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DOE OSTI2022

Revisit NGC 5466 tidal stream with Gaia , SDSS/SEGUE, and LAMOST

ABSTRACT By mining the data from Gaia Early Data Release 3, Sloan Digital Sky Survey/Sloan Extension for Galactic Understanding and Exploration Data Release 16, and Large Sky Area Multi-Object Fiber Spectroscopic Telescope Data Release 8, 11 member stars of the NGC 5466 tidal stream are detected and 7 of them are newly identified. To reject contaminators, a variety of cuts are applied in sky position, colour–magnitude diagram, metallicity, proper motion, and radial velocity. We compare our data to a mock stream generated by modelling the cluster’s disruption under a smooth Galactic potential plus the Large Magellanic Cloud (LMC). The concordant trends in phase space between the model and observations imply that the stream might have been perturbed by the LMC. The two most distant stars among the 11 detected members trace the stream’s length to 60° of sky, supporting and extending the previous length of 45°. Given that NGC 5466 is so distant and potentially has a longer tail than previously thought, we expect that the NGC 5466 tidal stream could be a useful tool in constraining the Milky Way gravitational field.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1984

Wide-angle radio tail QSOs as members of clusters of galaxies. II - Direct optical observations and spectroscopy of QSO fields

Deep, direct observations are obtained for the fields of 19 QSOs (redshift values 0.3-1.5) whose 'excess' galaxy counts in 1.17-sq arcmin fields centered on the QSOs confirm QSO redshift cosmological interpretations at the 99-percent confidence level. It is in addition indicated with 97-percent confidence that wide angle radio tail QSOs lie in richer concentrations of galaxies than undistorted QSOs. The results obtained also confirm that the 'excess' galaxies detected in the QSO fields have the same redshifts as the QSOs. These observations indicate that deep CCD observations of the fields of radio tail QSOs would provide a sample of distant clusters of galaxies at known redshifts (those of the QSOs).

Hintzen, P.↗

NASA NTRS2012

Hybrid Collaborative Learning for Classification and Clustering in Sensor Networks

Traditionally, nodes in a sensor network simply collect data and then pass it on to a centralized node that archives, distributes, and possibly analyzes the data. However, analysis at the individual nodes could enable faster detection of anomalies or other interesting events as well as faster responses, such as sending out alerts or increasing the data collection rate. There is an additional opportunity for increased performance if learners at individual nodes can communicate with their neighbors. In previous work, methods were developed by which classification algorithms deployed at sensor nodes can communicate information about event labels to each other, building on prior work with co-training, self-training, and active learning. The idea of collaborative learning was extended to function for clustering algorithms as well, similar to ideas from penta-training and consensus clustering. However, collaboration between these learner types had not been explored. A new protocol was developed by which classifiers and clusterers can share key information about their observations and conclusions as they learn. This is an active collaboration in which learners of either type can query their neighbors for information that they then use to re-train or re-learn the concept they are studying. The protocol also supports broadcasts from the classifiers and clusterers to the rest of the network to announce new discoveries. Classifiers observe an event and assign it a label (type). Clusterers instead group observations into clusters without assigning them a label, and they collaborate in terms of pairwise constraints between two events [same-cluster (mustlink) or different-cluster (cannot-link)]. Fundamentally, these two learner types speak different languages. To bridge this gap, the new communication protocol provides four types of exchanges: hybrid queries for information, hybrid "broadcasts" of learned information, each specified for classifiers-to-clusterers, and clusterers-to-classifiers. The new capability has the potential to greatly expand the in situ analysis abilities of sensor networks. Classifiers seeking to categorize incoming data into different types of events can operate in tandem with clusterers that are sensitive to the occurrence of new kinds of events not known to the classifiers. In contrast to current approaches that treat these operations as independent components, a hybrid collaborative learning system can enable them to learn from each other.

Wagstaff, Kiri L.↗

NASA NTRS2019

Application of High-Dimensional Fuzzy K-Means Cluster Analysis to CALIOP/CALIPSO Version 4.1 Cloud-Aerosol Discrimination

This study applies fuzzy k-means (FKM) cluster analyses to a subset of the parameters reported in the CALIPSO lidar level 2 data products in order to classify the layers detected as either clouds or aerosols. The results obtained are used to assess the reliability of the cloud–aerosol discrimination (CAD) scores reported in the version 4.1 release of the CALIPSO data products. FKM is an unsupervised learning algorithm, whereas the CALIPSO operational CAD algorithm (COCA) takes a highly supervised approach. Despite these substantial computational and architectural differences, our statistical analyses show that the FKM classifications agree with the COCA classifications for more than 94 % of the cases in the troposphere. This high degree of similarity is achieved because the lidar-measured signatures of the majority of the clouds and the aerosols are naturally distinct, and hence objective methods can independently and effectively separate the two classes in most cases. Classification differences most often occur in complex scenes (e.g., evaporating water cloud filaments embedded in dense aerosol) or when observing diffuse features that occur only intermittently (e.g., volcanic ash in the tropical tropopause layer). The two methods examined in this study establish overall classification correctness boundaries due to their differing algorithm uncertainties. In addition to comparing the outputs from the two algorithms, analysis of sampling, data training, performance measurements, fuzzy linear discriminants, defuzzification, error propagation, and key parameters in feature type discrimination with the FKM method are further discussed in order to better understand the utility and limits of the application of clustering algorithms to space lidar measurements. In general, we find that both FKM and COCA classification uncertainties are only minimally affected by noise in the CALIPSO measurements, though both algorithms can be challenged by especially complex scenes containing mixtures of discrete layer types. Our analysis results show that attenuated backscatter and color ratio are the driving factors that separate water clouds from aerosols; backscatter intensity, depolarization, and mid-layer altitude are most useful in discriminating between aerosols and ice clouds; and the joint distribution of backscatter intensity and depolarization ratio is critically important for distinguishing ice clouds from water clouds.

Zeng, Shan↗

DOE OSTI2023

The cross-correlation of galaxies in absorption with the Lyman α forest

ABSTRACT We present the first clustering measurement of Strong Blended Lyman α (SBLA) absorption systems by measuring their cross-correlation with the Lyman α forest. SBLAs are a new population of absorbers detected within the Lyman α forest. We find a bias of 2.329 ± 0.057, consistent with that of Damped Lyman α absorbers (DLAs). For DLAs, we recover a bias of 2.331 ± 0.057 larger than previously reported. We also find a redshift space distortion parameter β = 0.417 ± 0.010, also consistent with the recovered value for DLAs (β = 0.416 ± 0.010). This is consistent with SBLA and DLA systems tracing different portions of the circumgalactic medium of a broadly common population of galaxies. Given these common clustering properties, we combined them to perform a cross-correlation of galaxies in absorption with the Lyα forest. We find that the BAO scale uncertainty of this new measurement is 1.75 × that of Lyα autocorrelation and 1.6 × that of the quasar cross-correlation with the Lyα forest. We note that the current preferred metal contamination model for fitting the correlation functions with respect to the Lyα forest is not realistic enough for SBLA systems, likely due to their status as high redshift precision sites of high metal enrichment. Mock spectra including SBLA systems and their associated metal absorption are required to understand this sample fully. We conclude that SBLAs have the potential to complement the standard Lyα cosmological analyses in future surveys.

Astronomy & Astrophysics↗

DOE OSTI2022

Relationship and distribution of Salmonella enterica serovar I 4,[5],12:i:- strain sequences in the NCBI Pathogen Detection database

Background: Of the > 2600 Salmonella serovars, Salmonella enterica serovar I 4,[5],12:i:- (serovar I 4,[5],12:i:-) has emerged as one of the most common causes of human salmonellosis and the most frequent multidrug-resistant (MDR; resistance to ≥3 antimicrobial classes) nontyphoidal Salmonella serovar in the U.S. Serovar I 4,[5],12:i:- isolates have been described globally with resistance to ampicillin, streptomycin, sulfisoxazole, and tetracycline (R-type ASSuT) and an integrative and conjugative element with multi-metal tolerance named Salmonella Genomic Island 4 (SGI-4). Results: We analyzed 13,612 serovar I 4,[5],12:i:- strain sequences available in the NCBI Pathogen Detection database to determine global distribution, animal sources, presence of SGI-4, occurrence of R-type ASSuT, frequency of antimicrobial resistance (AMR), and potential transmission clusters. Genome sequences for serovar I 4,[5],12:i:- strains represented 30 countries from 5 continents (North America, Europe, Asia, Oceania, and South America), but sequences from the United States (59%) and the United Kingdom (28%) were dominant. The metal tolerance island SGI-4 and the R-type ASSuT were present in 71 and 55% of serovar I 4,[5],12:i:- strain sequences, respectively. Sixty-five percent of strain sequences were MDR which correlates to serovar I 4,[5],12:i:- being the most frequent MDR serovar. The distribution of serovar I 4,[5],12:i:- strain sequences in the NCBI Pathogen Detection database suggests that swine-associated strain sequences were the most frequent food-animal source and were significantly more likely to contain the metal tolerance island SGI-4 and genes for MDR compared to all other animal-associated isolate sequences. Conclusions: Our study illustrates how analysis of genomic sequences from the NCBI Pathogen Detection database can be utilized to identify the prevalence of genetic features such as antimicrobial resistance, metal tolerance, and virulence genes that may be responsible for the successful emergence of bacterial foodborne pathogens.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Emerging Per- and Polyfluoroalkyl Substances in Tap Water from the American Healthy Homes Survey II

Humans experience widespread exposure to anthropogenic per- and polyfluoroalkyl substances (PFAS) through various media, which can lead to a wide range of negative health impacts. Tap water is an important source of exposure in communities with any degree of contamination but routine or large-scale PFAS monitoring often depends on targeted analytical methods limited to measuring specific PFAS. We analyzed 680 tap water samples from the American Healthy Homes Survey II for PFAS using non-targeted analysis (NTA) to expand the range of detectable PFAS. Based on detection frequency and relative abundance, about half of the identified PFAS were found only by NTA. We identified (with varying degrees of confidence) 75 distinct PFAS, including 57 exclusively detected by NTA. The identified PFAS are members of seven structural subclasses differentiated by their head groups and degree of fluorination. Clustering analysis categorized the PFAS into four coabundance groups dominated by specific PFAS subclasses. One group uniquely identified by NTA contains zwitterionic PFAS and other PFAS transformation products which are likely associated with aqueous firefighting foam contaminants in a small number of spatially correlated samples. These results help further characterize the scope of exposure to emerging PFAS experienced by the U.S. population via tap water and augment nationwide targeted-PFAS monitoring programs.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗

DOE OSTI2022

Search for Supernova Neutrinos and Constraint on the Galactic Star Formation Rate with the KamLAND Data

We present the results of a search for core-collapse supernova neutrinos, using long-term KamLAND data from 2002 March 9 to 2020 April 25. We focus on the electron antineutrinos emitted from supernovae in the energy range of 1.8–111 MeV. Supernovae will make a neutrino event cluster with the duration of ~10 s in the KamLAND data. We find no neutrino clusters and give the upper limit on the supernova rate to be 0.15 yr –1 with a 90% confidence level. The detectable range, which corresponds to a >95% detection probability, is 40–59 kpc and 65–81 kpc for core-collapse supernovae and failed core-collapse supernovae, respectively. This paper proposes to convert the supernova rate obtained by the neutrino observation to the Galactic star formation rate. Assuming a modified Salpeter-type initial mass function, the upper limit on the Galactic star formation rate is <(17.5–22.7) M ⊙ yr –1 with a 90% confidence level.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

First Constraints on Growth Rate from Redshift-space Ellipticity Correlations of SDSS Galaxies at 0.16 < z < 0.70

We report the first constraints on the growth rate of the universe, f(z)σ 8 (z), with intrinsic alignments (IAs) of galaxies. We measure the galaxy density-intrinsic ellipticity cross-correlation and intrinsic ellipticity autocorrelation functions over 0.16 < z < 0.7 from luminous red galaxies (LRGs) and LOWZ and CMASS galaxy samples in the Sloan Digital Sky Survey (SDSS) and SDSS-III BOSS survey. We detect clear anisotropic signals of IA due to redshift-space distortions. By combining measured IA statistics with the conventional galaxy clustering statistics, we obtain tighter constraints on the growth rate. The improvement is particularly prominent for the LRG, which is the brightest galaxy sample and known to be strongly aligned with underlying dark matter distribution; using the measurements on scales above 10 h -1 Mpc, we obtain $f{\sigma }_{8}={0.5196}_{-0.0354}^{+0.0352}$ (68% confidence level) from the clustering-only analysis and $f{\sigma }_{8}={0.5322}_{-0.0291}^{+0.0293}$ with clustering and IA, meaning 19% improvement. The constraint is in good agreement with the prediction of general relativity, f σ 8 = 0.4937 at z = 0.34. For LOWZ and CMASS samples, the improvement of constraints on f σ 8 is found to be 10% and 3.5%, respectively. Our results indicate that the contribution from IA statistics for cosmological constraints can be further enhanced by carefully selecting galaxies for a shape sample.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1983

Absorption by halo gas in the direction of M13

A high velocity cloud in the direction 1 = 59 degrees, b = 41 degrees is detected in absorption at approximately -80 km/s in high dispersion IUE spectra of the blue star Barnard 29 in the globular cluster M13. The cloud is also seen in the H I 21 cm emission data of Kerr and Knapp (1972). Its radial velocity agrees with Giovanelli's data (1980, 1981) for high velocity clouds seen in this general direction of the sky. The cloud's motion is incompatible with the suggestions that neutral halo gas corotates with disk gas. The motion could be explained if neutral halo gas rotates more slowly than disk gas with increasing distance from the galactic plane. Because of our very limited understanding of the actual motions of halo gas, the scale height of this gas is best derived from plots of N sin b versus z for galactic and extragalactic stars.

De Boer, K. S.↗

NASA NTRS1986

Highly ionized atoms in cooling gas

The ionization of low density gas cooling from a high temperature was calculated. The evolution during the cooling is assumed to be isochoric, isobaric, or a combination of these cases. The calculations are used to predict the column densities and ultraviolet line luminosities of highly ionized atoms in cooling gas. In a model for cooling of a hot galactic corona, it is shown that the observed value of N(N V) can be produced in the cooling gas, while the predicted value of N(Si IV) falls short of the observed value by a factor of about 5. The same model predicts fluxes of ultraviolet emission lines that are a factor of 10 lower than the claimed detections of Feldman, Brune, and Henry. Predictions are made for ultraviolet lines in cooling flows in early-type galaxies and clusters of galaxies. It is shown that the column densities of interest vary over a fairly narrow range, while the emission line luminosities are simply proportional to the mass inflow rate.

Edgar, R. J.↗

NASA NTRS1986

Highly ionized atoms in cooling gas

The ionization of low density gas cooling from a high temperature was calculated. The evolution during the cooling is assumed to be isochoric, isobaric, or a combination of these cases. The calculations are used to predict the column densities and ultraviolet line luminosities of highly ionized atoms in cooling gas. In a model for cooling of a hot galactic corona, it is shown that the observed value of N(N V) can be produced in the cooling gas, while the predicted value of N(Si IV) falls short of the observed value by a factor of about 5. The same model predicts fluxes of ultraviolet emission lines that are a factor of 10 lower than the claimed detections of Feldman, Bruna, and Henry. Predictions are made for ultraviolet lines in cooling flows in early-type galaxies and clusters of galaxies. It is shown that the column densities of interest vary over a fairly narrow range, while the emission line luminosities are simply proportional to the mass inflow rate.

Edgar, Richard J.↗

NASA NTRS2006

A 70 Kiloparsec X-Ray Tail in the Cluster A3627

We present the discovery of a 70 kpc X-ray tail behind the small late-type galaxy ESO 137-001, in the nearby, hot (T=6.5 keV) merging cluster A3627, from both Chandra and XMM-Newton observations. The tail has a length-to-width ratio of approx. 10. It is luminous (L(0.5-2keV) approx 1041 ergs/s), with a temperature of approx. 0.7 keV and an X-ray gas mass of approx 10(exp 9) solar masses (approx 10% of the galaxy's stellar mass). We interpret this tail as the stripped interstellar medium of ESO 137-001 mixed with the hot cluster medium, with this blue galaxy being converted into a gas-poor galaxy. Three X-ray point sources are detected in the axis of the tail, which may imply active star formation there. The straightness and narrowness of the tail also imply that the turbulence in the intracluster medium is not strong on scales of 20-70 kpc.

Sun, M.↗

NASA NTRS2011

Collaborative Clustering for Sensor Networks

Traditionally, nodes in a sensor network simply collect data and then pass it on to a centralized node that archives, distributes, and possibly analyzes the data. However, analysis at the individual nodes could enable faster detection of anomalies or other interesting events, as well as faster responses such as sending out alerts or increasing the data collection rate. There is an additional opportunity for increased performance if individual nodes can communicate directly with their neighbors. Previously, a method was developed by which machine learning classification algorithms could collaborate to achieve high performance autonomously (without requiring human intervention). This method worked for supervised learning algorithms, in which labeled data is used to train models. The learners collaborated by exchanging labels describing the data. The new advance enables clustering algorithms, which do not use labeled data, to also collaborate. This is achieved by defining a new language for collaboration that uses pair-wise constraints to encode useful information for other learners. These constraints specify that two items must, or cannot, be placed into the same cluster. Previous work has shown that clustering with these constraints (in isolation) already improves performance. In the problem formulation, each learner resides at a different node in the sensor network and makes observations (collects data) independently of the other learners. Each learner clusters its data and then selects a pair of items about which it is uncertain and uses them to query its neighbors. The resulting feedback (a must and cannot constraint from each neighbor) is combined by the learner into a consensus constraint, and it then reclusters its data while incorporating the new constraint. A strategy was also proposed for cleaning the resulting constraint sets, which may contain conflicting constraints; this improves performance significantly. This approach has been applied to collaborative clustering of seismic and infrasonic data collected by the Mount Erebus Volcano Observatory in Antarctica. Previous approaches to distributed clustering cannot readily be applied in a sensor network setting, because they assume that each node has the same view of the data set. A view is the set of features used to represent each object. When a single data set is partitioned across several computational nodes, distributed clustering works; all objects have the same view. But when the data is collected from different locations, using different sensors, a more flexible approach is needed. This approach instead operates in situations where the data collected at each node has a different view (e.g., seismic vs. infrasonic sensors), but they observe the same events. This enables them to exchange information about the likely cluster membership relations between objects, even if they do not use the same features to represent the objects.

Wagstaff. Loro :/↗

NASA NTRS2019

CMB-HD: an Ultra-Deep, High-Resolution Millimeter-Wave Survey over Half the Sky

A millimeter-wave survey over half the sky, that spans frequencies in the range of 30 to 350 gigahertz, and that is both an order of magnitude deeper and of higher-resolution than currently funded surveys would yield an enormous gain in understanding of both fundamental physics and astrophysics. By providing such a deep, high-resolution millimeter-wave survey (about 0.5 microK-arcminutes noise and 15 arcseconds resolution at 150 gigahertz), CMB-HD (Cosmic Microwave Background - Henry Draper catalog entry) will enable major advances. It will allow 1) the use of gravitational lensing of the primordial microwave background to map the distribution of matter on small scales (k approximately equal to 10 h per megaparsec), which probes dark matter particle properties. It will also allow 2) measurements of the thermal and kinetic Sunyaev-Zel’dovich effects on small scales to map the gas density and gas pressure profiles of halos over a wide field, which probes galaxy evolution and cluster astrophysics. In addition, CMB-HD would allow us to cross critical thresholds in fundamental physics: 3) ruling out or detecting any new, light (less than 0.1 electronvolts), thermal particles, which could potentially be the dark matter, and 4) testing a wide class of multi-field models that could explain an epoch of inflation in the early Universe. Such a survey would also 5) monitor the transient sky by mapping the full observing region every few days, which opens a new window on gamma-ray bursts, novae, fast radio bursts, and variable active galactic nuclei. Moreover, CMB-HD would 6) provide a census of planets, dwarf planets, and asteroids in the outer Solar System, and 7) enable the detection of exo-Oort clouds around other solar systems, shedding light on planet formation. The combination of CMB-HD with contemporary ground and space-based experiments will also provide powerful synergies. CMB-HD will deliver this survey in 5 years of observing 20,000 square degrees, using two new 30-meter-class off-axis cross-Dragone telescopes to be located at Cerro Toco in the Atacama Desert. The telescopes will field about 2.4 million detectors (600,000 pixels) in total. The CMB-HD survey will be made publicly available, with usability and accessibility a priority.

Sehgal, Neelima↗

NASA NTRS2019

CMB-HD: An Ultra-Deep, High-Resolution Millimeter-Wave Survey Over Half the Sky

A millimeter-wave survey over half the sky, that spans frequencies in the range of 30 to 350 GHz, and that is both an order of magnitude deeper and of higher-resolution than currently funded surveys would yield an enormous gain in understanding of both fundamental physics and astrophysics. By providing such a deep, high-resolution millimeter-wave survey (about 0.5 Karcmin noise and 15 arcsecond resolution at 150 GHz), CMB-HD will enable major advances.It will allow 1.) the use of gravitational lensing of the primordial microwave background to map the distribution of matter on small scales (k 10 hMpc1), which probes dark matter particle properties. It will also allow 2.) measurements of the thermal and kinetic Sunyaev-Zel'dovich effects on small scales to map the gas density and gas pressure profiles of halos over a wide field,which probes galaxy evolution and cluster astrophysics. In addition, CMB-HD would allow us to cross critical thresholds in fundamental physics: 3.) ruling out or detecting any new, light (< 0:1 eV), thermal particles, which could potentially be the dark matter, and 4.) testing a wide class of multi-field models that could explain an epoch of inflation in the early Universe. Such a survey would also 5.) monitor the transient sky by mapping the full observing region every few days,which opens a new window on gamma-ray bursts, novae, fast radio bursts, and variable active galactic nuclei. Moreover, CMB-HD would 6.) provide a census of planets, dwarf planets, andasteroids in the outer Solar System, and 7.) enable the detection of exo-Oort clouds around othersolar systems, shedding light on planet formation. The combination of CMB-HD with contemporaryground and space-based experiments will also provide powerful synergies. CMB-HD willdeliver this survey in 5 years of observing 20,000 square degrees, using two new 30-meter-classoff-axis cross-Dragone telescopes to be located at Cerro Toco in the Atacama Desert. The telescopeswill field about 2.4 million detectors (600,000 pixels) in total. The CMB-HD survey willbe made publicly available, with usability and accessibility a priority.

Sehgal, Neelima↗

NASA NTRS1989

Abundances of the elements in the intergalactic medium

A summary of the X-ray and optical spectroscopic data on emission lines from elements in clusters of galaxies is presented. The best data exists for Fe. For the about 30 well-observed clusters, the mean Fe abundance is about 0.4 solar, but there appears to be a real variation of at least a factor of 2. X-ray emission lines from O, Mg, Si and S have been detected, and their abundances are within a factor of a few of solar. Analysis of optical emission lines from cooling gas due to O, N, and S also indicates abundances not too far from solar values. The implications of the presence of heavy elements in the intergalactic medium of clusters for the evolution of the chemical abundances of galaxies are discussed.

Mushotzky, Richard↗

DOE OSTI2024

Enhanced Detection of Primary Biological Aerosol Particles Using Machine Learning and Single-Particle Measurement

Accurately identifying primary biological aerosol particles (PBAPs) using analytical techniques poses inherent challenges due to their resemblance to other atmospheric carbonaceous particles. Here, we present a study of an enhanced method for detecting PBAPs by combining single-particle measurement with advanced supervised machine learning (SML) techniques. We analyzed ambient particles from a variety of environments and lab-generated standards, focusing on chemical composition for traditional rule-based and clustering approaches and incorporating morphological features into the SML approaches, neural networks and XGBoost, for improved accuracy. This study demonstrates that SML methods outperform traditional methods in quantifying PBAPs, achieving significant improvements in precision, recall, F1-score, and accuracy, leading to an increased number of detected PBAPs by at least 19%. The adaptability of the proposed XGBoost-based SML model is showcased in comparison to traditional methods in categorizing PBAPs for blind data sets from different geographical locations. Two field case studies were investigated, over agricultural land and Amazonia rain forest, representing relatively low and high concentrations of PBAPs, respectively, where XGBoost consistently detected up to 3.5 times more PBAPs than traditional methods. Precise detection of PBAPs in the atmosphere could significantly improve the prediction of climatic impacts by them.

42 ENGINEERING↗