The Deep Space Network
The objectives, functions, and organization of the Deep Space Network are summarized. The Deep Space Instrumentation Facility, the Ground Communications Facility, and the Network Control System are described.
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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Watch and learn—a generalized approach for transferrable learning in deep neural networks via physical principles
Transfer learning refers to the use of knowledge gained while solving a machine learning task and applying it to the solution of a closely related problem. Such an approach has enabled scientific breakthroughs in computer vision and natural language processing where the weights learned in state-of-the-art models can be used to initialize models for other tasks which dramatically improve their performance and save computational time. Here we demonstrate an unsupervised learning approach augmented with basic physical principles that achieves fully transferrable learning for problems in statistical physics across different physical regimes. By coupling a sequence model based on a recurrent neural network to an extensive deep neural network, we are able to learn the equilibrium probability distributions and inter-particle interaction models of classical statistical mechanical systems. Our approach, distribution-consistent learning, DCL, is a general strategy that works for a variety of canonical statistical mechanical models (Ising and Potts) as well as disordered interaction potentials. Using data collected from a single set of observation conditions, DCL successfully extrapolates across all temperatures, thermodynamic phases, and can be applied to different length-scales. This constitutes a fully transferrable physics-based learning in a generalizable approach.
97 MATHEMATICS AND COMPUTING↗
Photometric redshifts from SDSS images with an interpretable deep capsule network
ABSTRACT Studies of cosmology, galaxy evolution, and astronomical transients with current and next-generation wide-field imaging surveys like the Rubin Observatory Legacy Survey of Space and Time are all critically dependent on estimates of photometric redshifts. Capsule networks are a new type of neural network architecture that is better suited for identifying morphological features of the input images than traditional convolutional neural networks. We use a deep capsule network trained on ugriz images, spectroscopic redshifts, and Galaxy Zoo spiral/elliptical classifications of ∼400 000 Sloan Digital Sky Survey galaxies to do photometric redshift estimation. We achieve a photometric redshift prediction accuracy and a fraction of catastrophic outliers that are comparable to or better than current methods for SDSS main galaxy sample-like data sets (r ≤ 17.8 and zspec ≤ 0.4) while requiring less data and fewer trainable parameters. Furthermore, the decision-making of our capsule network is much more easily interpretable as capsules act as a low-dimensional encoding of the image. When the capsules are projected on a two-dimensional manifold, they form a single redshift sequence with the fraction of spirals in a region exhibiting a gradient roughly perpendicular to the redshift sequence. We perturb encodings of real galaxy images in this low-dimensional space to create synthetic galaxy images that demonstrate the image properties (e.g. size, orientation, and surface brightness) encoded by each dimension. We also measure correlations between galaxy properties (e.g. magnitudes, colours, and stellar mass) and each capsule dimension. We publicly release our code, estimated redshifts, and additional catalogues at https://biprateep.github.io/encapZulate-1.
79 ASTRONOMY AND ASTROPHYSICS↗
The deep space network
The objectives, functions, and organization of the Deep Space Network are summarized along with deep space station, ground communication, and network operations control capabilities. Mission support of ongoing planetary/interplanetary flight projects is discussed with emphasis on Viking orbiter radio frequency compatibility tests, the Pioneer Venus orbiter mission, and Helios-1 mission status and operations. Progress is also reported in tracking and data acquisition research and technology, network engineering, hardware and software implementation, and operations.
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Complex multicomponent spectrum analysis with Deep Neural Network
Here, in this paper, we present the use of deep neural networks to estimate physical parameters from complex optical emission spectra of the D β /H β transition. Specifically, we focus on estimating the radio frequency electric field vector of the lower hybrid wave and isotope ratio within the scrape-off-layer plasma of the WEST tokamak. Fitting the spectral data using a traditional non-linear least squares analysis requires many free parameters and is computationally expensive, rendering the data unusable for real-time control. By implementing relatively small neural networks, the physical parameters can be directly extracted from the spectral data with reasonable accuracy in a few milliseconds. The deep neural network prediction can serve as input for a reduced model using least-squares fitting or for real-time control. We show that deep neural networks can be an effective tool for analyzing complex multicomponent spectra, providing a speedup of more than 10 5 times compared to least residual analysis, with an accuracy of 0.5% for the isotope ratio, and 0.09 kV/cm and 0.38 kV/cm for the RF radial and poloidal electric field respectively.
47 OTHER INSTRUMENTATION↗
The Impact of Traffic Prioritization on Deep Space Network Mission Traffic
A select number of missions supported by NASA's Deep Space Network (DSN) are demanding very high data rates. For example, the Kepler Mission was launched March 7, 2009 and at that time required the highest data rate of any NASA mission, with maximum rates of 4.33 Mb/s being provided via Ka band downlinks. The James Webb Space Telescope will require a maximum 28 Mb/s science downlink data rate also using Ka band links; as of this writing the launch is scheduled for a June 2014 launch. The Lunar Reconnaissance Orbiter, launched June 18, 2009, has demonstrated data rates at 100 Mb/s at lunar-Earth distances using NASA's Near Earth Network (NEN) and K-band. As further advances are made in high data rate space telecommunications, particularly with emerging optical systems, it is expected that large surges in demand on the supporting ground systems will ensue. A performance analysis of the impact of high variance in demand has been conducted using our Multi-mission Advanced Communications Hybrid Environment for Test and Evaluation (MACHETE) simulation tool. A comparison is made regarding the incorporation of Quality of Service (QoS) mechanisms and the resulting ground-to-ground Wide Area Network (WAN) bandwidth necessary to meet latency requirements across different user missions. It is shown that substantial reduction in WAN bandwidth may be realized through QoS techniques when low data rate users with low-latency needs are mixed with high data rate users having delay-tolerant traffic.
quality of service↗
Deep space network support of the manned space flight network for Apollo, volume 3
The Deep Space Network (DSN) activities in support of Project Apollo during the period of 1971 and 1972 are reported. Beginning with the Apollo 14 mission and concluding with the Apollo 17 mission, the narrative includes, (1) a mission description, (2) the NASA support requirements placed on the DSN, and, (3) a comprehensive account of the support activities provided by each committed DSN deep space communication station. Associated equipment and activities of the three elements of the DSN (the Deep Space Instrumentation Facility (DSIF), the Space Flight Operations Facility (SFOF), and the Ground Communications Facility (GCF)) used in meeting the radio-metric and telemetry demands of the missions are documented.
Hartley, R. B.↗
The deep space network - Space programs summary, volume 2
Deep Space Network systems engineering and analysis research projects
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Space programs summary no. 37-66, volume 2 for the period 1 September - 31 October 1970. The deep space network
Operations and research development of Deep Space Network facilities
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The deep space network, volume 10
Progress on the Deep Space Network (DSN) supporting research and technology is reported. The objectives, functions and facilities of the DSN are described along with the mission support for the following: interplanetary flight projects, planetary flight projects, and manned space flight projects. Work in advanced engineering and communications systems is reported along with changes in hardware and software configurations in the DSN/MSFN tracking stations.
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The deep space network
Various ongoing projects within the Deep Space Network are discussed, including mission support, communications, data processing, and network operations.
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A study of the charged particle calibration requirements for the Deep Space Network
A study made of the Deep Space Network charged particle calibration requirements is discussed. The effects of charged particles on navigation and timing systems were reviewed and it was proposed that a system based upon the Global Positioning System satellites be used to measure the charged particle content of the ionosphere. The system would be required to measure the total electron content of the ionosphere to the order of 10 to the 16th power electron per square meter. Two types of systems were suggested as possible candidates for making these measurements.
Townes, S. A.↗
Deep space network resource scheduling approach and application
Deep Space Network (DSN) resource scheduling is the process of distributing ground-based facilities to track multiple spacecraft. The Jet Propulsion Laboratory has carried out extensive research to find ways of automating this process in an effort to reduce time and manpower costs. This paper presents a resource-scheduling system entitled PLAN-IT with a description of its design philosophy. The PLAN-IT's current on-line usage and limitations in scheduling the resources of the DSN are discussed, along with potential enhancements for DSN application.
Eggemeyer, William C.↗
The Deep Space Network scheduling problem
We describe the Deep Space network's scheduling problem based on a user requriement language.
space communication↗
The deep space network, volume 13
The objectives, functions, and organization of the Deep Space Network are summarized. The deep space instrumentation facility, the ground communications facility, and the network control system are described. Other areas reported include: Helios Mission support, DSN support of the Mariner Mars 1971 extended mission, Mariner Venus/Mercury 1973 mission support, Viking mission support, radio science, tracking and ground-based navigation, network control and data processing, and deep space stations.
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Enhanced Ionization Charge Identification in the Short-Baseline Neutrino Program Neutrino Detectors with Deep Neural Networks
We present a deep neural net-based region of interest detection method (DNN ROI) for signal processing in the liquid argon time projection chambers of the Short-Baseline Neutrino (SBN) Program, SBND and ICARUS. DNN ROI addresses limitations of the traditional wire-by-wire thresholding algorithm by leveraging the full two-dimensional detector readout and cross-plane matching information. To account for detector performance variations, we explore training with augmented samples. We find that DNN ROI outperforms the traditional method in both low-level ROI identification performance and high-level reconstruction metrics for high-energy cosmic and accelerator neutrino interaction products, while also being more robust against detector variations, with or without sample augmentation.
Abratenko, P. [Tufts U.]↗
A Framework for the Analysis of Deep Neural Networks in Autonomous Aerospace Applications using Bayesian Statistics
Deep Neural Networks (DNNs) are considered to be key components in many autonomous systems. Applications range from vision-based obstacle avoidance to intelligent/learning control and planning. Safety-critical applications as found in the aerospace domain require that the behavior of the DNN is validated and tested rigorously for safety of the autonomous system (AUS). In this paper, we present a framework to support testing of DNNs and the analysis of the network structure. Our framework employs techniques from statistical modeling and active learning to effectively generate test cases for DNN safety testing and performance analysis. We will present results of a case study on a physics-based Deep recurrent residual neural network (DR-RNN), which has been trained to emulate the aerodynamics behavior of a fixed-wing aircraft.
Deep Neural networks↗
Neural Scaling Laws of Deep ReLU and Deep Operator Network: A Theoretical Study
Neural scaling laws play a pivotal role in the performance of deep neural networks and have been observed in a wide range of tasks. However, a complete theoretical framework for understanding these scaling laws remains underdeveloped. In this paper, we explore the neural scaling laws for deep operator networks, which involve learning mappings between function spaces, with a focus on the Chen and Chen style architecture. These approaches, which include the popular Deep Operator Network (DeepONet), approximate the output functions using a linear combination of learnable basis functions and coefficients that depend on the input functions. We establish a theoretical framework to quantify the neural scaling laws by analyzing its approximation and generalization errors. We articulate the relationship between the approximation and generalization errors of deep operator networks and key factors such as network model size and training data size. Moreover, we address cases where input functions exhibit low-dimensional structures, allowing us to derive tighter error bounds. These results also hold for deep ReLU networks and other similar structures. Our results offer a partial explanation of the neural scaling laws in operator learning and provide a theoretical foundation for their applications.
97 MATHEMATICS AND COMPUTING↗