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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Development of ARGOS (Active Response Gravity Offload System) Offloading Assessments and Methodology for Lunar EVA Simulations

The Active Response Gravity Offload System (ARGOS) at NASA Johnson Space Center (JSC) is an analog environment that can offload pressurized suited subjects for various reduced gravity simulations. The suit is suspended from a robotic overhead crane by a cable connected to the suit via a gimbal with an adjustable pivot point (i.e. offload attachment). There has been increased interest in providing planetary pressurized suited training at ARGOS in preparation for lunar missions. Determination of the appropriate gimbal pivot point location for a given subject is vital for a high-fidelity functional lunar simulation. Interactions between the pivot point location and human-spacesuit center of gravity (CG) can result in righting moments that may lead to artificially stable or unrealistically challenging configurations. Changing the pivot point location is time consuming and repeated adjustment can result in loss of valuable pressurized suited time. This paper aims to share knowledge obtained from the offloading characterization efforts during pressurized suited testing at ARGOS and document the ongoing process to define an appropriate pivot point location through iterative quantitative and qualitative assessments. Human-spacesuit CG locations for the ARGOS lunar simulation were estimated using a 3D body scan and density model combined with spacesuit hardware CAD and specifications. Early pilot testing of the gimbal revealed that setting the pivot point coincident with the modeled CG location was not always possible due to the current gimbal design, and small pivot point shifts had noticeable effects on subject stability. Fourteen subjects performed a series of CG-related tasks in the Exploration Extravehicular Mobility Unit (xEMU) to assess simulation characteristics. Through iterative testing, this task list evolved to streamline the process needed to efficiently identify a suitable pivot point for a given subject. The developed methodology will be critical for pivot point selection during astronaut training in the ARGOS environment.

Sarah L. Jarvis↗
DOE OSTI2025

Monitoring the Structural Health of the Stage-Four Gibbs Resistor In Order to Maintain a Functioning Pulse-Forming Network

The Dual-Axis Radiographic Hydrodynamic Test (DARHT) facility at Los Alamos National Laboratory (LANL) is a critical facility used for nuclear weapons research and development (Los Alamos National Laboratory). Its primary function is to provide high-resolution, real-time images of the behavior of materials under extreme conditions, specifically during the hydrodynamic testing of nuclear weapons surrogates. The facility uses advanced radiographic techniques, such as dual-axis X-ray imaging, to capture detailed snapshots of these materials as they react to high-pressure environments. DARHT plays a key role in maintaining the safety, security, and reliability of the U.S. nuclear arsenal, supporting the Stockpile Stewardship Program. The facility helps ensure that nuclear weapons perform as designed without the need for nuclear tests. Its dual-axis radiography provides more precise data than traditional single-axis imaging, offering a comprehensive view of the internal dynamics of a weapon's primary stage.

42 ENGINEERING↗
DOE OSTI2026

Predicting Li-Ion Battery Capacity Fade Using Early-Life Data and a Hybrid Data-Driven Gaussian Process-Bayesian Regression Approach

Accurately predicting Li-ion battery capacity trajectories using early-life data can dramatically improve battery-life understandings and be used to rapidly evaluate design/cost/performance trade-offs when developing new battery materials. Accurate early-life predictions enable researchers to quickly iterate over cell designs and material precursor properties without consistently cycling cells to failure. To this end, we present a toolbox that uses a combined Gaussian Process and Bayesian regression approach that capitalizes on signals other than just capacity (e.g., dQ/dV, voltage drops) to rapidly predict capacity-fade trajectories. The prediction tool uses Bayesian regression to fit functional forms, e.g., power law, sigmoids, etc., to predict capacity-fade dynamics. By fitting functional forms, the capacity fade can be interrogated at any point in the future, allowing for early cell-failure prediction. Additionally, Bayesian regression allows for accurate uncertainty estimates that account for cell-to-cell variability (aleatoric uncertainty) and the lack of observation data (epistemic uncertainty). By only using early cycle data to predict the capacity fade trajectory, uncertainty bounds at end-of-life can be extremely large. The large uncertainty bounds are further exacerbated because there is no systematic way to define the prior distribution of the functional forms' parameters. We improve our the predicted trajectory confidence interval of our predicted trajectory using two methods. First, we shows that a small amount of held-out cycling data is sufficientuse some train cells, that have been cycled to failure to derive information regarding the appropriate prior distributions for the functional forms' parameters of the functional form, effectively leading to data-driven priors.. We propose constructing the data-driven priors by first running a Bayesian regression starting with uninformed priors to generate intermediate cell-specific posterior parameter distributions. These posterior distributions are combined using a Ggaussian mixture model for each parameter to create the data-driven priors. These mixture models serve as the data-driven prior distributions for the parameters for. Second, we derive multiple features, e.g., C_dchg 0.5 DoD 0.5, log (|mean(dQ/dV_(w_3-w_0 ) (V)|), etc., from the train cellsheld-out cycling data, identify which the features are that best predicting capacity at early/mid-life cycles, and then create Ggaussian process regression models that are used for predicting capacity at early/mid-life cycles for the test cells (see blue dots with error bars in Fig 1b). Finally, these predicted data-points are used in addition to the actual early cycle data capacity fade to construct the Bayesian regression trajectory for the test cell s. Notably. We note that these two methods are complementary and can be combined with each other. We evaluate the performance of our proposed method on an testing open-source dataset from Iowa State University and Iowa Lakes Community College (ISU-ILCC). This dataset comprises of 251 nickel-manganese-cobalt/graphite Lithium-ion cells that are cycled under 63 different conditions. We compute the mean average percentage error (MAPE) and negative log predictive density (NLPD) to quantify the efficacy of our method. Our initial findings suggest that, when only few observations are available, for test cells, when using only Bayesian regression with uninformed priors, a power law functional provides the most accurate predictions. with very few data points. However, asHowever, a the number of data points increases, a twin sigmoidal function becomes more accurate as the number of observations further increases. We also find that using as little as 10% of the data set towards generating data-driven priors can lead to significant improvement in prediction accuracy when using early cycle data. Lastly, we found that augmenting early-cycle data with Gaussian process-predicted capacity data for Bayesian regression greatly improves the prediction accuracy. We will present a comprehensive comparison of our methods to other methods available in the literature and apply this method to additional battery datasets.

42 ENGINEERING↗
DOE OSTI2026

Membrane Composition Influences Expression Yield of Plant Cytochrome P450s in E. coli Lysate-Based Cell-Free Systems

Plant cytochrome P450 enzymes are central to natural product biosynthesis, but remain difficult to express in microbial hosts due to their transmembrane nature. Lysate-based, cell-free expression systems allow supplementation with artificial membranes to support the expression and translocation of transmembrane proteins. We developed a framework to systematically test liposomal membrane compositions to enhance the plant P450 expression yield. Adjustments to common phospholipid ratios or the addition of plant galactolipids had minimal impact on expression. In contrast, blended liposomes containing Egg PC, sterol-conjugated phospholipids, and PEGylated lipids produced concentration-dependent increases in expression. Expression of an Escherichia coli mechanosensitive channel and three plant P450s improved more than 2-fold, with some P450s showing up to 14-fold enhancement. Furthermore, these findings highlight membrane composition as a key determinant of the P450 expression yield in cell-free expression systems. While P450 activity was not measured, these findings provide a framework for future workflows toward achieving functional plant transmembrane enzymes for the bioproduction of natural products.

TXTL↗
DOE OSTI2025

Strong Coupling of Iridium and Boron–Carbon-Nitride Support for Enhanced Acidic Water Oxidation

Supporting iridium (Ir)-based materials on stable and suitable substrates is an efficient strategy to improve their catalytic performance for the oxygen evolution reaction (OER) in harsh acidic environments. Herein, we report a series of Ir catalysts supported on boron–carbon-nitride (BCN), denoted as Ir/BCN. Detailed experiments combined with density functional theory calculations demonstrate strong interactions between Ir nanoparticles (NPs) and the BCN support, significantly contributing to the improved OER performance. Among the synthesized catalysts, the Ir/BCN-75 catalyst outperforms commercial Ir black and IrO 2 in both half-cell and proton exchange membrane water electrolysis (PEMWE) tests. Specially, the Ir/BCN-75 catalyst achieves a high current density of 2.94 A cm –2 at 1.9 V with a low Ir loading of 0.26 mg cm –2 in PEMWE measurements, exceeding most Ir-based catalysts reported to date. The BCN support interacts strongly with Ir, wherein Ir–N bonding directs the Ir NP growth and enhances electrocatalytic activity, while Ir–B bonding contributes to the stability of the Ir/BCN catalyst.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Phylogenetic and ecological drivers of the avian lung mycobiome and its potentially pathogenic component

Vertebrate lungs contain diverse microbial communities, but little is known about the drivers of community composition or consequences for health. Microbiome assembly by processes such as dispersal, coevolution, and host-switching can be probed with comparative surveys; however, few studies exist for lung microbiomes, particularly for the fungal component, the mycobiome. Distinguishing among fungal taxa that are generalist or specialist symbionts, potential pathogens, or incidentally inhaled spores is urgent because of potential for emerging diseases. Here, we characterize the avian lung mycobiome and test the relative influences of environment, phylogeny, and functional traits. We used metabarcoding and culturing from 195 lung samples representing 32 bird species across 20 families. We identified 526 fungal taxa as estimated by distinct sequence types (zOTUs) including many opportunistic pathogens. These were predominantly from the phylum Ascomycota (79%) followed by Basidiomycota (16%) and Mucoromycota (5%). Yeast and yeast-like taxa (Malassezia, Filobasidium, Saccharomyces, Meyerozyma, and Aureobasidium) and filamentous fungi (Cladosporium, Alternaria, Neurospora, Fusarium, and Aspergillus) were abundant. Lung mycobiomes were strongly shaped by environmental exposure, and further modulated by host identity, traits, and phylogenetic affinities. Our results implicate migratory bird species as potential vectors for long-distance dispersal of opportunistically pathogenic fungi.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2022

Ultra-Pure, High Endurance Liquid Bladder with Volume Sensor for Space Applications

The NASA Portable Life Support System (PLSS) for the Exploration Extravehicular Mobility Unit (xEMU) incorporates a Feedwater Supply Assembly (FSA) to store consumable cooling water. The FSA accepts pure water prior to each Extra-Vehicular Activity (EVA), then supplies this water to a membrane evaporator at ambient suit pressure during the eight hour EVA. The FSA must function reliably for hundreds of EVAs over 15 years of service without introducing contamination that may accumulate and foul the membrane. We present a unique design that combines the benefits of a flexible fluoropolymer bladder with the strength and convenience of a rigid tank, providing an ultra-pure, ambient-pressure reservoir with fill-drain cycle life exceeding 3000, internal pressure tolerance exceeding 100 psi, high volumetric efficiency, near-zero dead volume, optical transparency for visual inspection, simplified mounting, and very low mass. We also incorporate a novel volume sensor suitable for both microgravity and arbitrarily accelerated environments and capable of real-time monitoring with accuracy on the order of 2.5% full scale. Analytical predictions are supported by experimental test data from a mature subscale prototype.

PLSS Portable Life Support System FSA Feedwater Su↗
NASA NTRS1992

Advanced satellite communication system

The objective of this research program was to develop an innovative advanced satellite receiver/demodulator utilizing surface acoustic wave (SAW) chirp transform processor and coherent BPSK demodulation. The algorithm of this SAW chirp Fourier transformer is of the Convolve - Multiply - Convolve (CMC) type, utilizing off-the-shelf reflective array compressor (RAC) chirp filters. This satellite receiver, if fully developed, was intended to be used as an on-board multichannel communications repeater. The Advanced Communications Receiver consists of four units: (1) CMC processor, (2) single sideband modulator, (3) demodulator, and (4) chirp waveform generator and individual channel processors. The input signal is composed of multiple user transmission frequencies operating independently from remotely located ground terminals. This signal is Fourier transformed by the CMC Processor into a unique time slot for each user frequency. The CMC processor is driven by a waveform generator through a single sideband (SSB) modulator. The output of the coherent demodulator is composed of positive and negative pulses, which are the envelopes of the chirp transform processor output. These pulses correspond to the data symbols. Following the demodulator, a logic circuit reconstructs the pulses into data, which are subsequently differentially decoded to form the transmitted data. The coherent demodulation and detection of BPSK signals derived from a CMC chirp transform processor were experimentally demonstrated and bit error rate (BER) testing was performed. To assess the feasibility of such advanced receiver, the results were compared with the theoretical analysis and plotted for an average BER as a function of signal-to-noise ratio. Another goal of this SBIR program was the development of a commercial product. The commercial product developed was an arbitrary waveform generator. The successful sales have begun with the delivery of the first arbitrary waveform generator.

Staples, Edward J.↗
DOE OSTI2025

Rhodium‐Catalyzed Oxidative Alkenylation of Naphthalene: Factors That Influence Reaction at the β‐ versus α‐Position

The catalyst precursor [(η 2 -C 2 H 4 ) 2 Rh(μ-OAc)] 2 and in situ oxidant Cu(OPiv) 2 (OPiv = t-BuCOO – ) convert naphthalene and olefins (i.e., ethylene and propylene) to alkenylnaphthalenes. Under all reaction conditions tested, the functionalization is selective for the β-position of naphthalene with the highest observed β:α ratio >20:1. The β-selectivity is catalyst controlled, but oxidant identity, ethylene pressure, and olefin identity influence the ratio of β-alkenylation to α-alkenylation. The concentration of HOPiv and naphthalene do not have an effect on the β:α ratio under the reaction conditions tested. Arenes similar to naphthalene (i.e., o-xylene and 1,2,3,4-tetrahydronaphthalene) give quantitative selectivity for alkenylation at the position β to the substituent. Using propylene as the olefin for naphthalene alkenylation, the β:α ratio is 32(7):1. and the anti-Markovnikov to Markovnikov ratio is 16(2):1.

Alkenes↗
DOE OSTI2025

The three-point form factor of Tr ϕ 3 to six loops

We study the three-point form factor of the length-three half-BPS operator (Tr ϕ 3 ) in planar $\mathcal{N}$ = 4 Super-Yang-Mills theory, using analyticity and integrability methods. We find that the functions describing the form factor in perturbation theory live in the same restrictive space of multiple polylogarithms as the one describing the form factor of the stress-tensor operator (Tr ϕ 2 ). Furthermore, we find that the leading-order data in the collinear limit provided by the form factor operator product expansion (FFOPE) is enough to fix the form factor uniquely, at least through six loops. We perform various tests of our results using the subleading FFOPE corrections. We also analyze the form factor in the Regge limit where two Mandelstam invariants are large; we obtain a compact representation for the form factor in this limit which is valid to all orders in the coupling.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Criticality analysis of nuclear binding energy neural networks

Machine learning methods, in particular deep learning methods such as artificial neural networks (ANNs) with many layers, have become widespread and useful tools in nuclear physics. However, these ANNs are typically treated as ‘black boxes’, with their architecture (width, depth, and weight/bias initialization) and the training algorithm and parameters chosen empirically by optimizing learning based on limited exploration. We test a non-empirical approach to understanding and optimizing nuclear physics ANNs by adapting a criticality analysis based on renormalization group flows in terms of the hyperparameters for weight/bias initialization, training rates, and the ratio of depth to width. This treatment utilizes the statistical properties of neural network initialization to find a generating functional for network outputs at any layer, allowing for a path integral formulation of the ANN outputs as a Euclidean statistical field theory. We use a prototypical example to test the applicability of this approach: a simple ANN for nuclear binding energies. We find that with training using a stochastic gradient descent optimizer, the predicted criticality behavior is realized, and optimal performance is found with critical tuning. However, the use of an adaptive learning algorithm leads to somewhat superior results without concern for tuning and thus obscures the analysis. Nevertheless, the criticality analysis offers a way to look within the black box of ANNs, which is a first step towards potential improvements in network performance beyond using adaptive optimizers.

artificial neural network↗
DOE OSTI2025

Pollinator habitat in solar facilities has potential to support high diversity of bee species

Conversion of agricultural and degraded lands to solar energy production provides opportunities to support diverse bee communities—and their pollination services—by creating habitat at solar facilities. Habitat restoration for bees at solar facilities follows the general principle of installing a diverse flowering plant community and assuming bees will come. However, selecting seed mixes for solar facilities is challenging because selected plants need to establish in the unique abiotic conditions of solar facilities and not interfere with solar energy production (tall plants may shade panels). Recent research tested commercially available and bespoke pollinator mixes for establishment success in Minnesota solar facilities and identified 14 forb species that established successfully over three years of the experiment. We ask an important follow-up question: how many native bee species, and which bee species, are these 14 plant species capable of supporting? We used a plant-bee interaction data set from Minnesota to calculate the number of bee species that would be supported by the 14 plant species for the duration of their flight seasons using a published bee species richness function. We found 122 unique bee species (24% of Minnesota diversity) may be supported for their entire flight seasons by the 14 focal plants.

14 SOLAR ENERGY↗
DOE OSTI2026

Particle Filter Based Inference Testing

The primary intent of PAR-FIT (Particle Filter based Inference Testing) is to provide hard inductive evidence that a machine learning model is capable and proven for an individual test input. By examining training data used to form the underlying model functional correlation, an estimate of the reliability that a model will make the correct prediction can be made. The Sequential Probability Ratio Test is used to derive a qualitative evaluation for reliability based on hypothesis testing. The PAR-FIT framework achieves this by implementing a particle filter and the sequential probability ratio test algorithms on the machine learning model training data to determine relevancy of new individual test samples to the training dataset. The kernel function evaluates the local proximity and density of training data used to derive a prediction outcome. Particles are used to probabilistically determine which training data to evaluate for proximity. For test samples that are within a close proximity to and surrounded by multiple training data points, the evaluated reliability of the prediction is high. For test samples that are anomalies not represented by the training dataset, in low density data clusters, or are far from existing data points, the evaluated reliability is low as insufficient training evidence exists to suggest the model is capable of making the correct prediction. Sequential Probability Ratio Test is further used to determine when a hypothesis on whether a signal can be rejected or accepted for use. The ratio test collects sequence information from the particle filter to test whether the signal is anomalous or normal via hypothesis testing of the underlying distributions.

Chen, Edward [Idaho National Laboratory (INL), Ida↗
DOE OSTI2025

Toward Standardized Microscale Tensile Testing for Two‐Photon Polymerization‐Fabricated Materials in Liquid

Two-photon polymerization (TPP) enables the fabrication of intricate 3D microstructures with submicron precision, offering significant potential in biomedical applications like tissue engineering. In such applications, to print materials and structures with defined mechanics, it is crucial to understand how TPP printing parameters impact the material properties in a physiologically relevant liquid environment. Herein, an experimental approach utilizing microscale tensile testing (μTT) for the systematic measurement of TPP-fabricated microfibers submerged in liquid as a function of printing parameters is introduced. Using a diurethane dimethacrylate-based resin, the influence of printing parameters on microfiber geometry is first explored, demonstrating cross-sectional areas ranging from 1 to 36 μm 2 . Tensile testing reveals Young's moduli between 0.5 and 1.5 GPa and yield strengths from 10 to 60 MPa. The experimental data show an excellent fit with the Ogden hyperelastic polymer model, which enables a detailed analysis of how variations in writing speed, laser power, and printing path influence the mechanical properties of TPP microfibers. The μTT method is also showcased for evaluating multiple commercial resins and for performing cyclic loading experiments. Collectively, this study builds a foundation toward a standardized microscale tensile testing framework to characterize the mechanical properties of TPP printed structures.

mechanical characterization↗
DOE OSTI2025

Efficient online quantum circuit learning with no upfront training

Optimization is a promising candidate for studying the utility of variational quantum algorithms (VQAs). However, evaluating cost functions using quantum hardware introduces runtime overheads that limit exploration. Surrogate-based methods can reduce calls to a quantum computer, yet existing approaches require hyperparameter pre-training and have been tested only on small problems. Here, we show that surrogate-based methods can enable successful optimization at scale, without pre-training, by using radial basis function interpolation (RBF) to construct an adaptive, hyperparameter-free surrogate. Using the surrogate as an acquisition function drives hardware queries to the vicinity of the true optima. For 16-qubit random 3-regular Max-Cut instances with the Quantum Approximate Optimization Algorithm (QAOA), our method outperforms state-of-the-art approaches, without considering their upfront training costs. Furthermore, we successfully optimize QAOA circuits for 127-qubit random Ising models on an IBM processor using 10 4 −10 5 measurements. Strong empirical performance demonstrates the promise of automated surrogate-based learning for large-scale VQA applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

An Innovative Zero-Liquid Discharge Intermediate-Cold-Liquid Eutectic-Freeze Desalination System

A novel freeze desalination technology is presented for purification of water produced from various industrial processes, including oil and gas extraction. The main innovative idea is introducing a water-immiscible intermediate-cold-liquid (ICL) medium which absorbs the cold from a refrigeration cycle and transfers it to a freezing chamber where it is mixed with brine. The cold ICL enters the freezing chamber at temperatures between -10°C to -30°C. Precooled brine in injected into the freezing chamber and pure water freezes out of the brine upon mixing with the ICL. The rate of freezing inside the chamber is controlled by adjusting the flow rate of the cold ICL relative to the inlet brine. Both eutectic freeze and non-eutectic freezing are possible by controlling the temperature of the freezing chamber. The solid-liquid slurry created in the freezing chamber flows to a filter separation unit after passing through a separation column. The ice crystals are separated from the liquid by a rotary filter device. The collected ice is rinsed by a centrifugal unit. The rinsed ice is melted to produce fresh water. The developed technology resolves the issues common to current freeze-desalination systems while offering superior heat transfer performance ascribed to direct-contact between the cooling medium and the brine in addition to producing solid salt and pure water. The system operates under atmospheric pressure. The low operation temperature minimizes the corrosion issues common in systems exposed to brine. Also, due to the low operating temperature, there are no concerns about evaporation of volatile organics from the produced water during the treatment. The developed technology effectively eliminates the use of chemicals for pretreatment of brine. The system can be constructed from commercially available components. Extensive testing using a lab-scale prototype by using synthetic brine and actual produced water with TDS values from 50,000 ppm to 300,000 ppm showed that freshwater could be achieved with relatively small dependency on the quality of the input brine. The quality of the treated water was found to be mainly a function of the duration of the centrifugal process. Treated water with TDS as low as 200 ppm could be achieved in the tests. Thermo-economic modeling coupled with system level thermochemical process model were developed for prediction of the LCOW. The LCOW was predicted to be $\$$0.50 to $\$$0.90 per barrel of produced water. The potential markets for deployment of the technology are the oil and gas, in-land thermoelectric powerplants, RO desalination plants, and mining industry.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Bayesian Optimization of Non-Invariant Systems with Constraints Developed for Application to the ECR Ion Source VENUS

In this work, we consider the optimization of non-invariant systems with both safety and control constraints. We present a new approach based on Bayesian optimization for the dynamic, safe and controlled optimization of such systems. Although there are other possible use cases, we focus on the application to the electron cyclotron resonance ion source VENUS. From experimental data, we have observed that VENUS behaves to first order as a non-invariant dynamic system with moving areas of instability. Our novel approach aims at providing a tool that can maintain system optimization in a safe way. This is accomplished by making sure the objective function, the beam current in the case of VENUS, does not fall under an operational minimum, while simultaneously requiring the optimization to avoid areas where VENUS is unstable. We compare the result of our approach on synthetic data modeled to mimic the behavior of VENUS with two methods from the literature, a standard Bayesian optimizer and a safe Bayesian optimizer, both adapted to deal with dynamic systems. A cross Student T-test is conducted to show the significance of the improvement given by the new method we introduce here, regarding the two preexisting methods we compared to. The results of the tests conducted on synthetic data show that the proposed method succeeds at maintaining the system optimized and obeys the predefined constraints better than the literature methods explored.

Bayesian optimization↗
NASA NTRS2006

The Solar-B Mission: First Light, Future Plans and Community Participation

The Solar-B spacecraft was launched from the Uchinoura Space Center into a circular, sun-synchronous, polar orbit by the Japanese Aerospace Exploration Agency in late September 2006. The spacecraft carries thee scientific instruments designed to follow the flow of magnetic energy from the photosphere to the corona to improve our understanding of both steady state and transient energy release. This goal will be achieved through coordinated observations of three highly advanced solar telescopes developed cooperatively by teams from Japan, the United States and the United Kingdom. The three telescopes are a 0.5m aperture, diffraction limited, solar optical telescope (SOT), an X-ray telescope (XRT) designed for full sun imaging with 1.0 arcsec pixels and an EUV imaging spectrometer (EIS) with an order of magnitude improvement in sensitivity over past instruments. The SOT focal plane contains three instruments, a spectropolarimeter for measuring vector magnetic fields, a broadband filter imager for recording images of the photosphere and chromosphere at the highest resolution the telescope is capable of, and a narrow band filter imager that will record Doppler grams and vector magnetograms. The XRT has broad temperature coverage and a spatial a resolution three times as high as Yohkoh. EIS covers a broad range of transition region and coronal temperatures in two spectral bands. Both XRT and EIS have 2 arcsec spatial resolution (1 arcsec pixels). Instrument first light occurred after five weeks on orbit to allow for out gassing and the opening of the telescopes doors. The initial observation sequences are designed to test the functionality of the different operating modes and for calibration. After this commissioning phase is complete a series of observations are planned to demonstrate the ability of the instruments to meet NASA's mission minimum success criteria. Data is downloaded every orbit to the Norwegian high latitude ground station at Svalbard. The data are transmitted to ISAS where they are reformatted into FITS files and archived as Level 0 data on the ISAS DARTS system. Once the initial observation period is complete, approximately six months after launch, the mission data will be open and freely available to researchers shortly after receipt at the DARTS data archive hosted in Japan and at NASA s Solar Data Analysis Center at the Goddard Space Flight Center. Scientific operations will be conducted from the ISAS facility in Sagamihara, Japan and the observatory will become available for performing joint operations with both ground and space based instruments and for conducting observing programs proposed by non-team members. This process will be described together with a status report from the initial operation of the observatory, showing examples of the first observations.

Davis, John M.↗