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Leung, K.

Publications and source records attributed to Leung, K..

At least 19 records

Ultra-sensitive radon assay using an electrostatic chamber in a recirculating system

Rare event searches such as neutrinoless double beta decay and Weakly Interacting Massive Particle detection require ultra-low background detectors. Radon contamination is a significant challenge for these experiments, which employ highly sensitive radon assay techniques to identify and select low-emission materials. This work presents the development of ultra-sensitive electrostatic chamber (ESC) instruments designed to measure radon emanation in a recirculating gas loop, for future lower background experiments. Unlike traditional methods that separate emanation and detection steps, this system allows continuous radon transport and detection. This is made possible with a custom-built recirculation pump. A Python-based analysis framework, PyDAn, was developed to process and fit time-dependent radon decay data. Radon emanation rates are given for various materials measured with this instrument. A radon source of known activity provides an absolute calibration, enabling statistically-limited minimal detectable activities of 20 µBq. These devices are powerful tools for screening materials in the development of low-background particle physics experiments.

47 OTHER INSTRUMENTATION↗

RADARSAT Processing System at ASF

This paper outlines the ASF (Alaska SAR[synthetic aperture radar] Facility) Radarsat data processing requirements as driven by the science users and describes the Radarsat processing system design and implementaiton approach to meet the challenge of providing ASF with and integrated operational SAR image production facility. Design and implementation attributes that facilitate syswtem growth in handling future SAR missions such as Envisat and HIROS are also addressed.

ASF↗

Alaska Satellite Facility ASF5 SAR Data Processing System Overview

The Alaska SAR Facility (ASF) situated at the University of Alaska Fairbanks (UAF) is currently undergoing an extensive development effort to increase its capabilities in handling new sensors and in the areas of throughput, data handling, archive, and distribution [15].

SAR↗

SAR data processing adaptation for Magellan Cycle II

The authors describe the effort made at the Magellan SAR Data Processing Facility in adapting existing data processors to handle data collected from various imaging experiments. The different data processing requirements imposed by the various types of data are discussed, the tradeoffs and compromises made to work within the constraint of the existing systems are explained, and summary processing results obtained to date are given.

Leung, K.↗

SAR data processing for the Magellan prime mission

The Magellan prime mission involves mapping the planet of Venus once around its rotational axis. The Magellan synthetic aperture radar (SAR) data processing facility processes all SAR data collected by the Magellan spacecraft into image data on an orbit-by-orbit basis. About 1700 million bits of radar data were collected. A complete description of the Magellan SAR Data Processing Facility is provided with emphasis on key design features of the data processors that satisfied the project data processing requirements. A summary of the attained data processing performance is included, as well as a brief discussion of some of the constraints and considerations regarding the applicability of the processors to meeting the data processing goals anticipated for the follow-on mission phases (i.e., cycles II, III, and beyond).

Leung, K.↗

Implementation and performance of the Magellan digital correlator subsystem

The Magellan synthetic aperture radar (SAR) produces Venus surface images from data collected by the SAR carried on board the Magellan spacecraft. The core of the primary Magellan SAR processor is the digital correlator subsystem (DCS). The pipeline DSC architecture enables the Magellan primary SAR processor (PSP) to achieve real-time data processing capability. The implementation and performance of the DSC are described. Hardware (H/W) constraints that influenced the processing algorithm design are highlighted.

Chen, M.↗