Engineering topics

Huang, Nicholas

Publications and source records attributed to Huang, Nicholas.

3 records

DOE OSTI2022

Performance and characterization of the SPT-3G digital frequency-domain multiplexed readout system using an improved noise and crosstalk model

The third-generation South Pole Telescope camera (SPT-3G) improves upon its predecessor (SPTpol) by an order of magnitude increase in detectors on the focal plane. The technology used to read out and control these detectors, digital frequency-domain multiplexing (DfMUX), is conceptually the same as used for SPTpol, but extended to accommodate more detectors. A nearly 5× expansion in the readout operating bandwidth has enabled the use of this large focal plane, and SPT-3G performance meets the forecasting targets relevant to its science objectives. However, the electrical dynamics of the higher-bandwidth readout differ from predictions based on models of the SPTpol system due to the higher frequencies used and parasitic impedances associated with new cryogenic electronic architecture. To address this, we present an updated derivation for electrical crosstalk in higher-bandwidth DfMUX systems and identify two previously uncharacterized contributions to readout noise, which become dominant at high bias frequency. The updated crosstalk and noise models successfully describe the measured crosstalk and readout noise performance of SPT-3G. These results also suggest specific changes to warm electronics component values, wire-harness properties, and SQUID parameters, to improve the readout system for future experiments using DfMUX, such as the LiteBIRD space telescope.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2019

SPT-3G Computing

SPT-3G, the third generation camera on the South Pole Telescope (SPT), was deployed in the 2016-2017 Austral summer season. The SPT is a 10-meter telescope located at the geographic South Pole and designed for observations in the millimeter-wave and submillimeter-wave regions of the electromagnetic spectrum. The SPT is primarily used to study the cosmic microwave background (CMB). The upgraded camera produces an order of magnitude more data than the previous generations of SPT cameras. The telescope is expected to collect a petabyte (PB) of data over course of five years, which is a significantly larger data volume than any other CMB telescope in operation. The increase in data rate required radical changes to the SPT computing model both at the South Pole and University of Chicago. This paper will describe the overall integration of distributed storage and compute resources into a common interface, deployment of on-site data reduction and storage infrastructure, and the usage of the Open Science Grid (OSG) by the SPT collaboration.

Riedel, Benedikt↗

DOE OSTI2019

Classification of Sunyaev-Zel’dovich Galaxy Clusters using Deep Learning

Galaxy clusters are the largest gravitationally bound objects in the universe. One way to find and characterize them is through the Sunyaev-Zel'dovich (SZ) effect in millimeter wave observations of the cosmic microwave background (CMB). From cluster abundance across redshifts and cluster masses, we can infer the growth rate of the universe and thereby extract information about dark energy and cosmic structure evolution. This work represents a first step in applying convolutional neural networks (CNNs) to extract this information. We train a CNN on realistic simulations of the millimeter-wave sky to find galaxy clusters through classification and then to measure their gas mass through regression. We employ relatively simple CNN architectures on a suite of simulated CMB maps, each of which has a different combination of signals and noise: SZ effect, CMB signal, instrument noise, and different foregrounds. In our initial experiments we find varying classification succe ss --- worsening as more of the aforementioned contaminants are included. For regression, the correlation between the predicted and input SZ signal is clear for the case of maps with SZ, CMB, and instrument noise. While the performance does not beat current methods, the CNN is easier to set up and is less susceptible to the influence of highly structured noise terms, such as galactic dust.

79 ASTRONOMY AND ASTROPHYSICS↗