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DOE OSTI2014

Modeling X-ray emission around galaxies

Extended X-ray emission can be studied by spatial surface brightness measurements or by spectral analysis, but the two methods can disagree at low intensity levels. Here we present an improved method for spatial analysis that can be extended to include spectral information simultaneously. We construct a model for the entire image in a given energy band and generate a likelihood function to compare the model to the data. A critical goal is disentangling vignetted and unvignetted backgrounds through their different spatial distributions. Employing either maximum likelihood or Markov Chain Monte Carlo, we can derive probability distributions for the source and background parameters together, or we can fit and subtract the background, leaving the description of the source non-parametric. We calibrate this method against a variety of simulated images, and apply it to Chandra observations of the hot gaseous halo around the elliptical galaxy NGC 720. We follow the emission below a tenth of the background and infer a hot gas mass within 35 kpc of 4-5 × 10{sup 9} M {sub ☉}, with some indication that the profile continues to at least 50 kpc and that it steepens. We derive stronger constraints on the surface brightness profile than previous studies that employed the spectral method, and we show that the density profiles inferred from these studies are in conflict with the observed surface brightness profile. Contrary to a previous claim, we find that the X-ray halo does not contain the full complement of missing baryons within the virial radius.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

Exploring the non-Gaussianity of the cosmic infrared background and its weak gravitational lensing

ABSTRACT Gravitational lensing deflects the paths of photons, altering the statistics of cosmic backgrounds and distorting their information content. We take the cosmic infrared background (CIB), which provides plentiful information about galaxy formation and evolution, as an example to probe the effect of lensing on non-Gaussian statistics. Using the Websky simulations, we first quantify the non-Gaussianity of the CIB, revealing additional detail on top of its well-measured power spectrum. To achieve this, we use needlet-like multipole-band filters to calculate the variance and higher-point correlations. Using our simulations, we show the two-, three- and four-point spectra, and compare our calculated power spectra and bispectra to Planck values. We then lens the CIB, shell-by-shell with corresponding convergence maps, to capture the broad redshift extent of both the CIB and its lensing convergence. The lensing of the CIB changes the three- and four-point functions by a few tens of per cent at large scales, unlike with the power spectrum, which changes by less than two per cent. We expand our analyses to encompass the full intensity probability distribution functions (PDFs) involving all n-point correlations as a function of scale. In particular, we use the relative entropy between lensed and unlensed PDFs to create a spectrum of templates that can allow estimation of lensing. The underlying CIB model is missing the important role of star bursting, which we test by adding a stochastic lognormal term to the intensity distributions. The novel aspects of our filtering and lensing pipeline should prove useful for any radiant background, including line intensity maps.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

Latent Stochastic Differential Equations for Modeling Quasar Variability and Inferring Black Hole Properties

Quasars are bright and unobscured active galactic nuclei (AGN) thought to be powered by the accretion of matter around supermassive black holes at the centers of galaxies. The temporal variability of a quasar’s brightness contains valuable information about its physical properties. The UV/optical variability is thought to be a stochastic process, often represented as a damped random walk described by a stochastic differential equation (SDE). Upcoming wide-field telescopes such as the Rubin Observatory Legacy Survey of Space and Time (LSST) are expected to observe tens of millions of AGN in multiple filters over a ten year period, so there is a need for efficient and automated modeling techniques that can handle the large volume of data. Latent SDEs are machine learning models well suited for modeling quasar variability, as they can explicitly capture the underlying stochastic dynamics. In this work, we adapt latent SDEs to jointly reconstruct multivariate quasar light curves and infer their physical properties such as the black hole mass, inclination angle, and temperature slope. Our model is trained on realistic simulations of LSST ten year quasar light curves, and we demonstrate its ability to reconstruct quasar light curves even in the presence of long seasonal gaps and irregular sampling across different bands, outperforming a multioutput Gaussian process regression baseline. Our method has the potential to provide a deeper understanding of the physical properties of quasars and is applicable to a wide range of other multivariate time series with missing data and irregular sampling.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2019

KCMP Minnesota Tall Tower Ammonia Observations and Emission Estimates Dataset 2017-2018

This data set contains NH3 mixing ratios, net ecosystem NH3 exchange, and supporting information measured at a tall tower (KCMP, 244 meter) site near St. Paul, Minnesota, USA. The data include NH3 and H2O mixing ratios measured at the 100 meter level, as well as NH3 mixing ratio measured at the 56 meter level. Wind data was measured using a sonic anemometer at the 185 meter level. Also included in this data set are values of wet N and NH3 deposition from 2016 - 2018. This data was collected to improve understanding of the ammonia and reactive nitrogen budget for the region.Ammonia mixing ratios were filtered for periods when the NH3 analyzer measured anomalous (i.e. 10 mmol mol-1) water vapor mixing ratios based on comparison with the IRGA (LI7000). Further, we filtered out NH3 observations obtained during strongly condensing conditions. These periods were identified when the water vapor mixing ratio differences between the 100 m and 56 m levels were more negative than -0.5 mmol mol-1 (i.e. when the water vapor gradient was strong and directed toward the land surface). Finally, we filtered out NH3 fluxes when the atmosphere was strongly stable (i.e. when (z-d)/L exceeded a value of +4.5) and subject to decoupling from the surface fluxes. Approximately 35.1% of the hourly flux data were either missing due to instrument failure or filtered according to the above quality control procedures.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Predictive analytics of selections of russet potatoes

We explore the application of machine learning algorithms specifically to enhance the selection process of Russet potato (Solanum tuberosum L.) clones in breeding trials by predicting their suitability for advancement. This study addresses the challenge of efficiently identifying high-yield, disease-resistant, and climate-resilient potato varieties that meet processing industry standards. Leveraging manually collected data from trials in the state of Oregon, we investigate the potential of a wide variety of state-of-the-art binary classification models. The dataset includes 1086 clones, with data on 38 attributes recorded for each clone, focusing on yield, size, appearance, and frying characteristics, with several control varieties planted consistently across four Oregon regions from 2013 to 2021. We conduct a comprehensive analysis of the dataset that includes preprocessing, feature engineering, and imputation to address missing values. We focus on several key metrics such as accuracy, F1-score, and Matthews correlation coefficient (MCC) for model evaluation. The top-performing models, namely a feedforward neural network classifier (Neural Net), a histogram-based gradient boosting classifier (HGBC), and a support vector machine classifier (SVM), demonstrate consistent and significant results. To further validate our findings, we conducted a simulation study using the aims, data-generating mechanisms, estimands, methods, and performance measures (ADEMP) framework, simulating different data-generating scenarios to assess model robustness and performance through true positive, true negative, false positive, and false negative distributions, area under the receiver operating characteristic curve (AUC-ROC) and MCC. The simulation results highlight that non-linear models like SVM and HGBC consistently show higher AUC-ROC and MCC than logistic regression, thus outperforming the traditional linear model across various distributions, and emphasizing the importance of model selection and tuning in agricultural trials. Variable selection further enhances model performance and identifies influential features in predicting trial outcomes. The findings emphasize the potential of machine learning in streamlining the selection process for potato varieties, offering benefits such as increased efficiency, substantial cost savings, and judicious resource utilization. Our study contributes insights into precision agriculture and showcases the relevance of advanced technologies for informed decision-making in breeding programs.

60 APPLIED LIFE SCIENCES↗

NASA NTRS2023

Potentially Underestimated Gas Flaring Activities—A New Approach to Detect Combustion Using Machine Learning and NASA’s Black Marble Product Suite

Monitoring changes in greenhouse gas (GHG) emission is critical for assessing climate mitigation efforts towards the Paris Agreement goal. A crucial aspect of science-based GHG monitoring is to provide objective information for quality assurance and uncertainty assessment of the reported emissions. Emission estimates from combustion events (gas flaring and biomass burning) are often calculated based on activity data (AD) from satellite observations, such as those detected from the visible infrared imaging radiometer suite (VIIRS) onboard the Suomi-NPP and NOAA-20 satellites. These estimates are often incorporated into carbon models for calculating emissions and removals. Consequently, errors and uncertainties associated with AD propagate into these models and impact emission estimates. Deriving uncertainty of AD is therefore crucial for transparency of emission estimates but remains a challenge due to the lack of evaluation data or alternate estimates. This work proposes a new approach using machine learning (ML) for combustion detection from NASA's Black Marble product suite and explores the assessment of potential uncertainties through comparison with existing detections. We jointly characterize combustion using thermal and light emission signals, with the latter improving detection of probable weaker combustion with less distinct thermal signatures. Being methodologically independent, the differences in ML-derived estimates with existing approaches can indicate the potential uncertainties in detection. The approach was applied to detect gas flares over the Eagle Ford Shale, Texas. We analyzed the spatio-temporal variations in detections and found that approximately 79.04% and 72.14% of the light emission-based detections are missed by ML-derived detections from VIIRS thermal bands and existing datasets, respectively. This improvement in combustion detection and scope for uncertainty assessment is essential for comprehensive monitoring of resulting emissions and we discuss the steps for extending this globally.

gas flaring↗

DOE OSTI2017

Assessing the urban carbon footprint: An overview

All cities present environmental sustainability issues, above all regarding greenhouse gas (GHG) emissions, and specifically carbon dioxide (CO{sub 2}), that directly affect climate change. Consequently, it is very important to quantify and report their Carbon Footprint (CF) for implementing national and international policies/strategies aimed at mitigating and adapting these concerns. The Urban Carbon Footprint (UCF), indeed, has been recognized as the more valuable choice to inform, specifically, decision makers about city environmental sustainability. Several accounting systems and inventory methods have been taken into account to perform UCF, highlighting the complexity of the topic and generating very often confusion among users. In this context, the authors aim to summarize what has been done and what is going on with UCFs, trying to classify them according to some principal dimensions. Thus, they divide UFCs in two main categories namely: “spatial” or “direct”, with a limited amount of data requested, and “economic” or “life cycle based”, more or less data inclusive according to the accounting systems considered. Furthermore, they observe that there is not a “global agreed-upon protocol” yet, neither is there a specific model shared among researchers, even if some steps have been made towards this direction (Relative Carbon Footprint - RCF, Publicly Available Specification – PAS 2070 and Global Protocol for Community scale - GPC). Consequently, it is necessary to complete and standardize, in the short term, the accounting and reporting frameworks, in order to compare different UCFs for adopting shared climate strategies and actions at global level. - Highlights: •Cities present environmental sustainability issues. •GHG emissions - specifically carbon dioxide - directly affect climate change. •UCF is a tool to support policy makers towards city environmental sustainability. •There is not a “global agreed-upon protocol” to perform UFC. •There are still missing points to be considered to provide a sharing model.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2022

Resilient, Rural, and Revolutionary: Salisbury Square's Direct-Current Affordable Microgrid Community: Preprint

The technology to interconnect buildings with a dedicated direct-current (DC) power distribution network is in place today; what is missing is a turnkey approach to designing a DC microgrid - and the business models allowing such systems to be deployed, owned, and operated at scale. To close this gap, the Salisbury Square Development Team, comprising clean-energy experts, has engineered a resilient community DC microgrid for an affordable housing community in Randolph, Vermont. Ten single-family, occupant-owned residences and 12 multifamily rental units will share locally generated and stored solar energy via a DC power distribution bus capable of operating during extended grid outages. With a DC power distribution network in place, each home will be equipped with high-efficiency DC lighting and appliances, operating alongside alternating current (AC) appliances, even during an islanded mode of operation. To obtain a comprehensive understanding of what is possible and achievable, the Team collaborated with the local utility, regulatory agencies, a national laboratory, energy-as-service providers, and vendors. The collaborators evaluated microgrid typologies, business models, and energy modeling, and analyzed electrification and resilience. Further, the Team applied the URBANoptTM (Urban Renewable Building and Neighborhood optimization, NREL 2022) software development kit (SDK) to Salisbury Square's single-family and multifamily buildings to validate workflows and identify needs for advanced capability. This paper addresses the barriers to entry, scalability, and impact on residents and system ownership. It also examines the analysis that informed the design and engineering of the DC microgrid and the opportunities to streamline the process.

DER↗

DOE OSTI2019

Dreaming awake: disentangling the underlying physics in case of a SUSY-like discovery at the LHC

The purpose of this review is to investigate what kind of physics can be extracted at the Large Hadron Collider (LHC), assuming a discovery is made in events with missing transverse momentum, as generically expected in supersymmetry (SUSY) with R-parity conservation. To set the scene, we first discuss the collider phenomenology of the six possible electroweakino benchmark scenarios, as they provide valuable insight into what one might be facing at the LHC. We review the existing methods for mass reconstruction from measured kinematic endpoints in the distributions of suitable variables, e.g. the invariant masses of various sets of visible decay products, as well as the M T2 and the M 2 types of variables. We propose to extend the application of these methods to the various topologies of fully hadronic final states, possibly with hadronically reconstructed massive bosons (W, Z or h). We test the idea with a simplified simulation of events in the main electroweakino benchmark scenarios. We find that the fully hadronic events allow the complete determination of the relevant mass spectrum. For comparison, we also review the potential of the standard kinematic endpoint methods for final states involving leptons from the decays of (on-shell or off-shell) sleptons. We find that with 300 fb -1 , the statistics for the leptonic events is very marginal and they look less promising than the fully hadronic channels. This corresponds to a complete reversal of the usual paradigm, where leptonic events comprised the gold-plated SUSY channels. Finally, we put together all available information and summarize what level of understanding of the underlying physics can be achieved. We show that, as a by-product of the mass reconstruction, it is also possible to determine the production cross sections and decay branching ratios, which in turn enable us to pinpoint the underlying model.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Coupling Metabolic Source Isotopic Pair Labeling and Genome Wide Association for Metabolite and Gene Annotation in Plants (Final Technical Report)

In this project, we applied our labeling pipeline to Arabidopsis and sorghum by feeding tissues with isotopically labeled versions of commercially available amino acids to identify all metabolite features that incorporate the label. In sorghum, we fed five accessions, sampled across the diversity of sorghum, to identify the precursor-of-origin for metabolites that vary between accessions as well as those that may be missing from a single reference genotype. This provided us with precursor-of-origin annotation for thousands of unknown metabolites. We then used GWA to map genes responsible for the synthesis of precursor-of-origin classified metabolites. For sorghum leaf and root ducible metabolites, we performed untargeted metabolomics on leaf and root tissues from 300 diverse genotyped sorghum inbred lines. The amino acid precursor-of-origin metabolite library were then used to identify the corresponding metabolites in the GWA data sets and to identify novel gene-metabolite associations. Finally, we utilized existing and newly generated sequenced EMS mutants of sorghum to validate the predicted gene-metabolite relationships that our labelling analysis identified. In parallel, we conducted similar feeding experiments in Arabidopsis to categorize metabolites based on precursor-of-origin, identify those that vary across our existing Arabidopsis metabolite GWA dataset, and identify genes required for the synthesis of each metabolite. To provide an independent test of gene annotation and pathway involvement, we tested the GWA gene-metabolite associations in Arabidopsis by analyzing the metabolic phenotypes of gene knockouts. Genes of particular interest from both sorghum and Arabidopsis were studied in detail by directly measuring the activity of the corresponding enzymes following heterologous expression. In summary, this work classified as-yet-unknown amino acid-derived metabolites and identified genes involved in their production generated through “omics” technologies. This information was used to validate gene function and identify new metabolism in Arabidopsis and sorghum.

09 BIOMASS FUELS↗

DOE OSTI2014

Overview of the forthcoming AAPM TG-158 Report, 'Measurements and calculations of doses outside the treatment volume from External Beam Radiation Therapy' - Paper 85

The evolution of techniques and technology in radiotherapy has greatly improved our ability to deliver higher tumor doses while minimizing the dose to the adjacent organs at risk. However, this improved conformality has not removed the problem of doses to normal tissues outside the target volume. Whether these out-of-field doses arise from photons, electrons, protons or neutrons, there are unique challenges that many medical physicists may have limited knowledge about or experience with. With modern treatment planning systems, high dose regions and areas within the primary beam path are typically well described. However, their accuracy beyond a few centimeters outside the treatment field edge are usually poor. In these cases, alternate methods are required to assess the out-of-field dose. Such assessments require additional care to avoid potentially large errors associated with a variety of unique complications in performing dosimetry outside the treatment field. Furthermore, physicists will not always have an a priori estimate of the expected dose, and, as a result, large dosimetric errors are more easily missed. Low radiation doses outside the treatment volume are a concern because they can cause deleterious effects to the patient. The risk of late effects from the secondary radiation may be more evident today because of the success of cancer screening and modern therapies that have increased the number of cancer patients who survive and live long enough for the adverse radiation effects on healthy tissues to manifest. Simultaneously, radiotherapy procedures have become more complex, making the estimation of low out-of-field doses even more challenging. Over the past 30 years, numerous studies have been published detailing non-target doses from various radiotherapy treatments (for a detailed review see Xu et al. However, dosimetry data were obtained using a variety of measurement or computational techniques. An overview of dosimetry techniques for measuring low-level non-target doses is not available. Improved, or at least valid, dosimetry is important for several reasons. In clinical care (e.g. fetal dose estimates), large dose errors can lead to inappropriate or unnecessary treatment decisions, with potentially serious consequences to the patient. In a broader sense of somatic effects, more accurate dosimetry will improve patient risk assessment, understanding of dose responses, treatment planning, and design of the radiation delivery machines. Once reliable dosimetry data are available, it is then the clinician's responsibility to assess the risk vs. benefit of the radiotherapy treatment to make a sound and informed assessment of all possible treatment outcomes. (author)

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2015

Short timescale photometric and polarimetric behavior of two BL Lacertae type objects

Context. Blazars are astrophysical sources whose emission is dominated by non-thermal processes, typically interpreted as synchrotron and inverse Compton emission. Although the general picture is rather robust and consistent with observations, many aspects are still unexplored. Aims. Polarimetric monitoring can offer a wealth of information about the physical processes in blazars. Models with largely different physical ingredients can often provide almost indistinguishable predictions for the total flux, but usually are characterized by markedly different polarization properties. We explore, with a pilot study, the possibility to derive structural information about the emitting regions of blazars by means of a joint analysis of rapid variability of the total and polarized flux at optical wavelengths. Methods. Short timescale (from tens of seconds to a couple of minutes) optical linear polarimetry and photometry for two blazars, BL Lacertae and PKS 1424+240, was carried out with the PAOLO polarimeter at the 3.6 m Telescopio Nazionale Galileo. Several hours of almost continuous observations were obtained for both sources. Results. Our intense monitoring allowed us to draw strongly different scenarios for BL Lacertae and PKS 1424+240, with the former characterized by intense variability on time-scales from hours to a few minutes and the latter practically constant in total flux. Essentially the same behavior is observed for the polarized flux and the position angle. The variability time-scales turned out to be as short as a few minutes, although involving only a few percent variation of the flux. The polarization variability time-scale is generally consistent with the total flux variability. Total and polarized flux appear to be essentially uncorrelated. However, even during our relatively short monitoring, different regimes can be singled out. Conclusions. No simple scenario is able to satisfactorily model the very rich phenomenology exhibited in our data. As a result, detailed numerical simulations show that the emitting region should be characterized by some symmetry, and the inclusion of turbulence for the magnetic field may constitute the missing ingredient for a more complete interpretation of the data.

1424+240↗

DOE OSTI2014

MO-PIS-Exhibit Hall-01: Tools for TG-142 Linac Imaging QA I

Partners in Solutions is an exciting new program in which AAPM partners with our vendors to present practical “hands-on” information about the equipment and software systems that we use in our clinics. The therapy topic this year is solutions for TG-142 recommendations for linear accelerator imaging QA. Note that the sessions are being held in a special purpose room built on the Exhibit Hall Floor, to encourage further interaction with the vendors. Automated Imaging QA for TG-142 with RIT Presentation Time: 2:45 – 3:15 PM This presentation will discuss software tools for automated imaging QA and phantom analysis for TG-142. All modalities used in radiation oncology will be discussed, including CBCT, planar kV imaging, planar MV imaging, and imaging and treatment coordinate coincidence. Vendor supplied phantoms as well as a variety of third-party phantoms will be shown, along with appropriate analyses, proper phantom setup procedures and scanning settings, and a discussion of image quality metrics. Tools for process automation will be discussed which include: RIT Cognition (machine learning for phantom image identification), RIT Cerberus (automated file system monitoring and searching), and RunQueueC (batch processing of multiple images). In addition to phantom analysis, tools for statistical tracking, trending, and reporting will be discussed. This discussion will include an introduction to statistical process control, a valuable tool in analyzing data and determining appropriate tolerances. An Introduction to TG-142 Imaging QA Using Standard Imaging Products Presentation Time: 3:15 – 3:45 PM Medical Physicists want to understand the logic behind TG-142 Imaging QA. What is often missing is a firm understanding of the connections between the EPID and OBI phantom imaging, the software “algorithms” that calculate the QA metrics, the establishment of baselines, and the analysis and interpretation of the results. The goal of our brief presentation will be to establish and solidify these connections. Our talk will be motivated by the Standard Imaging, Inc. phantom and software solutions. We will present and explain each of the image quality metrics in TG-142 in terms of the theory, mathematics, and algorithms used to implement them in the Standard Imaging PIPSpro software. In the process, we will identify the regions of phantom images that are analyzed by each algorithm. We then will discuss the process of the creation of baselines and typical ranges of acceptable values for each imaging quality metric.

07 ISOTOPE AND RADIATION SOURCES↗

DOE OSTI2024

Dual-frequency (Ka-band and G-band) radar estimates of liquid water content profiles in shallow clouds

The profile of the liquid water content (LWC) in clouds provides fundamental information for understanding the internal structure of clouds, their radiative effects, propensity to precipitate, and degree of entrainment and mixing with the surrounding environment. In principle, differential absorption techniques based on coincident dual-frequency radar reflectivity observations have the potential to provide the LWC profile. Previous differential frequency radar reflectivity (DFR) efforts were challenged by the fact that the measurable differential attenuation for small quantities of LWC is usually comparable to the system measurement error. This typically renders the retrieval impractical, as the uncertainty can become many times greater than the retrieved value itself. Theoretically, this drawback can be mitigated following two interconnected approaches: (1) increasing the frequency separation between the dual-frequency radar system to measure greater differential attenuation and (2) increasing the radar operating frequency to reduce the instrument measurement random error. Our recently developed 239 GHz radar was deployed during the Eastern Pacific Cloud Aerosol Precipitation Experiment (EPCAPE) along with a variety of collocated remote sensing and in situ instruments. We have combined Ka-band (35 GHz) and G-band (239 GHz) observations to retrieve the LWC from more than 100 vertical profiles of shallow clouds with typical amounts of LWC smaller than 1 g m -3 . We theoretically and experimentally demonstrate that the Ka-band and G-band pair of frequencies offers at least a 65 % relative improvement in the LWC retrieval sensitivity compared to previous works reported in the literature using lower-frequency radars. This new technique provides a missing capability to determine the LWC in the challenging low liquid water path (LWP) range (< 200 g m -2 ) and suggests a way forward to characterize microphysical and dynamical processes more precisely in shallow clouds.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Quantum algorithmic measurement

There has been recent promising experimental and theoretical evidence that quantum computational tools might enhance the precision and efficiency of physical experiments. However, a systematic treatment and comprehensive framework are missing. Here we initiate the systematic study of experimental quantum physics from the perspective of computational complexity. To this end, we define the framework of quantum algorithmic measurements (QUALMs), a hybrid of black box quantum algorithms and interactive protocols. We use the QUALM framework to study two important experimental problems in quantum many-body physics: determining whether a system’s Hamiltonian is time-independent or time-dependent, and determining the symmetry class of the dynamics of the system. We study abstractions of these problems and show for both cases that if the experimentalist can use her experimental samples coherently (in both space and time), a provable exponential speedup is achieved compared to the standard situation in which each experimental sample is accessed separately. Our work suggests that quantum computers can provide a new type of exponential advantage: exponential savings in resources in quantum experiments.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2021

Validation of Scale-Derived Ages in Wild Juvenile and Adult Steelhead Using Parental-Based Tagging

Abstract Accurate age information is a critical component in fisheries monitoring, management, and research. The ability to assign accurate ages to fish by using nonlethal structures is vital in calculating cohort productivity for fish species with low abundances and variable life histories. A common nonlethal method to assign ages is by interpretation of patterns in scales. Validation of ages is not easily obtained for free-ranging fishes; however, the development of genetic “tags” has expanded age validation opportunities. In this study, we used parental-based genetic tagging to validate ages determined from scales collected from juvenile and adult steelhead Oncorhynchus mykiss in two streams within the Snake River basin. Juvenile scale ages were in agreement with 93% of the known ages. Adult scale ages were in agreement with 89% of the known ages. Although overall bias in juvenile ages was very low, we saw a slight positive bias in younger fish (young of the year and age 1) and a small negative bias in older juveniles (ages 2–4). A small negative bias in ages of adults was a consequence of errors in freshwater age. The errors observed did not significantly bias age compositions because the 90% confidence intervals about the age proportions based on scales contained the known proportions; therefore, scale analysis is an acceptable method for assigning ages to Snake River steelhead. We discuss the value of validated known-age scales as a reference collection, and as an illustrative use, we constructed a circulus count guide to aid technicians in identifying missing annuli and distinguishing true from false annuli. To demonstrate the use of the circulus guidelines, we reanalyzed samples with age discrepancies, applying the circulus count data and the known age to identify mistakes.

Reinhardt, Leslie↗

NASA NTRS2002

Commercial Research and Development: Power to Explore, Opportunities from Discovery

The technical and economic goals of commercial use of space are laudable, and are addressed as a high priority by almost every national space program and most major aerospace companies the world over. Yet, the focus of most organizational agendas and discussions tends to focus on one or two very narrow enabling aspects of this potentially large technological and economic opportunity. While government sponsored commercial launch activities and private space platforms are an integral part of efforts to leverage the commercial use of space, these activities are possibly one of the smallest parts of creating, a viable and sustainable market for the commercial use of space. Most of the current programs usually do not appropriately address some of the critical issues of the current, already interested, potential space user communities. Current programs place the focus of the majority of the user requirements on the vehicle payload weight and mass performance considerations as the primary payload economical factor in providing a commercial market with a stimulating price for gaining access to the space environment. The larger user challenges of transformation from Earth-based research and development approaches to space environment approaches are not addressed early enough in programs to impact the new business considerations of potential users. Currently, space-based research and development user activities require a large user investment in time, in development of new areas of support expertise, in development of new systems, in risk of schedule to completion, and in long term capital positioning. The larger opportunities for stimulating a strong market driven interest in commercial use of space that could result from the development of vehicle payload "leap ahead technologies" for users are being missed, and there is a real risk of limiting the potentially broader market base to support a more technologically advanced and economically lucrative outcome. A major driving force for strengthening the commercial space activities is not only the technological advances in launch vehicle, or newer satellites, but the myriad of enabling payloads technologies that could, as a goal, result in an almost transparent facilitation to regular CD a, -n access to space and microgravity environments by the future users from the existing Earth-based research and development organizations market segments. Rather than focusing only on developing high lift performance launch vehicles and then developing payloads to fit them, the real focus from a business model perspective should to be on the customer payloads requirements, and on designing launch vehicles and platforms systems for a space transportation and facility infrastructure to support all aspects of the business model for the user market. To harness the full potential of space commercialization, new efforts need to be made to comprehensively examine all the critical business model areas for commercial research, development, and manufacturing in space so as to identify specific products and efforts; to determine how such operations must be both similar to and different from current Earth-based activities; to evaluate the enabling technological devices, processes and efforts so that like efforts can be addressed in a synergistic fashion for maximum user cost effectiveness; to delineate the services that are both needed and can be provided by such activities; and to use this information to drive design and development of space commercialization efforts and policy.

Casas, Joseph C.↗

NASA NTRSDate not supplied

Lessons from Earth Bioaerosol Sampling for Venus Habitability & Biosignature Science

Venus’s clouds are often discussed as a potential habitat. Several high-priority science goals – cloud aerosol composition, internal radiative flux, and aerosol residence time and circulation models – will improve our understanding of Venus in an astrobiology context. Lessons from Earth’s aerobiosphere can inform these requirements and interpretation of results. A stable Venus aerobiosphere would need microorganisms to reproduce faster than they settle out due to gravity. This is a joint constraint of the potential nutrients and energy influx, bioenergetic costs such as desiccation and radiation damage, and aerosol dynamics. Though life cycles involving S- and Fe-based redox metabolism have been proposed, at an optimistic estimate of 75% H2SO4, Venus aerosol water activity (aw) is still ~0.02, far below the observed microbial growth limit of ~0.6. Long-term desiccation with brief spurts of repair, growth and reproduction in response to transient water influx (perhaps volcanism) is the most likely model for Earth-like life on Venus – a ‘desert bloom’ scenario. In Earth’s troposphere, clouds can carry 103 – 105 cells/mL, some metabolically active. A better analogue may be Earth’s stratospheric sulfate aerosol layer: supercooled sulfuric acid aerosols (acid weight fraction 0.6 – 0.85, 0.1 – 1 µm diameter, 0.1 – 1 cm-3) with little water activity, long residence times, high UV radiation, and only sporadic influx from surface particle sources. Viable cells isolated from this region are rare (~102 cells/m3), though ‘hot spots’ at tropospheric mixing occur. Cells are primarily in inactive forms such as spores, and it is unclear if any are associated with sulfate aerosols or simply co-located. Given the heterogeneities in Venus clouds and the model of a sparse, largely dormant ecosystem, a single transect on descent would most likely pass through a low-water, inactive region, missing potential signs of habitability or biosignatures. Even a passive aerial platform (balloon) may end up following a single air mass over its lifetime. A better strategy for Venus would target an aerial region with some upwelling from surface sources, and take multiple transects separated in time and space. Earth’s stratospheric bioaerosol population could provide a starting point for estimating the required lower volume and detection limits.

Earth↗