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Bruce Dean

Publications and source records attributed to Bruce Dean.

7 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2023

The Science Performance of JWST as Characterized in Commissioning

This paper characterizes the actual science performance of the James Webb Space Telescope (JWST), as determined from the six month commissioning period. We summarize the performance of the spacecraft, telescope, science instruments, and ground system, with an emphasis on differences from pre-launch expectations. Commissioning has made clear that JWST is fully capable of achieving the discoveries for which it was built. Moreover, almost across the board, the science performance of JWST is better than expected; in most cases, JWST will go deeper faster than expected. The telescope and instrument suite have demonstrated the sensitivity, stability, image quality, and spectral range that are necessary to transform our understanding of the cosmos through observations spanning from near-earth asteroids to the most distant galaxies.

Infrared astronomy↗
NASA NTRS2022

Phasing the Webb Telescope

The James Webb Space Telescope (JWST) is a segmented deployable telescope, currently operating at L2. The telescope utilizes 6 degrees of freedom for adjustment of the Secondary Mirror (SM) and 7 degrees of freedom for adjustment of each of its 18 segments in the Primary Mirror (PM). After deployment, the PM segments and the SM arrived in their correct optical positions to within a ~1 mm, with accordingly large wavefront errors. A Wavefront Sensing and Controls (WFSC) process was executed to adjust each of these optical elements in order to correct the deployment errors and produce diffraction-limited images across the entire science field. This paper summarizes the application of the WFSC process.

JWST↗
NASA NTRSDate not supplied

The James Webb Space Telescope: Its Commissioning and Technology

The James Webb Space Telescope (JWST) launched on December 25th, 2021. During the first 120 days of the mission the telescope and mirrors were deployed and the 18 primary segments and secondary mirror were aligned from millimeters to nanometers. This process led to an optical system that is limited only by the laws of physics. JWST is the first segmented, large cryogenic telescope that has been in flown in space and its deployment was an incredible first step in the advancement of large space telescopes. This talk will discuss the origin of several key technologies as well as the commissioning process from the perspective of the telescope including launch, deployments, alignments. It will culminate with a summary of how well the telescope is performing.

James Webb Space Telescope↗
NASA NTRSDate not supplied

Introduction to Image-Based Wavefront Sensing

The James Webb Space Telescope (JWST) is easily one of the most advanced devices ever created by humans, bridging a gap between the macroscopic and quantum worlds in terms of how accurately we can align mirrors. It is the product of exhaustive testing, countless innovations, and the international collaboration between 1000’s of people working tirelessly for more than 20 years. In this talk we discuss the physics of image-based wavefront sensing: one of the core technologies that was utilized to complete the final alignment of the segmented mirror.

Wavefront Sensing↗
NASA NTRSDate not supplied

Using Artificial Intelligence and Machine Learning to Enhance Mission Design and Operations of the Habitable Worlds Observatory (HWO)

One key aspect in the development of HWO is the early deployment of artificial intelligence (AI) and machine learning (ML) to enhance mission science and operations. Our subtask group is part of the HWO AI/ML working group and focuses on AI and ML for mission operations. Our task group seeks to educate other HWO working groups about AI and ML capabilities for mission operations, investigate how to bridge technology gaps, and enable new capabilities particularly in the areas of observational scheduling, instrument health monitoring, and downlink operations. We focus on mission tasking / scheduling both for mission analysis in development and operations. AI and ML for mission scheduling includes: tools to support proposal calls and review, ensuring fairness in calls for proposals, community peer reviews and ease workloads, as well as in-flight and ground software development (e.g., using natural language processing (NLP) to support process automation from requirements). AI and ML for the mission’s development and operations include 1) anomaly detection and prediction (from onboard and ground based tools) to monitor the spacecraft’s health, 2) ground-based automated scheduling for mission operations including long-term and short-term planning and maintenance, and 3) flight system flexible execution (as flight proven for Spitzer and JWST) to enable robust execution despite execution variations, and 4) data analysis for prioritization (e.g., real-time data evaluation leading to autonomous actions and adjustments, high-priority identification, onboard data compression, etc.). Incorporation of ML and AI will enable HWO to address the major science questions related to exoplanet characterization, general astrophysics, and solar system exploration and also extend the boundaries of space mission technologies.

Mark Moussa↗