Validation of the HERA Phase I Epoch of Reionization 21 cm Power Spectrum Software Pipeline

We describe the validation of the HERA Phase I software pipeline by a series of modular tests, building up to an end-to-end simulation. The philosophy of this approach is to validate the software and algorithms used in the Phase I upper-limit analysis on wholly synthetic data satisfying the assumptions of that analysis, not addressing whether the actual data meet these assumptions. We discuss the organization of this validation approach, the specific modular tests performed, and the construction of the end-to-end simulations. We explicitly discuss the limitations in scope of the current simulation effort. With mock visibility data generated from a known analytic power spectrum and a wide range of realistic instrumental effects and foregrounds, we demonstrate that the current pipeline produces power spectrum estimates that are consistent with known analytic inputs to within thermal noise levels (at the 2σ level) for k > 0.2h Mpc−1 for both bands and fields considered. Our input spectrum is intentionally amplified to enable a strong “detection” at k ∼ 0.2 h Mpc−1—at the level of ∼25σ—with foregrounds dominating on larger scales and thermal noise dominating at smaller scales. Our pipeline is able to detect this amplified input signal after suppressing foregrounds with a dynamic range (foreground to noise ratio) of ≳107. Our validation test suite uncovered several sources of scale-independent signal loss throughout the pipeline, whose amplitude is well-characterized and accounted for in the final estimates. We conclude with a discussion of the steps required for the next round of data analysis.

Transformation of Water Wave Spectra into Time Series of Surface Elevation

Spectral wave modelling is widely used to simulate large-scale wind–wave processes due to its low computation cost and relatively simpler formulation, in comparison to phase-resolving or hydrodynamic models. However, some applications require a time-domain representation of sea waves. This article proposes a methodology to transform the wave spectrum into a time series of water surface elevation for applications that require a time-domain representation of ocean waves. The proposed method uses a generated phase spectrum and the inverse Fourier transform to turn the wave spectrum into a time series of water surface elevation. The consistency of the methodology is then verified. The results show that it is capable of correctly transforming the wave spectrum, and the significant wave height of the resulting time series is within 5% of that of the input spectrum.

Optimization of GEM-based detector readout electrode structure for SXR imaging of tokamak plasma

The paper presents an optimization of a readout structure of the GEM-based detector designed for X-ray imaging for DTT tokamak in the energy range of 2–15 keV. The readout electrode of approximately 100 cm2 surface is composed of hexagonal pixels connected in a way that allows reducing the actual number of signal pixels (electronics channels). At the same time, based on time coincidence analysis, it makes possible to unambiguously identify the position of the recorded X-ray photon. For the input spectrum, the Detective Quantum Efficiency (DQE) of the detector was calculated using the Geant4 program and the spatial distributions of electron avalanches at the readout electrode were simulated using the Garfield++ program. These were conducted for a given energy range of radiation and a statistical distribution consistent with the shape of the spectrum considering the DQE of the detector. As a result, the size of a single hexagonal pixel was proposed to capture the position of the recorded radiation quanta in an optimal and effective way.

The Application of Silicon-Filtered Beam in the Validation of Iron Cross Sections by Deep Penetration Experiments

This paper summarizes the issue of the validation of the silicon-filtered neutron beam transport in the deep neutron transport penetration experiment in iron. Iron is an essential structural material important for nuclear technology. The use of a silicon-filtered beam is a very interesting method because some significant peaks occur in the spectrum, helping to study selected wide energy regions during the deep neutron transport in the iron. The detailed characterization of the silicon-filtered beam has been performed in the past as well. Therefore, the input spectrum for the penetration experiments is well-known. The character of the input spectrum is reflecting the fine structure of the silicon cross section in region 1–8 MeV. Based on the agreement between calculated and measured attenuation in groups located within the neutron flux peaks, one can reveal possible problems in neutron transport description. The results are confirming satisfactory agreement of neutron transport description in ENDF/B-VII.1 in the majority of energy regions, while in the interval 4.7–6 MeV, underprediction in attenuation can be observed. This seems to be a consequence of discrepancies in the angular distribution of scattered neutrons. These results constitute an advance to previously performed integral experiments characterizing the neutron transport in iron using 252Cf(s.f) and 235U(nth;fiss).

Fast Search Method Based on Vector Quantization for Raman Spectroscopy Identification

In spectroscopy, matching a measured spectrum to a reference spectrum in a large database is often computationally intensive. To solve this problem, we propose a novel fast search algorithm that finds the most similar spectrum in the database. The proposed method is based on principal component transformation and provides results equivalent to the traditional full search method. To reduce the search range, hierarchical clustering is employed, which divides the spectral data into multiple clusters according to the similarity of the spectrum, allowing the search to start at the cluster closest to the input spectrum. Furthermore, a pilot search was applied in advance to further accelerate the search. Experimental results show that the proposed method requires only a small fraction of the computational complexity required by the full search, and it outperforms the previous methods.

Intensity Simulation of a Fourier Transform Infrared Spectrometer

This paper introduces an intensity simulation for the Fourier transform infrared spectrometer whose core element is the Michelson interferometer to provide support for the on-orbit monitoring of the instrument and to improve the data processing and application of the Fourier transform spectrometer. The Geostationary Interferometric Infrared Imager (GIIRS) aboard on Fengyun-4B (FY-4B) satellite, which will be launched in 2020, aims to provide hyperspectral infrared observations. An intensity simulation of the Michelson interferometer based on the GIIRS’s instrument parameters is systematically analyzed in this paper. Off-axis effects and non-linearity response are two important factors to be considered in this simulation. Off-axis effects mainly cause the wavenumber shift to induce a large brightness temperature error compared with the input spectrum, and the non-linearity response reduces the energy received by the detector. Then, off-axis effects and a non-linearity response are added to the input spectrum successively to obtain the final spectrum. Off-axis correction and non-linearity correction are also developed to give a full simulation process. Comparing the corrected spectrum with the input spectrum, we can see that the brightness temperature errors have a magnitude of 10−3 K, and this fully proves the reliability and rationality of the whole simulation process.

Compressive Sensing Spectroscopy Using a Residual Convolutional Neural Network

Compressive sensing (CS) spectroscopy is well known for developing a compact spectrometer which consists of two parts: compressively measuring an input spectrum and recovering the spectrum using reconstruction techniques. Our goal here is to propose a novel residual convolutional neural network (ResCNN) for reconstructing the spectrum from the compressed measurements. The proposed ResCNN comprises learnable layers and a residual connection between the input and the output of these learnable layers. The ResCNN is trained using both synthetic and measured spectral datasets. The results demonstrate that ResCNN shows better spectral recovery performance in terms of average root mean squared errors (RMSEs) and peak signal to noise ratios (PSNRs) than existing approaches such as the sparse recovery methods and the spectral recovery using CNN. Unlike sparse recovery methods, ResCNN does not require a priori knowledge of a sparsifying basis nor prior information on the spectral features of the dataset. Moreover, ResCNN produces stable reconstructions under noisy conditions. Finally, ResCNN is converged faster than CNN.

Performance Barriers for Single-Degree-of-Freedom Energy Harvesters Under Generic Stochastic Excitation

We develop performance criteria for the objective comparison of different classes of single-degree-of-freedom oscillators under stochastic excitation. For each family of oscillators, these objective criteria take into account the maximum possible energy harvested for a given response level, which is a quantity that is directly connected to the size of the harvesting configuration. We prove that the derived criteria are invariant with respect to magnitude or temporal rescaling of the input spectrum and they depend only on the relative distribution of energy across different harmonics of the excitation. We then compare three different classes of linear and nonlinear oscillators and using stochastic analysis tools we illustrate that in all cases of excitation spectra (monochromatic, broadband, white-noise) the optimal performance of all designs cannot exceed the performance of the linear design.