Space-time sampling strategies for electronically steerable incoherent scatter radar

Incoherent scatter radar (ISR) systems allow researchers to peer into the ionosphere via remote sensing of intrinsic plasma parameters. ISR sensors have been used since the 1950s and until the past decade were mainly equipped with a single mechanically steerable antenna. As such, the ability to develop a two or three dimensional picture of the plasma parameters in the ionosphere has been constrained by the relatively slow mechanical steering of the antennas. A newer class of systems using electronically steerable array (ESA) antennas have broken the chains of this constraint, allowing researchers to create 3-D reconstructions of plasma parameters. There have been many studies associated with reconstructing 3-D fields of plasma parameters, but there has not been a systematic analysis into the sampling issues that arise. Also, there has not been a systematic study as to how to reconstruct these plasma parameters in an optimum sense as opposed to just using different forms of interpolation. The research presented here forms a framework that scientists and engineers can use to plan experiments with ESA ISR capabilities and to better analyze the resulting data. This framework attacks the problem of space-time sampling by ESA ISR systems from the point of view of signal processing, simulation and inverse theoretic image reconstruction. We first describe a physics based model of incoherent scatter from the ionospheric plasma, along with processing methods needed to create the plasma parameter measurements. Our approach leads to development of the space-time ambiguity function, forming a theoretical foundation of the forward model for ISR. This forward model is novel in that it takes into account the shape of the antenna beam and scanning method along with integration time to develop the proper statistics for a desired measurement precision. Once the forward model is developed, we present the simulation method behind the Simulator for ISR (SimISR). SimISR uses input plasma parameters over space and time and creates complex voltage samples in a form similar to that produced by a real ISR system. SimISR allows researchers to evaluate different experiment configurations in order to efficiently and accurately sample specific phenomena. We present example simulations using input conditions derived from a multi-fluid ionosphere model and reconstructions using standard interpolation techniques. Lastly, methods are presented to invert the space-time ambiguity function using techniques from image reconstruction literature. These methods are tested using SimISR to quantify accurate plasma parameter reconstruction over a simulated ionospheric region

Project Tech Top study of lunar, planetary and solar topography Final report

Data acquisition techniques for information on lunar, planetary, and solar topograph

연소 환경 진단을 위한 고속 및 고정확도 화염 자발광 분광법에 관한 연구

학위논문(박사) -- 서울대학교대학원 : 공과대학 기계항공공학부, 2023. 2. 도형록.최근 연소의 효율성, 안정성 및 성능을 유지하면서 환경 문제에 대응하기 위해 많은 연소 기술이 개발되었다. 이러한 기술이 적용된 연소기를 최적으로 작동하기 위해 연소기 내부 화학 반응 영역의 가스 특성은 빠르고 정확하게 감시되고 즉각적으로 제어되어야 한다. 가스 특성을 측정하기 위해 화염 자발광 분광신호를 사용하는 화염 방출 분광법(FES, Flame Emission Spectroscopy)은 실시간으로 정확한 가스 특성 측정을 제공할 수 있는 기법의 하나다. 이는 화염 방출 분광법이 자발광을 이용한 비침입식 광학 측정으로 정확도가 높으며 광 검출 장비만을 사용하는 실험 특성으로 인해 고속 측정이 가능하기 때문이다. 그러나 정보 수집 속도를 높이기 위해 광 검출 장치의 노출 시간이 짧아짐에 따라, 화염의 자발광 분광신호의 신호 대 잡음비(SNR, Signal to Noise Ratio)가 낮아지고 FES 측정의 정확도가 감소한다. 그 뿐만 아니라 신호 대 잡음비가 높은 신호 수집이 가능하여도, 자발광으로 가스 특성을 직접 예측하는 것은 화학 발광의 완전한 화학 반응 경로 모델링이 요구되어 어렵다. 이에 따라 분광 신호로부터 가스 특성을 예측하는 것은 자발광과 가스 특성을 상호 연관시키는 보정 절차(Calibration process)에 의해 달성된다. 기존의 방법은 들뜬 상태인 화학종의 분광신호 면적 비율과 같은 국부적인 분광신호 특징을 추출하고, 이 특징의 변화를 가스 특성과 연관 지어 일대일 보정 곡선을 이용한다. 그렇지만 분광신호 특징의 변화가 항상 단조로운 것은 아니므로 보정 프로세스가 간단하지 않다. 본 논문에서는 화염 방출 분광법을 이용한 가스 특성 예측의 시간 분해능과 정확도를 개선하기 위한 체계를 제안하였다. 1) 합성곱 신경망(CNN, Convolutional Neural Network) 구조에 기반한 신호 처리와 2) 적합 직교 분해(POD, Proper Orthogonal Decomposition) 및 크리깅 기법(Kriging Method)을 포함한 차수 축소 모델(ROM, Reduced Order Model) 보정을 결합한 데이터 기반 보정 체계 기법이다. 분광신호의 적합 직교 분해 기저를 포함한 손실 함수 및 잡음이 있는 데이터와 깨끗한 신호의 데이터 쌍으로 학습되는 딥 러닝 신경망은 정보 손실을 최소화하면서 신호 대 잡음비를 향상할 수 있다. 처리된 화염 분광신호를 가스 특성에 높은 연관성을 가지는 분광신호의 직교 적합 분해 기저의 계수로 차수를 축소하고, 이 계수로 크리깅 모델을 활용하여 가스 특성 예측을 하였다. 결과적으로 제안된 보정 체계는 짧은 노출 시간을 가지는 신호를 사용하여 압력 및 연료 당량비 같은 다중 가스 특성의 예측 정확도를 향상할 수 있다. 제안된 새로운 기법을 실제 고압 메탄-공기 화염 신호에 적용하여, 고정확도 감지기로 측정된 실험값과 비교 및 제안된 기법의 정확도 및 정밀도를 분석하였다. 압력 및 연료 당량비의 특성 예측 정확도 및 정밀도는 임의의 실시간 측정을 나타내는 평가 분광 데이터(보정 체계 학습에 사용되지 않음)의 가스 특성 예측 평균 상대 오차(REP, average Relative Errors of Prediction) 및 평균 상대 표준 편차(RSD, average Relative Standard deviation)를 사용하여 정량화되었다. 제안된 방법은 메탄-공기 화염의 당량비(0.8 – 1.2)와 압력(1 – 10 bar)의 넓은 시험 범위에서 짧은 노출 시간(0.05, 0.2, 0.4 초)의 화염 자발광 신호를 이용하여 연소 조건의 가스 특성을 정확하게 예측하였다. 이를 통해 제안된 체계가 높은 정확도와 높은 시간 분해능의 화염 방출 분광법을 가능하게 할 수 있음을 확인하였다.Remarkable combustion techniques have been developed to deal with environmental issues while maintaining the efficiency, stability, and performance of combustion. To operate recent combustors optimally, the gas properties in the reaction zones should be monitored quickly and accurately as well as instantaneously controlled. Flame emission spectroscopy (FES) is one of the candidate solutions for providing accurate gas properties measurements in real-time. This is because FES is a non-intrusive optical method that uses spontaneous and instantaneous emission spectra to estimate gas properties with the simplest experimental setup utilizing only detector systems. However, as the exposure time is reduced to increase the data acquisition rate, the signal-to-noise ratio (SNR) of the flame emission spectrum decreases as well reducing the accuracy of the FES measurements. On the other hand, even if it is possible to collect signals with a high SNR, it is challenging to predict gas properties directly from the signals because of the requirement for complete chemical reaction path modeling of chemiluminescence. Therefore, predicting gas properties from emission spectra is achieved by a calibration process that correlates flame emission with gas properties. Conventional methods utilize one-to-one calibration functions by extracting local spectral features, such as band intensity ratio, and matching the features to gas properties. Nevertheless, the variations of spectral features are not always monotonic which makes the calibration process not straightforward. This study mainly discusses the framework for improving the temporal resolution and accuracy of FES for predicting gas properties. A data-driven calibration framework that combines 1) deep learning-based denoising based on the convolutional neural network (CNN) architecture as a signal preprocessor, and 2) data-driven calibration technique using a reduced order model (ROM) consisting of proper orthogonal decomposition (POD) and Kriging model is proposed. A deep learning neural network supervised on data pairs of noisy and clean signals with a loss function that utilizes POD of the spectrum can enhance the SNR of the short-gated spectra with minimal information loss. Then, the POD method with a Kriging model mapping flame emission spectrum to the target gas properties predicts the gas properties from the processed spectra. To sum up, the proposed calibration method can improve prediction accuracy of gas properties such as equivalence ratio and pressure using short-gated noisy signals. The proposed combustion diagnosis method was applied to actual spontaneous flame emission spectra in high-pressure conditions. The gas property predictions of the proposed method were compared with experimental values measured by high-precision and high-accuracy sensors to estimate the accuracy and precision of the proposed method. The prediction accuracy and precision of the proposed method were evaluated using the average relative errors of prediction (REP) and the average relative standard deviation (RSD) of the gas predictions from the test spectrum data (not used for model training). The proposed method was investigated under combustion conditions in broad test ranges of equivalence ratio (0.8 – 1.2) and pressure (1 – 10 bar) of methane-air flame, taking a short-gated (0.05, 0.2, and 0.4 s) flame emission spectrum with low SNR as input. It was confirmed that the proposed framework enables flame emission spectroscopy to achieve high accuracy and fast temporal resolution.Chapter 1. Introduction 1 1.1 Background and Motivation 1 1.2 Previous studies 8 1.2.1 Mechanism of flame emission 8 1.2.2 Calibration process of flame emission spectroscopy (FES) 16 1.2.3 Deep learning-based denoising 17 1.3 Outline of the Dissertation 22 1.4 Contributions 24 Chapter 2. Experimental result of flame emission 26 2.1 Flame emission measurement 26 2.1.1 Experimental setup 26 2.1.2 Uncertainty quantification of experimental measurement 32 2.1.3 Noise analysis of spectral signal 34 2.2 Computational simulation of flame properties 37 2.3 Characteristics of flame emission 39 Chapter 3. Development of Data-driven Calibration Process 47 3.1 Overview of data-driven calibration process 47 3.2 Calibration framework based on data-driven approach 49 3.2.1 Training and test dataset 52 3.2.2 Proper orthogonal decomposition (POD) 53 3.2.3 Kriging model 56 3.2.4 Global sensitivity analysis (GSA): Sobol sensitivity indices 59 3.2.5 Evaluation of accuracy and precision of calibration process 61 3.3 Validation of data-driven calibration process 63 3.3.1 POD of flame emission spectra 63 3.3.2 Parametric study using Global sensitivity analysis (GSA) 68 3.3.3 Validation of Kriging model 71 3.4 Results of data-driven calibration process 73 3.4.1 Calibration result on experimental data 73 3.4.2 Wavelength range effect on calibration accuracy 78 3.4.3 Calibration result on simulation data 80 Chapter 4. Development of Deep Learning-Based Denoising 83 4.1 Overview of fast time-resolved FES 83 4.2 Deep learning-based denoising process 86 4.2.1 Training and test dataset 86 4.2.2 Neural network architecture 88 4.2.3 Loss function 93 4.3 Results of data processing 94 4.3.1 Denoising with the proposed CNN 94 4.3.2 Neural network architecture and loss function 100 4.3.3 Computational Efficiency 107 Chapter 5. Framework for fast time-resolved and high accuracy FES 111 5.1 Overview of proposed framework 111 5.2 Fast time-resolved and high accuracy FES 115 5.2.1 Calibration and prediction of gas properties 115 5.2.2 Neural network architecture and loss function 118 5.2.3 Hyperparameter search 123 5.2.4 Noise level sensitivity analysis 128 5.2.5 Exposure time 131 Chapter 6. Conclusions 133 APPENDIX A 137 A.1 Result of computational simulation 137 A.2 Denoising performance of neural networks with MSE loss 140 A.3 Denoising performance of neural networks with MSE and POD loss 143 A.4 Calibration and prediction performance of neural network architecture trained by MSE and POD loss 146 REFERENCES 153 ABSTRACT (KOREAN) 160박

Velocity Dispersion Measurements of Milky Way Globular Clusters with VLT/X-shooter Spectroscopy

We have observed 29 globular clusters in the Magellanic Clouds and the Milky Way with VLT/X-shooter, a spectrograph with an exceptionally large spectral range from the ultraviolet to the near-infrared at moderately high resolution. The observations have been performed in drift-scan mode, where the telescope is slewed across the cluster during integration. Our comprehensive cascade of reduction steps allows for an uncertainty less than 10% in the absolute flux calibration and less than 0.02 angstrom in the wavelength calibration of the reduced spectra. For a subset of eleven clusters, for which accurate Hubble Space Telescope photometry is available, we construct detailed synthetic composite spectra based on their stellar populations, and subsequently use them as spectral templates to measure the velocity dispersion profile and radial velocity profile for each cluster. The obtained radial velocities indicate ordered rotation of some clusters. We use the central velocity dispersions to compute the dynamical masses and mass-to-light ratios for our sample. The sample median mass-to-light ratio is 1.7 M_sun / L_sun and fully consistent with a cluster mass that is entirely made up of stars and their remnants. In conjunction with our kinematic results follow-up numerical simulations will help to constrain the cluster mass profiles

Asteroseismology and Interferometry

Asteroseismology provides us with a unique opportunity to improve our understanding of stellar structure and evolution. Recent developments, including the first systematic studies of solar-like pulsators, have boosted the impact of this field of research within Astrophysics and have led to a significant increase in the size of the research community. In the present paper we start by reviewing the basic observational and theoretical properties of classical and solar-like pulsators and present results from some of the most recent and outstanding studies of these stars. We centre our review on those classes of pulsators for which interferometric studies are expected to provide a significant input. We discuss current limitations to asteroseismic studies, including difficulties in mode identification and in the accurate determination of global parameters of pulsating stars, and, after a brief review of those aspects of interferometry that are most relevant in this context, anticipate how interferometric observations may contribute to overcome these limitations. Moreover, we present results of recent pilot studies of pulsating stars involving both asteroseismic and interferometric constraints and look into the future, summarizing ongoing efforts concerning the development of future instruments and satellite missions which are expected to have an impact in this field of research.Comment: Version as published in The Astronomy and Astrophysics Review, Volume 14, Issue 3-4, pp. 217-36

Polarimetric weather radar:from signal processing to microphysical retrievals

Accurate modelling of liquid, solid and mixed-phase precipitation requires a thorough understanding of phenomena occurring at various spatial and temporal scales. At the smallest scales, precipitation microphysics defines all the processes occurring at the level where precipitation is a discrete process. The knowledge of these microphysical processes originates from the interpretation of snowfall and rainfall measurements collected with various sensors. Direct sampling, performed with in-situ instruments, provides data of superior quality. However, the development of remote sensing (and dual-polarization radar in particular) offers a noteworthy alternative: large domains can in fact be sampled in real time and with a single instrument. The drawback is obviously the fact that radars measure precipitation indirectly. Only through appropriate interpretation radar data can be translated into physical mechanisms of precipitation. This thesis contributes to the effort to decode polarimetric radar measurements into microphysical processes or microphysical quantities that characterize precipitation. The first part of the work is devoted to radar data processing. In particular, it focuses on how to obtain high resolution estimates of the specific differential phase shift, a very important polarimetric variable with significant meteorological importance. Then, hydrometeor classification, i.e. the first qualitative microphysical aspect that may come to mind, is tackled and two hydrometeor classification methods are proposed. One is designed for polarimetric radars and one for an in-situ instrument: the two-dimensional video disdrometer. These methods illustrate the potential that supervised and unsupervised techniques can have for the interpretation of meteorological measurements. The combination of in-situ measurements and polarimetric data (including hydrometeor classification) is exploited in the last part of the thesis, devoted to the microphysics of snowfall and in particular of rimed precipitation. Riming is shown to be an important factor leading to significant accumulation of snowfall in the alpine environment. Additionally, the vertical structure of rimed precipitation is examined and interpreted

Probing the connection between the intergalactic medium and galaxies with quasar absorption-line spectroscopy

In this thesis, we examine the relationship between the metal-enriched intergalactic medium (IGM) and galaxies at z < 1. In particular, we investigate the nature and consequence of feedback from active galactic nuclei (AGN) and supernovae, which shape the evolution of galaxies and are responsible for enriching the IGM with metals. The IGM is surveyed in ultraviolet (UV) absorption lines against background quasars (QSOs), whilst galaxies are surveyed in emission by means of optical photometry and spectroscopy. Simulated samples of IGM absorption systems and galaxies are also extracted from the Evolution and Assembly of GaLaxies and their Environments (EAGLE) cosmological hydrodynamical simulation for critical comparison with the data. We present the results of two primary studies that are designed to address key questions on the nature and consequence of feedback: 1. We examine complex absorption profiles in the spectrum of a QSO at z ~ 1, that trace a metal-rich outflow originating from the host galaxy. We show that these absorption profiles originate from dense, sub-pc scale gas clumps at distances of a few kpc from the central AGN. The gas is likely to be dynamically unstable, and is potentially far from ionization equilibrium. We favour a scenario in which the clumps are formed in-situ, and are entrained in a hot (T > 10^6 K) outflowing wind that may trace the majority of the mass, but is undetected in the UV. These observations provide a detailed set of constraints on the nature of feedback in QSO host galaxies. 2. We investigate the distribution and dynamics of metal-enriched gas around galaxies at z < 1 through the two-point cross- and auto-correlation functions of OVI absorbers and galaxies (abridged

Simulating 3D Radiation Transport, a modern approach to discretisation and an exploration of probabilistic methods

Light, or electromagnetic radiation in general, is a profound and invaluable resource to investigate our physical world. For centuries, it was the only and it still is the main source of information to study the Universe beyond our planet. With high-resolution spectroscopic imaging, we can identify numerous atoms and molecules, and can trace their physical and chemical environments in unprecedented detail. Furthermore, radiation plays an essential role in several physical and chemical processes, ranging from radiative pressure, heating, and cooling, to chemical photo-ionisation and photo-dissociation reactions. As a result, almost all astrophysical simulations require a radiative transfer model. Unfortunately, accurate radiative transfer is very computationally expensive. Therefore, in this thesis, we aim to improve the performance of radiative transfer solvers, with a particular emphasis on line radiative transfer. First, we review the classical work on accelerated lambda iterations and acceleration of convergence, and we propose a simple but effective improvement to the ubiquitously used Ng-acceleration scheme. Next, we present the radiative transfer library, Magritte: a formal solver with a ray-tracer that can handle structured and unstructured meshes as well as smoothed-particle data. To mitigate the computational cost, it is optimised to efficiently utilise multi-node and multi-core parallelism as well as GPU offloading. Furthermore, we demonstrate a heuristic algorithm that can reduce typical input models for radiative transfer by an order of magnitude, without significant loss of accuracy. This strongly suggests the existence of more efficient representations for radiative transfer models. To investigate this, we present a probabilistic numerical method for radiative transfer that naturally allows for uncertainty quantification, providing us with a mathematical framework to study the trade-off between computational speed and accuracy. Although we cannot yet construct optimal representations for radiative transfer problems, we point out several ways in which this method can lead to more rigorous optimisation