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This research focuses on the kinematics and evolution of the hot phase of the interstellar medium in the Galaxy. The plan is to measure the UV spectra for all hot stars observed with International Ultraviolet Explorer (IUE), in order to identify and measure the main component and any high velocity components to the interstellar lines. A total of 1200 stars are candidates for inclusion in this study.
This research focuses on the kinematics and evolution of the hot phase of the interstellar medium in the Galaxy. The plan was to measure the UV spectra of all hot stars observed with IUE, in order to identify and measure the main component and any high velocity components to the interstellar lines. Collection of data from higher resolution instruments on HST has been proposed for some of the interesting lines of sight. IUE spectra of 240 stars up to 8 kpc in 2 quadrants of the galactic plane have been examined to (1) estimate the total column density per kpc as a function of direction and distance, and (2) to obtain a lower limit to the number of high velocity components to the interstellar lines, thus giving an approximation of the number of conductive interfaces encountered per line of sight. By determining an approximation to the number of components per unit distance we aim to derive statistics on interfaces between hot and cold gas in the Galaxy. We find that 20% of the stars in this sample show at least one high velocity component in the C IV interstellar line. Two successful FUSE programs address this research and collected data for several of the lines of sight identified as locations of hot, expanding gas with the IUE data. One FUSE program is complete for the Vela SNR region. Data from another FUSE program to investigate the Cygnus superbubble region are being analyzed.
Aircraft engine noise research and development depends on the ability to study and predict the noise created by each engine component in isolation. Fan noise testing, however, requires a significant support system including a drive mechanism to turn the fan, a device to smooth the flow into the fan, and a stand to raise the fan off the ground each of which has the potential to create its own noise. A methodology was therefore developed to improve the data quality for the 9x15 Low Speed Wind Tunnel (LSWT) at the NASA Glenn Research Center that identifies three noise sources: fan noise, jet noise, and rig noise. The jet noise and rig noise was then measured by mounting a scale model of the 9x15 LSWT setup in a jet rig to simulate everything except the rotating machinery that characterizes fan noise. The data showed that the spectra measured in the LSWT has a strong rig noise component at frequencies as high as 3 kHz depending on the fan and speed. The jet noise was determined to be significantly lower than the rig noise. A mathematical model for the rig noise was then developed using a multi-dimensional least squares fit to the rig noise data. This allows the rig noise to be subtracted or removed, depending on the amplitude of the rig noise relative to the fan noise, at any given frequency, observer angle, or nozzle pressure ratio. The impact of isolating the fan noise with this method on spectra, overall power level (OAPWL), and Effective Perceived Noise Level (EPNL) is studied.
We present a theoretical construct that nominally underlies spectroscopic data analysis of multi-temperature plasmas, known as the differential emission measure (DEM). From a data analytic perspective, the DEM formalism is used to derive temperature distributions from line spectra that are formed in the presence of temperature gradients and by time integrations of evolving plasmas. From a modeling perspective, DEMs are convenient intermediaries between radiation hydrodynamics simulations and spectroscopic measurements acquired in the laboratory. The DEM concept and its associated methodologies were originally developed by spectroscopists working with astrophysical data. We borrow from these earlier investigations. In this manuscript, intended primarily as a tutorial, we discuss the basic concepts, but also augment various aspects of the theory by the way of extension and example, including a detailed treatment of various weighting and averaging schemes, intended to mitigate ambiguities that often arise when reporting temperature information. We focus on high-temperature plasmas that are not in local thermodynamic equilibrium and the x-ray spectra that they produce, although the core ideas presented here are applicable to spectroscopy in other energy bands. A few examples involving the derivation and manipulation of model DEMs in simple geometries are provided.
Replacing classical exciton model with the better founded Multi-step Direct (MSD) and Multistep Compound (MSC) mechanisms has been impeded by incapability of the latter models to describe central part of the neutron emission spectra at incident neutron energies of about 14 MeV and above. We have ascribed this deficiency to the decrease of absorption to the MSC mechanism resulting from the concept of gradual absorption. We were able to obtain very good reproduction of experimentally measured neutron spectra using MSD/MSC calculations when this option was turned off. Such treatment is, however, at odds with the fundamental distinction between MSD and MSC mechanisms that should proceed through the chain of open (P-space) and closed (Q-space) configurations respectively. By blocking gradual absorption we allow the first stage of MSC to be fully populated from the incident channel that at higher incident energies is impossible. We discuss various attempts of addressing the problem that, so far, remains open. In addition, we present an evidence for much tighter spin distribution of particle-hole states than normally assumed.
We present new X-ray and optical spectra of the old nova CP Pup (nova Pup 1942) obtained with Chandra and the CTIO 4m telescope. The X-ray spectrum reveals a multi-temperature optically thin plasma reaching a maximum temperature of 36+19 keV 16 absorbed by local complex neutral material. The time resolved optical spectroscopy confirms the presence of the 1.47 hr period, with cycle-to-cycle amplitude changes, as well as of an additional long term modulation which is suggestive either of a longer pe- riod or of non-Keplerian velocities in the emission line regions. These new observational facts add further support to CP Pup as a magnetic cataclysmic variable (mCV). We compare the mCV and the non-mCV scenarios and while we cannot conclude whether CP Pup is a long period system, all observational evidences point at an intermediate polar (IP) type CV.
Dedicated spectrometers for terahertz radiation with [0.3, 30] THz frequencies using traditional optomechanical interferometry are substantially less common than their infrared and microwave counterparts. This paper presents the design and initial performance measurements of a tabletop Fourier transform spectrometer (FTS) for multi-terahertz radiation using infrared optics in a Michelson arrangement. This is coupled to a broadband pyroelectric photodetector designed for [0.1, 30] THz frequencies. We measure spectra of narrowband and broadband input radiation to characterize the performance of this instrument above 10 THz, where signal-to-noise is high. This paves the groundwork for planned upgrades to extend below 10 THz. We also briefly discuss potential astroparticle physics applications of such FTS instruments to broadband axion dark matter searches, whose signature comprises low-rate monochromatic photons with unknown frequency.
The design and use of an infrared Fourier transform spectrometer which has been used for observations of laboratory, stratospheric, and astronomical spectra are described. The spectrometer has a spectral resolution of 0.032/cm and has operated in the mid-infrared (12 to 13 microns) as well as the far-infrared (40 to 140 microns), using both bolometer and photoconductor cryogenic detectors. The spectrometer is optically sized to accept an f/9 beam from the multi-mirror telescope (MMT). The optical and electronic design are discussed, including remote operation of the spectrometer on a balloon-borne 102-cm telescope. The performance of the laser-controlled, screw-driven moving cat's-eye mirror is discussed. Segments of typical far-infrared balloon flight spectra, lab spectra, and mid-infrared MMT spectra are presented. Data reduction, interferogram processing, artifact removal, wavelength calibration, and intensity calibration methods are discussed. Future use of the spectrometer is outlined.
Deuterium retention was measured on neutron irradiated tungsten samples where one side of the samples had a visually clean metallic luster and the opposite side appeared to have a reacted surface film. Deuterium plasma exposure and subsequent thermal desorption from the reacted surface side produced spectra with larger total deuterium desorption at lower temperatures than from the clean surface side. For neutron irradiation, these W disk samples were installed in an irradiation capsule in such a way that one side of W sample was in contact with the surface of another W sample, and the opposite side was in contact with a SiC temperature monitor. The composition of the reacted surface was investigated using X-ray photoelectron spectroscopy and showed that SiC had interdiffused into the W samples. Neutron enhanced diffusion likely contributed to this as SiC and W are stable at temperatures exceeding the irradiation temperature. Results highlight the need to consider the surface chemistry of samples in drawing conclusions on hydrogen isotope retention of W materials and also illustrate the complexity of multi-material nuclear environments expected in fusion devices.
Hubble Space Telescope images with the Wide Field Planetary Camera 2 of the young, oxygen-rich, Crab-like supernova remnant SNR0540-69.3 in the Large Magellanic Cloud (LMC) reveal details of the emission distribution and the relationship between the expanding ejecta and synchrotron nebula. The emission distributions appear very similar to those seen in the Crab nebula, with the ejecta located in a thin envelope surrounding the synchrotron nebula. The [O III] emission is more extended than other tracers, forming a faint "skin" around the denser filaments and synchrotron nebula, as also observed in the Crab. The [O III] exhibits somewhat different kinematic structure in long-slit spectra, including a more extended high-velocity emission halo not seen in images. Yet even the fastest expansion speeds in SNR 0540 s halo are slow when compared to most other young supernova remnants, though the Crab nebula has similar slow expansion speeds. We show a striking correspondence between the morphology of the synchrotron nebula observed in an optical continuum filter with that recently resolved in Chandra X-ray images. We argue that the multi-component kinematics and filamentary morphology of the optical emission-line features likely result from magnetic Rayleigh-Taylor instabilities that form as the synchrotron nebula expands and sweeps up ejecta, as seen in the Crab nebula. Our images and spectra help to refine our understanding of SNR 0540 in several more detailed respects: they confirm the identification of H(alpha)+[N II] in the red spectrum, they show that the systemic velocity of SNR 0540 is not significantly different from that of the LMC, and they hint at a lower Ne abundance than the Crab (potentially indicating a more massive progenitor star).
Abstract One of the most effective ways to advance the performance of quantum computers and quantum sensors is to increase the number of qubits or quantum resources in the system. A major technical challenge that must be solved to realize this goal for trapped-ion systems is scaling the delivery of optical signals to many individual ions. In this paper we demonstrate an approach employing waveguides and multi-mode interferometer splitters to optically address multiple 171 Yb + ions in a surface trap by delivering all wavelengths required for full qubit control. Measurements of hyperfine spectra and Rabi flopping were performed on the E2 clock transition, using integrated waveguides for delivering the light needed for Doppler cooling, state preparation, coherent operations, and detection. We describe the use of splitters to address multiple ions using a single optical input per wavelength and use them to demonstrate simultaneous Rabi flopping on two different transitions occurring at distinct trap sites. This work represents an important step towards the realization of scalable integrated photonics for atomic clocks and trapped-ion quantum information systems.
The physics mechanism behind the acceleration of electrons to energies much higher than the laser ponderomotive potential is reported. While electrons with these energies have been observed for the last twenty years at the one percent level, it is only with the recent advent of high power lasers with extended pulse durations, large focal spots, and high intensities that significant numbers (>10%) of the so-called “superponderomotive” electrons have been observed. Evidence is provided that the dominant acceleration mechanism of superponderomotive electrons is direct acceleration by the electromagnetic (EM) wave of the laser as they co-propagate in an under-dense plasma; one-dimensional particle-in-cell simulations yield electron spectra with typically ten times the ponderomotive temperature. Direct acceleration requires tens of plasma wavelengths of under-dense plasma, as well as pulse durations of tens of plasma periods, conditions that can be found in large focal spot multi-picosecond laser pulses. It is shown that the plasma simply provides the source of accelerated electrons and a variety of methods to inject a significant population of electrons into the EM waves at relativistic energy.
NASA Langley Research Center (LaRC) initiated the development of the miniature Raman-Fluorescence spectrograph and Lidar system with Langley investment funds. Later, a proposal called “Combined Raman, Fluorescence, and Lidar Multi-Sensor (RFLMS) Instrument Development Program” under NASA Mars Instrument Development Project (MIDP) was jointly proposed by University of Hawaii and NASA LaRC in 2008 and was selected for funding. Finally, Raman, Fluorescence, and Lidar prototype instrument has been integrated onto a rover system and demonstrated at NASA LaRC. Surface and atmospheric characterizations are performed with this prototype instrument from a robotic platform to investigate and identify the water, ice, and dry-ice at a 15 meter distance as well as conducting atmospheric aerosol and cloud distributions profiling. Recently acquired Raman spectra from water, ice, and dry-ice as well as atmospheric characterization results [refs. 1-2] are discussed in this study. The objective of this study is to develop a remote Raman-Fluorescence spectroscopy and Lidar multi-sensor instrument capable for investigation and identification of minerals, organics, and biogenic materials as well as conducting atmospheric studies of Mars, Moon, Asteroids/Comets, Europa, Titan, Venus, and other planets from rovers and landers.
Infrared spectroscopy is a cost-effective, non-destructive, and environmentally benign technology that is increasingly recognized as an important solution for meeting the global demand for soil data. While both near-infrared (NIR) and mid-infrared (MIR) diffuse reflectance spectroscopy enable rapid estimation of soil properties, they present a significant trade-off: NIR offers superior scalability and lower operational costs, whereas MIR provides higher analytical fidelity by capturing fundamental molecular vibrations. In this study, we propose a self-supervised, multi-fidelity learning framework designed to bridge this gap. Our approach leverages large-scale MIR spectral libraries to learn a compact, transferable latent representation, into which NIR spectra are subsequently aligned for downstream prediction. The workflow consists of pretraining a latent model on a large MIR library, adapting the representation using a smaller paired NIR–MIR dataset, and evaluating generalization on an independent external test set. Across a range of chemical and physical soil properties, we found that MIR-derived embeddings improved prediction accuracy relative to baseline models that used raw MIR inputs. Predictions derived from the spectrum conversion (NIR to MIR) task did not match the performance of the original MIR spectra but were similar or superior to predictive performance of NIR-only models, suggesting the unified spectral latent space can effectively leverage the larger and more diverse MIR dataset for prediction of soil properties not well represented in current NIR libraries.
Transition Edge Sensor (TES) spectrometers for hard X-ray beamline science will enable improved X-ray emission and absorption spectroscopy in the information-rich 2 to 20keV energy range. We are building a TES-based instrument for time Advanced Photon Source (APS) synchrotron, to be made available to beamline users. 24-pixel prototype arrays have recently been fabricated and tested. the first, spectroscopy measurements using these arrays are promising, with a best single-pixel energy resolution of 11.2 eV and saturation energy > 20 keV. We present a series of recent X-ray Fluorescence measurements involving transition metal elements and multi-element samples with closely spaced emission lines, in particular a Cu-Ni-Co thin film and a foil of Cu and Hf, The TES-measured spectra are directly compared to spectra measured with silicon drift detectors at an APS beamline, demonstrating the improved X-ray science made possible by TES spectrometers.
Multi-dimensional spectral-imaging is a mainstay of the scanning probe and electron microscopies, micro-Raman, and various forms of chemical imaging. In many cases, individual spectra can be fit to a specific functional form, with the model parameter maps, providing direct insight into material properties. Since spectra are often acquired across a spatial grid of points, spatially adjacent spectra are likely to be similar to one another; yet, this fact is almost never used when considering parameter estimation for functional fits. On datasets tried here, we show that by utilizing proximal information, whether it be in the spatial or spectral domains, it is possible to improve the reliability and increase the speed of such functional fits by ~2-3x, as compared to random priors. We explore and compare three distinct new methods: (1) spatially averaging neighborhood spectra, and propagating priors based on functional fits to the averaged case, (2) hierarchical clustering-based methods where spectra are grouped hierarchically based on response, with the priors propagated progressively down the hierarchy, and (3) regular clustering without hierarchical methods with priors propagated from fits to cluster means. Our results highlight that utilizing spatial and spectral neighborhood information is often critical for accurate parameter estimation in noisy environments, which we show for ferroelectric hysteresis loops acquired on a prototypical PbTiO3 thin film with piezoresponse spectroscopy. This method is general and applicable to any spatially measured spectra where functional forms are available. Examples include exploring the superconducting gap with tunneling spectroscopy, using the Dynes formula, or current-voltage curve fits in conductive atomic force microscopy mapping. Here we explore the problem for ferroelectric hysteresis, which, given its large parameter space, constitutes a more difficult task than, for example, fitting current-voltage curves with a Schottky emission formula.
The COVID-19 pandemic demonstrated how a novel, elusive, and diffuse biological threat can engender uncertainty and misinformation, and it underscored the need for flexible analytical modalities agnostic to the identity of biological material. Yet even before the pandemic, recognition of the limitations of the current, list-based approach, which focuses on known pathogens and biotoxins, and of the importance of agent-agnostic biodetection was growing within the biosecurity community. In a 2018 report on “Biodefense in the Age of Synthetic Biology,” for example, the National Academy of Sciences stated that “an overreliance on the Select Agent List is a systematic weakness affecting many aspects of the United States’ current biodefense mitigation capability”. More recently, a group of biodefense researchers proposed the identification and adoption of “bioagent-agnostic signatures (BASs)” as a way of detecting and characterizing not only existing agents but also novel ones, an approach they believe will “enable a more flexible and resilient biodefense posture”. Indeed, the future of biodetection requires us to begin developing novel analytics that can identify anomalies and/or characteristics that indicate a potential threat, whether known or unknown, without looking for a specific signature that has been identified previously. To assess potential threats more rapidly, it is critical to develop agnostic artificial intelligence (AI)/machine learning (ML) systems that can be employed for real-time assessment of the nature and source of a perturbation. Such systems should be multi scale and multi-dimensional, integrating sensor data from a range of biological, chemical, and physical application spaces. Emerging deep learning (DL) models demonstrate exceptional promise for identification of discriminatory features within multi-dimensional datasets. DL models have the capacity to recognize and encode highly complex patterns in a wide range of input data modalities, including images, text, and biological/chemical/physical spectra. As such, they can execute a wide range of assessments and determinations that have traditionally required a human operator. The promise of advances in DL is apparent in the realm of human health and medicine. DL models have been validated for evaluating a variety of clinical threats to human health in a range of contexts, including infection and cancer, and they demonstrated improved performance in predicting stroke relative to human neurologists in some categories of data. Continuously evolving advances in AI/ML are expected to support more efficient evaluation of raw sequence, spectroscopy, and spectrometry data. For instance, recent advances and deployment of large language models (LLM) such as Generative Pre training Transformer (GPT) and Bidirectional Encoder Representations from Transformers (BERT) have already motivated application of these models for biological function prediction. As frameworks such as LLMs become larger and more complex in their representations, their capacity to serve as pre-trained models that can be fine-tuned for biological/biodetection purposes will similarly be amplified. While existing and emerging AI/ML have found broad applicability and use cases in the clinical sciences, development for environmental evaluation and biodetection has been limited. Functionalizing such capabilities for this purpose requires an understanding of the existing technical landscape and how the respective tools and algorithms are currently being employed. This landscape awareness then allows an assessment of the current practical capabilities of existing models and the anticipated requirements and development efforts that will be needed to adapt available algorithms for biodetection applications relevant to DHS. Leveraging expertise in biodetection, ML, and operational biodetection, the effort described in this report is comprised of a systematic landscape assessment (Subtask 2.1), comparative evaluation (Subtask 2.2), and formulation of a value proposition (Subtask 2.3) for the prospect of ML-enabled, agnostic biodetection from raw, or minimally-processed, datasets.
The COVID-19 pandemic demonstrated how a novel, elusive, and diffuse biological threat can engender uncertainty and misinformation, and it underscored the need for flexible analytical modalities agnostic to the identity of biological material. Yet even before the pandemic recognition of the limitations of the current, list-based approach, which focuses on known pathogens and biotoxins, and of the importance of agent-agnostic biodetection, was growing within the biosecurity community. In a 2018 report on “Biodefense in the Age of Synthetic Biology,” for example, the National Academy of Sciences stated that “an overreliance on the Select Agent List is a systematic weakness affecting many aspects of the United States’ current biodefense mitigation capability." More recently, a group of biodefense researchers proposed the identification and adoption of “bioagent-agnostic signatures (BASs)” as a way of detecting and characterizing not only existing agents but also novel ones, an approach they believe will “enable a more flexible and resilient biodefense posture." Indeed, the future of biodetection requires us to begin developing novel analytics that can identify anomalies and/or characteristics that indicate a potential threat, whether known or unknown, without looking for a specific signature that has been identified previously. To assess potential threats more rapidly, it is critical to develop agnostic artificial intelligence (AI)/machine learning (ML) systems that can be employed for real-time assessment of the nature and source of a perturbation. Such systems should be multiscale and multi-dimensional, integrating sensor data from a range of biological, chemical, and physical application spaces. Emerging deep learning (DL) models demonstrate exceptional promise for identification of discriminatory features within multi-dimensional datasets. DL models have the capacity to recognize and encode highly complex patterns in a wide range of input data modalities, including images, text, and biological/chemical/physical spectra. As such, they can execute a wide range of assessments and determinations that have traditionally required a human operator.