Related papers: BeyondPlanck VI. Noise characterization and modell…
A pulsar's pulse profile gets broadened at low frequencies due to dispersion along the line of sight or due to multi-path propagation. The dynamic nature of the interstellar medium makes both of these effects time-dependent and introduces…
We investigate phase and frequency estimation with different measurement strategies under the effect of collective phase noise. First, we consider the standard linear estimation scheme and present an experimentally realisable optimization…
We explore the use of Gibbs sampling in estimating the noise properties of individual pulsars and illustrate its effectiveness using the NANOGrav 11-year data set. We find that Gibbs sampling noise modeling (GM) is more efficient than the…
Due to the sheer complexity of the Laser Interferometer Space Antenna (LISA) space mission, data gaps arising from instrumental irregularities and/or scheduled maintenance are unavoidable. Focusing on merger-dominated massive black hole…
We propose a general method to carry out a valid Bayesian analysis of a finite-dimensional `targeted' parameter in the presence of a finite-dimensional nuisance parameter. We apply our methods to causal inference based on estimating…
We present a method of subtracting the foreground contamination for the measurement of CMB polarization. We calculate the resultant errors on CMB polarization and temperature-polarization cross correlation power spectra for the high…
In recent years, decentralized sensor networks have garnered significant attention in the field of state estimation owing to enhanced robustness, scalability, and fault tolerance. Optimal fusion performance can be achieved under fully…
We have constructed timing solutions for 81 gamma-ray pulsars covering more than five years of Fermi data. The sample includes 37 radio-quiet or radio-faint pulsars which cannot be timed with other telescopes. These timing solutions and the…
The Low Frequency Instrument on board the PLANCK satellite is designed to give the most accurate map ever of the CMB anisotropy of the whole sky over a broad frequency band spanning 27 to 77 GHz. It is made of an array of 22…
We investigated the use of the Bayesian inference to restore noise-degraded images under conditions of spatially correlated noise. The generative statistical models used for the original image and the noise were assumed to obey…
We describe the processing of the 531 billion raw data samples from the High Frequency Instrument (hereafter HFI), which we performed to produce six temperature maps from the first 473 days of Planck-HFI survey data. These maps provide an…
This paper studies the problem of parameter estimation in resonant, acoustic fluid-structure interaction problems over a wide frequency range. Problems with multiple resonances are known to be subjected to local minima, which represents a…
Self-heterodyne beat note measurements are widely used for the experimental characterization of the frequency noise power spectral density (FN-PSD) and the spectral linewidth of lasers. The measured data, however, must be corrected for the…
This paper describes the processing applied to the Planck High Frequency Instrument (HFI) cleaned, time-ordered information to produce photometrically calibrated maps in temperature and (for the first time) in polarization. The data from…
The Gaussian phase noise of intensity time series is demonstrated to be drastically reduced when the raw voltage data are digitally filtered through an arbitrarily large number $n$ of orthornormal bandpass profiles (eigen-filters) sharing…
Bayesian Neural Networks with Latent Variables (BNN+LVs) capture predictive uncertainty by explicitly modeling model uncertainty (via priors on network weights) and environmental stochasticity (via a latent input noise variable). In this…
This study presents a nonlinear signal processing method for accurate radar-based heartbeat interval estimation by exploiting the periodicity of higher-order harmonics inherent in heartbeat signals. Unlike conventional approaches that…
This study proposes a novel approach to quantifying uncertainties of constitutive relations inferred from noisy experimental data using inverse modelling. We focus on electrochemical systems in which charged species (e.g., Lithium ions) are…
Existing learning-based denoising methods typically train models to generalize the image prior from large-scale datasets, suffering from the variability in noise distributions encountered in real-world scenarios. In this work, we propose a…
Wide-bandgap (WBG) technologies offer unprecedented improvements in power system efficiency, size, and performance, but also introduce unique sensor corruption and cybersecurity risks in industrial control systems (ICS), particularly due to…