Engineering topics

Wang, Yuhan

Publications and source records attributed to Wang, Yuhan.

3 records

DOE OSTI2024

The Simons Observatory: Design, Optimization, and Performance of Low-Frequency Detectors

The Simons Observatory (SO) is a cosmic microwave background (CMB) experiment located in the Atacama Desert in Chile that will make precise temperature and polarization measurements over six spectral bands ranging from 27 to 285 GHz. Three small aperture telescopes (SATs) and one large aperture telescope (LAT) will house ~60,000 detectors and cover angular scales between one arcminute and tens of degrees. We present the performance of the dichroic, low-frequency (LF) lenslet-coupled sinuous antenna transition-edge sensor (TES) bolometer arrays with bands centered at 27 and 39 GHz. The LF focal plane will primarily characterize Galactic synchrotron emission as a critical part of foreground subtraction from CMB data. We will discuss the design, optimization, and current testing status of these pixels.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

The Simons Observatory: Design, Integration, and Testing of the Small Aperture Telescopes

The Simons Observatory (SO) is a cosmic microwave background survey experiment that includes small-aperture telescopes (SATs) observing from an altitude of 5200 m in the Atacama Desert in Chile. The SO SATs will cover six spectral bands between 27 and 280 GHz to search for primordial B-modes to a sensitivity of σ(r) = 0.002, with quantified systematic errors well below this value. Each SAT is a self-contained cryogenic telescope with a 35° field of view, 42 cm diameter optical aperture, 40 K half-wave plate, 1 K refractive optics, and <0.1 K focal plane that holds >12,000 transition edge sensor detectors. We describe the nominal design of the SATs and present details about the integration and testing for one operating at 93 and 145 GHz.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2024

Macroscopic Traffic Modeling Using Probe Vehicle Data: A Machine Learning Approach

Abstract The macroscopic fundamental diagram (MFD) captures an orderly relationship among traffic flow, density, and speed at the network level. It is a simple yet powerful tool for modeling traffic dynamics in large urban networks with broad application in traffic control and management. However, empirically derived MFDs in urban regions require high-resolution traffic data from the network. Having the network flow and vehicular density estimated at the (granular) census tract level using vehicle probe data, we apply machine learning methods to predict the MFDs across U.S. urban areas and capture the impacts of location-specific input features on the network flow–density relationships at a large scale. The results show that, among the four tested machine learning approaches (Random Forest, XGBoost, Support Vector Machine, and Neural Network), XGBoost delivers the best performance in predicting network traffic flow based on vehicular density and location attributes. Using interaction Shapley Additive explanation (SHAP) values and partial correlation analysis, we examine the factors influencing MFD shapes across different locations. Our empirical findings reveal that across U.S. urban areas, network topology, transportation infrastructure, and land use are primary factors shaping MFD curves, while demand and trip-related factors play a lesser role. Specifically, higher ranking roads, centrality, and development levels correlate positively with network capacity and critical density, whereas negative associations are observed for network connectivity, mixed-use development, and road roughness levels.

Jin, Ling↗