Search NASA⌕ Search

NASA NTRS · 20100031716

SCDU Testbed Automated In-Situ Alignment, Data Acquisition and Analysis

Abstract

In the course of fulfilling its mandate, the Spectral Calibration Development Unit (SCDU) testbed for SIM-Lite produces copious amounts of raw data. To effectively spend time attempting to understand the science driving the data, the team devised computerized automations to limit the time spent bringing the testbed to a healthy state and commanding it, and instead focus on analyzing the processed results. We developed a multi-layered scripting language that emphasized the scientific experiments we conducted, which drastically shortened our experiment scripts, improved their readability, and all-but-eliminated testbed operator errors. In addition to scientific experiment functions, we also developed a set of automated alignments that bring the testbed up to a well-aligned state with little more than the push of a button. These scripts were written in the scripting language, and in Matlab via an interface library, allowing all members of the team to augment the existing scripting language with complex analysis scripts. To keep track of these results, we created an easily-parseable state log in which we logged both the state of the testbed and relevant metadata. Finally, we designed a distributed processing system that allowed us to farm lengthy analyses to a collection of client computers which reported their results in a central log. Since these logs were parseable, we wrote query scripts that gave us an effortless way to compare results collected under different conditions. This paper serves as a case-study, detailing the motivating requirements for the decisions we made and explaining the implementation process.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Werne, Thomas A., Wehmeier, Udo J., Wu, Janet P., An, Xin, Goullioud, Renaud, Nemati, Bijan, Shao, Michael, Shen, Tsae-Pyng J., Wang, Xu, Weilert, Mark A., Zhai, Chengxing. 2010-06-27. SCDU Testbed Automated In-Situ Alignment, Data Acquisition and Analysis. https://ntrs.nasa.gov/citations/20100031716

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

Graph theory inspired anomaly detection at the LHC

Designing model-independent anomaly detection algorithms for analyzing LHC data remains a central challenge in the search for new physics, due to the high dimensionality of collider events. In this work, we develop a graph autoencoder as an unsupervised, model-agnostic tool for anomaly detection, using the LHC Olympics dataset as a benchmark. By representing jet constituents as a graph, we introduce a method to systematically control the information available to the model through sparse graph constructions that serve as physically motivated inductive biases. Specifically, (1) we construct graph autoencoders based on locally rigid Laman graphs and globally rigid unique graphs, and (2) we explore the clustering of jet constituents into subjets to interpolate between high- and low-level input representations. We obtain the best performance, measured in terms of the Significance Improvement Characteristic curve for an intermediate level of subjet clustering and certain sparse unique graph constructions. We further investigate the role of graph connectivity in jet classification tasks. Our results demonstrate the potential of leveraging graph-theoretic insights to refine and increase the interpretability of machine learning tools for collider experiments.

Automation↗

Accelerating template generation in resonant anomaly detection searches with optimal transport

We introduce Resonant Anomaly Detection with Optimal Transport (RAD-OT), a method for generating signal templates in resonant anomaly detection searches. RAD-OT leverages the fact that the samples from the conditional probability density of the target features vary approximately linearly along the optimal transport path connecting the resonant feature. This does not assume that the conditional density itself is linear with the resonant feature, allowing RAD-OT to efficiently capture multimodal relationships, changes in resolution, etc. By solving the optimal transport problem, RAD-OT can quickly build a template by interpolating between the background distributions in two sideband regions. We demonstrate the performance of RAD-OT using the LHC Olympics R&D dataset, where we find comparable sensitivity and improved stability with respect to deep learning-based approaches.

Automation↗

Automated Collision Avoidance (ACA) and Automated Return to Course (ARTC) Requirement and Guidance Review: Final

This report's objectives were to review regulatory and guidance documents to identify requirements and considerations for the design and operation of automated systems that perform the functions of automated collision avoidance (ACA) and/or automated return to course (ARTC). The importance of this work is twofold: 1) to help focus efforts addressing new automated collision avoidance and return to course systems requirements and considerations, and 2) in pulling together available requirements from multiple sources, generate a master resource for these automated system requirements and considerations. Reviewed documents include regulations, industry standards and research papers that specifically address ACA and ARTC systems, other automatedl control and guidance systems, ICAO (International Civil Aviation Organization) and Eurocontrol documents.

Automation↗