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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

No more active galactic nuclei in clumpy disks than in smooth galaxies at z ∼ 2 in CANDELS/3D-HST

We use CANDELS imaging, 3D-HST spectroscopy, and Chandra X-ray data to investigate if active galactic nuclei (AGNs) are preferentially fueled by violent disk instabilities funneling gas into galaxy centers at 1.3 < z < 2.4. We select galaxies undergoing gravitational instabilities using the number of clumps and degree of patchiness as proxies. The CANDELS visual classification system is used to identify 44 clumpy disk galaxies, along with mass-matched comparison samples of smooth and intermediate morphology galaxies. We note that despite being mass-matched and having similar star formation rates, the smoother galaxies tend to be smaller disks with more prominent bulges compared to the clumpy galaxies. The lack of smooth extended disks is probably a general feature of the z ∼ 2 galaxy population, and means we cannot directly compare with the clumpy and smooth extended disks observed at lower redshift. We find that z ∼ 2 clumpy galaxies have slightly enhanced AGN fractions selected by integrated line ratios (in the mass-excitation method), but the spatially resolved line ratios indicate this is likely due to extended phenomena rather than nuclear AGNs. Meanwhile, the X-ray data show that clumpy, smooth, and intermediate galaxies have nearly indistinguishable AGN fractions derived from both individual detections and stacked non-detections. The data demonstrate that AGN fueling modes at z ∼ 1.85—whether violent disk instabilities or secular processes—are as efficient in smooth galaxies as they are in clumpy galaxies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

A Proxy Method to Bridge LCA Data Gaps Using Automated Material Classification and Probabilistic Under-Specification

Life cycle assessments (LCAs) are essential for understanding the environmental impacts of material production. However, gaps in life cycle inventory (LCI) data for material and chemical inputs present a key challenge for LCA practitioners, especially in the early design stages. Strategies for filling in these gaps require additional time and expertise, which can hinder the LCA’s completion. This study combined automatic material classification and probabilistic under-specification to create a time-efficient method to fill material LCI data gaps. To illustrate the proposed method, proxy environmental impact distributions were generated using publicly available material LCI data classified into the ChemOnt chemical taxonomy using the open-source chemical classification software ClassyFire. Input materials with data gaps were then classified into the same taxonomy, where proxy environmental impact values could be selected from the available distributions to quickly fill in any data gaps. Although these methods were applied to classify material production processes available in the Federal LCA Commons and Ecoinvent databases, they can be applied to any LCA database. This study shows that classifying materials by their chemical structure produces taxonomies with increased granularity relative to industrial classification, improving the ability of under-specified proxy data to be used for differentiating the environmental impacts of competing designs.

biological databases↗
DOE OSTI2017

Application of visible and near-infrared spectroscopy to classification of Miscanthus species

Here, the feasibility of visible and near infrared (NIR) spectroscopy as tool to classify Miscanthus samples was explored in this study. Three types of Miscanthus plants, namely, M. sinensis , M. sacchariflorus and M. fIoridulus , were analyzed using a NIR spectrophotometer. Several classification models based on the NIR spectra data were developed using line discriminated analysis (LDA), partial least squares (PLS), least squares support vector machine regression (LSSVR), radial basis function (RBF) and neural network (NN). The principal component analysis (PCA) presented rough classification with overlapping samples, while the models of Line_LSSVR, RBF_LSSVR and RBF_NN presented almost same calibration and validation results. Due to the higher speed of Line_LSSVR than RBF_LSSVR and RBF_NN, we selected the line_LSSVR model as a representative. In our study, the model based on line_LSSVR showed higher accuracy than LDA and PLS models. The total correct classification rates of 87.79 and 96.51% were observed based on LDA and PLS model in the testing set, respectively, while the line_LSSVR showed 99.42% of total correct classification rate. Meanwhile, the lin_LSSVR model in the testing set showed correct classification rate of 100, 100 and 96.77% for M. sinensis , M. sacchariflorus and M. fIoridulus , respectively. The lin_LSSVR model assigned 99.42% of samples to the right groups, except one M. fIoridulus sample. The results demonstrated that NIR spectra combined with a preliminary morphological classification could be an effective and reliable procedure for the classification of Miscanthus species.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Appropriate “marginal” farmlands for second‐generation biofuel crops in North Carolina

Abstract Current research on bioenergy crops shows that perennial grasses can yield substantial amounts of dry biomass with relatively low inputs of water and fertilizer. In order to minimize competition with food production, it has been suggested that bioenergy crops could be directed to land areas less suitable for commodity crops, commonly referred to as “marginal” lands. These are land units with inherent limitations to vegetative growth and production, which may be due to several factors (soil physical and chemical properties, climatic conditions, etc.). However the term “marginal” is an adjective with imprecise meaning, and objective criteria for determining “marginal” lands for siting bioenergy crops are necessary. Here we propose that such criteria may be based on soil survey classifications and realistic yield estimates, and we show an example of its use to justify site selection for bioenergy crops in different regions of North Carolina.

09 BIOMASS FUELS↗
DOE OSTI2025

Automation for Electric Vehicle Battery Pack Disassembly

Battery‐electric vehicles (EVs) are growing exponentially. The demand for these batteries is expected to increase sevenfold by 2035. The EV batteries reach their end of life when the capacity fades to 70%–80% of new, with some being removed from the primary applications with even lower levels of degradation. These batteries can be used in less demanding applications. The disassembly process is currently manual, slow, unsafe, and expensive. Automation is needed to increase the throughput. EV battery packs feature various continually changing designs and form factors, which limit the usefulness of deterministically programmed robotic solutions. The conceptual robotic disassembly of EV batteries has attracted the attention of researchers. However, while many approaches have been proposed, practical implementations are lacking. Here, we review proposed concepts for EV battery disassembly and describe the selected approach, with elements of partial solutions validated in a laboratory setting, including the selection of commercial solutions, the development of custom end effectors, and methodologies for detection, localization, and classification of fasteners. The computer vision tasks employed an overhead 2D camera to detect the type of battery pack and approximate localization of fasteners, and a 3D camera mounted on the robotic arm for precise localization (position and tilt) and classification.

Islam, Abu [Rochester Inst. of Technology, Rochest↗
DOE OSTI2013

PROPERTIES OF M31. II. A CEPHEID DISK SAMPLE DERIVED FROM THE FIRST YEAR OF PS1 PANDROMEDA DATA

We present a sample of Cepheid variable stars toward M31 based on the first year of regular M31 observations of the PS1 survey in the r{sub P1} and i{sub P1} filters. We describe the selection procedure for Cepheid variable stars from the overall variable source sample and develop an automatic classification scheme using Fourier decomposition and the location of the instability strip. We find 1440 fundamental mode (classical {delta}) Cep stars, 126 Cepheids in the first overtone mode, and 147 belonging to the Population II types. Two hundred ninety-six Cepheids could not be assigned to one of these classes and three hundred fifty-four Cepheids were found in other surveys. These 2009 Cepheids constitute the largest Cepheid sample in M31 known so far and the full catalog is presented in this paper. We briefly describe the properties of our sample in its spatial distribution throughout the M31 galaxy, in its age properties, and we derive an apparent period-luminosity relation (PLR) in our two bands. The Population I Cepheids nicely follow the dust pattern of the M31 disk, whereas the 147 Type II Cepheids are distributed throughout the halo of M31. We outline the time evolution of the star formation in the major ring found previously and find an age gradient. A comparison of our PLR to previous results indicates a curvature term in the PLR.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2011

THE VLA SURVEY OF CHANDRA DEEP FIELD SOUTH. V. EVOLUTION AND LUMINOSITY FUNCTIONS OF SUB-MILLIJANSKY RADIO SOURCES AND THE ISSUE OF RADIO EMISSION IN RADIO-QUIET ACTIVE GALACTIC NUCLEI

We present the evolutionary properties and luminosity functions of the radio sources belonging to the Chandra Deep Field South Very Large Array survey, which reaches a flux density limit at 1.4 GHz of 43 {mu}Jy at the field center and redshift {approx}5 and which includes the first radio-selected complete sample of radio-quiet active galactic nuclei (AGNs). We use a new, comprehensive classification scheme based on radio, far- and near-IR, optical, and X-ray data to disentangle star-forming galaxies (SFGs) from AGNs and radio-quiet from radio-loud AGNs. We confirm our previous result that SFGs become dominant only below 0.1 mJy. The sub-millijansky radio sky turns out to be a complex mix of SFGs and radio-quiet AGNs evolving at a similar, strong rate; non-evolving low-luminosity radio galaxies; and declining radio powerful (P {approx}> 3 x 10{sup 24} W Hz{sup -1}) AGNs. Our results suggest that radio emission from radio-quiet AGNs is closely related to star formation. The detection of compact, high brightness temperature cores in several nearby radio-quiet AGNs can be explained by the coexistence of two components, one non-evolving and AGN related and one evolving and star formation related. Radio-quiet AGNs are an important class of sub-millijansky sources, accounting for {approx}30% of the sample and {approx}60% of all AGNs, and outnumbering radio-loud AGNs at {approx}< 0.1 mJy. This implies that future, large area sub-millijansky surveys, given the appropriate ancillary multiwavelength data, have the potential of being able to assemble vast samples of radio-quiet AGNs, bypassing the problems of obscuration that plague the optical and soft X-ray bands.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

The K2 Ecliptic Plane Input Catalog (EPIC) and stellar classifications of 138,600 targets in campaigns 1–8

The K2 Mission uses the Kepler spacecraft to obtain high-precision photometry over ≈80 day campaigns in the ecliptic plane. The Ecliptic Plane Input Catalog (EPIC) provides coordinates, photometry, and kinematics based on a federation of all-sky catalogs to support target selection and target management for the K2 mission. We describe the construction of the EPIC, as well as modifications and shortcomings of the catalog. Kepler magnitudes (Kp) are shown to be accurate to ≈0.1 mag for the Kepler field, and the EPIC is typically complete to Kp ≈ 17 (Kp ≈ 19 for campaigns covered by Sloan Digital Sky Survey). We furthermore classify 138,600 targets in Campaigns 1–8 (≈88% of the full target sample) using colors, proper motions, spectroscopy, parallaxes, and galactic population synthesis models, with typical uncertainties for G-type stars of ≈3% in T{sub eff}, ≈0.3 dex in log g, ≈40% in radius, ≈10% in mass, and ≈40% in distance. Our results show that stars targeted by K2 are dominated by K–M dwarfs (≈41% of all selected targets), F–G dwarfs (≈36%), and K giants (≈21%), consistent with key K2 science programs to search for transiting exoplanets and galactic archeology studies using oscillating red giants. However, we find significant variation of the fraction of cool dwarfs with galactic latitude, indicating a target selection bias due to interstellar reddening and increased contamination by giant stars near the galactic plane. We discuss possible systematic errors in the derived stellar properties, and differences with published classifications for K2 exoplanet host stars. The EPIC is hosted at the Mikulski Archive for Space Telescopes (MAST): http://archive.stsci.edu/k2/epic/search.php.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Background-Aware 3-D Point Cloud Segmentation With Dynamic Point Feature Aggregation

With the proliferation of LiDAR sensors and 3-D vision cameras, 3-D point cloud analysis has attracted significant attention in recent years. In this article, we propose a novel 3-D point cloud learning network, referred to as dynamic point feature aggregation network (DPFA-Net), by selectively performing the neighborhood feature aggregation (FA) with dynamic pooling and an attention mechanism. DPFA-Net has two variants for semantic segmentation and classification of 3-D point clouds. As the core module of the DPFA-Net, we propose an FA layer, in which features of the dynamic neighborhood of each point are aggregated via a self-attention mechanism. In contrast to other segmentation models, which aggregate features from fixed neighborhoods, our approach can aggregate features from different neighbors in different layers providing a more selective and broader view to the query points and focusing more on the relevant features in a local neighborhood. In addition, to further improve the performance of semantic segmentation, we exploit the background–foreground (BF) information and present two novel approaches, namely, two-stage BF-Net and BF regularization. Experimental results show that the proposed DPFA-Net achieves the state-of-the-art overall accuracy score of 89.22% for semantic segmentation on the Stanford large-scale 3-D Indoor Spaces (S3DIS) dataset and provides consistently satisfactory performance across different tasks of semantic segmentation, part segmentation, and 3-D object classification. Furthermore, our model achieves 93.1% accuracy on the ModelNet40 dataset and provides a mean shape intersection-over-union (IoU) value of 85.5% for part segmentation on the ShapeNet-Part dataset. It is a also computationally more efficient compared to other methods.

3-D↗
DOE OSTI2022

Plasma image classification using cosine similarity constrained convolutional neural network

Plasma jets are widely investigated both in the laboratory and in nature. Astrophysical objects such as black holes, active galactic nuclei and young stellar objects commonly emit plasma jets in various forms. With the availability of data from plasma jet experiments resembling astrophysical plasma jets, classification of such data would potentially aid in not only investigating the underlying physics of the experiments but also the study of astrophysical jets. In this work we use deep learning to process all of the laboratory plasma images from the Caltech Spheromak Experiment spanning two decades. We found that cosine similarity can aid in feature selection, classify images through comparison of feature vector direction and be used as a loss function for the training of AlexNet for plasma image classification. We also develop a simple vector direction comparison algorithm for binary and multi-class classification. Using our algorithm we demonstrate 93 % accurate binary classification to distinguish unstable columns from stable columns and 92 % accurate five-way classification of a small, labelled data set which includes three classes corresponding to varying levels of kink instability.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2012

THE CHANDRA MULTI-WAVELENGTH PROJECT: OPTICAL SPECTROSCOPY AND THE BROADBAND SPECTRAL ENERGY DISTRIBUTIONS OF X-RAY-SELECTED AGNs

From optical spectroscopy of X-ray sources observed as part of the Chandra Multi-wavelength Project (ChaMP), we present redshifts and classifications for a total of 1569 Chandra sources from our targeted spectroscopic follow-up using the FLWO/1.5 m, SAAO/1.9 m, WIYN 3.5 m, CTIO/4 m, KPNO/4 m, Magellan/6.5 m, MMT/6.5 m, and Gemini/8 m telescopes, and from archival Sloan Digital Sky Survey (SDSS) spectroscopy. We classify the optical counterparts as 50% broad-line active galactic nuclei (AGNs), 16% emission line galaxies, 14% absorption line galaxies, and 20% stars. We detect QSOs out to z {approx} 5.5 and galaxies out to z {approx} 3. We have compiled extensive photometry, including X-ray (ChaMP), ultraviolet (GALEX), optical (SDSS and ChaMP-NOAO/MOSAIC follow-up), near-infrared (UKIDSS, Two Micron All Sky Survey, and ChaMP-CTIO/ISPI follow-up), mid-infrared (WISE), and radio (FIRST and NVSS) bands. Together with our spectroscopic information, this enables us to derive detailed spectral energy distributions (SEDs) for our extragalactic sources. We fit a variety of template SEDs to determine bolometric luminosities, and to constrain AGNs and starburst components where both are present. While {approx}58% of X-ray Seyferts (10{sup 42} erg s{sup -1} < L{sub 2-10keV} <10{sup 44} erg s{sup -1}) require a starburst event (>5% starburst contribution to bolometric luminosity) to fit observed photometry only 26% of the X-ray QSO (L{sub 2-10keV} >10{sup 44} erg s{sup -1}) population appear to have some kind of star formation contribution. This is significantly lower than for the Seyferts, especially if we take into account torus contamination at z > 1 where the majority of our X-ray QSOs lie. In addition, we observe a rapid drop of the percentage of starburst contribution as X-ray luminosity increases. This is consistent with the quenching of star formation by powerful QSOs, as predicted by the merger model, or with a time lag between the peak of star formation and QSO activity. We have tested the hypothesis that there should be a strong connection between X-ray obscuration and star formation but we do not find any association between X-ray column density and star formation rate both in the general population or the star-forming X-ray Seyferts. Our large compilation also allows us to report here the identification of 81 X-ray Bright Optically inactive Galaxies, 78 z > 3 X-ray sources, and eight Type-2 QSO candidates. Also, we have identified the highest redshift (z = 5.4135) X-ray-selected QSO with optical spectroscopy.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Optical spectroscopic observations of blazars and γ-ray blazar candidates in the Sloan digital sky survey data release nine

We present an analysis of the optical spectra available in the Sloan Digital Sky Survey data release nine (SDSS DR9) for the blazars listed in the ROMA-BZCAT and for the γ-ray blazar candidates selected according to their IR colors. First, we adopt a statistical approach based on Monte Carlo simulations to find the optical counterparts of the blazars listed in the ROMA-BZCAT catalog. Then, we crossmatched the SDSS spectroscopic catalog with our selected samples of blazars and γ-ray blazar candidates, searching for those with optical spectra available to classify our blazar-like sources and, whenever possible, to confirm their redshifts. Our main objectives are to determine the classification of uncertain blazars listed in the ROMA-BZCAT and to discover new gamma-ray blazars. For the ROMA-BZCAT sources, we investigated a sample of 84 blazars, confirming the classification for 20 of them and obtaining 18 new redshift estimates. For the γ-ray blazars, indicated as potential counterparts of unassociated Fermi sources or with uncertain nature, we established the blazar-like nature of 8 out of the 27 sources analyzed and confirmed 14 classifications.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

The K giant stars from the LAMOST survey data. I. Identification, metallicity, and distance

We present a support vector machine classifier to identify the K giant stars from the LAMOST survey directly using their spectral line features. The completeness of the identification is about 75% for tests based on LAMOST stellar parameters. The contamination in the identified K giant sample is lower than 2.5%. Applying the classification method to about two million LAMOST spectra observed during the pilot survey and the first year survey, we select 298,036 K giant candidates. The metallicities of the sample are also estimated with an uncertainty of 0.13 ∼ 0.29 dex based on the equivalent widths of Mg{sub b} and iron lines. A Bayesian method is then developed to estimate the posterior probability of the distance for the K giant stars, based on the estimated metallicity and 2MASS photometry. The synthetic isochrone-based distance estimates have been calibrated using 7 globular clusters with a wide range of metallicities. The uncertainty of the estimated distance modulus at K = 11 mag, which is the median brightness of the K giant sample, is about 0.6 mag, corresponding to ∼30% in distance. As a scientific verification case, the trailing arm of the Sagittarius stream is clearly identified with the selected K giant sample. Moreover, at about 80 kpc from the Sun, we use our K giant stars to confirm a detection of stream members near the apo-center of the trailing tail. These rediscoveries of the features of the Sagittarius stream illustrate the potential of the LAMOST survey for detecting substructures in the halo of the Milky Way.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Carbon- and Oxygen-rich stars in MaStar: identification and classification

ABSTRACT Carbon- and Oxygen-rich stars populating the Thermally Pulsing Asymptotic Giant Branch (TP-AGB) phase of stellar evolution are relevant contributors to the spectra of ∼1 Gyr old populations. Atmosphere models for these types are uncertain, due to complex molecules and mass-loss effects. Empirical spectra are then crucial, but samples are small due to the short (∼3 Myr) TP-AGB lifetime. Here we exploit the vastness of the MaNGA Stellar library MaStar (∼60 000 spectra) to identify C and O-rich type stars. We define an optical colour selection with cuts of (g − r) > 2 and (g − i) < 1.55(g − r) − 0.07, calibrated with known C- and O-rich spectra. This identifies C- and O-rich stars along clean, separated sequences. An analogue selection is found in V, R, I bands. Our equation identifies C-rich and O-rich spectra with predictive performance metric F1-scores of 0.72 and 0.74 (over 1), respectively. We finally identify 41 C- and 87 O-rich type AGB stars in MaStar, 5 and 49 of which do not have a SIMBAD counterpart. We also detect a sample of non-AGB, dwarf C-stars. We further design a fitting procedure to classify the spectra into broad spectral types, by using as fitting templates empirical C- and O-rich spectra. We find remarkably good fits for the majority of candidates and categorize them into C- and O-rich bins following existing classifications, which correlate to effective temperature. Our selection models can be applied to large photometric surveys (e.g. Euclid, Rubin). The classified spectra will facilitate future evolutionary population synthesis models.

Hill, Lewis (ORCID:0000000180528080)↗
DOE OSTI2019

From the outside looking in: what can Milky Way analogues tell us about the star formation rate of our own galaxy?

ABSTRACT The Milky Way has been described as an anaemic spiral, but is its star formation rate (SFR) unusually low when compared to its peers? To answer this question, we define a sample of Milky Way analogues (MWAs) based on stringent cuts on the best literature estimates of non-transient structural features for the Milky Way. This selection yields only 176 galaxies from the whole of the SDSS DR7 spectroscopic sample which have morphological classifications in Galaxy Zoo 2, from which we infer SFRs from two separate indicators. The mean SFRs found are $\log (\rm {SFR}_{SED}/\rm {M}_{\odot }~\rm {yr}^{-1})=0.53$ with a standard deviation of 0.23 dex from SED fits, and $\log (\rm {SFR}_{W4}/\rm {M}_{\odot }~\rm {yr}^{-1})=0.68$ with a standard deviation of 0.41 dex from a mid-infrared calibration. The most recent estimate for the Milky Way’s SFR of $\log (\rm {SFR}_{MW}/\rm {M}_{\odot }~\rm {yr}^{-1})=0.22$ fits well within 2$\sigma$ of these values, where $\sigma$ is the standard deviation of each of the SFR indicator distributions. We infer that the Milky Way, while being a galaxy with a somewhat low SFR, is not unusual when compared to similar galaxies.

Fraser-McKelvie, Amelia↗
DOE OSTI2021

A Cyber-Physical Anomaly Detection for Wide-Area Protection Using Machine Learning

Wide-area protection scheme (WAPS) provides system-wide protection by detecting and mitigating small and large-scale disturbances that are difficult to resolve using local protection schemes. As this protection scheme is evolving from a substation-based distributed remedial action scheme (DRAS) to the control center-based centralized RAS (CRAS), it presents severe challenges to their cybersecurity because of its heavy reliance on an insecure grid communication, and its compromise would lead to system failure. This article presents an architecture and methodology for developing a cyber-physical anomaly detection system (CPADS) that utilizes synchrophasor measurements and properties of network packets to detect data integrity and communication failure attacks on measurement and control signals in CRAS. The proposed machine leaning-based methodology applies a rules-based approach to select relevant input features, utilizes variational mode decomposition (VMD) and decision tree (DT) algorithms to develop multiple classification models, and performs final event identification using a rules-based decision logic. Here, we have evaluated the proposed methodology of CPADS using the IEEE 39 bus system for several performance measures (accuracy, recall, precision, and F-measure) in a cyber-physical testbed environment. Furthermore, our experimental results reveal that the proposed algorithm (VMD-DT) of CPADS outperforms the existing machine learning classifiers during noisy and noise-free measurements while incurring an acceptable processing overhead.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2014

SU-E-T-49: Automatic Beam Angle Determination for Lung IMRT Planning Using a Beam Configuration Atlas

Purpose: To present a technique to automatically determine beam angle configurations for lung IMRT planning based on the patient-specific anatomy and tumor geometry. Methods: The relationship between individual patient anatomy and proper beam configurations was learned from high quality clinical plans in three steps. First, a beam configuration atlas was obtained by classifying 60 lung IMRT plans into 6 beam configuration clusters based on a dissimilarity measure defined between different beam configurations. A beam configuration template was extracted from each cluster to form an atlas. Second, a beam efficiency index map (EI map) was constructed to characterize the geometry of the tumor relative to the lungs, the body and other OARs along each candidate beam direction. Finally, the EI maps of the clinical cases and the cluster assignments of their beam configurations were paired to train a Bayesian classification model. This technique was validated by leave-one-out cross validation with 16 cases randomly selected from the original dataset. An IMRT plan (autobeam plan) for each test case was generated using the beam configuration template according to the cluster assignment given by the model and was compared with the corresponding clinical plan. Results: The dosimetric parameters (mean±S.D. in percentage of prescription dose) in the auto-beam plans and in the clinical plans, respectively, and the p-values by a paired ttest (in parenthesis) are: lung Dmean: 16.3±9.3, 18.6±7.4 (0.48), esophagus Dmean: 28.4±18, 30.7±19.3 (0.02), Heart Dmean: 21.5±17.5,21.1±17.2 (0.76), Spinal Cord D2%: 48±23, 51.2±21.8 (0.01), PTV dose homogeneity (D2%–D99%): 22±27.4, 20.4±12.8 (0.10).The dose reductions by the autobeam plans in esophagus Dmean and cord D02 are statistically significant but the differences (<4%) may not be clinically significant. The other dosimetric parameters are not statistically different. Conclusion: Plans generated by the automatic beam angle determination method can achieve dosimetric quality equivalent to that of clinical plans. Partially supported by NIH/NCI under grant #R21CA161389 and a master research grant by Varian Medical System.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Improved Spectral Classification of Local K/M Dwarfs in SDSS Surveys. I. Chemodynamic Validation and Metallicity Calibration

An examination of 109,276 spectra of low-mass stars in the Sloan Digital Sky Survey (SDSS) data archive, collected pre-2010, provides a broad collection of K/M (sub)dwarfs tracing the local (d ≲ 250 pc) population of the thin-disk, thick-disk, and halo, based on 3D kinematics. These populations have distinct metallicities and kinematics, which should be reflected in lower-mass members. One complication is measuring metallicities of M dwarfs from low-resolution spectroscopy or photometry alone remains a challenge, even with the availability of Gaia data. To better characterize physical parameters of low-mass stars observed by SDSS, we define a set of 536 improved, empirical K/M dwarf classification templates to more accurately determine spectral subtypes (K5.5–M8.5) and morphology classes (MCs) (0.5–12.5). We select the best-fit template to every archival SDSS spectrum in the range 5000–8000 Å based on minimum χ 2 value. We then use secondary metallicity estimators to calibrate the most likely [Fe/H] values corresponding to the assigned MC. We confirm that the most metal-rich K/M dwarfs have thin-disk kinematics, and we observe the known effect of Galactic radial metallicity migration, which we confirm is strongly imprinted in the chemodynamics of the local disk M dwarfs. Confirmation of these chemodynamic trends validates the precision of our improved templates for estimating [Fe/H] for M (sub)dwarfs, notably at the low-metallicity end (–3 < [Fe/H] < –1). Substructure in the chemodynamics of the most metal-poor K/M subdwarfs begins to distinguish between local Gaia-Enceladus and local in situ halo objects. This methodology opens the door to use the ubiquitous, local K/M (sub)dwarfs to trace local Galactic chemodynamics with the highest possible resolution.

79 ASTRONOMY AND ASTROPHYSICS↗