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DOE OSTI2014

SU-E-T-157: Evaluation and Comparison of Doses to Pelvic Lymph Nodes and to Point B with 3D Image Guided Treatment Planning for High Dose Brachytherapy for Treatment of Cervical Cancer

Purpose: To estimate and compare the doses received by the obturator, external and internal iliac lymph nodes and point Methods: CT-MR fused image sets of 15 patients obtained for each of 5 fractions of HDR brachytherapy using tandem and ring applicator, were used to generate treatment plans optimized to deliver a prescription dose to HRCTV-D90 and to minimize the doses to organs at risk (OARs). For each set of image, target volume (GTV, HRCTV) OARs (Bladder, Rectum, Sigmoid), and both left and right pelvic lymph nodes (obturator, external and internal iliac lymph nodes) were delineated. Dose-volume histograms (DVH) were generated for pelvic nodal groups (left and right obturator group, internal and external iliac chains) Per fraction DVH parameters used for dose comparison included dose to 100% volume (D100), and dose received by 2cc (D2cc), 1cc (D1cc) and 0.1 cc (D0.1cc) of nodal volume. Dose to point B was compared with each DVH parameter using 2 sided t-test. Pearson correlation were determined to examine relationship of point B dose with nodal DVH parameters. Results: FIGO clinical stage varied from 1B1 to IIIB. The median pretreatment tumor diameter measured on MRI was 4.5 cm (2.7– 6.4cm).The median dose to bilateral point B was 1.20 Gy ± 0.12 or 20% of the prescription dose. The correlation coefficients were all <0.60 for all nodal DVH parameters indicating low degree of correlation. Only 2 cc of obturator nodes was not significantly different from point B dose on t-test. Conclusion: Dose to point B does not adequately represent the dose to any specific pelvic nodal group. When using image guided 3D dose-volume optimized treatment nodal groups should be individually identified and delineated to obtain the doses received by pelvic nodes.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2019

ENSEMBLE KALMAN FILTERS FOR RELIABILITY ESTIMATION IN PERFUSION INFERENCE

We consider the solution of inverse problems in dynamic contrast–enhanced imaging by means of ensemble Kalman filters. Our quantity of interest is blood perfusion, i.e., blood flow rates in tissue. While existing approaches to compute blood perfusion parameters for given time series of radiological measurements mainly rely on deterministic, deconvolution–based methods, we aim at recovering probabilistic solution information for given noisy measurements. To this end, we model radiological image capturing as a sequential data assimilation process and solve it by an ensemble Kalman filter. Thereby, we recover deterministic results as an ensemble–based mean and are able to compute reliability information such as probabilities for the perfusion to be in a given range. Our target application is the inference of blood perfusion parameters in the human brain. A numerical study shows promising results for artificial measurements generated by a digital perfusion phantom.

Engineering↗

DOE OSTI2024

Physics informed neural network can retrieve rate and state friction parameters from acoustic monitoring of laboratory stick-slip experiments

Various machine learning (ML) and deep learning (DL) techniques have been recently applied to the forecasting of laboratory earthquakes from friction experiments. The magnitude and timing of shear failures in stick-slip cycles are predicted using features extracted from the recorded ultrasonic or acoustic emission (AE) signals. In addition, the Rate and State Friction (RSF) constitutive laws are extensively used to model the frictional behavior of faults. In this work, we use data from shear experiments coupled with passive acoustic (variance, kurtosis, and AE rate) interleaved with active source ultrasonic monitoring (transmitted wave amplitude) to develop physics-informed neural network (PINN) models incorporating the RSF law and AE rate generation equation with wave amplitude serving as a proxy for friction state variable. This PINN framework allows learning RSF parameters from stick-slip experiments rather than measuring them through a series of velocity step experiments. We observe that when the stick-slip cycles are irregular, the PINN models outperform the data-driven DL models. Transfer learning (TL) PINN models are also developed by pre-training on data collected at one normal stress level followed by forecasting shear failures and retrieving RSF parameters at other stress levels (i.e., with different recurrence intervals) after retraining on a limited amount of new data. Our findings suggest that TL models perform better compared to standalone models. Both standalone and TL PINN-estimated RSF parameters and their ground truth values show excellent agreements thus demonstrating that RSF parameters can be retrieved from laboratory stick-slip experiments using the corresponding acoustic data and that the transmitted wave amplitude provides a good representation of the evolving frictional state during stick-slips.

58 GEOSCIENCES↗

DOE OSTI2022

Error analysis of numerical methods for thick diffusive neutron transport problems on Shishkin mesh

A thin layer will develop at the boundary if the incoming angular flux is anisotropic in thick diffusive neutron transport problems. Solving such singularly perturbed problems, which have non-smooth solutions with singularity near the boundary, is computationally challenging. Standard finite difference schemes on a uniform mesh cannot yield ε-uniform convergence, where ε is a small parameter, while it can be achieved on a suitable piecewise-uniform Shishkin mesh. We present a formal error analysis of the diamond difference (DD) method and step difference (SD) method for solving the S{sub N} neutron transport equation. The analysis can be extended to other finite difference methods. Numerical results are presented to confirm the error estimates and the advantages of the Shishkin mesh. (author)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2021

Simulating Dopant Diffusion in a Detalied Porous Structure

The National Ignition Facility (NIF) at Lawrence Livermore National Laboratory (LLNL) uses highpowered lasers to explore cutting edge fusion technology. Specialized optics, known as fiber lasers, generate and amplify the NIF’s high-powered lasers that facilitate fusion experiments. Fiber lasers are glass fibers that are doped with heavy elements to stimulate light emission. The fiber laser doping processing uses a predeposited layer of porous silica nanoparticles on the surface of the glass to absorb a dopant rich salt solution. Our project simulated the diffusion of dopants into porous silica with prescribed colloidal stacking geometries using COMSOL Multiphysics. With these simulations we identified the porosity and tortuosity of the porous nanoparticle structures as the primary parameters that will dictate how the dopants will diffusive into the porous surface of the glass. Porosity and Tortuosity are geometric parameters that can easily be input into more detailed simulations to estimate an effective diffusivity within the porous layer. Future work should account for surface reactions to accurately simulate the full-scale diffusion of the solution doping process.

36 MATERIALS SCIENCE↗

NASA NTRSDate not supplied

Development of Carbon Flux Model Using ABI Data Over the Conterminous US

The satellite-driven carbon flux estimation has been playing important role to estimate continental-scale carbon budget. One of the biggest recent advances in the satellite-driven carbon flux modeling is utilization of high-frequent geostationary satellites to estimate diurnal cycle in carbon fluxes. The satellite based carbon flux estimation used the polar orbiting satellite sensors (e.g., Moderate Resolution Imaging Spectroradiometer (MODIS)), which allow us to observe target regions only once during the day. The new generation of geostationary satellite sensors provide frequent observations, often less than every 10 minutes. Here, we use GOES Advanced Baseline Imager (ABI) data to estimate hourly NEE over the conterminous US. We used the Terrestrial Observation Prediction System (TOPS) model for estimating hourly NEE. TOPS is a diagnostic ecosystem process model that simulates the fluxes of carbon and water through vegetation in response to climate variability. For the climate input, we developed hourly climate data using the same algorithm with NASA Earth Exchange Gridded Daily Meteorology (NEX-GDM) datasets based on machine learning techniques. The hourly climate data includes precipitation, maximum temperature, minimum temperature, dew point temperature, and solar radiation were derived from the Geostationary observations. The spatial patterns of ecosystem parameters used in TOPS are optimized using satellite Solar Induced Fluorescence (SIF) data. The high frequency GPP estimations from geostationary satellite sensors make it comparable to the instantaneous SIF data than daily GPP. We also used Ameriflux data for optimization of model parameters and the validation of the output. The derived data addresses the diurnal dynamics of carbon cycling at large scales and should help in reducing the uncertainties in carbon budget studies.

geostationary satellite↗

DOE OSTI2015

A whole wilderness of warning: Assessing economic consequences of nuclear releases - 15494

The consequences of the Fukushima Dai-ichi disaster may provide a useful analogue for assessing the consequences of an accident involving the transport of spent nuclear fuel in the United States. An unexamined, to date, aspect of the Fukushima Dai-ichi disaster is the wide disagreement about the economic consequences of the release. There exist a variety of different estimates and different models and methods for performing these estimates, but not a systematic way to address the range of issues such a disaster generated. This paper examines the sources, methods, data, and assumptions used to prepare economic consequence estimates of the disaster. This paper compares and contrasts these estimates and evaluates them for their reliability. The wide dispersion of consequence assessments as defined by various media and agency impact reports in the post-Fukushima era are evidence that such variability does not serve the industry, regulators, government or researchers well. While a protocol exists to gauge the intensity of an incident (INES Scale) no pre-existing methodology exists to measure the various socioeconomic impacts associated with a radiological disaster, be it induced by a natural disaster incident or human initiated. This paper argues that an improved method will be necessary and suggests some parameters for such a method. Nevada has started the dialogue on such a comprehensive methodology (Ballard 2012, Ballard 2014). Despite this effort by a stakeholder, the regulatory agencies involved in nuclear waste shipments in the United States appear reluctant to begin a dialogue about how to address the full range and scope of issues. Resolution of this problem will be a necessary prerequisite to any large scale shipping program. It will be necessary to adopt a standardized methodology to assess consequences of a radiological emergency and incorporate this into the environmental impact statement process for the shipping program. This analytical deficiency should be addressed by these agencies/regulators and in preparation for the potential movement of nuclear waste in the quantities necessary to service the nuclear power industry and to address the existing spent nuclear fuel (SNF) stockpiles across the United States. It will be necessary to demonstrate to stakeholders that the benefits of shipping radioactive substances this dangerous outweigh the costs of a possible accident or terrorist incident. The nuclear energy industry should support funding for these assessment studies in order to provide a basis for discussions about the costs and benefits of the shipments. The studies would provide stakeholders with clarity about the possible consequences of the program. The studies will also assist Japan in its efforts to understand what their disaster means for that country. This paper will begin the dialogue on how such analytical protocols could be constructed to pre-stage the research necessary to assess such consequences. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2016

Estimating the Maximum Achievable Productivity in Outdoor Ponds: Microalgae Biomass Growth Modeling and Climate Simulated Culturing

To address the challenge of determining the maximum achievable monthly, seasonal, and annual biomass productivity for selected promising strains in outdoor ponds at any geographic location of interest, we developed the following four step integrated strategy. First, the strain is characterized in terms of its specific growth rate response to temperature and light, including darkness, as well as its light attenuation characteristics. Second, these species-specific parameters serve as inputs to PNNL’s biomass growth model that, in conjunction with the sunlight and pond water temperature predictions provided by PNNL’s Biomass Assessment Tool, is used to predict the respective biomass productivities for tens of thousands of hypothetical pond locations in the United States. Third, monthly and annual biomass productivity maps are created to identify the geographic location(s) of optimum biomass productivity and to generate light and water temperature “scripts” (time series) for hypothetical ponds at the best site(s). Finally, the maximum achievable biomass productivity for the given strain is confirmed by conducting culture experiments in PNNL’s indoor LED-lighted and temperature-controlled ponds under climate-simulated conditions, using the previously determined light and temperature scripts. This integrated strategy provides an efficient and low risk approach to screen promising strains for their potential to exhibit high biomass productivities, and to validate their model-predicted performance in climate-simulation ponds before transitioning to cultivation in outdoor raceways.

biomass assessment tool↗

DOE OSTI2016

Precision ephemerides for gravitational wave searches. II. Cyg X-2

Accreting neutron stars in low-mass X-ray binaries are candidate high-frequency persistent gravitational wave sources. These may be detectable with next-generation interferometers such as Advanced LIGO/VIRGO within this decade. However, the search sensitivity is expected to be limited principally by the uncertainty in the binary system parameters. We combine new optical spectroscopy of Cyg X-2 obtained with the Liverpool Telescope with available historical radial velocity data, which gives us improved orbital parameter uncertainties based on a 44 year baseline. We obtained an improvement of a factor of 2.6 in the orbital period precision and a factor of 2 in the epoch of inferior conjunction T {sub 0}. The updated orbital parameters imply a mass function of 0.65 ± 0.01 M {sub ⊙}, leading to a primary mass (M {sub 1}) of 1.67 ± 0.22 M {sub ⊙} (for i = 62. {sup ∘}5±4{sup ∘}). In addition, we estimate the likely orbital parameter precision through to the expected Advanced LIGO and VIRGO detector observing period and quantify the corresponding improvement in sensitivity via the required number of templates.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

High-energy neutrino emission from magnetized jets of rapidly rotating protomagnetars

ABSTRACT Relativistic jets originating from protomagnetar central engines can lead to long duration gamma-ray bursts (GRBs) and are considered potential sources of ultra-high-energy cosmic rays and secondary neutrinos. We explore the propagation of such jets through a broad range of progenitors, from stars which have shed their envelopes to supergiants which have not. We use a semi-analytical spin-down model for the strongly magnetized and rapidly rotating protoneutron star (PNS) to investigate the role of central engine properties such as the surface dipole field strength, initial rotation period, and jet opening angle on the interactions and dynamical evolution of the jet-cocoon system. With this model, we determine the properties of the relativistic jet, the mildly relativistic cocoon, and the collimation shock in terms of system parameters such as the time-dependent jet luminosity, injection angle, and density profile of the stellar medium. We also analyse the criteria for a successful jet breakout, the maximum energy that can be deposited into the cocoon by the relativistic jet, and structural stability of the magnetized outflow relative to local instabilities. Lastly, we compute the high-energy neutrino emission as these magnetized outflows burrow through their progenitors. Precursor neutrinos from successful GRB jets are unlikely to be detected by IceCube, which is consistent with the results of previous works. On the other hand, we find that high-energy neutrinos may be produced for extended progenitors like blue and red supergiants, and we estimate the detectability of neutrinos with next generation detectors such as IceCube-Gen2.

Astronomy & Astrophysics↗

DOE OSTI2016

Axial, scalar, and tensor charges of the nucleon from 2+1+1-flavor Lattice QCD

We present results for the isovector axial, scalar, and tensor charges 𝑔$^{𝑢−𝑑}_{𝐴}$, 𝑔$^{𝑢−𝑑}_{𝑆}$, and 𝑔$^{𝑢−𝑑}_{𝑇}$ of the nucleon needed to probe the Standard Model and novel physics. The axial charge is a fundamental parameter describing the weak interactions of nucleons. The scalar and tensor charges probe novel interactions at the TeV scale in neutron and nuclear 𝛽-decays, and the flavor-diagonal tensor charges 𝑔$^{𝑢}_{𝑇}$, 𝑔$^{𝑑}_{𝑇}$, and 𝑔$^{𝑠}_{𝑇}$ are needed to quantify the contribution of the quark electric dipole moment (EDM) to the neutron EDM. The lattice-QCD calculations were done using nine ensembles of gauge configurations generated by the MILC Collaboration using the highly improved staggered quarks action with 2+1+1 dynamical flavors. These ensembles span three lattice spacings 𝑎 ≈0.06,0.09, and 0.12 fm and light-quark masses corresponding to the pion masses 𝑀 𝜋 ≈ 135,225, and 315 MeV. High-statistics estimates on five ensembles using the all-mode-averaging method allow us to quantify all systematic uncertainties and perform a simultaneous extrapolation in the lattice spacing, lattice volume, and light-quark masses for the connected contributions. Our final estimates, in the $\overline{MS}$ scheme at 2 GeV, of the isovector charges are 𝑔$^{𝑢−𝑑}_{𝐴}$ = 1.195⁢(33)⁢(20), 𝑔$^{𝑢−𝑑}_{𝑆}$ = 0.97⁢(12)⁢(6), and 𝑔$^{𝑢−𝑑}_{𝑇}$ = 0.987⁢(51)⁢(20). The first error includes statistical and all systematic uncertainties except that due to the extrapolation Ansatz, which is given by the second error estimate. Combining our estimate for 𝑔$^{𝑢−𝑑}_{𝑆}$ with the difference of light quarks masses (𝑚 𝑑 − 𝑚 𝑢 ) QCD = 2.67⁢(35) MeV given by the Flavor Lattice Average Group, we obtain (𝑀 𝑁 − 𝑀 𝑃 ) QCD = 2.59⁢(49) MeV.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2014

SU-E-QI-03: Compartment Modeling of Dynamic Brain PET - The Effect of Scatter and Random Corrections On Parameter Errors

Purpose: To investigate the effects of corrections for random and scattered coincidences on kinetic parameters in brain tumors, by using ten Monte Carlo (MC) simulated dynamic FLT-PET brain scans. Methods: The GATE MC software was used to simulate ten repetitions of a 1 hour dynamic FLT-PET scan of a voxelized head phantom. The phantom comprised six normal head tissues, plus inserted regions for blood and tumor tissue. Different time-activity-curves (TACs) for all eight tissue types were used in the simulation and were generated in Matlab using a 2-tissue model with preset parameter values (K1,k2,k3,k4,Va,Ki). The PET data was reconstructed into 28 frames by both ordered-subset expectation maximization (OSEM) and 3D filtered back-projection (3DFBP). Five image sets were reconstructed, all with normalization and different additional corrections C (A=attenuation, R=random, S=scatter): Trues (AC), trues+randoms (ARC), trues+scatters (ASC), total counts (ARSC) and total counts (AC). Corrections for randoms and scatters were based on real random and scatter sinograms that were back-projected, blurred and then forward projected and scaled to match the real counts. Weighted non-linearleast- squares fitting of TACs from the blood and tumor regions was used to obtain parameter estimates. Results: The bias was not significantly different for trues (AC), trues+randoms (ARC), trues+scatters (ASC) and total counts (ARSC) for either 3DFBP or OSEM (p<0.05). Total counts with only AC stood out however, with an up to 160% larger bias. In general, there was no difference in bias found between 3DFBP and OSEM, except in parameter Va and Ki. Conclusion: According to our results, the methodology of correcting the PET data for randoms and scatters performed well for the dynamic images where frames have much lower counts compared to static images. Generally, no bias was introduced by the corrections and their importance was emphasized since omitting them increased bias extensively.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

Deep Learning Based Approach to Integrate MyShake's Trigger Data with ShakeAlert for Faster and Robust EEW Alerts

Earthquake Early Warning (EEW) systems depend on the dense seismic network to make fast and accurate earthquake detections to issue useful early alerts. The recent development and implementation of the ShakeAlert system is relying on the west coast seismic network to detect and estimate the earthquake parameters in real-time. While working on making improvements on the current system, one potential approach is to include more seismic recordings from various sources, such as the low-cost smartphone seismic network, such as the MyShake network, or the Google Android smartphones, since these smartphone seismic networks have much more portable stations (phones) to potentially provide useful data to the system. This report summarizes the initial exploration of using a deep learning approach to combine the data from both traditional seismic stations and the smartphone data in California. Due to the heterogeneity nature of the data, we aggregate data from the phones (using simulation data), as well as that from traditional seismic stations to grid cells. By generating real-time triggering ratio grid cell maps, the designed deep learning algorithm can process the data from multiple sources and detect the earthquake faster than only using that from a traditional seismic network.

58 GEOSCIENCES↗

NASA NTRS2007

NASA Tech Briefs, December 2007

Topics include: Ka-Band TWT High-Efficiency Power Combiner for High-Rate Data Transmission; Reusable, Extensible High-Level Data-Distribution Concept; Processing Satellite Imagery To Detect Waste Tire Piles; Monitoring by Use of Clusters of Sensor-Data Vectors; Circuit and Method for Communication Over DC Power Line; Switched Band-Pass Filters for Adaptive Transceivers; Noncoherent DTTLs for Symbol Synchronization; High-Voltage Power Supply With Fast Rise and Fall Times; Waveguide Calibrator for Multi-Element Probe Calibration; Four-Way Ka-Band Power Combiner; Loss-of-Control-Inhibitor Systems for Aircraft; Improved Underwater Excitation-Emission Matrix Fluorometer; Metrology Camera System Using Two-Color Interferometry; Design and Fabrication of High-Efficiency CMOS/CCD Imagers; Foam Core Shielding for Spacecraft CHEM-Based Self-Deploying Planetary Storage Tanks Sequestration of Single-Walled Carbon Nanotubes in a Polymer PPC750 Performance Monitor Application-Program-Installer Builder Using Visual Odometry to Estimate Position and Attitude Design and Data Management System Simple, Script-Based Science Processing Archive Automated Rocket Propulsion Test Management Online Remote Sensing Interface Fusing Image Data for Calculating Position of an Object Implementation of a Point Algorithm for Real-Time Convex Optimization Handling Input and Output for COAMPS Modeling and Grid Generation of Iced Airfoils Automated Identification of Nucleotide Sequences Balloon Design Software Rocket Science 101 Interactive Educational Program Creep Forming of Carbon-Reinforced Ceramic-Matrix Composites Dog-Bone Horns for Piezoelectric Ultrasonic/Sonic Actuators Benchtop Detection of Proteins Recombinant Collagenlike Proteins Remote Sensing of Parasitic Nematodes in Plants Direct Coupling From WGM Resonator Disks to Photodetectors Using Digital Radiography To Image Liquid Nitrogen in Voids Multiple-Parameter, Low-False-Alarm Fire-Detection Systems Mosaic-Detector-Based Fluorescence Spectral Imager Plasmoid Thruster for High Specific-Impulse Propulsion Analysis Method for Quantifying Vehicle Design Goals Improved Tracking of Targets by Cameras on a Mars Rover Sample Caching Subsystem Multistage Passive Cooler for Spaceborne Instruments GVIPS Models and Software Stowable Energy-Absorbing Rocker-Bogie Suspensions

Source record↗

NASA NTRS2013

Automating Hyperspectral Data for Rapid Response in Volcanic Emergencies

In a volcanic emergency, time is of the essence. It is vital to quantify eruption parameters (thermal emission, effusion rate, location of activity) and distribute this information as quickly as possible to decision-makers in order to enable effective evaluation of eruption-related risk and hazard. The goal of this work was to automate and streamline processing of spacecraft hyperspectral data, automate product generation, and automate distribution of products. Visible and Short-Wave Infrared Images of volcanic eruption in Iceland in May 2010." class="caption" align="right">The software rapidly processes hyperspectral data, correcting for incident sunlight where necessary, and atmospheric transmission; detects thermally anomalous pixels; fits data with model black-body thermal emission spectra to determine radiant flux; calculates atmospheric convection thermal removal; and then calculates total heat loss. From these results, an estimation of effusion rate is made. Maps are generated of thermal emission and location (see figure). Products are posted online, and relevant parties notified. Effusion rate data are added to historical record and plotted to identify spikes in activity for persistently active eruptions. The entire process from start to end is autonomous. Future spacecraft, especially those in deep space, can react to detection of transient processes without the need to communicate with Earth, thus increasing science return. Terrestrially, this removes the need for human intervention.

Davies, Ashley G.↗

NASA NTRS2018

Impacts of Snow and Cloud Covers on Satellite-Derived PM 2.5 Levels

Satellite aerosol optical depth (AOD) has been widely employed to evaluate ground fine particle (PM 2.5 ) levels, whereas snow/cloud covers often lead to a large proportion of non-random missing AOD. As a result, the fully covered and unbiased PM 2.5 estimates will be hard to generate. Among the current approaches to deal with the data gap issue, few have considered the cloud-AOD relationship and none of them have considered the snow-AOD relationship. This study examined the impacts of snow and cloud covers on AOD and PM 2.5 and made full-coverage PM 2.5 predictions with the consideration of these impacts. To estimate the missing AOD, daily gap-filling models with snow/cloud fractions and meteorological covariates were developed using the random forest algorithm. By using these models in New York State, a daily AOD data set with a 1-km resolution was generated with a complete coverage. The“out-of-bag” R 2 of the gap-filling models averaged 0.93 with an interquartile range from 0.90 to 0.95. Subsequently, a random forest-based PM 2.5 prediction model with the gap-filled AOD and covariates was built to predict fully covered PM 2.5 estimates. A ten-fold cross-validation for the prediction model showed a good performance with an R 2 of 0.82. In the gap-filling models, the snow fraction was of higher significance in the snow season compared with the rest of the year. The prediction models fitted with/without the snow fraction also suggested the discernible changes in PM 2.5 patterns, further confirming the significance of this parameter. Compared with the methods without considering snow and cloud covers, our PM 2.5 prediction surfaces showed more spatial details and reflected small-scale terrain-driven PM 2.5 patterns. The proposed methods can be generalized to the areas with extensive snow/cloud covers and large proportions of missing satellite AOD for predicting PM 2.5 levels with high resolutions and complete coverage.

AOD↗

DOE OSTI2022

Photometric redshifts from SDSS images with an interpretable deep capsule network

ABSTRACT Studies of cosmology, galaxy evolution, and astronomical transients with current and next-generation wide-field imaging surveys like the Rubin Observatory Legacy Survey of Space and Time are all critically dependent on estimates of photometric redshifts. Capsule networks are a new type of neural network architecture that is better suited for identifying morphological features of the input images than traditional convolutional neural networks. We use a deep capsule network trained on ugriz images, spectroscopic redshifts, and Galaxy Zoo spiral/elliptical classifications of ∼400 000 Sloan Digital Sky Survey galaxies to do photometric redshift estimation. We achieve a photometric redshift prediction accuracy and a fraction of catastrophic outliers that are comparable to or better than current methods for SDSS main galaxy sample-like data sets (r ≤ 17.8 and zspec ≤ 0.4) while requiring less data and fewer trainable parameters. Furthermore, the decision-making of our capsule network is much more easily interpretable as capsules act as a low-dimensional encoding of the image. When the capsules are projected on a two-dimensional manifold, they form a single redshift sequence with the fraction of spirals in a region exhibiting a gradient roughly perpendicular to the redshift sequence. We perturb encodings of real galaxy images in this low-dimensional space to create synthetic galaxy images that demonstrate the image properties (e.g. size, orientation, and surface brightness) encoded by each dimension. We also measure correlations between galaxy properties (e.g. magnitudes, colours, and stellar mass) and each capsule dimension. We publicly release our code, estimated redshifts, and additional catalogues at https://biprateep.github.io/encapZulate-1.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2015

Spontaneous magnetization of a vacuum in the hot Universe and intergalactic magnetic fields

We review the spontaneous magnetization of the vacuum of non-Abelian gauge fields at high temperature. The standard model of particles is investigated as a particular example. By using both analytic methods of quantum field theory and gauge field theory on a lattice, we determine the Abelian (chromo)magnetic fields in the restored phase of the model at high temperatures T ≥ T{sub ew}. The fields are stable and temperature dependent, B = B(T). We investigate the mechanisms of the field stabilization in detail. The screening parameters for electric and magnetic fields—the Debye, m{sub D}(B, T), and magnetic, m{sub magn}(B, T), masses—are calculated. It is shown that, in the field presence, the former one is smaller than at zero field. The magnetic mass of the (chromo)magnetic fields is determined to be zero, as for usual U(1) magnetic field. We also show that the vacuum magnetization stops at temperatures below the electroweak phase transition temperature, T ≤ T{sub ew}, when a scalar condensate creates. These properties make reasonable a possibility that the intergalactic magnetic fields observed recently were spontaneously generated in the hot Universe at the reheating epoch due to vacuum polarization of non-Abelian gauge fields. We present a procedure for estimating the field strengths B(T) at different temperatures. In particular, the value of B(T{sub ew}) ∼ 10{sup 14} G, at T{sub ew} is estimated with taking into consideration the observed intergalactic magnetic field B{sub 0} ∼ 10{sup −15} G. The magnetic field scale is also estimated. Some model dependent peculiarities of the phenomena studied are briefly discussed.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗