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At least 973 records · Page 54
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Luminous red galaxies: selection and classification by combining optical and infrared photometry

We describe a new method of combining optical and infrared photometry to select luminous red galaxies (LRGs) at redshifts z>0.6. We explore this technique using a combination of optical photometry from CFHTLS and Hubble Space Telescope, infrared photometry from the Wide-Field Infrared Survey Explorer satellite, and spectroscopic or photometric redshifts from the DEEP2 Galaxy Redshift Survey or COSMOS. We present a variety of methods for testing the success of our selection and present methods for optimization given a set of rest-frame color and redshift requirements. We have tested this selection in two different regions of the sky, the COSMOS and Extended Groth Strip (EGS) fields, to reduce the effect of cosmic/sample variance. We have used these methods to assemble large samples of LRGs for two different ancillary programs as a part of the Sloan Digital Sky Survey (SDSS)-III/Baryon Oscillation Spectroscopic Survey. This technique is now being used to select ∼600,000 LRG targets for SDSS-IV/Extended Baryon Oscillation Spectroscopic Survey, which began observations in Fall 2014, and will be adapted for the proposed Dark Energy Spectroscopic Instrument survey. We have found that these methods can select high-redshift LRGs efficiently with minimal stellar contamination; this is extremely difficult to achieve with selections that rely on optical photometry alone.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

VOLATILE LOSS AND CLASSIFICATION OF KUIPER BELT OBJECTS

Observations indicate that some of the largest Kuiper Belt Objects (KBOs) have retained volatiles in the gas phase (e.g., Pluto), while others have surface volatiles that might support a seasonal atmosphere (e.g., Eris). Since the presence of an atmosphere can affect their reflectance spectra and thermal balance, Schaller and Brown examined the role of volatile escape driven by solar heating of the surface. Guided by recent simulations, we estimate the loss of primordial N{sub 2} for several large KBOs, accounting for escape driven by UV/EUV heating of the upper atmosphere as well as by solar heating of the surface. For the latter we present new simulations and for the former we scale recent detailed simulations of escape from Pluto using the energy limited escape model validated recently by molecular kinetic simulations. Unlike what has been assumed to date, we show that unless the N{sub 2} atmosphere is thin (<∼10{sup 18} N{sub 2} cm{sup −2}) and/or the radius small (<∼200–300 km), escape is primarily driven by the UV/EUV radiation absorbed in the upper atmosphere. This affects the discussion of the relationship between atmospheric loss and the observed surface properties for a number of the KBOs examined. Our long-term goal is to connect detailed atmospheric loss simulations with a model for volatile transport for individual KBOs.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

CLASSIFICATION OF STELLAR ORBITS IN AXISYMMETRIC GALAXIES

It is known that two supermassive black holes (SMBHs) cannot merge in a spherical galaxy within a Hubble time; an emerging picture is that galaxy geometry, rotation, and large potential perturbations may usher the SMBH binary through the critical three-body scattering phase and ultimately drive the SMBH to coalesce. We explore the orbital content within an N-body model of a mildly flattened, non-rotating, SMBH-embedded elliptical galaxy. When used as the foundation for a study on the SMBH binary coalescence, the black holes bypassed the binary stalling often seen within spherical galaxies and merged on gigayear timescales. Using both frequency-mapping and angular momentum criteria, we identify a wealth of resonant orbits in the axisymmetric model, including saucers, that are absent from an otherwise identical spherical system and that can potentially interact with the binary. We quantified the set of orbits that could be scattered by the SMBH binary, and found that the axisymmetric model contained nearly six times the number of these potential loss cone orbits compared to our equivalent spherical model. In this flattened model, the mass of these orbits is more than three times that of the SMBH, which is consistent with what the SMBH binary needs to scatter to transition into the gravitational wave regime.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

Bayesian high-redshift quasar classification from optical and MID-IR photometry

We identify 885,503 type 1 quasar candidates to i≲22 using the combination of optical and mid-IR photometry. Optical photometry is taken from the Sloan Digital Sky Survey-III: Baryon Oscillation Spectroscopic Survey (SDSS-III/BOSS), while mid-IR photometry comes from a combination of data from the Wide-field Infrared Survey Explorer (WISE) “AllWISE” data release and several large-area Spitzer Space Telescope fields. Selection is based on a Bayesian kernel density algorithm with a training sample of 157,701 spectroscopically confirmed type 1 quasars with both optical and mid-IR data. Of the quasar candidates, 733,713 lack spectroscopic confirmation (and 305,623 are objects that we have not previously classified as photometric quasar candidates). These candidates include 7874 objects targeted as high-probability potential quasars with 3.5 3.5 than the traditional mid-IR selection “wedges” and to 2.2 3. This catalog paves the way for luminosity-dependent clustering investigations of large numbers of faint, high-redshift quasars and for further machine-learning quasar selection using Spitzer and WISE data combined with other large-area optical imaging surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Cyclotron radiation emission spectroscopy signal classification with machine learning in project 8

The Cyclotron Radiation Emission Spectroscopy (CRES) technique pioneered by Project 8 measures cyclotron radiation from individual electrons in a background magnetic field to construct a highly precise energy spectrum for beta decay studies and other applications. The detector, magnetic trap geometry, and electron dynamics give rise to a multitude of complex electron signal structures which carry information about distinguishing physical traits. Here, we develop machine learning models to classify CRES signals with high accuracy based on these traits, improve the resultant frequency spectrum, and offer the potential for a sophisticated analysis which will help Project 8 achieve tritium endpoint measurement in the future.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Plasma confinement state classification in fusion power plants: Profile reflectometer and ensemble diagnostics

As Fusion Pilot Plants (FPPs) are increasingly viewed as within reach, many engineering challenges remain. Not many diagnostics are expected to be available in a reactor environment. Survivability, maintainability, and limited port space substantially restrict the number of FPP-relevant diagnostics. One remaining challenge is developing tools and devices to extract plasma state information necessary for controlling an FPP from a limited subset of diagnostics. This work is part of an overarching project to address this challenge. The specific diagnostic subset to be used in FPPs is still under debate. We take the approach of developing machine-learning-based tools for different significant plasma state parameters, using already known FPP-viable diagnostics. Previously we developed a plasma confinement mode classifier utilizing the Electron Cyclotron Emission (ECE) diagnostic. Here, we expand on this by developing a Profile Reflectometer (PR) based classifier with 97% test accuracy, and an ensemble model that combines the ECE and PR models into a single model, achieving 99% test accuracy.

Clark, Randall [Univ. of California, San Diego, CA↗
DOE OSTI2024

Photon classification with Gradient Boosted Trees at CLAS12

Dihadron semi-inclusive deep inelastic scattering (SIDIS) of 10.6 GeV longitudinally polarized electrons off the proton has been measured using the CLAS12 detector at Jefferson Lab. Two separate channels, π + π 0 and π - π 0 , were analyzed, requiring the reconstruction of diphoton pairs. Here, in this analysis, we addressed the problem of false neutral particles being reconstructed by CLAS12's event builder, polluting the otherwise physical combinatorial background underneath the π 0 peak. A photon classifier using a Gradient Boosted Trees (GBTs) architecture was trained with Monte Carlo simulations to reduce the amount of background π 0 's. We show that the nearest-neighbor features learned by the model lead to a substantial increase in signal vs. background discrimination compared to previous CLAS12 π^0 analyses. The machine learning approach recovers several times more dihadron statistics for the dataset.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

InClass nets: independent classifier networks for nonparametric estimation of conditional independence mixture models and unsupervised classification

Abstract Conditional independence mixture models (CIMMs) are an important class of statistical models used in many fields of science. We introduce a novel unsupervised machine learning technique called the independent classifier networks (InClass nets) technique for the nonparameteric estimation of CIMMs. InClass nets consist of multiple independent classifier neural networks (NNs), which are trained simultaneously using suitable cost functions. Leveraging the ability of NNs to handle high-dimensional data, the conditionally independent variates of the model are allowed to be individually high-dimensional, which is the main advantage of the proposed technique over existing non-machine-learning-based approaches. Two new theorems on the nonparametric identifiability of bivariate CIMMs are derived in the form of a necessary and a (different) sufficient condition for a bivariate CIMM to be identifiable. We use the InClass nets technique to perform CIMM estimation successfully for several examples. We provide a public implementation as a Python package called RainDancesVI.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2019

Machine-Learning Classification Suggests That Many Alphaproteobacterial Prophages May Instead Be Gene Transfer Agents

Abstract Many of the sequenced bacterial and archaeal genomes encode regions of viral provenance. Yet, not all of these regions encode bona fide viruses. Gene transfer agents (GTAs) are thought to be former viruses that are now maintained in genomes of some bacteria and archaea and are hypothesized to enable exchange of DNA within bacterial populations. In Alphaproteobacteria, genes homologous to the “head–tail” gene cluster that encodes structural components of the Rhodobacter capsulatus GTA (RcGTA) are found in many taxa, even if they are only distantly related to Rhodobacter capsulatus. Yet, in most genomes available in GenBank RcGTA-like genes have annotations of typical viral proteins, and therefore are not easily distinguished from their viral homologs without additional analyses. Here, we report a “support vector machine” classifier that quickly and accurately distinguishes RcGTA-like genes from their viral homologs by capturing the differences in the amino acid composition of the encoded proteins. Our open-source classifier is implemented in Python and can be used to scan homologs of the RcGTA genes in newly sequenced genomes. The classifier can also be trained to identify other types of GTAs, or even to detect other elements of viral ancestry. Using the classifier trained on a manually curated set of homologous viruses and GTAs, we detected RcGTA-like “head–tail” gene clusters in 57.5% of the 1,423 examined alphaproteobacterial genomes. We also demonstrated that more than half of the in silico prophage predictions are instead likely to be GTAs, suggesting that in many alphaproteobacterial genomes the RcGTA-like elements remain unrecognized.

Evolutionary Biology↗
DOE OSTI2022

The miniJPAS survey quasar selection – I. Mock catalogues for classification

In this series of papers, we employ several machine learning (ML) methods to classify the point-like sources from the miniJPAS catalogue, and identify quasar candidates. Since no representative sample of spectroscopically confirmed sources exists at present to train these ML algorithms, we rely on mock catalogues. In this first paper, we develop a pipeline to compute synthetic photometry of quasars, galaxies, and stars using spectra of objects targeted as quasars in the Sloan Digital Sky Survey . To match the same depths and signal-to-noise ratio distributions in all bands expected for miniJPAS point sources in the range 17.5 ≤ r < 24, we augment our sample of available spectra by shifting the original r-band magnitude distributions towards the faint end, ensure that the relative incidence rates of the different objects are distributed according to their respective luminosity functions, and perform a thorough modelling of the noise distribution in each filter, by sampling the flux variance either from Gaussian realizations with given widths, or from combinations of Gaussian functions. Finally, we also add in the mocks the patterns of non-detections which are present in all real observations. Although the mock catalogues presented in this work are a first step towards simulated data sets that match the properties of the miniJPAS observations, these mocks can be adapted to serve the purposes of other photometric surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Automating galaxy morphology classification using k -nearest neighbours and non-parametric statistics

ABSTRACT Morphology is a fundamental property of any galaxy population. It is a major indicator of the physical processes that drive galaxy evolution and in turn the evolution of the entire Universe. Historically, galaxy images were visually classified by trained experts. However, in the era of big data, more efficient techniques are required. In this work, we present a k-nearest neighbours based approach that utilizes non-parametric morphological quantities to classify galaxy morphology in Sloan Digital Sky Survey images. Most previous studies used only a handful of morphological parameters to identify galaxy types. In contrast, we explore 1023 morphological spaces (defined by up to 10 non-parametric statistics) to find the best combination of morphological parameters. Additionally, while most previous studies broadly classified galaxies into early types and late types or ellipticals, spirals, and irregular galaxies, we classify galaxies into 11 morphological types with an average accuracy of ${\sim} 80\!-\!90 \, {{\rm per\, cent}}$ per T-type. Our method is simple, easy to implement, and is robust to varying sizes and compositions of the training and test samples. Preliminary results on the performance of our technique on deeper images from the Hyper Suprime-Cam Subaru Strategic Survey reveal that an extension of our method to modern surveys with better imaging capabilities might be possible.

Mukundan, Kavya↗
DOE OSTI2019

A heuristic classification of woody plants based on contrasting shade and drought strategies

Woody plants vary in their adaptations to drought and shade. For a better prediction of vegetation responses to drought and shade within dynamic global vegetation models, it is critical to group species into functional types with similar adaptations. One of the key challenges is that the adaptations are generally determined by a large number of plant traits that may not be available for a large number of species. In this study, we present two heuristic woody plant groups that were separated using cluster analysis in a three-dimensional trait–environment space based on three key metrics for each species: mean xylem embolism resistance, shade tolerance and habitat aridity. The two heuristic groups separate these species into tolerators and avoiders. Furthermore, the tolerators either rely on their high embolism resistance to tolerate drought in arid habitats (e.g., Juniperus and Prunus ) or rely on high shade tolerance to withstand shaded conditions in wet habitats (e.g., Picea , Abies and Acer ). In contrast, all avoiders have low embolism resistance and low shade tolerance. In arid habitats, avoiders tend to minimize catastrophic embolism (e.g., most Pinus species) while in wet habitats, they may survive despite low shade tolerance (e.g., Betula , Populus , Alnus and Salix ). Because our approach links traits to the environmental conditions, we expect it could be a promising framework for predicting changes in species composition, and therefore ecosystem function, under changing environmental conditions.

59 BASIC BIOLOGICAL SCIENCES↗