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NASA NTRS1992

Fractionation of carbon isotopes by phytoplankton and estimates of ancient CO2 levels

Reports of the 13C content of marine particulate organic carbon are compiled and on the basis of GEOSECS data and temperatures, concentrations, and isotopic compositions of dissolved CO2 in the waters in which the related phytoplankton grew are estimated. In this way, the fractionation of carbon isotopes during photosynthetic fixation of CO2 is found to be significantly correlated with concentrations of dissolved CO2. Because ancient carbon isotopic fractionations have been determined from analyses of sedimentary porphyrins [Popp et al., 1989], the relationship between isotopic fractionation and concentrations of dissolved CO2 developed here can be employed to estimate concentrations of CO2 dissolved in ancient oceans and, in turn, partial pressures of CO2 in ancient atmospheres. The calculations take into account the temperature dependence of chemical and isotopic equilibria in the dissolved-inorganic-carbon system and of air-sea equilibria. Paleoenvironmental temperatures for each sample are estimated from reconstructions of paleogeography, latitudinal temperature gradients, and secular changes in low-latitude sea surface temperature. It is estimated that atmospheric partial pressures of CO2 were over 1000 micro atm 160 - 100 Ma ago, then declined to values near 300 micro atm during the next 100 Ma. Analysis of a high-resolution record of carbon isotopic fractionation at the Cenomanian-Turonian boundary suggests that the partial pressure of CO2 in the atmosphere was drawn down from values near 840 micro atm to values near 700 micro atm during the anoxic event.

NASA Discipline Exobiology↗

NASA NTRS1995

The Relative Reactivity of Deoxyribose and Ribose: Did DNA Come Before RNA?

If it is assumed that there was a precursor to the ribose-phosphate backbone of RNA in the preRNA world (such as peptide nucleic acid), then the entry of various sugars into the genetic material may be related to the stability and non-enzymatic reactivity of the aldose. The rate of decomposition of 2-deoxyribose has been determined to be 1/3 that of ribose. In addition we have measured the amount of free aldehyde by H-1 and C-13 NMR and find that it has approximately 0.15% free aldehyde compared to 0.05% for ribose at 25 C. This suggests that deoxyribose would be significantly more reactive with early bases in the absence of enzymes. This is confirmed by urazole and deoxyribose reacting to form the deoxynucleoside 45 times faster as 25 C than urazole reacts with ribose to form the Ribonucleoside. Urazole is a potential precursor of uracil and is a plausible prebiotic compound which reacts with aldoses to form nucleosides. Thus the non-enzymatic reactivity of deoxyribose would favor its early use over ribose until enzymes could change the relative reactivities. Most of the reasons that RNA is presumed to have come before DNA are extrapolations back from contemporary metabolism (e.g. the abundance of ribose based coenzymes, the biosynthesis of histidine, deoxyribonucleotides are synthesized from ribonucleotides, etc.). It is very difficult to reconstruct biochemical pathways much before the last common ancestor, and it is even more difficult to do more than guess at the biochemistry of very early self-replicating systems. Thus we believe that these reasons are not compelling and that the non-enzymatic chemistry may be more important than enzymatic pathways for constructing the earliest of biochemical pathways. While the RNA world has been discussed at great length, there has not been an exploration of the transition out of the RNA world. We have constructed many possible schemes of genetic takeover events from preRNA to modern DNA, RNA, protein system which could generate the RNA metabolic fossils we see today.

Dworkin, Jason P.↗

NASA NTRS2003

The ATIC Long Duration Balloon Project

Long Duration Balloon (LDB) scientific experiments, launched to circumnavigate the south pole over Antarctica, have particular advantages compared to Shuttle or other Low Earth Orbit (LEO) missions in terms of cost, weight, scientific 'duty factor' and work force development. The Advanced Thin Ionization Calorimeter (ATIC) cosmic ray astrophysics experiment is a good example of a university-based project that takes full advantage of current LDB capability. The ATIC experiment is currently being prepared for its first LDB science flight that will investigate the charge composition and energy spectra of primary cosmic rays over the energy range from about 10(exp 10) to 10(exp 14) eV. The instrument is built around a fully active, Bismuth Germanate (BGO) ionization calorimeter to measure the energy deposited by the cascades formed by particles interacting in a thick carbon target. A highly segmented silicon matrix, located above the target, provides good incident charge resolution plus rejection of the 'backscattered' particles from the interaction. Trajectory reconstruction is based on the cascade profile in the BGO calorimeter, plus information from the three pairs of scintillator hodoscope layers in the target section above it. A full evaluation of the experiment was performed during a test flight occurring between 28 December 2000 and 13 January 2001 where ATIC was carried to an altitude of approx. 37 km above Antarctica by an approx. 850,000 cu m helium filled balloon for one circumnavigation of the continent. All systems behaved well, the detectors performed as expected, more than 43 gigabytes of engineering and cosmic ray event data was returned and these data are now undergoing preliminary data analysis. During the coming 2002-2003 Antarctica summer season, we are preparing for a ATIC science flight with approx. 15 to 30 days of continuous data collection in the near-space environment of LDB float altitudes.

Guzik, T. G.↗

NASA NTRS2013

Skipped Stage Modeling and Testing of the CPAS Main Parachutes

The Capsule Parachute Assembly System (CPAS) has undergone the transition from modeling a skipped stage event using a simulation that treats a cluster of parachutes as a single composite canopy to the capability of simulating each parachute individually. This capability along with data obtained from skipped stage flight tests has been crucial in modeling the behavior of a skipping canopy as well as the crowding effect on non-skipping ("lagging") neighbors. For the finite mass inflation of CPAS Main parachutes, the cluster is assumed to inflate nominally through the nominal fill time, at which point the skipping parachute continues inflating. This sub-phase modeling method was used to reconstruct three flight tests involving skipped stages. Best fit inflation parameters were determined for both the skipping and lagging canopies.

Varela, Jose G.↗

NASA NTRS2020

Application of Sparse Identification of Nonlinear Dynamics for Physics-Informed Learning

Advances in machine learning and deep neural networks has enabled complex engineering tasks like image recognition, anomaly detection, regression, and multi-objective optimization, to name but a few. The complexity of the algorithm architecture, e.g., the number of hidden layers in a deep neural network, typically grows with the complexity of the problems they are required to solve, leaving little room for interpreting (or explaining) the path that results in a specific solution. This drawback is particularly relevant for autonomous aerospace and aviation systems, where certifications require a complete understanding of the algorithm behavior in all possible scenarios. Including physics knowledge in such data-driven tools may improve the interpretability of the algorithms, thus enhancing model validation against events with low probability but relevant for system certification. Such events include, for example, spacecraft or aircraft sub-system failures, for which data may not be available in the training phase. This paper investigates a recent physics-informed learning algorithm for identification of system dynamics, and shows how the governing equations of a system can be extracted from data using sparse regression. The learned relationships can be utilized as a surrogate model which, unlike typical data-driven surrogate models, relies on the learned underlying dynamics of the system rather than large number of fitting parameters. The work shows that the algorithm can reconstruct the differential equations underlying the observed dynamics using a single trajectory when no uncertainty is involved. However, the training set size must increase when dealing with stochastic systems, e.g., nonlinear dynamics with random initial conditions.

Corbetta, Matteo↗

DOE OSTI2025

A Curated Dataset of Regional Meteor Events with Simultaneous Optical and Infrasound Observations (2006–2011)

We present a curated, openly accessible dataset of 71 regional meteor events simultaneously recorded by optical and infrasound instrumentation between 2006 and 2011. These events were captured during an observational campaign using the all-sky cameras of the Southern Ontario Meteor Network and the co-located Elginfield Infrasound Array. Each entry provides optical trajectory measurements, infrasound waveforms, and atmospheric specification profiles. The integration of optical and acoustic data enables robust linkage between observed acoustic signals and specific points along meteor trajectories, offering new opportunities to examine shock wave generation, propagation, and energy deposition processes. This release fills a critical observational gap by providing the first validated, openly accessible archive of simultaneous optical–infrasound meteor observations that supports trajectory reconstruction, acoustic propagation modeling, and energy deposition analyses. By making these data openly available in a structured format, this work establishes a durable reference resource that advances reproducibility, fosters cross-disciplinary research, and underpins future developments in meteor physics, atmospheric acoustics, and planetary defense.

astrometry↗

NASA NTRS2022

Can Machine Learning Reveal Precursors of Reversals of the Geomagnetic Axial Dipole Field?

It is well known that the axial dipole part of Earth’s magnetic field reverses polarity, so that the magnetic North Pole becomes the South Pole and vice versa. The timing of reversals is well documented for the past 160 Myr, but the conditions that lead to a reversal are still not well understood. It is not known if there are reliable ‘precursors’ of reversals (events that indicate that a reversal is upcoming) or what they might be. We investigate if machine learning (ML) techniques can reliably identify precursors of reversals based on time-series of the axial magnetic dipole field. The basic idea is to train a classifier using segments of time-series of the axial magnetic dipole. This training step requires modification of standard ML techniques to account for the fact that we are interested in rare events—a reversal is unusual, while a non-reversing field is the norm. Without our tweak, the ML classifiers lead to useless predictions. Perhaps even more importantly, the usable observational record is limited to 0–2 Ma and contains only five reversals, necessitating that we determine if the data are even sufficient to reliably train and validate an ML algorithm. To answer these questions we use several ML classifiers (linear/non-linear support vector machines and long short-term memory networks), invoke a hierarchy of numerical models (from simplified models to 3-D geodynamo simulations), and two palaeomagnetic reconstructions (PADM2M and Sint-2000). The performance of the ML classifiers varies across the models and the observational record and we provide evidence that this is not an artefact of the numerics, but rather reflects how ‘predictable’ a model or observational record is. Studying models of Earth’s magnetic field via ML classifiers thus can help with identifying shortcomings or advantages of the various models. For Earth’s magnetic field, we conclude that the ability of ML to identify precursors of reversals is limited, largely due to the small amount and low frequency resolution of data, which makes training and subsequent validation nearly impossible. Put simply: the ML techniques we tried are not currently capable of reliably identifying an axial dipole moment (ADM) precursor for geomagnetic reversals. This does not necessarily imply that such a precursor does not exist, and improvements in temporal resolution and length of ADM records may well offer better prospects in the future.

Kyle Gwirtz↗

DOE OSTI2024

Improved accuracy and robustness of electron density profiles from JET’s X-mode frequency-modulated continuous-wave reflectometers

JET’s frequency-modulated continuous wave (FMCW) reflectometers have been operating well with the current design since 2005, and density profiles have been automatically calculated intershot since then. However, the calculated profiles had long suffered from several shortcomings: poor agreement with other diagnostics, sometimes inappropriately moving radially by several centimeters, elevated levels of radial jitter, and persistent wriggles (strong unphysical oscillations). Here, in this research, several techniques are applied to the reflectometry data analysis, and the shortcomings are significantly improved. Starting with improving the equilibrium reconstruction that estimates the background magnetic field, adding a ripple correction in the reconstructed magnetic field profile, and adding new inner-wall reflection positions estimated through ray-tracing, these changes not only improve the agreement of reconstructed profiles to other diagnostics but also solve density profile wriggles that were present during band transitions. Other smaller but also persistent wriggles were also suppressed by applying a localized correction to the measured beat frequency where persistent oscillations are present. Finally, the burst analysis method, as introduced by Varela et al., has been implemented to extract the beat frequency from stacked spectrograms. Due to the strong suppression of spurious reflections, the radial jitter that sometimes would span several centimeters has been strongly reduced. The stacking of spectrograms has also been shown to be very useful for stacking recurring events, like small gas puff modulations, and extracting transport coefficients that would otherwise be below the noise level.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2022

High-resolution mapping reveals hotspots and sex-biased recombination in Populus trichocarpa

Abstract Fine-scale meiotic recombination is fundamental to the outcome of natural and artificial selection. Here, dense genetic mapping and haplotype reconstruction were used to estimate recombination for a full factorial Populus trichocarpa cross of 7 males and 7 females. Genomes of the resulting 49 full-sib families (N = 829 offspring) were resequenced, and high-fidelity biallelic SNP/INDELs and pedigree information were used to ascertain allelic phase and impute progeny genotypes to recover gametic haplotypes. The 14 parental genetic maps contained 1,820 SNP/INDELs on average that covered 376.7 Mb of physical length across 19 chromosomes. Comparison of parental and progeny haplotypes allowed fine-scale demarcation of cross-over regions, where 38,846 cross-over events in 1,658 gametes were observed. Cross-over events were positively associated with gene density and negatively associated with GC content and long-terminal repeats. One of the most striking findings was higher rates of cross-overs in males in 8 out of 19 chromosomes. Regions with elevated male cross-over rates had lower gene density and GC content than windows showing no sex bias. High-resolution analysis identified 67 candidate cross-over hotspots spread throughout the genome. DNA sequence motifs enriched in these regions showed striking similarity to those of maize, Arabidopsis, and wheat. These findings, and recombination estimates, will be useful for ongoing efforts to accelerate domestication of this and other biomass feedstocks, as well as future studies investigating broader questions related to evolutionary history, perennial development, phenology, wood formation, vegetative propagation, and dioecy that cannot be studied using annual plant model systems.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Retrieval of energy spectra for all flavours of neutrinos from core-collapse supernova with multiple detectors

ABSTRACT We present a new method by which to retrieve energy spectrum for all falvours of neutrinos from core-collapse supernova (CCSN). In the retrieval process, we do not assume any analytic formulas to express the energy spectrum of neutrinos but rather take a direct way of spectrum reconstruction from the observed data; the singular value decomposition algorithm with a newly developed adaptive energy-gridding technique is adopted. We employ three independent reaction channels having different flavour sensitivity to neutrinos. Two reaction channels, inverse beta decay on proton and elastic scattering on electrons, from a water Cherenkov detector such as Super-Kamiokande (SK) and Hyper-Kamiokande (HK), and a charged current reaction channel with Argon from the Deep Underground Neutrino Experiment (DUNE) are adopted. Given neutrino oscillation models, we iteratively search the neutrino energy spectra at the CCSN source until they provide the consistent event counts in the three reaction channels. We test the capability of our method by demonstrating the spectrum retrieval to a theoretical neutrino data computed by our recent three-dimensional CCSN simulation. Although the energy spectrum with either electron-type or electron-type antineutrinos at the CCSN source has relatively large error compared to that of other species, the joint analysis with HK + DUNE or SK + DUNE will provide precise energy spectrum of all flavours of neutrinos at the source. Finally, we discuss perspectives for improvements of our method by using neutrino data of other detectors.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Multitask Machine Learning of Collective Variables for Enhanced Sampling of Rare Events

Computing accurate reaction rates is a central challenge in computational chemistry and biology because of the high cost of free energy estimation with unbiased molecular dynamics. In this work, a data-driven machine learning algorithm is devised to learn collective variables with a multitask neural network, where a common upstream part reduces the high dimensionality of atomic configurations to a low dimensional latent space and separate downstream parts map the latent space to predictions of basin class labels and potential energies. Here, the resulting latent space is shown to be an effective low-dimensional representation, capturing the reaction progress and guiding effective umbrella sampling to obtain accurate free energy landscapes. This approach is successfully applied to model systems including a 5D Müller Brown model, a 5D three-well model, the alanine dipeptide in vacuum, and an Au(110) surface reconstruction unit reaction. It enables automated dimensionality reduction for energy controlled reactions in complex systems, offers a unified and data-efficient framework that can be trained with limited data, and outperforms single-task learning approaches, including autoencoders.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Multiplicity dependence of ${\Xi }_{\text{c}}^{+}$ and ${\Xi }_{\text{c}}^{0}$ production in pp collisions at $\sqrt{s}=13$ TeV

The first measurement at midrapidity (|y| < 0.5) of the production yield of the strange-charm baryons $Ξ$$^{+}_{c}$ and $Ξ$$^{0}_{c}$ as a function of transverse momentum (p T ) in different charged-particle multiplicity classes in proton-proton collisions at $\sqrt{s}$ = 13 TeV with the ALICE experiment at the LHC is reported. The $Ξ$$^{+}_{c}$ baryon is reconstructed via the $Ξ$$^{+}_{c}$ → $Ξ$ – π + π + decay channel in the range 4 < p T < 12 GeV/c, while the $Ξ$$^{0}_{c}$ baryon is reconstructed via both the $Ξ$$^{0}_{c}$ → $Ξ$ – π + and $Ξ$$^{0}_{c}$ → $Ξ$ – e + ν e decay channels in the range 2 < p T < 12 GeV/c. The baryon-to-meson ($Ξ$$^{0}_{c}$ + /D 0 ) and the baryon-to-baryon ($Ξ$$^{0}_{c}$ + /Λ$^{+}_{c}$) production yield ratios show no significant dependence on multiplicity. In addition, the observed yield ratios are not described by theoretical predictions that model charm-quark fragmentation based on measurements at e + e − and e − p colliders, indicating differences in the charm-baryon production mechanism in pp collisions. A comparison with different event generators and tunings, including different modelling of the hadronisation process, is also discussed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2022

Montage electron tomography of vitrified specimens

Cryo-electron tomography provides detailed views of macromolecules in situ. However, imaging a large field of view to provide more cellular context requires reducing magnification during data collection, which in turn restricts the resolution. To circumvent this trade-off between field of view and resolution, we have developed a montage data collection scheme that uniformly distributes the dose throughout the specimen. In this approach, sets of slightly overlapping circular tiles are collected at high magnification and stitched to form a composite projection image at each tilt angle. These montage tilt-series are then reconstructed into massive tomograms with a small pixel size but a large field of view. For proof-of-principle, we applied this method to the thin edge of HeLa cells. Thon rings to better than 10 Å were detected in the montaged tilt-series, and diverse cellular features were observed in the resulting tomograms. These results indicate that the additional dose required by this technique is not prohibitive to performing structural analysis to intermediate resolution across a large field of view. We anticipate that montage tomography will prove particularly useful for lamellae, increase the likelihood of imaging rare cellular events, and facilitate visual proteomics.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2020

Determination of jet calibration and energy resolution in proton–proton collisions at $\sqrt{s} = 8~\hbox {TeV}$ using the ATLAS detector

The jet energy scale, jet energy resolution, and their systematic uncertainties are measured for jets reconstructed with the ATLAS detector in 2012 using proton–proton data produced at a centre-of-mass energy of 8 TeV with an integrated luminosity of 20fb -1 . Jets are reconstructed from clusters of energy depositions in the ATLAS calorimeters using the anti-k t algorithm. A jet calibration scheme is applied in multiple steps, each addressing specific effects including mitigation of contributions from additional proton–proton collisions, loss of energy in dead material, calorimeter non-compensation, angular biases and other global jet effects. The final calibration step uses several in situ techniques and corrects for residual effects not captured by the initial calibration. Furthermore, these analyses measure both the jet energy scale and resolution by exploiting the transverse momentum balance in γ + jet, Z + jet, dijet, and multijet events. A statistical combination of these measurements is performed. In the central detector region, the derived calibration has a precision better than 1% for jets with transverse momentum 150 GeV < p T < 1500 GeV, and the relative energy resolution is (8.4 ± 0.6)% for p T =100 GeV and (23 ± 2)% for p T =20 GeV. The calibration scheme for jets with radius parameter R = 1.0, for which jets receive a dedicated calibration of the jet mass, is also discussed.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2021

Commissioning the DIRC Detector and Searching for Axion-like Particles at GlueX

This thesis centers around problems in the study of the strong nuclear force. The GlueX DIRC, a Cherenkov radiation-based detector, was proposed to upgrade the particle identification capability of the GlueX experiment, which aims to perform quantitative tests of Quantum Chromodynamics in the nonperturbative regime by searching for and studying hybrid mesons. This thesis describes the construction, commissioning, reconstruction, and calibration of the GlueX DIRC detector. Originally proposed to solve the strong CP problem, axions and axion-like particles are hypothetical pseudoscalar particles found in many proposed extensions to the Standard Model of particle physics. This thesis presents a search for photoproduction of axion-like particles using data in photon-proton interactions collected by the GlueX experiment at Jefferson Laboratory in the $\gamma\gamma$ and $\pi^+\pi^-\pi^0$ final states of the axion-like particles. In addition, the Monte Carlo modeling of the strong interaction at low energies leads to challenges known as the event generator tuning problem. This thesis presents a novel approach to the Monte Carlo event generator tuning problem using Bayesian optimization.

Yang, Yunjie↗

DOE OSTI2026

Demonstration of reconstruction-free static magnetic control of DIII-D plasma with deep reinforcement learning

This paper presents the development and experimental validation of a reinforcement learning (RL)-based magnetic controller on the DIII-D tokamak. The controller directly maps raw magnetic diagnostic signals to actuator commands, replacing the traditional isoflux control algorithm based on equilibrium reconstruction. Four RL controllers are trained using the Soft Actor–Critic algorithm with an asymmetric Actor–Critic architecture in the NSFsim simulator. All controllers are deployed in the DIII-D Plasma Control System and operated with a 4 kHz feedback loop. Two randomization strategies are evaluated during training: evolving kinetic profiles and fixed kinetic profiles within each episode. The latter approach is found to better capture experimental deviations in the current density profile and to provide overall improved control performance. Robust operation is demonstrated across heating power scans in both L- and H-mode plasmas, as well as during transient events such as L–H transitions and pellet injections. Control errors in plasma shape and radial position remained within 1.5–2.0 cm and 1 cm, respectively. A notable discrepancy was observed in the vertical X-point position, with errors of up to approximately 4 cm, attributed to the current density distribution mismatches between simulations and experiments.

DIII-D↗

DOE OSTI2025

The "Fake" Supernova Neutrinos of SBND: Using Muons Decaying at Rest to Study the Charged Current MeV-Scale $V_e$-Ar Cross Section

In a Type-II core collapse supernova, 99% of the total amount of energy is released in the form of neutrinos. However, the nearest supernova explosion in the last few hundred years was in 1987, over thirty years ago. It was also the first from which particle detectors around the world managed to detect the neutrino flux, yielding important information about their properties and the explosion mechanism. Neutrinos are the most weakly interacting subatomic fundamental particles known to date, and they act as a cooling mechanism for the star. Out of the six neutrino flavours currently known, the majority exiting the explosion are electron neutrinos, ve. Neutrino experiments using liquid argon (LAr) as their detector medium are unique at probing this specific channel. In the meantime, however, until the next explosion, we can still practise for the big event. With the Short Baseline Near Detector (SBND) experiment at Fermilab, we can study how electron neutrinos of the same energy coming from the Booster Neutrino Beam (BNB) interact in LAr, and measure the cross section. As BNB measurements are mostly tailored to higher energy neutrinos, I will present an overview of how SBND is aiming to select the low-energy neutrino candidates from muons Decaying At Rest (DAR) in the absorber and target within the BNB beam pipe. This is not a straightforward feat, encompassing special difficulties that include beam and flux systematic uncertainty simulation, system trigger needs and particular reconstruction efforts, and so I will present how each challenge is being addressed in SBND. This will be a novel measurement in the neutrino particle physics community, and will prove fundamental for the next generation of supernova neutrino detectors.

Kotsiopoulou, Lucy [Edinburgh U.]↗

NASA NTRS2021

Advances in Land Surface Models and Indicators for Drought Monitoring and Prediction

Millions of people across the globe are affected by droughts every year, and recent droughts have highlighted the considerable agricultural impacts and economic costs of these events. Monitoring the state of droughts depends on integrating multiple indicators that each capture particular aspects of hydrologic impact and various types and phases of drought. As the capabilities of land surface models and remote sensing have improved, important physical processes such as dynamic, interactive vegetation phenology, groundwater, and snowpack evolution now support a range of drought indicators that better reflect coupled water, energy, and carbon cycle processes. In this work, we discuss these advances, including newer classes of indicators that can be applied to improve the characterization of drought onset, severity, and duration. We utilize a new model-based drought reconstruction to illustrate the role of dynamic phenology and groundwater in drought assessment. Further, through case studies on flash droughts, snow droughts, and drought recovery, we illustrate the potential advantages of advanced model physics and observational capabilities, especially from remote sensing, in characterizing droughts.

Christa D Peters-lidard↗