Related papers: Recovering Intrinsic Pulsar Profiles and Scatterin…
The stable rotation of young pulsars is often interrupted by two non-deterministic phenomena: glitches and red timing noise. Timing noise provides insights into plasma and nuclear physics under extreme conditions. The framework leverages…
New developments in quantitative sputter depth profiling during the past ten years are reviewed, with special emphasis on the experimental achievement of ultrahigh depth resolution (below 2 nm for sputtered depths larger than 10 nm). In…
We are concerned with the inverse scattering problems associated with incomplete measurement data. It is a challenging topic of increasing importance in many practical applications. Based on a prototypical working model, we propose a…
The strict periodicity of pulsars is the primary source of information we have to learn about their nature and environment, it allows us to challenge general relativity and measure gravitational waves. Identifying such a periodicity from a…
Extracting precise pulse times of arrival (TOAs) and their uncertainties is the first and most fundamental step in high-precision pulsar timing. In the classical method, TOAs are derived from total intensity pulse profiles of pulsars via…
We consider the problem of recovering signals from their power spectral density. This is a classical problem referred to in literature as the phase retrieval problem, and is of paramount importance in many fields of applied sciences. In…
We develop a physics-informed neural networks (PINNs) framework for the inverse scattering problem in nuclear physics and apply it to the $P_{3/2}$ partial wave of neutron-alpha elastic scattering. The radial potential is represented by a…
In this study we used Hydrus-1D to simulate water infiltration from a ring infiltrometer. We generated water content profiles at each time step of infiltration, based on a particular value of the saturated hydraulic conductivity while…
We consider an inverse acoustic scattering problem in simultaneously recovering an embedded obstacle and its surrounding inhomogeneous medium by formally determined far-field data. It is shown that the knowledge of the scattering amplitude…
We propose a novel Bayesian optimization (BO) procedure aimed at identifying the ``profile optima'' of a deterministic black-box computer simulation that has a single control parameter and multiple nuisance parameters. The profile optima…
Spectral clustering algorithms typically require a priori selection of input parameters such as the number of clusters, a scaling parameter for the affinity measure, or ranges of these values for parameter tuning. Despite efforts for…
We develop a novel algorithm for large-scale holographic reconstruction of 3D particle fields. Our method is based on a multiple-scattering beam propagation method (BPM) combined with sparse regularization that enables recovering dense 3D…
In this work, we investigate sensing parameter estimation in the presence of clutter in perceptive mobile networks (PMNs) that integrate radar sensing into mobile communications. Performing clutter suppression before sensing parameter…
This paper investigates the application of a data-driven technique based on retrospective cost optimization to optimize the frequency of mass injection into an S-shaped diffuser, with the objective of maximizing the pressure recovery.…
In this paper, we present a deep learning (DL) algorithm for channel estimation in communication systems. We consider the time-frequency response of a fast fading communication channel as a two-dimensional image. The aim is to find the…
Clustering algorithms can help reconstruct the assembly history of the Milky Way by identifying groups of stars sharing similar properties in a kinematical or chemical abundance space. Despite being promising tools, their efficiency has not…
Matrix recovery from sparse observations is an extensively studied topic emerging in various applications, such as recommendation system and signal processing, which includes the matrix completion and compressed sensing models as special…
We introduce pinta, a pipeline for reducing the upgraded Giant Metre-wave Radio Telescope (uGMRT) raw pulsar timing data, developed for the Indian Pulsar Timing Array experiment. We provide a detailed description of the workflow and usage…
Combining multi-spectral satellite data and machine learning has been suggested as a method for monitoring plastic pollutants in the ocean environment. Recent studies have made theoretical progress regarding the identification of marine…
In this article, a time-domain calibration procedure is proposed for pulsed Terahertz Integrated Circuits (TIC) used in on-chip applications, where the conventional calibration methods are not applicable. The proposed post-detection method…