameters from the Lyman-alpha forest flux fluctuations as alternative standard simulation-based techniques. As a first application, we re-analyze publicly available one-dimensional power spectrum (FPS) data Sloan Digital Sky Survey (SDSS). Our analysis relies on informative priors EFT which combination of public hydrodynamic simulation and emulator data. Assuming concordance model, our one-parameter...

Gravitational lensing data is frequently collected at low resolution due to instrumental limitations and observing conditions. Machine learning-based super-resolution techniques offer a method enhance the of these images, enabling more precise measurements effects better understanding matter distribution in system. This enhancement can significantly improve our knowledge mass within galaxy its environment, as well properties background source being lensed. Traditional typically learn mapping...

Cosmological models are often motivated and formulated in the language of particle physics, using quantities such as axion decay constant, but tested against data ostensibly physical quantities, energy density ratios, assuming uniform priors on latter. This approach neglects model from fundamental theory, including physics string preference for sub-Planckian constants. We introduce a novel to learning theory-informed Bayesian inference normalizing flows (NF), flexible generative machine...

The next decade is expected to see a tenfold increase in the number of strong gravitational lenses, driven by new wide-field imaging surveys. To discover these rare objects, efficient automated detection methods need be developed. In this work, we assess performance three domain adaptation techniques -- Adversarial Discriminative Domain Adaptation (ADDA), Wasserstein Distance Guided Representation Learning (WDGRL), and Supervised (SDA) enhancing lens-finding algorithms trained on simulated...