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X-ray luminosity functions (XLFs) of Active Galactic Nuclei (AGN) trace the growth and evolution of supermassive black hole populations across cosmic time, however, current XLF models are poorly constrained at redshifts of z>6, with a lack…
Bayesian neural networks (BNNs) provide a formalism to quantify and calibrate uncertainty in deep learning. Current inference approaches for BNNs often resort to few-sample estimation for scalability, which can harm predictive performance,…
In this paper, we study the cosmological implications of the 100 square degree Weak Lensing survey (the CFHTLS-Wide, RCS, VIRMOS-DESCART and GaBoDS surveys). We combine these weak lensing data with the cosmic microwave background (CMB)…
We study the spectral properties of the unresolved cosmic X-ray background (CXRB) in the 1.5-7.0 keV energy band with the aim of providing an observational constraint on the statistical properties of those sources that are too faint to be…
Flow matching with clean-data prediction has shown that regressing the clean point can exploit low-dimensional structure more effectively than predicting an ambient noised quantity. We ask whether this principle remains useful after images…
Wavelet shrinkage estimators are widely applied in several fields of science for denoising data in wavelet domain by reducing the magnitudes of empirical coefficients. In nonparametric regression problem, most of the shrinkage rules are…
We show using a realistic Hamiltonian-type model that definite outcomes of quantum measurements may emerge from quantum evolution of pure states, i.e quantum dynamics provides a deterministic collapse of the wavefunction in a quantum…
We forecast combined future constraints from the cosmic microwave background and large-scale structure on the models of primordial non-Gaussianity. We study the generalized local model of non-Gaussianity, where the parameter f_NL is…
We investigate the cosmological information in higher-order statistics of the cosmic microwave background (CMB) lensing convergence field for a near-term experiment with noise properties similar to the Simons Observatory (SO). Using a fully…
The synthesis model for the cosmic X-ray background (XRB) -based on the integrated emission of Active Galactic Nuclei (AGNs)- is complemented with new observational results. We adopt the most recent estimates of the AGN X-ray luminosity…
The XENON1T experiment is the most recent stage of the XENON Dark Matter Search, aiming for the direct detection of Weakly Interacting Massive Particles (WIMPs). To reach its projected sensitivity, the background has to be reduced by two…
Gravitational wave echoes offer a unique probe of the near-horizon structure of astrophysical black holes, beyond the standard ''black hole spectroscopy''. Theoretical waveform predictions, however, remain uncertain, motivating robust…
We consider the problem of robust deconvolution, and particularly the recovery of an unknown deterministic signal convolved with a known filter and corrupted by additive noise. We present a novel, non-iterative data-driven approach.…
The XENON1T experiment searches for dark matter particles through their scattering off xenon atoms in a 2 tonne liquid xenon target. The detector is a dual-phase time projection chamber, which measures simultaneously the scintillation and…
In recent years, deep neural networks showed their exceeding capabilities in addressing many computer vision tasks including scene flow prediction. However, most of the advances are dependent on the availability of a vast amount of dense…
In the DP-model, gravity-related spontaneous wave function collapses suppress undesirable Schr\"odinger Cat states. We derive the equations of the model for the hydrodynamic-elastic (acoustic) modes in a bulk. Two particular features are…
We compute the critical density of collapse for spherically symmetric overdensities in a class of f(R) modified gravity models. For the first time we evolve the Einstein, scalar field and non-linear fluid equations, making the minimal…
Diffusion models generate high-dimensional data with remarkable quality, yet how their training efficiently learns the score function, bypassing the curse of dimensionality when data is supported on low-dimensional manifolds, remains…
Deep learning models are trained to minimize the error between the model's output and the actual values. The typical cost function, the Mean Squared Error (MSE), arises from maximizing the log-likelihood of additive independent, identically…
A relativistic version of the Schr{\"o}dinger-Newton equation is analyzed within the recently proposed Grave de Peralta approach [L. Grave de Peralta, {\em Results Phys.} {\bf 18} (2020) 103318], which include relativistic effects by a…