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At least 163 records · Page 9

Low Power Consumption Lasers for Miniature Optical Spectrometers for Trace Gas Analysis

The air quality of any manned spacecraft needs to be continuously monitored in order to safeguard the health of the crew. Air quality monitoring grows in importance as mission duration increases. Due to the small size, low power draw, and performance reliability, semiconductor laser-based instruments are viable candidates for this purpose. Achieving a minimum instrument size requires lasers with emission wavelength coinciding with the absorption of the fundamental absorption lines of the target gases, which are mostly in the 3.0-5.0 micron wavelength range. In this paper we report on our progress developing high wall plug efficiency type-I quantum-well GaSb-based diode lasers operating at room temperatures in the spectral region near 3.0-3.5 micron and quantum cascade (QC) lasers in the 4.0-5.0 micron range. These lasers will enable the development of miniature, low-power laser spectrometers for environmental monitoring of the spacecraft.

Distributed Feedback (DFB) Diode Lasers↗

Low Power Consumption Laser for Next Generation Miniature Optical Spectrometers for Trace Gas Analysis

The air quality of any manned spacecraft needs to be continuously monitored in order to safeguard the health of the crew. Air quality monitoring grows in importance as mission duration increases. Due to the small size, low power draw, and performance reliability, semiconductor laser-based instruments are viable candidates for this purpose. Achieving a minimum instrument size requires lasers with emission wavelength coinciding with the absorption of the fundamental absorption lines of the target gases, which are mostly in the 3.0-5.0 mu m wavelength range. In this paper we report on our progress developing high wall plug efficiency type-I quantum-well GaSb-based diode lasers operating at room temperatures in the spectral region near 3.0-3.5 mu m and quantum cascade (QC) lasers in the 4.0-5.0 mu m range. These lasers will enable the development of miniature, low-power laser spectrometers for environmental monitoring of the spacecraft

distributed feedback laser (DFB↗

New technologies for UV detectors

Several technologies are currently being developed, leading to substantial improvements in the performance of UV detectors or significant reductions in power or weight. Four technologies discussed are (1) thin-film coatings to enhance the UV sensitivity of CCD's, (2) highly innovative magnet assemblies that dramatically reduce weight and result in virtually no external flux, (3) new techniques for curving microchannel plates (MCP's) so that single plates can be used to prevent ion feedback and present highly localized charge clouds to an anode structure, and (4) high-performance alternatives to glass-based MCP's. In item (2), for example, very robust magnets are made out of rare earth materials such as samarium cobalt, and cladding magnets are employed to prevent flux from escaping from the detector into the external environment. These new ultralight magnet assemblies are able to create strong, exceptionally uniform magnetic fields for image intensification and focusing of photoelectrons. The principle advantage of such detectors is the quantum efficiencies of 70-80 percent obtained throughout ultraviolet wavelengths (900-2000 A), the highest of any device. Despite the improvements achieved under item (3), high-performance alternatives to conventional glass-based MCP's potentially offer three distinct new advantages that include (1) a 30-100-fold improvement in dynamic range resulting in correspondingly higher signal-to-noise ratios, (2) the use of pure dielectric and semiconductor materials that will not outgas contaminants that eventually destroy photocathodes, and (3) channels that have constant spacing providing long-ranged order since the plates are made using photolithography techniques from the semiconductor industry. The manufacturers of these advanced-technology MCP's, however, are a couple of years away from actually producing a functioning image intensifier. In contrast to the use of CCD's for optical, ground based observations, there is no single detector technology in the ultraviolet that dominates or is as universally suitable for all applications. Thus, several technological problems, recent advances, and the impact that these new enabling technologies represent for UV applications are addressed.

Joseph, C. L.↗

Dynamics of homogeneous cavitation with pressure feedback

Theoretical studies of homogeneous cavitation have largely been based on the classical nucleation theory. However, existing cavitation models cannot adequately describe its dynamics at nanosecond timescale, which has been called for in other fields. We develop a model coupling nucleation kinetics with cavity growth and pressure feedback as saturation mechanisms. Our numerical studies reveal the exponential dependence of cavitation characteristics such as saturation cavity density and most probable cavity radius on model parameters: Tolman length and initial pressure. This work also sheds light on the possibility of accurately determining Tolman length, whose value has a large spread in the literature.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Sandia Academic Alliance Program Collaboration Report: 2020-2021 Accomplishments

University partnerships play an essential role in sustaining Sandia’s vitality as a national laboratory. The SAA is an element of Sandia’s broader University Partnerships program, which facilitates recruiting and research collaborations with dozens of universities annually. The SAA program has two three-year goals. SAA aims to realize a step increase in hiring results, by growing the total annual inexperienced hires from each out-of-state SAA university. SAA also strives to establish and sustain strategic research partnerships by establishing several federally sponsored collaborations and multi-institutional consortiums in science & technology (S&T) priorities such as autonomy, advanced computing, hypersonics, quantum information science, and data science. The SAA program facilitates access to talent, ideas, and Research & Development facilities through strong university partnerships. Earlier this year, the SAA program and campus executives hosted John Myers, Sandia’s former Senior Director of Human Resources (HR) and Communications, and senior-level staff at Georgia Tech, U of Illinois, Purdue, UNM, and UT Austin. These campus visits provided an opportunity to share the history of the partnerships from the university leadership, tours of research facilities, and discussions of ongoing technical work and potential recruiting opportunities. These visits also provided valuable feedback to HR management that will help Sandia realize a step increase in hiring from SAA schools. The 2020-2021 Collaboration Report is a compilation of accomplishments in 2020 and 2021 from SAA and Sandia’s valued SAA university partners.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Self-Supervised Dynamical Systems

Some progress has been made in a continuing effort to develop mathematical models of the behaviors of multi-agent systems known in biology, economics, and sociology (e.g., systems ranging from single or a few biomolecules to many interacting higher organisms). Living systems can be characterized by nonlinear evolution of probability distributions over different possible choices of the next steps in their motions. One of the main challenges in mathematical modeling of living systems is to distinguish between random walks of purely physical origin (for instance, Brownian motions) and those of biological origin. Following a line of reasoning from prior research, it has been assumed, in the present development, that a biological random walk can be represented by a nonlinear mathematical model that represents coupled mental and motor dynamics incorporating the psychological concept of reflection or self-image. The nonlinear dynamics impart the lifelike ability to behave in ways and to exhibit patterns that depart from thermodynamic equilibrium. Reflection or self-image has traditionally been recognized as a basic element of intelligence. The nonlinear mathematical models of the present development are denoted self-supervised dynamical systems. They include (1) equations of classical dynamics, including random components caused by uncertainties in initial conditions and by Langevin forces, coupled with (2) the corresponding Liouville or Fokker-Planck equations that describe the evolutions of probability densities that represent the uncertainties. The coupling is effected by fictitious information-based forces, denoted supervising forces, composed of probability densities and functionals thereof. The equations of classical mechanics represent motor dynamics that is, dynamics in the traditional sense, signifying Newton s equations of motion. The evolution of the probability densities represents mental dynamics or self-image. Then the interaction between the physical and metal aspects of a monad is implemented by feedback from mental to motor dynamics, as represented by the aforementioned fictitious forces. This feedback is what makes the evolution of probability densities nonlinear. The deviation from linear evolution can be characterized, in a sense, as an expression of free will. It has been demonstrated that probability densities can approach prescribed attractors while exhibiting such patterns as shock waves, solitons, and chaos in probability space. The concept of self-supervised dynamical systems has been considered for application to diverse phenomena, including information-based neural networks, cooperation, competition, deception, games, and control of chaos. In addition, a formal similarity between the mathematical structures of self-supervised dynamical systems and of quantum-mechanical systems has been investigated.

Zak, Michail↗

A Discrete Component Low-Noise Preamplifier Readout for a Linear (1x16) SiC Photodiode Array

A compact, low-noise and inexpensive preamplifier circuit has been designed and fabricated to optimally readout a common cathode (1x16) channel 4H-SiC Schottky photodiode array for use in ultraviolet experiments. The readout uses an operational amplifier with 10 pF capacitor in the feedback loop in parallel with a low leakage switch for each of the channels. This circuit configuration allows for reiterative sample, integrate and reset. A sampling technique is given to remove Johnson noise, enabling a femtoampere level readout noise performance. Commercial-off-the-shelf acquisition electronics are used to digitize the preamplifier analogue signals. The data logging acquisition electronics has a different integration circuit, which allows the bandwidth and gain to be independently adjusted. Using this readout, photoresponse measurements across the array between spectral wavelengths 200 nm and 370 nm are made to establish the array pixels external quantum efficiency, current responsivity and noise equivalent power.

low-noise preamplifier↗

Controlled removal of hydrogen atoms from H-terminated silicon surfaces

The controlled formation of dangling bond structures on a H-terminated silicon surface is the first step in an atomically precise method of fabrication of silicon quantum electronic devices. An ultrahigh vacuum scanning tunneling microscope (STM) tip is used to selectively desorb hydrogen atoms from a Si(100)-2x1:H surface by injecting electrons with the sample held at a positive bias voltage. The authors propose a lithography method that allows the STM to operate under negative bias imaging conditions and simultaneously desorb H atoms as required. A high frequency signal is added to the negative bias voltage to deliver the required energy for hydrogen removal. The resulting current at this frequency and its harmonics are filtered to minimize their effect on the operation of the STM’s feedback control loop. The authors show that the chance of tip-sample crash during the lithography process is reduced by employing this method. They also demonstrate that this approach offers a significant potential for controlled and precise removal of H atoms from a H-terminated silicon surface and thus may be used for the fabrication of practical silicon-based atomic-scale devices.

42 ENGINEERING↗

An adaptive approach to machine learning for compact particle accelerators

Abstract Machine learning (ML) tools are able to learn relationships between the inputs and outputs of large complex systems directly from data. However, for time-varying systems, the predictive capabilities of ML tools degrade if the systems are no longer accurately represented by the data with which the ML models were trained. For complex systems, re-training is only possible if the changes are slow relative to the rate at which large numbers of new input-output training data can be non-invasively recorded. In this work, we present an approach to deep learning for time-varying systems that does not require re-training, but uses instead an adaptive feedback in the architecture of deep convolutional neural networks (CNN). The feedback is based only on available system output measurements and is applied in the encoded low-dimensional dense layers of the encoder-decoder CNNs. First, we develop an inverse model of a complex accelerator system to map output beam measurements to input beam distributions, while both the accelerator components and the unknown input beam distribution vary rapidly with time. We then demonstrate our method on experimental measurements of the input and output beam distributions of the HiRES ultra-fast electron diffraction (UED) beam line at Lawrence Berkeley National Laboratory, and showcase its ability for automatic tracking of the time varying photocathode quantum efficiency map. Our method can be successfully used to aid both physics and ML-based surrogate online models to provide non-invasive beam diagnostics.

97 MATHEMATICS AND COMPUTING↗

Consistent inclusion of triple substitutions within a coupled cluster based static quantum embedding theory

We have previously proposed the MPCC static embedding framework for quantum chemistry that self-consistently couples a high-level coupled cluster (CC) treatment of the fragment (active region) with a lower level, Møller–Plesset perturbation treatment of the environment. Our initial implementation was limited to single and double (SD) substitutions, with CCSD for the fragment and first-order perturbative SD amplitudes for the environment. Here, we extend the MPCC embedding treatment to triple substitutions, which is essential for achieving chemical accuracy in energy differences. To this end, we employ a CCSDT solver for the fragment subsystem. For the environment subsystem, we construct a perturbative estimate of the triples amplitudes, explicitly accounting for feedback from all fragment amplitudes. The resulting approach is denoted MPCCSDT(pt). We further introduce a more complete formulation in which feedback from the environment amplitudes to the fragment amplitudes is also included. This scheme involves an iterative treatment of the environment triples amplitudes and is denoted MPCCSDT(it). In addition, we assess the accuracy of the previously proposed low-level method by introducing a modified low-level approach that incorporates a lowest-order treatment of selected long-range effects, including spin fluctuations and charge polarization. All resulting approaches may be viewed as post-CCSD(T) methods. We therefore consider test cases for which CCSD(T) exhibits substantial deviations from CCSDT. These include (i) single- and triple-bond stretching in F 2 and N 2 , (ii) bond dissociation energies of selected molecules from the W4-11 dataset, and (iii) total atomization energies of transition metal hydrides. Our results demonstrate that inclusion of triples amplitudes at the fragment level alone is insufficient; a perturbative treatment of the environment triples amplitudes is required. For many energy-difference applications, feedback from the environment triples amplitudes to the fragment amplitudes is not essential, but it does play a role in the very challenging CoH and FeH molecules. A very interesting finding from our study is that in some challenging cases, we need an improved (second-order) perturbative method for the SD amplitudes, going beyond the first-order one used in our earlier work. In conclusion, considering both cost and accuracy, the MP2CCSDT(pt) model is the most promising for future applications among the candidates considered here.

Shee, Avijit [University of California, Berkeley, ↗

Triviality of quantum trajectories close to a directed percolation transition

Here we study quantum circuits consisting of unitary gates, projective measurements, and control operations that steer the system toward a pure absorbing state. Two types of phase transition occur as the rate of these control operations is increased: a measurement-induced entanglement transition, and a directed percolation transition into the absorbing state (taken here to be a product state). In this work, we show analytically that these transitions are generically distinct, with the quantum trajectories becoming disentangled before the absorbing state transition is reached, and we analyze their critical properties. We introduce a simple class of models where the measurements in each quantum trajectory define an effective tensor network (ETN)—a subgraph of the initial spacetime graph where nontrivial time evolution takes place. By analyzing the entanglement properties of the ETN, we show that the entanglement and absorbing-state transitions coincide only in the limit of the infinite local Hilbert-space dimension. Focusing on a Clifford model which allows numerical simulations for large system sizes, we verify our predictions and study the finite-size crossover between the two transitions at large local Hilbert space dimension. We give evidence that the entanglement transition is governed by the same fixed point as in hybrid circuits without feedback.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Measurements of Temperature and Enthalpy in NASA Ames 60-MW Arcjet using Atomic Oxygen and Atomic Nitrogen Absorption

We report on measurements of temperature and enthalpy in the 60-MW Interaction Heating Facility (IHF) Arcjet at the NASA Ames Research Center. These quantities were measured at sub-second temporal resolution using tunable diode laser absorption spectroscopy, employing four Distributed Feedback (DFB) lasers to simultaneously probe the arcjet flow. Optical access was achieved through a specially-designed optical disk mounted in series with the modules of the arc heater in the add-air plenum of the column, which is positioned between the downstream electrodes and the nozzle. Gas at this measurement location serves as the reservoir condition for the ensuing supersonic expansion. Conditions in the reservoir are extreme, with temperatures between 5000-8000 K and pressures between 1-9 atm. Absorption from the 5S state of atomic oxygen at 777.2 nm and the 4P state of atomic nitrogen at 868.0 nm, two electronically-excited quantum states, formed the basis of the flow characterization measurements. Fiber-coupled laser light was pitched through the IHF via 1/8" ports in the optical disks, permitting optical access with minimal intrusion to the flow. Data was collected at 3 nominal conditions, a Low condition (1600 A, 2000 V, 0.13 kg/s), a Medium condition (3500 A, 5700 V, 0.61 kg/s), and a High condition (5900 A, 6800 V, 0.84 kg/s). Measurements suggest agreement within 1-2% between excited oxygen and excited nitrogen thermometry, indicating approximate thermal and chemical equilibration of the arcjet reservoir state, a result in keeping with long-standing assumptions of CFD modelling. Spatial gradients are found in the flow due to enthalpy loss to the facility walls, but the measured spatial non-uniformities are consistent with axi-symmetry. Measurements of specific enthalpy in the reservoir are also made utilizing excited oxygen and excited nitrogen absorption under the assumption of chemical equilibration.

Peter M. Finch↗

Autonomous fabrication of tailored defect structures in 2D materials using machine learning-enabled scanning transmission electron microscopy

Materials with tailored quantum properties can be engineered from atomic-scale assembly techniques, but existing methods often lack the agility and accuracy to precisely and intelligently control the manufacturing process. Here, we demonstrate a fully autonomous approach for fabricating atomic-level defects using electron beams in scanning transmission electron microscopy (STEM) that combines advanced machine learning and automated beam control. As a proof of concept, we achieved controlled fabrication of MoS-nanowire (MoS-NW) edge structures by iterative and targeted exposure of MoS 2 monolayer to a focused electron beam to selectively eject sulfur atoms, utilizing high-angle annular dark-field (HAADF) imaging for feedback-controlled monitoring of structural evolution of defects. A machine learning framework combining a random forest model and a convolutional neural network (CNN) was developed to decode the HAADF image and accurately identify atomic positions and species. This atomic-level information was then integrated into an autonomous decision-making platform, which applied predefined fabrication strategies to instruct beam control about atomic sites to be ejected. The selected sites were subsequently exposed to a localized electron beam using an FPGA-controlled scan routine with precise control over beam positioning and duration. While the MoS-NW edge structures produced exhibit promising mechanical and electronic properties, the proposed methods to build the autonomous fabrication framework is material-agnostic and can be extended to other 2D materials for the creation of diverse defect structures and heterostructures beyond Mo S2 .

Engineering↗

Flexoelectricity and New Phenomena

It is easy to miss the scientific implications of our recent work on Triboelectricity. Everyone knows that rubbing and contact can produce static electricity; less appreciated is that the thermodynamic driver has been an open question since static electricity was first observed by Thales of Miletus around 585 BC. People think they understand it, for instance one common explanation that can still be found in the current literature is that differences in the work function drives charge transfer, often called the Volta-Helmholtz hypothesis. As summarized in 1967 by Harper, this fails to explain many experimental observations, for instance that charging can occur when two pieces of the same material are rubbed against each other. We are the first to place triboelectricity on a solid foundation rooted in quantum mechanics – the flexoelectric effect. We were able to explain a significant number of previously unexplained phenomena: 1) Bipolar tribocurrents associated with stick-slip, due to the change in sign of the strain gradients. 2) A one-third power scaling of tribocurrents with indentation force. 3) Tribocharging when two identical materials are used, these being due to local variations in the asperities so there are usually local potential differences. 4) Inhomogeneous charging of insulators, related to the statistical nature of asperities. 5) An experimentally observed reversal in the sign of charge transfer for negative and positive curvature, which is related to a change in the sign of the strain gradient. Exploiting our DOE prior funded work on flexoelectricity, we obtained semi-quantitative matching to existing experimental measurements of the surface charge in triboelectric experiments. The work has been well received in the literature. The work has been the focus of a number of popular science press articles, and also formed the basis for a Podcast for children 6-10 “The Rise and Fall of Static Man” posted in December 2019 by NPR as part of their “Wow in the World” series. I was also briefly interviewed by the Chicago PBS station in January 2020. This work are significant for a wide range of energy applications; to quote from an independent source: "Triboelectric power has plenty of potential, says Wenzhuo Wu, an assistant professor of engineering at Purdue. If the basics of static electricity are better understood, we could maximize the efficiency of wind or wave power generators, Wu says. The body's own movement could be used to power internal medical devices. Imagine being able to create a roof shaped to harness the power of a raindrop — the friction of the rain passing over the surface — to generate triboelectricity, powering the building below it." This is the start of new science, some of which we already partially understand such as the role of band bending in charge transfer. We need to understand charge transfer combining elasticity, quantum mechanics, band bending and defect states. These directly involve several of the DOE Grand Challenges: "How do we control material processes at the level of electrons? How do remarkable properties of matter emerge from complex correlations of the atomic or electronic constituents and how can we control these properties? How do we characterize and control matter away—especially very far away— from equilibrium?" I will argue that this work truly falls into the class of disruptive science; it is not just a simple extension, linear science. Not everyone will accept the approach. Since we explain far more about triboelectricity than anyone before, the preponderance of evidence supports the model. The feedback I have received is that many agree with the work, to quote: "The model makes sense, says Michael McAlpine, a professor of engineering at the University of Minnesota. "It's such a simple explanation, I was surprised I didn't put my finger on that," McAlpine says." The proposal received strong reviews. It was also publicized on the Department of Energy Web Site.

16 TIDAL AND WAVE POWER↗

Artificial Intelligence for Autonomous Molecular Design: A Perspective

Domain-aware artificial intelligence has been increasingly adopted in recent years to expedite molecular design in various applications, including drug design and discovery. Recent advances in areas such as physics-informed machine learning and reasoning, software engineering, high-end hardware development, and computing infrastructures are providing opportunities to build scalable and explainable AI molecular discovery systems. This could improve a design hypothesis through feedback analysis, data integration that can provide a basis for the introduction of end-to-end automation for compound discovery and optimization, and enable more intelligent searches of chemical space. Several state-of-the-art ML architectures are predominantly and independently used for predicting the properties of small molecules, their high throughput synthesis, and screening, iteratively identifying and optimizing lead therapeutic candidates. However, such deep learning and ML approaches also raise considerable conceptual, technical, scalability, and end-to-end error quantification challenges, as well as skepticism about the current AI hype to build automated tools. To this end, synergistically and intelligently using these individual components along with robust quantum physics-based molecular representation and data generation tools in a closed-loop holds enormous promise for accelerated therapeutic design to critically analyze the opportunities and challenges for their more widespread application. This article aims to identify the most recent technology and breakthrough achieved by each of the components and discusses how such autonomous AI and ML workflows can be integrated to radically accelerate the protein target or disease model-based probe design that can be iteratively validated experimentally. Taken together, this could significantly reduce the timeline for end-to-end therapeutic discovery and optimization upon the arrival of any novel zoonotic transmission event. Our article serves as a guide for medicinal, computational chemistry and biology, analytical chemistry, and the ML community to practice autonomous molecular design in precision medicine and drug discovery.

59 BASIC BIOLOGICAL SCIENCES↗

Rapid Cryogenic Electrical Characterization of Materials and Devices Using Gifford-McMahon Cryocoolers

Thin-film heterostructures are necessary building blocks for superconducting and phononic quantum computing devices. Many new generations of quantum hardware demand extensive materials research to optimize performances at cryogenic temperatures (below 10 K). Here, we demonstrate compact cryogenic measurement systems capable of reaching sub-10K temperatures in less than three hours with the ability to measure AC/DC resistance and dielectric properties of thin-film materials. Our platform utilizes Gifford-McMahon (GM) cryocoolers as effective tools for providing high throughput cooling-warming cycles. We successfully used the GM-based measurement systems to measure 1) the superconducting transition temperature for Nb thin films (T c ~7.8 K), and 2) the temperature dependence of the dielectric constant in SiO 2 thin films down to 10 K. The fast electrical characterization feedback will be critical in developing robust materials and components for cryogenic computing devices.

36 MATERIALS SCIENCE↗

Accelerated Selectrion of Optimal Perovskite Alloys for Solar PV using a Combined Quantum and Machine Learning Hierachiral Approach

The project aims to: (i) accelerate the discovery of “Missing HP alloys” by combining quantum mechanics and artificial intelligence machine learning approaches, and (ii) analyze the stabilities of candidate alloys, including those that do not pass selection filters (and are thus expected to degrade over time) to decipher the nature of the instabilities to guide the development of durable solar cell materials. Successful candidates will be subjected to validation experiments at NREL's state-of-the-art facilities. The discoveries this effort will provide will be directly testable and implementable and will greatly impact U.S. progress in HP PV as they will provide clear direction and motivation for experimental studies including specific material synthetic targets, device optimization, and device stability protocols. A key advantage of this effort is the feedback and guidance provided by the Industry Collaborative Work Group that we established to coordinate academic and national lab research with industry needs. The proposed work will provide a basis for and direct the development of robust and reliable HP PV. It will also provide a timely, valuable and extensive roadmap to the experimental HP PV community to enable it to focus its efforts on improving and fine-tuning promising HP compositions that this effort predicts will likely be the best performers rather than wandering in the vast chemical space for decades spending enormous resources mostly evaluating unpromising candidate materials.

14 SOLAR ENERGY↗

Enhancements to a Superconducting Quantum Interference Device (SQUID) Multiplexer Readout and Control System

Far-infrared detector arrays such as the 16x32 superconducting bolometer array for the SAFIRE instrument (flying on the SOFIA airborne observatory) require systems of readout and control electronics to provide translation between a user-driven, digital PC and the cold, analog world of the cryogenic detector. In 2001, the National Institute of Standards and Technology (NIST) developed their Mark III electronics for purposes of control and readout of their 1x32 SQUID Multiplexer chips. We at NASA s Goddard Space Flight Center acquired a Mark 111 system and subsequently designed upgrades to suit our and our collaborators purposes. We developed an arbitrary, programmable multiplexing system that allows the user to cycle through rows in a SQUID array in an infinite number of combinations. We provided hooks in the Mark III system to allow readout of signals from outside the Mark 111 system, such as telescope status information. Finally, we augmented the heart of the system with a new feedback algorithm implementation, flexible diagnostic tools, and informative telemetry.

Forgione, J.↗