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DOE OSTI2020

A robust clustering algorithm for analysis of composition-dependent organic aerosol thermal desorption measurements

Abstract. One of the challenges of understanding atmospheric organic aerosol (OA) particles stems from its complex composition. Mass spectrometry is commonly used to characterize the compositional variability of OA. Clustering of a mass spectral dataset helps identify components that exhibit similar behavior or have similar properties, facilitating understanding of sources and processes that govern compositional variability. Here, we developed an algorithm for clustering mass spectra, the noise-sorted scanning clustering (NSSC), appropriate for application to thermal desorption measurements of collected OA particles from the Filter Inlet for Gases and AEROsols coupled to a chemical ionization mass spectrometer (FIGAERO-CIMS). NSSC, which extends the common density-based special clustering of applications with noise (DBSCAN) algorithm, provides a robust, reproducible analysis of the FIGAERO temperature-dependent mass spectral data. The NSSC allows for the determination of thermal profiles for compositionally distinct clusters of mass spectra, increasing the accessibility and enhancing the interpretation of FIGAERO data. Applications of NSSC to several laboratory biogenic secondary organic aerosol (BSOA) systems demonstrate the ability of NSSC to distinguish different types of thermal behaviors for the components comprising the particles along with the relative mass contributions and chemical properties (e.g., average molecular formula) of each mass spectral cluster. For each of the systems examined, more than 80 % of the total mass is clustered into 9–13 mass spectral clusters. Comparison of the average thermograms of the mass spectral clusters between systems indicates some commonality in terms of the thermal properties of different BSOA, although with some system-specific behavior. Application of NSSC to sets of experiments in which one experimental parameter, such as the concentration of NO, is varied demonstrates the potential for mass spectral clustering to elucidate the chemical factors that drive changes in the thermal properties of OA particles. Further quantitative interpretation of the thermograms of the mass spectral clusters will allow for a more comprehensive understanding of the thermochemical properties of OA particles.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1993

Possibilistic clustering for shape recognition

Clustering methods have been used extensively in computer vision and pattern recognition. Fuzzy clustering has been shown to be advantageous over crisp (or traditional) clustering in that total commitment of a vector to a given class is not required at each iteration. Recently fuzzy clustering methods have shown spectacular ability to detect not only hypervolume clusters, but also clusters which are actually 'thin shells', i.e., curves and surfaces. Most analytic fuzzy clustering approaches are derived from Bezdek's Fuzzy C-Means (FCM) algorithm. The FCM uses the probabilistic constraint that the memberships of a data point across classes sum to one. This constraint was used to generate the membership update equations for an iterative algorithm. Unfortunately, the memberships resulting from FCM and its derivatives do not correspond to the intuitive concept of degree of belonging, and moreover, the algorithms have considerable trouble in noisy environments. Recently, the clustering problem was cast into the framework of possibility theory. Our approach was radically different from the existing clustering methods in that the resulting partition of the data can be interpreted as a possibilistic partition, and the membership values may be interpreted as degrees of possibility of the points belonging to the classes. An appropriate objective function whose minimum will characterize a good possibilistic partition of the data was constructed, and the membership and prototype update equations from necessary conditions for minimization of our criterion function were derived. The ability of this approach to detect linear and quartic curves in the presence of considerable noise is shown.

Keller, James M.↗

NASA NTRS2011

Ka-Band Site Characterization of the NASA Near Earth Network in Svalbard, Norway

Critical to NASA s rapid migration toward Ka-Band is the comprehensive characterization of the communication channels at NASA's ground sites to determine the effects of the atmosphere on signal propagation and the network's ability to support various classes of users in different orbits. Accordingly, NASA has initiated a number of studies involving the ground sites of its Near Earth and Deep Space Networks. Recently, NASA concluded a memorandum of agreement (MOA) with the Norwegian Space Centre of the Kingdom of Norway and began a joint site characterization study to determine the atmospheric effects on Ka-Band links at the Svalbard Satellite Station in Norway, which remains a critical component of NASA s Near Earth Communication Network (NEN). System planning and design for Ka-band links at the Svalbard site cannot be optimally achieved unless measured attenuation statistics (e.g. cumulative distribution functions (CDF)) are obtained. In general, the CDF will determine the necessary system margin and overall system availability due to the atmospheric effects. To statistically characterize the attenuation statistics at the Svalbard site, NASA has constructed a ground-based monitoring station consisting of a multi-channel total power radiometer (25.5 - 26.5 GHz) and a weather monitoring station to continuously measure (at 1 second intervals) attenuation and excess noise (brightness temperature). These instruments have been tested in a laboratory environment as well as in an analogous outdoor climate (i.e. winter in Northeast Ohio), and the station was deployed in Svalbard, Norway in May 2011. The measurement campaign is planned to last a minimum of 3 years but not exceeding a maximum of 5 years.

Acosta, R.↗

DOE OSTI2026

Optimal binning of correlated measurements

Experimental measurements are commonly represented on a discrete grid, requiring a balance between granularity and statistical noise. Two strategies have traditionally been used to improve such representations: selecting an appropriate bin width to control discretization error and applying kernel-based smoothing to suppress fluctuations. Despite their shared goal, these approaches have largely developed independently, without a unified statistical description of how discretization and correlation jointly determine measurement precision. Here, we extend the discussion of optimal interval averaging to a correlation-aware setting by Gaussian process regression, which explicitly accounts for correlations among neighboring bins. Starting from first principles, we derive the mean-squared error of discretized measurements and obtain closed-form asymptotic expressions for the optimal bin width and correlation length. When recast in reduced variables, the theory reveals distinct universal scaling laws governing the error in the correlation-free and correlation-controlled regimes. Characterized by intrinsically smooth intensity profiles and counting-based statistics, neutron scattering measurements are well suited for demonstrating the enhanced error contraction enabled by inter-bin correlations. We show that such improvement is achievable over the experimentally accessible Q-range and across multiple instruments and material systems. These results show that explicitly accounting for correlations systematically reshapes the limits of precision in discretized, noise-limited measurements. More broadly, the framework provides a transferable statistical foundation for optimizing data representation, inference, and experimental design across the physical and data sciences.

Tung, Chi-Huan [ORNL] (ORCID:0000000221972074)↗

NASA NTRS2003

Characterizing the X-Ray Spectrum of the Galactic Halo

The goal of this project is to determine the spectrum of the Galactic halo's soft X-ray emission. These photons are emitted by hot, diffuse gas hundreds to thousands of parsecs from the Galactic plane. Thus, the emission is weak, can be confused with locally produced photons, and must be distinguished from noise. My co-I has made significant progress on determining the background. I have been working on a complementary aspect of the project: computer simulations of the hot gas in the local and distant regions.

Source record↗

DOE OSTI2025

Accurate determination of uranium isotope abundances by wavelength modulation spectroscopy in atomic beams

The design and demonstration of an optical analysis system based on wavelength modulation spectroscopy in an atomic beam for uranium isotope abundance determinations is presented. This system probes the uranium 5f 3 6d7s 2 ( 5 L 6 ) → 5f 2 6d 2 7s 2 ( 5 K 5 ) transition at 861.031 nm, which is considered to be the most suitable transition for uranium isotopic analysis. A new laser characterization strategy was developed for the conditions where optimum laser wavelength modulation depth was small compared to the free spectral range (FSR) of etalons. Two capabilities enabled the higher-precision determination of isotope abundances of atomic beams: (1) reduction of low-frequency additive noise, especially the noise caused by black-body radiation and (2) suppression of non-absorption transmission losses. The performance of this system was validated with uranium samples of various isotopic compositions. Further, by comparing the measurements using natural uranium samples between the direct absorption and the wavelength modulation approaches, a 21-fold decrease in uncertainty of the integrated absorbance and a 6.8-fold improvement in the 1-σ precision of the number density were achieved. In addition, by comparing the results using uranium oxide samples, a 6.1-fold decrease in the uncertainty of inferred isotope abundance was obtained. These results demonstrate that the 1f-normalized 2f wavelength modulation spectroscopy (WMS-2f/1f) technique enables higher-precision analysis of atomic beams.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

DOE OSTI2026

Associated Particle Imaging of Neutron Inelastic Scatter: 3-D Reconstruction, Capabilities, and Challenges

Associated particle imaging (API) offers unique advantages for 3-D imaging of neutron inelastic scatter, including single-view tomographic imaging and data acquisition when access is limited to only one side of the interrogated object. However, widespread adoption of neutron inelastic scatter imaging has been impeded by several inherent challenges, most prominently spatial resolution, self-attenuation, and statistical noise. Here, in this work, the capabilities and challenges of neutron inelastic scatter imaging are investigated. Instead of focusing on a single imaging application, we identify imaging principles that hold for various neutron inelastic scatter imaging techniques. The primary challenges for 3-D imaging are characterized. The inherent spatial resolution in the time-of-flight (TOF) dimension is derived based on the known system timing resolution and scan geometry. Three reconstruction algorithms are described and demonstrated, including the introduction of modern iterative reconstruction incorporating a physics-based system model. Simulation is leveraged to demonstrate imaging capability with varying coincidence count levels and system timing resolution. An example of measured data with both back-scatter and forward-scatter detector positioning is presented. System design characteristics and their effects on image quality are identified. The imaging framework presented in this article has the potential to facilitate growth of 3-D neutron inelastic scatter API by identifying applications that are a good match for the technique and by targeting system development resources toward the requirements of a specific imaging task.

Associated particle imaging (API)↗

DOE OSTI2022

Performance of novel VUV-sensitive Silicon Photo-Multipliers for nEXO

Liquid xenon time projection chambers are promising detectors to search for neutrinoless double beta decay (0νββ), due to their response uniformity, monolithic sensitive volume, scalability to large target masses, and suitability for extremely low background operations. The nEXO collaboration has designed a tonne-scale time projection chamber that aims to search for 0νββ of 136 Xe with projected half-life sensitivity of 1.35 x 10 28 yr. To reach this sensitivity, the design goal for nEXO is ≤1% energy resolution at the decay Q-value (2458.07 ± 0.31 keV). Reaching this resolution requires the efficient collection of both the ionization and scintillation produced in the detector. The nEXO design employs Silicon Photo-Multipliers (SiPMs) to detect the vacuum ultra-violet, 175 nm scintillation light of liquid xenon. This paper reports on the characterization of the newest vacuum ultra-violet sensitive Fondazione Bruno Kessler VUVHD3 SiPMs specifically designed for nEXO, as well as new measurements on new test samples of previously characterised Hamamatsu VUV4 Multi Pixel Photon Counters (MPPCs). Various SiPM and MPPC parameters, such as dark noise, gain, direct crosstalk, correlated avalanches and photon detection efficiency were measured as a function of the applied over voltage and wavelength at liquid xenon temperature (163 K). The results from this study are used to provide updated estimates of the achievable energy resolution at the decay Q-value for the nEXO design.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2020

Utah FORGE: Phase 3 Magnetotelluric (MT) Data

New high-quality tensor MT data at 122 sites, including the vertical magnetic field and utilizing ultra-remote referencing, have been acquired over the Utah FORGE project area. The results will be used to delineate the densities of faults and fractures in crystalline basement rocks, to define the heat sources, and to derive baseline 3D resistivity structure for later MT monitoring of temporal changes in resistivity structure following well stimulation in the EGS reservoir. There are three files here related to Utah FORGE magnetotelluric (MT) data acquisition and processing. The FORGE MT EDIs zip file contains the observed MT responses in industry-standard EDI format. For each site, there is an EDI response file that utilized a local independent reference for noise cancellation, and a file that utilized a distant reference cancelling noise associated with the DC transmission line of the Delta IPP passing down the west side of Milford Valley. These two site files could be merged as appropriate. The FORGE Model Cell Center file contains the model volume of the 3D Forge MT inversion for characterizing the resistivity structure in the project area. It was derived using finite element inversion methodology described in Wannamaker et al, in the attached FORGE Phase3 Geophysics paper, from the MT observation EDI files. It is ASCII format (.dat) and entries are defined at top of the file in a simple x-y-z-Rho listing in UTM coordinates. The element layers drape the topography so the Rho value layers are not purely horizontal slices. This greatly simplifies the listing.

15 GEOTHERMAL ENERGY↗

NASA NTRS1984

Absorption features in the 5-8 micron spectra of protostars

High signal-to-noise ratio spectra in the range of 5-8 microns of four sources embedded in molecular clouds are examined using low-temperature laboratory measurements of the 5-8-micron spectra of simple molecules and their mixtures. The absorption, apparent in all four sources, is characterized by highly distinct features ranging from two relatively narrow bands at 6.0 and 6.8 microns in W33A to a broad, shallow, and partially structured feature extending from 5.2 to 7.8 microns in Mon R2-IRS2, BN, and NGC2264. The first feature (W33A) is explained by the OH bending mode in H2O and the CH deformation modes in saturated hydrocarbons; while the second feature (Mon R2-IRS2-type) is explained by the presence of a mixture of saturated and unsaturated hydrocarbons possibly containing strongly electronegative groups.

Tielens, A. G. G. M.↗

NASA NTRS1994

Space VLBI telecommunication characteristics, protection criteria, and frequency sharing

A brief description of the technical characteristics of space VLBI is made, emphasizing the VLBI cross-correlation process. The signal-to-noise ratio of the cross-correlation process should be maintained as large as possible for the duration of the observation. Protection of this process from unwanted interference is a primary objective. The telecommunication radio links required in a space VLBI system are identified and characterized. Maximum bandwidths are suggested, as well as the minimum carrier frequencies required for the telemetering and the phase-transfer radio links. Planned space VLBI system models-Radioastron (Russia), VLBI Space Observatory Project (VSOP) (Japan), and the DSN orbiting VLBI subnet. (United States)--are taken as a baseline to determine the interference criteria. It is concluded that existing interference criteria for near-Earth research satellites are suitable for the protection of the space VLBI systems planned.

Gutierrez-Luaces, B. O.↗

NASA NTRS2004

Weak-light Phase-locking for LISA

The long armlengths of the LISA interferometer, and the finite aperture of the telescope, leads to an optical power attenuation of approximately equal to 10(exp -10) of the transmitted to received light. Simple reflection at the end of the arm is therefore not an optimum interferometric design. Instead, a local laser is offset phase-locked to the weak incoming beam, transferring the phase information of the incoming to the outgoing light. This paper reports on an experiment to characterize a weak light phase-locking scheme suitable for LISA in which a diode-pumped, Nd:YAG, non-planar ring oscillator (NPRO) is offset phase-locked to a low power (13pW) frequency stabilised master NPRO. Preliminary results of the relative phase noise of the slave laser shows shot noise limited performance above 0.4 Hz. Excess noise is observed at lower frequencies, most probably due to thermal effects in the optical arrangement and phase sensing electronics.

McNamara, Paul W.↗

NASA NTRS2007

Switched Band-Pass Filters for Adaptive Transceivers

Switched band-pass filters are key components of proposed adaptive, software- defined radio transceivers that would be parts of envisioned digital-data-communication networks that would enable real-time acquisition and monitoring of data from geographically distributed sensors. Examples of sensors to be connected to such networks include security cameras, radio-frequency identification units, and geolocation units based on the Global Positioning System. Through suitable software configuration and without changing hardware, these transceivers could be made to operate according to any of a number of complex wireless-communication standards that could be characterized by diverse modulation schemes, bandwidths, and data-handling protocols. The adaptive transceivers would include field-programmable gate arrays (FPGAs) and digital signal-processing hardware. In the receiving path of a transceiver, the incoming signal would be amplified by a low-noise amplifier (LNA). The output spectrum of the LNA would be processed by a band-pass filter operating in the frequency range between 900 MHz and 2.4 GHz. Then a down-converter would translate the signal to a lower frequency range to facilitate analog-to-digital conversion, which would be followed by baseband processing by one or more FPGAs. In the transmitting path, a digital stream would first be converted to an analog signal, which would then be up-converted to a selected frequency band before being applied to a transmitting power amplifier. The aforementioned band-pass filter in the receiving path would be a combination of resonant inductor-and-capacitor filters and switched band-pass filters. The overall combination would implement a switch function designed mathematically to exhibit desired frequency responses and to switch the signal in each frequency band to an analog-to-digital converter appropriate for that band to produce a digital intermediate-frequency signal for digital signal processing.

Wang, Ray↗

NASA NTRS2005

Near-Field Acoustical Characterization of Clustered Rocket Engines

This paper presents an approach for the prediction and characterization of the near-field acoustic levels from closely-spaced clustered rocket engines. The calculations are based on the method proposed by Eldred, wherein the flowfield from the clustered rockets is divided into two zones. Zone 1 contains the isolated nozzles which produce noise independently, and extends up to a distance where the individual flows completely mix to form an equivalent single nozzle flow. Zone 2 is occupied by the single mixed stream starting from the station where the jets merge. The acoustic fields from the two zones are computed separately on the basis of the NASA-SP method of Eldred developed for a single equivalent nozzle. A summation of the spectra for the two zones yields the total effective sound pressure level for the clustered engines. Under certain conditions of nozzle spacing and flow parameters, the combined sound pressure level spectrum for the clustered nozzles displays a double peak. Test cases are presented here to demonstrate the importance of hydrodynamic interactions responsible for the double peak in the sound spectrum in the case of clustered rocket nozzles, and the role of ground reflections in the case of non-interfering jets. A graphics interface (Rocket Acoustic Prediction Tool) has been developed to take into account the effects of clustered nozzles and ground reflections.

Kandula, Max↗

NASA NTRS2020

Detecting Seasonal Ice Dynamics in Satellite Images

Fully understanding how glaciers respond to environmental change will require new methods to help us identify the onset of ice acceleration events and observe how dynamic signals propagate within glaciers. In particular, observations of ice dynamics on seasonal timescales may offer insights into how a glacier interacts with various forcing mechanisms throughout the year. The task of generating continuous ice velocity time series that resolve seasonal variability is made more difficult by a spotty satellite record that contains no optical observations throughout the dark, polar winters. Furthermore, velocities obtained by feature tracking are marked by high noise when image pairs are separated by short time intervals and contain no direct insights into variability that occurs between images separated by long time intervals. In this paper, we describe a method of analyzing optical or SAR-derived feature-tracked velocities to characterize the magnitude and timing of seasonal ice dynamic variability. Our method is agnostic to data gaps and is able to recover climatological average winter velocities regardless of the availability of direct observations during winter. Using characteristic image acquisition times and error distributions from Antarctic image pairs in the ITS_LIVE dataset, we generate synthetic ice velocity time series, then apply our method to recover imposed magnitudes of seasonal variability within ±1.4 m yr−1. We then validate the techniques by comparing our results to GPS data collected on Russell Glacier in Greenland. The methods presented here may be applied to better understand how ice dynamic signals propagate on seasonal timescales, and what mechanisms control the flow of the world’s ice.

Chad A. Greene↗

NASA NTRS2020

Optical Design of the Earth Surface Mineral Dust Source Investigation (EMIT) Imaging Spectrometer

The Earth Surface Mineral Dust Source Investigation (EMIT) instrument is a high fidelity imaging spectrometer developed to characterize surface mineralogy of the Earth’s dust source regions over the spectral range of 380- 2500 nm and spectral sampling of 7.4 nm. EMIT will close the current knowledge gap in dust source mineral composition by collecting over 1 billion high signal-to-noise ratio spectra in this region of our planet. These new measurements will be used in conjunction with state-of-the-art Earth System Models to understand and reduce the uncertainty in the radiative forcing effect of mineral dust aerosols. EMIT will be deployed on the International Space Station that has an orbit that is well suited for measuring the arid land regions of the Earth. The optical design utilizes a Dyson spectrometer to reduce volume and mass for a fast (F/1.8) and wide swath (1240 samples) optical system. An overview of the EMIT optical design, development, and current status are discussed.

Green, Robert O.↗

NASA NTRSDate not supplied

Online Rapid Analysis of Laser Heterodyne Radiometer (LHR) Data Using the Planetary Spectrum Generator (PSG)

One of the biggest challenges in developing scientific instruments is not just the build and testing of an instrument, but the method for processing the data and producing a consistent, well characterized data product that can be confidently used by the public and scientific community. Raw data products are frequently an array of numbers that are a read-out of voltages. The challenge is to convert these arrays into meaningful information as well as remove noise and interferences. Because this can be a tedious and time-consuming, the goal is to automate the process so that data ca be processed rapidly and be available in real-time for event monitoring. Here we present a rapid analysis method for Laser Heterodyne Radiometer (LHR) data that can be used to analyze data taken from a range of LHR observation modes (column, limb, etc.). This online tool uses the versatile Planetary Spectrum Generator (PSG) and allows LHR users to quickly analyze their own data using a NASA Goddard Space Flight Center (GSFC) monitored capability to ensure quality and reproducibility in the data products. Background: Development of a miniaturized LHR (mini-LHR) as a ground instrument for measuring carbon dioxide (CO 2 ) and methane (CH 4 ) in the atmospheric column started in 2009 with the commercial availability of distributive feedback lasers (DFB) in wavelengths that could measure absorption of gases in the near-IR. As new DFB lasers, detectors and hardware emerged at different wavelengths, additional gases were added to the capability. The mini-LHR was adapted into an occultation-viewing CubeSat with Lawrence Livermore National Laboratory (LLNL) for observing CO 2 , CH 4 , and H 2 O in the limb and was launched in 2019. Other versions of the LHR technology have been designed for observation of water vapor in the lunar exosphere as well as observations of trace gases in planetary atmospheres and plumes from icy moons. The PSG is an online tool developed at GSFC that can be used for synthesizing Earth and planetary spectra (atmospheres and surfaces) over a broad range of wavelengths (0.1 μm to 100 mm) for any observatory, orbiter or lander. Spectra are simulated by combining several state-of-the-art radiative transfer models, spectroscopic databases, planetary databases, as well as modern-era retrospective analysis for research and applications, version 2 (MERRA-2) data set which provides meteorological inputs such as modeled surface pressure for calculating dry-air columns.

Emily Wilson↗

DOE OSTI2026

Image processing pipeline for AI-driven nanoparticle megalibrary characterization

Recent innovations have made it possible to produce megalibraries, millions of structurally and compositionally distinct nanoparticles on a chip. These megalibraries yield vast volumes of data that are impossible to analyze manually, necessitating the development of automated tools. In previous work, we created a binary classification machine learning model to select quality nanoparticle images for downstream analysis. In this work, we show that adding a custom image processing step before training can produce significantly higher-performing models in a fraction of the time and make them more robust to different image noise levels and microscope acquisition settings. The image processing pipeline proposed here effectively cleans raw nanoparticle images, enhances key features, and allows us to use much lower resolution images and simpler neural network model architectures. These features result in higher performance and significant cost savings. Experiments demonstrate superior performance relative to baseline, including an 18.2% improvement in recall and a 13.1% increase in accuracy. Given the high cost of downstream analysis, it is critical to minimize false positives, and our best-performing model reaches a precision of 95.9% and a weighted F-score of 95.1% on an unseen test set. Additionally, model training time is reduced from hours to less than a minute. We also show that, using this custom image processing pipeline, model performance is significantly improved at lower pixel resolutions compared to downsizing alone. We expect that adopting this pipeline for AI-driven automated nanoparticle characterization will allow researchers to rapidly and accurately analyze much greater volumes of data, thereby accelerating materials discovery.

77 NANOSCIENCE AND NANOTECHNOLOGY↗