Search

In 21-cm experimental cosmology, accurate characterization of a radio telescope's antenna beam response is essential to measure the 21-cm signal. Computational electromagnetic (CEM) simulations estimate the antenna beam pattern and frequency response by subjecting the EM model to different dependencies, or beam hyper-parameters, such as soil dielectric constant or orientation with the environment. However, it is computationally expensive to search all possible parameter spaces to optimize the antenna design or accurately represent the beam to the level required for use as a systematic model in 21-cm cosmology. We therefore present $\texttt{MEDEA}$, an emulator which rapidly and accurately generates farfield radiation patterns over a large hyper-parameter space. $\texttt{MEDEA}$ takes a subset of beams simulated by CEM software, spatially decomposes them into coefficients in a complete, linear basis, and then interpolates them to form new beams at arbitrary hyper-parameters. We test $\texttt{MEDEA}$ on an analytical dipole and two numerical beams motivated by upcoming lunar lander missions, and then employ $\texttt{MEDEA}$ as a model to fit mock radio spectrometer data to extract covariances on the input beam hyper-parameters. We find that the interpolated beams have RMS relative errors of at most $10^{-2}$ using 20 input beams or less, and that fits to mock data are able to recover the input beam hyper-parameters when the model and mock derive from the same set of beams. When a systematic bias is introduced into the mock data, extracted beam hyper-parameters exhibit bias, as expected. We propose several future extensions to $\texttt{MEDEA}$ to potentially account for such bias., Comment: 23 pages, 12 figures, accepted for publication in ApJ

We study a pair of budget- and performance-constrained weak submodular maximization problems. For computational efficiency, we explore the use of stochastic greedy algorithms which limit the search space via random sampling instead of the standard greedy procedure which explores the entire feasible search space. We propose a pair of stochastic greedy algorithms, namely, Modified Randomized Greedy (MRG) and Dual Randomized Greedy (DRG) to approximately solve the budget- and performance-constrained problems, respectively. For both algorithms, we derive approximation guarantees that hold with high probability. We then examine the use of DRG in robust optimization problems wherein the objective is to maximize the worst-case of a number of weak submodular objectives and propose the Randomized Weak Submodular Saturation Algorithm (Random-WSSA). We further derive a high-probability guarantee for when Random-WSSA successfully constructs a robust solution. Finally, we showcase the effectiveness of these algorithms in a variety of relevant uses within the context of Earth-observing LEO constellations which estimate atmospheric weather conditions and provide Earth coverage., Comment: 36 pages, 5 figures. A preliminary version of this article was presented at the 2023 American Control Conference (ACC). This version was submitted to Automatica

Near Infrared (NIR) spectroscopy is widely used in industrial quality control and automation to test the purity and grade of items. In this research, we propose a novel sensorized end effector and acquisition strategy to capture spectral signatures from objects and register them with a 3D point cloud. Our methodology first takes a 3D scan of an object generated by a time-of-flight depth camera and decomposes the object into a series of planned viewpoints covering the surface. We generate motion plans for a robot manipulator and end-effector to visit these viewpoints while maintaining a fixed distance and surface normal. This process is enabled by the spherical motion of the end-effector and ensures maximal spectral signal quality. By continuously acquiring surface reflectance values as the end-effector scans the target object, the autonomous system develops a four-dimensional model of the target object: position in an $R^3$ coordinate frame, and a reflectance vector denoting the associated spectral signature. We demonstrate this system in building spectral-spatial object profiles of increasingly complex geometries. We show the proposed system and spectral acquisition planning produce more consistent spectral signals than naive point scanning strategies. Our work represents a significant step towards high-resolution spectral-spatial sensor fusion for automated quality assessment., Comment: Presented at IROS 2024

We call for a theory of the particle-scale structure of materials that is based on the general notion of information rather than its special case of symmetry. An inherent limitation to the symmetry-based understanding of structure is described. The rapid decay in interaction strength with interparticle distance is used to argue for the representability of a system locally through the neighbourhoods of its constituent particles. The extracopularity coefficient E is presented as a local quantifier of information and compared to point group order |G|, a local quantifier of symmetry. The former is found to have nearly double the resolution of the latter for a set of commonly encountered coordination geometries. A proof is given for the generality of extracopularity over point symmetry. Some practical challenges and future perspectives are discussed., Comment: 6 pages, 4 figure. Title changed in this version. Content updated based on reviewer suggestions

In materials science, a wide range of properties of materials are governed by various types of energies, including thermal, physicochemical, structural, and mechanical energies. In 2005, Dr. Frans Spaepen used crystalline face-centered-cubic (fcc) copper as an example to discuss a variety of phenomena that are associated with energies. Inspired by his pioneering work, we broaden our analysis to include a selection of representative pure metals with fcc, hexagonal close-packed (hcp), and body-centered cubic (bcc) structures. Additionally, we extend our comparison to energies between pure metals and equiatomic binary, ternary, and multi-principal element alloys (sometimes also known as high-entropy alloys). Through an extensive collection of data and calculations, we compile energy tables that provide a comprehensive view of how structure and alloying influence the energy profiles of these metals and alloys. We highlight the significant impact of constituent elements on the energies of alloys compared to pure metals and reveal a notable disparity in mechanical energies among materials in fcc-, hcp- and bcc-structured metals and alloys. Furthermore, we discuss the underlying mechanisms behind these patterns and discuss the implications for structural transformations, providing insights into the broader context of these energy variations.

A Technical Reference for Autonomous Vehicles (AVs), with part 1 focusing on basic behaviour guidelines (TR68-1) is published with the intent to be a reference for evaluation of appropriated behaviour on Autonomous Vehicles for Singapore. This is based on applicability from Basic Theory of Driving (BTD) and Final Theory of Driving (FTD) which are the traffic code/rules for human driving. This report contains a consolidation of current guidelines from TR68-1, BTD and FTD. It will allow an initial identification of missing guidelines for AV behaviour on roads; however, it is difficult to identify conflicting rules or gaps in guidance without going into identified traffic situations. Identified situations for analysis were chosen from Centre of Excellence for Testing & Research of Autonomous Vehicle (CETRAN) assessment experience for further investigation. The outcome of the report proposes additional behaviour characteristics and guidelines to situations identified to close the gap between assessors and developers on expected AV behaviour. These recommendations could improve current guidelines for AV behavioural in assessment and generally for the local AV ecosystem for urban tropical roads in Singapore. These recommendations could also serve as inputs for future TR 68-1 revisions where a sample set of reference situations can help to define clearer expectations or requirements for AV behaviour in those situations. This will help Singapore push forward in better definition of the expected AV behaviour for AV systems., Comment: 69 pages

Although neural-network-based emulators enable efficient parameter estimation in 21-cm cosmology, the accuracy of such constraints is poorly understood. We employ nested sampling to fit mock data of the global 21-cm signal and high-$z$ galaxy ultraviolet luminosity function (UVLF) and compare for the first time the emulated posteriors obtained using the global signal emulator ${\tt globalemu}$ to the `true' posteriors obtained using the full model on which the emulator is trained using ${\tt ARES}$. Of the eight model parameters we employ, four control the star formation efficiency (SFE), and thus can be constrained by UVLF data, while the remaining four control UV and X-ray photon production, and the minimum virial temperature of star-forming halos ($T_{\rm min}$), and thus are uniquely probed by reionization and 21-cm measurements. For noise levels of 50 and 250 mK in the 21-cm data being jointly-fit, the emulated and `true' posteriors are consistent to within $1\sigma$. However, at lower noise levels of 10 and 25 mK, ${\tt globalemu}$ overpredicts $T_{\rm min}$ and underpredicts $\gamma_{\rm lo}$, an SFE parameter, by $\approx3-4\sigma$, while the `true' ${\tt ARES}$ posteriors capture their fiducial values within $1\sigma$. We find that jointly-fitting the mock UVLF and 21-cm data significantly improves constraints on the SFE parameters by breaking degeneracies in the ${\tt ARES}$ parameter space. Our results demonstrate the astrophysical constraints that can be expected for global 21-cm experiments for a range of noise levels from pessimistic to optimistic, and also the potential for probing redshift evolution of SFE parameters by including UVLF data., Comment: 19 pages, 8 figures, 2 tables. Accepted by ApJ

LiDAR (Light Detection and Ranging) is a useful sensing technique and an important source of data for autonomous vehicles (AVs). In this publication we present the results of a study undertaken to understand the impact of automotive paint on LiDAR performance along with a methodology used to conduct this study. Our approach consists of evaluating the average reflected intensity output by different LiDAR sensor models when tested with different types of automotive paints. The paints were chosen to represent common paints found on vehicles in Singapore. The experiments were conducted with LiDAR sensors commonly used by autonomous vehicle (AV) developers and OEMs. The paints used were also selected based on those observed in real-world conditions. This stems from a desire to model real-world performance of actual sensing systems when exposed to the physical world. The goal is then to inform regulators of AVs in Singapore of the impact of automotive paint on LiDAR performance, so that they can determine testing standards and specifications which will better reflect real-world performance and also better assess the adequacy of LiDAR systems installed for local AV operations. The tests were conducted for a combination of 13 different paint panels and 3 LiDAR sensors. In general, it was observed that darker coloured paints have lower reflection intensity whereas lighter coloured paints exhibited higher intensity values., Comment: 23 pages, 29 figures. This white paper was developed with support from the Urban Mobility Grand Challenge Fund by the Land Transport Authority of Singapore (No. UMGC-L010). For associated dataset, see https://researchdata.ntu.edu.sg/dataset.xhtml?persistentId=doi:10.21979/N9/CGDKMZ

Hyperspectral sensors have enjoyed widespread use in the realm of remote sensing; however, they must be adapted to a format in which they can be operated onboard mobile robots. In this work, we introduce a first-of-its-kind system architecture with snapshot hyperspectral cameras and point spectrometers to efficiently generate composite datacubes from a robotic base. Our system collects and registers datacubes spanning the visible to shortwave infrared (660-1700 nm) spectrum while simultaneously capturing the ambient solar spectrum reflected off a white reference tile. We collect and disseminate a large dataset of more than 500 labeled datacubes from on-road and off-road terrain compliant with the ATLAS ontology to further the integration and demonstration of hyperspectral imaging (HSI) as beneficial in terrain class separability. Our analysis of this data demonstrates that HSI is a significant opportunity to increase understanding of scene composition from a robot-centric context. All code and data are open source online: https://river-lab.github.io/hyper_drive_data

We describe the calibration and imaging heuristics developed and deployed in the ALMA interferometric data processing pipeline, as of ALMA Cycle 9. The pipeline software framework is written in Python, with each data reduction stage layered on top of tasks and toolkit functions provided by the Common Astronomy Software Applications package. This framework supports a variety of tasks for observatory operations, including science data quality assurance, observing mode commissioning, and user reprocessing. It supports ALMA and VLA interferometric data along with ALMA and NRO45m single dish data, via different stages and heuristics. In addition to producing calibration tables, calibrated measurement sets, and cleaned images, the pipeline creates a WebLog which serves as the primary interface for verifying the data quality assurance by the observatory and for examining the contents of the data by the user. Following the adoption of the pipeline by ALMA Operations in 2014, the heuristics have been refined through annual development cycles, culminating in a new pipeline release aligned with the start of each ALMA Cycle of observations. Initial development focused on basic calibration and flagging heuristics (Cycles 2-3), followed by imaging heuristics (Cycles 4-5), refinement of the flagging and imaging heuristics with parallel processing (Cycles 6-7), addition of the moment difference analysis to improve continuum channel identification (2020 release), addition of a spectral renormalization stage (Cycle 8), and improvement in low SNR calibration heuristics (Cycle 9). In the two most recent Cycles, 97% of ALMA datasets were calibrated and imaged with the pipeline, ensuring long-term automated reproducibility. We conclude with a brief description of plans for future additions, including self-calibration, multi-configuration imaging, and calibration and imaging of full polarization data., Comment: Accepted for publication by Publications of the Astronomical Society of the Pacific, 65 pages, 20 figures, 10 tables, 2 appendices. Small corrections and additions from the proof stage have been applied

Flare frequency distributions represent a key approach to addressing one of the largest problems in solar and stellar physics: determining the mechanism that counter-intuitively heats coronae to temperatures that are orders of magnitude hotter than the corresponding photospheres. It is widely accepted that the magnetic field is responsible for the heating, but there are two competing mechanisms that could explain it: nanoflares or Alfv\'en waves. To date, neither can be directly observed. Nanoflares are, by definition, extremely small, but their aggregate energy release could represent a substantial heating mechanism, presuming they are sufficiently abundant. One way to test this presumption is via the flare frequency distribution, which describes how often flares of various energies occur. If the slope of the power law fitting the flare frequency distribution is above a critical threshold, $\alpha=2$ as established in prior literature, then there should be a sufficient abundance of nanoflares to explain coronal heating. We performed $>$600 case studies of solar flares, made possible by an unprecedented number of data analysts via three semesters of an undergraduate physics laboratory course. This allowed us to include two crucial, but nontrivial, analysis methods: pre-flare baseline subtraction and computation of the flare energy, which requires determining flare start and stop times. We aggregated the results of these analyses into a statistical study to determine that $\alpha = 1.63 \pm 0.03$. This is below the critical threshold, suggesting that Alfv\'en waves are an important driver of coronal heating., Comment: 1,002 authors, 14 pages, 4 figures, 3 tables, published by The Astrophysical Journal on 2023-05-09, volume 948, page 71

Accurate detection of the cosmological 21-cm global signal requires galactic foreground models which can remove power over ~$10^6$. Although foreground and global signal models unavoidably exhibit overlap in their vector-spaces inducing bias error in the extracted signal, a second source of bias and error arises from inadequate foreground models, i.e. models which cannot fit spectra down to the noise level of the signal. We therefore test the level to which seven commonly employed foreground models -- including nonlinear and linear forward-models, polynomials, and maximally-smooth polynomials -- fit realistic simulated mock foreground spectra, as well as their dependence upon model inputs. The mock spectra are synthesized for an EDGES-like experiment and we compare all models' goodness-of-fit and preference using a Kolomogorov-Smirnov test of the noise-normalized residuals in order to compare models with differing, and sometimes indeterminable, degrees of freedom. For a single LST bin spectrum and p-value threshold of $p=0.05$, the nonlinear-forward model with 4 parameters is preferred ($p=0.99$), while the linear forward-model fits well with 6-7 parameters ($p=0.94,0.97$ respectively). The polynomials and maximally-smooth polynomials, like those employed by the EDGES and SARAS3 experiments, cannot produce good fits with 5 parameters for the experimental simulations in this work ($p<10^{-6}$). However, we find that polynomials with 6 parameters pass the KS-test ($p=0.4$), although a 9 parameter fit produces the highest p-value ($p\sim0.67$). When fitting multiple LST bins simultaneously, we find that the linear forward-model outperforms (a higher p-value) the nonlinear for 2, 5 and 10 LST bins. Importantly, the KS-test consistently identifies best-fit \textit{and} preferred models., Comment: 26 pages, 12 figures, accepted for publication by ApJ, minor revisions and edits from previous version

The Lunar Surface Electromagnetics Explorer 'LuSEE Night' is a low frequency radio astronomy experiment that will be delivered to the farside of the Moon by the NASA Commercial Lunar Payload Services (CLPS) program in late 2025 or early 2026. The payload system is being developed jointly by NASA and the US Department of Energy (DOE) and consists of a 4 channel, 50 MHz Nyquist baseband receiver system and 2 orthogonal $\sim$6m tip-to-tip electric dipole antennas. LuSEE Night will enjoy standalone operations through the lunar night, without the electromagnetic interference (EMI) of an operating lander system and antipodal to our noisy home planet., Comment: summary paper submitted to URSI GASS 2023

Notwithstanding decades of work, we still lack a satisfactory understanding of the structural relaxation that takes place as a liquid is rapidly cooled to form a glass. The present paper discusses a novel statistical characterization of this phenomenon in Kob-Andersen-type mixtures -- a simple yet powerful class of model glass formers. We use the variance of an order parameter called the extracopularity coefficient to measure the intensity of instantaneous fluctuations in local orientational order. This intensity is found to be nearly composition independent at the onset temperature of glassy dynamics for the standard Kob-Andersen mixture. We decompose these fluctuations into a density and symmetry contribution. Through the behavior of these contributions, we argue that the near composition independence prevails when the structure of the system equally resembles a liquid and a glass. We moreover report that the intensity of local-order fluctuations behaves like a heuristic measure of glass-forming ability, which could prove useful when exact methods are intractable., Comment: 6 pages, 5 figures

Coordination geometries describe how the neighbours of a central particle are arranged around it. Such geometries can be thought to lie in an abstract topological space; a model of this space could provide a mathematical basis for understanding physical transformations in crystals, liquids, and glasses. With this motivation, the present work proposes a metric model of the space of three-dimensional coordination geometries. This model is conceived through the generalisation of a local orientational order parameter and seems to be consistent with geometric intuition. It appears to suggest a taxonomy of coordination geometries with five main classes, each with a distinct character. A quantitative notion of orientational typicality is introduced and its interplay with orientational order is found to evidence a statistical regularity with respect to point symmetry. By the assertion of axioms on the topology of the space herein modelled, the range of structures that are possible to resolve with the order parameter in molecular dynamics simulations is greatly increased., Comment: 9 pages, 6 figures. Content updated

Consider a robot operating in an uncertain environment with stochastic, dynamic obstacles. Despite the clear benefits for trajectory optimization, it is often hard to keep track of each obstacle at every time step due to sensing and hardware limitations. We introduce the Safely motion planner, a receding-horizon control framework, that simultaneously synthesizes both a trajectory for the robot to follow as well as a sensor selection strategy that prescribes trajectory-relevant obstacles to measure at each time step while respecting the sensing constraints of the robot. We perform the motion planning using sequential quadratic programming, and prescribe obstacles to sense based on the duality information associated with the convex subproblems. We guarantee safety by ensuring that the probability of the robot colliding with any of the obstacles is below a prescribed threshold at every time step of the planned robot trajectory. We demonstrate the efficacy of the Safely motion planner through software and hardware experiments., Comment: 11 pages, submitted to Transactions on Robotics (T-RO)

Upcoming ground and space-based experiments may have sufficient accuracy to place significant constraints upon high-redshift star formation, Reionization, and dark matter (DM) using the global 21-cm signal of the intergalactic medium. In the early universe, when the relative abundance of low-mass DM halos is important, measuring the global signal would place constraints on the damping of structure formation caused by DM having a higher relic velocity (warm dark matter, or WDM) than in cold dark matter (CDM). Such damping, however, can be mimicked by altering the star formation efficiency (SFE) and difficult to detect because of the presence of Pop III stars with unknown properties. We study these various cases and their degeneracies with the WDM mass parameter $m_X$ using a Fisher matrix analysis. We study the $m_X = 7$ keV case and a star-formation model that parametrizes the SFE as a strong function of halo mass and include several variations of this model along with three different input noise levels for the likelihood; we also use a minimum halo virial temperature for collapse near the molecular cooling threshold. We find that when the likelihood includes only Pop II stars, $m_X$ is constrained to an uncertainty of $\sim 0.4$ keV for all models and noise levels at 68$\%$ CI. When the likelihood includes weak Pop III stars, $m_X \sim 0.3$ keV, and if Pop III star formation is relatively efficient, $m_X \sim 0.1$ keV uncertainty, with tight Pop III star-formation parameter constraints. Our results show that the global 21-cm signal is a promising test-bed for WDM models, even in the presence of strong degeneracies with astrophysical parameters., Comment: 22 pages, 9 figures, updated to version accepted by ApJ

Scheduled to launch in late 2021, the Imaging X-ray Polarimetry Explorer (IXPE) is a NASA Small Explorer Mission in collaboration with the Italian Space Agency (ASI). The mission will open a new window of investigation - imaging X-ray polarimetry. The observatory features 3 identical telescopes each consisting of a mirror module assembly with a polarization-sensitive imaging X-ray detector at the focus. A coilable boom, deployed on orbit, provides the necessary 4-m focal length. The observatory utilizes a 3-axis-stabilized spacecraft which provides services such as power, attitude determination and control, commanding, and telemetry to the ground. During its 2-year baseline mission, IXPE will conduct precise polarimetry for samples of multiple categories of X-ray sources, with follow-on observations of selected targets.

Structural cellular materials in nature, such as wood, trabecular bone, corals, and dentin combine complex biological functions with structural roles, such as skeletal support and impact protection1,2. They feature complex structural hierarchies from nano- to macroscale that enable optimization of both strength and toughness (flaw tolerance) simultaneously3-9. These hierarchies typically exhibit structural disorder in the arrangement of pores. The degree of disorder, however, has not been systematically quantified before, and its role in the mechanical performance of cellular biomaterials is generally unknown. Here we have applied Voronoi tessellations to quantify the cell size variation in 2D cross-sections of biological and engineered cellular materials, using a disorder parameter (d) ranging between 0 (highly disordered) to 1.0 (regular hexagonal honeycomb). We demonstrate that various plant, fungi, and animal cellular materials show characteristic ranges of disorder. Using 3D printed analogues and numerical methods, we demonstrate experimentally a range of pseudo-order (d=0.6 to 0.8) that exhibits a > 30% increase in fracture toughness (and equivalent strength) compared to hexagonal honeycombs (d=1.0) of equal density. Our results show this range of disorder is similar to that identified in the biological examples, which suggests convergent evolution. This optimal degree of structural disorder limits catastrophic failure, providing an evolutionary advantage for organism survival. Distributed structural damage limits cracks below a maximum threshold size and also enables tissue repair mechanisms after trauma. Our work shows that tailored disorder should be considered as a new design paradigm for digitally fabricated, lightweight architected materials to improve damage tolerance.