Reinterpretation of Classic Proton Charge Form Factor Measurements
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72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
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Comparative study of machine learning techniques for post-combustion carbon capture systems
Computational analysis of countercurrent flows in packed absorption columns, often used in solvent-based post-combustion carbon capture systems (CCSs), is challenging. Typically, computational fluid dynamics (CFD) approaches are used to simulate the interactions between a solvent, gas, and column's packing geometry while accounting for the thermodynamics, kinetics, heat, and mass transfer effects of the absorption process. These simulations can then be used explain a column's hydrodynamic characteristics and evaluate its CO 2 -capture efficiency. However, these approaches are computationally expensive, making it difficult to evaluate numerous designs and operating conditions to improve efficiency at industrial scales. In this work, we comprehensively explore the application of statistical ML methods, convolutional neural networks (CNNs), and graph neural networks (GNNs) to aid and accelerate the scale-up and design optimization of solvent-based post-combustion CCSs. We apply these methods to CFD datasets of countercurrent flows in absorption columns with structured packings characterized by several geometric parameters. We train models to use these parameters, inlet velocity conditions, and other model-specific representations of the column to estimate key determinants of CO 2 -capture efficiency without having to simulate additional CFD datasets. We also evaluate the impact of different input types on the accuracy and generalizability of each model. We discuss the strengths and limitations of each approach to further elucidate the role of CNNs, GNNs, and other machine learning approaches for CO 2 -capture property prediction and design optimization.
97 MATHEMATICS AND COMPUTING↗
Multiscale astrobiology with the Vera C. Rubin Observatory Legacy Survey of Space and Time
The Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST) presents an unprecedented opportunity to advance the search for astrobiologically relevant data across diverse astronomical environments. Through its extensive imaging capabilities, LSST will enhance our inventory of Solar System objects and assess their potential astrobiological conditions. Beyond the Solar System, LSST will survey billions of stars, contributing to the detection of exoplanets, characterizing planetary atmospheres at a zeroth-order level, identifying transient phenomena, and exploring conditions within habitable zones. In this paper, we highlight LSST’s unique contribution to astrobiology, complementing other missions and expanding the search for life beyond Earth in the coming decades.
79 ASTRONOMY AND ASTROPHYSICS↗
SNM Radiation Signature Classification Using Different Semi-Supervised Machine Learning Models
The timely detection of special nuclear material (SNM) transfers between nuclear facilities is an important monitoring objective in nuclear nonproliferation. Persistent monitoring enabled by successful detection and characterization of radiological material movements could greatly enhance the nuclear nonproliferation mission in a range of applications. Supervised machine learning can be used to signal detections when material is present if a model is trained on sufficient volumes of labeled measurements. However, the nuclear monitoring data needed to train robust machine learning models can be costly to label since radiation spectra may require strict scrutiny for characterization. Therefore, this work investigates the application of semi-supervised learning to utilize both labeled and unlabeled data. As a demonstration experiment, radiation measurements from sodium iodide (NaI) detectors are provided by the Multi-Informatics for Nuclear Operating Scenarios (MINOS) venture at Oak Ridge National Laboratory (ORNL) as sample data. Anomalous measurements are identified using a method of statistical hypothesis testing. After background estimation, an energy-dependent spectroscopic analysis is used to characterize an anomaly based on its radiation signatures. In the absence of ground-truth information, a labeling heuristic provides data necessary for training and testing machine learning models. Supervised logistic regression serves as a baseline to compare three semi-supervised machine learning models: co-training, label propagation, and a convolutional neural network (CNN). In each case, the semi-supervised models outperform logistic regression, suggesting that unlabeled data can be valuable when training and demonstrating value in semi-supervised nonproliferation implementations.
98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
Improving Monsoon Precipitation Prediction Using Combined Convolutional and Long Short Term Memory Neural Network
Precipitation downscaling is widely employed for enhancing the resolution and accuracy of precipitation products from general circulation models (GCMs). In this study, we propose a novel statistical downscaling method to foster GCMs’ precipitation prediction resolution and accuracy for the monsoon region. We develop a deep neural network composed of a convolution and Long Short Term Memory (LSTM) recurrent module to estimate precipitation based on well-resolved atmospheric dynamical fields. The proposed model is compared against the GCM precipitation product and classical downscaling methods in the Xiangjiang River Basin in South China. Results show considerable improvement compared to the European Centre for Medium-Range Weather Forecasts (ECMWF)-Interim reanalysis precipitation. Also, the model outperforms benchmark downscaling approaches, including (1) quantile mapping, (2) the support vector machine, and (3) the convolutional neural network. To test the robustness of the model and its applicability in practical forecasting, we apply the trained network for precipitation prediction forced by retrospective forecasts from the ECMWF model. Compared to the ECMWF precipitation forecast, our model makes better use of the resolved dynamical field for more accurate precipitation prediction at lead times from 1 day up to 2 weeks. This superiority decreases with the forecast lead time, as the GCM’s skill in predicting atmospheric dynamics is diminished by the chaotic effect. Finally, we build a distributed hydrological model and force it with different sources of precipitation inputs. Hydrological simulation forced with the neural network precipitation estimation shows significant advantage over simulation forced with the original ERA-Interim precipitation (with NSE value increases from 0.06 to 0.64), and the performance is only slightly worse than the observed precipitation forced simulation (NSE = 0.82). This further proves the value of the proposed downscaling method, and suggests its potential for hydrological forecasts.
54 ENVIRONMENTAL SCIENCES↗
Dynamical Mass Measurements of Contaminated Galaxy Clusters Using Machine Learning
We study dynamical mass measurements of galaxy clusters contaminated by interlopers and show that a modern machine learning algorithm can predict masses by better than a factor of two compared to a standard scaling relation approach. We create two mock catalogs from Multidark's publicly available N-body MDPL1 simulation, one with perfect galaxy cluster membership information and the other where a simple cylindrical cut around the cluster center allows interlopers to contaminate the clusters. In the standard approach, we use a power-law scaling relation to infer cluster mass from galaxy line-of-sight (LOS) velocity dispersion. Assuming perfect membership knowledge, this unrealistic case produces a wide fractional mass error distribution, with a width of ${\rm{\Delta }}\epsilon \approx 0.87$. Interlopers introduce additional scatter, significantly widening the error distribution further (${\rm{\Delta }}\epsilon \approx 2.13$). We employ the support distribution machine (SDM) class of algorithms to learn from distributions of data to predict single values. Applied to distributions of galaxy observables such as LOS velocity and projected distance from the cluster center, SDM yields better than a factor-of-two improvement (${\rm{\Delta }}\epsilon \approx 0.67$) for the contaminated case. Remarkably, SDM applied to contaminated clusters is better able to recover masses than even the scaling relation approach applied to uncontaminated clusters. We show that the SDM method more accurately reproduces the cluster mass function, making it a valuable tool for employing cluster observations to evaluate cosmological models.
79 ASTRONOMY AND ASTROPHYSICS↗
A selection of giant radio sources from NVSS
Results of the application of pattern-recognition techniques to the problem of identifying giant radio sources (GRSs) from the data in the NVSS catalog are presented, and issues affecting the process are explored. Decision-tree pattern-recognition software was applied to training-set source pairs developed from known NVSS large-angular-size radio galaxies. The full training set consisted of 51,195 source pairs, 48 of which were known GRSs for which each lobe was primarily represented by a single catalog component. The source pairs had a maximum separation of $20^{\prime} $ and a minimum component area of 1.87 square arcmin at the 1.4 mJy level. The importance of comparing the resulting probability distributions of the training and application sets for cases of unknown class ratio is demonstrated. The probability of correctly ranking a randomly selected (GRS, non-GRS) pair from the best of the tested classifiers was determined to be 97.8 ± 1.5%. The best classifiers were applied to the over 870,000 candidate pairs from the entire catalog. Images of higher-ranked sources were visually screened, and a table of over 1600 candidates, including morphological annotation, is presented. These systems include doubles and triples, wide-angle tail and narrow-angle tail, S- or Z-shaped systems, and core-jets and resolved cores. In conclusion, while some resolved-lobe systems are recovered with this technique, generally it is expected that such systems would require a different approach.
79 ASTRONOMY AND ASTROPHYSICS↗
Approximating Photo- z PDFs for Large Surveys
Modern galaxy surveys produce redshift probability density functions (PDFs) in addition to traditional photometric redshift (photo-z) point estimates. However, the storage of photo-z PDFs may present a challenge with increasingly large catalogs, as we face a trade-off between the accuracy of subsequent science measurements and the limitation of finite storage resources. This work presents qp, a Python package for manipulating parameterizations of one-dimensional PDFs, as suitable for photo-z PDF compression. We use qp to investigate the performance of three simple PDF storage formats (quantiles, samples, and step functions) as a function of the number of stored parameters on two realistic mock data sets, representative of upcoming surveys with different data qualities. We propose some best practices for choosing a photo-z PDF approximation scheme and demonstrate the approach on a science case using performance metrics on both ensembles of individual photo-z PDFs and an estimator of the overall redshift distribution function. We show that both the properties of the set of PDFs we wish to approximate and the fidelity metric(s) chosen affect the optimal parameterization. Additionally, we find that quantiles and samples outperform step functions, and we encourage further consideration of these formats for PDF approximation.
79 ASTRONOMY AND ASTROPHYSICS↗
The Photometric LSST Astronomical Time-series Classification Challenge PLAsTiCC: Selection of a Performance Metric for Classification Probabilities Balancing Diverse Science Goals
Classification of transient and variable light curves is an essential step in using astronomical observations to develop an understanding of the underlying physical processes from which they arise. However, upcoming deep photometric surveys, including the Large Synoptic Survey Telescope (LSST), will produce a deluge of low signal-to-noise data for which traditional type estimation procedures are inappropriate. Probabilistic classification is more appropriate for such data but is incompatible with the traditional metrics used on deterministic classifications. Furthermore, large survey collaborations like LSST intend to use the resulting classification probabilities for diverse science objectives, indicating a need for a metric that balances a variety of goals. We describe the process used to develop an optimal performance metric for an open classification challenge that seeks to identify probabilistic classifiers that can serve many scientific interests. The Photometric LSST Astronomical Time-series Classification Challenge (PLASTICC) aims to identify promising techniques for obtaining classification probabilities of transient and variable objects by engaging a broader community beyond astronomy. Using mock classification probability submissions emulating realistically complex archetypes of those anticipated of PLASTICC, we compare the sensitivity of two metrics of classification probabilities under various weighting schemes, finding that both yield results that are qualitatively consistent with intuitive notions of classification performance. We thus choose as a metric for PLASTICC a weighted modification of the cross-entropy because it can be meaningfully interpreted in terms of information content. Finally, we propose extensions of our methodology to ever more complex challenge goals and suggest some guiding principles for approaching the choice of a metric of probabilistic data products.
79 ASTRONOMY AND ASTROPHYSICS↗
Probabilistic Cosmological Mass Mapping from Weak Lensing Shear
We infer gravitational lensing shear and convergence fields from galaxy ellipticity catalogs under a spatial process prior for the lensing potential. We demonstrate the performance of our algorithm with simulated Gaussian-distributed cosmological lensing shear maps and a reconstruction of the mass distribution of the merging galaxy cluster Abell 781 using galaxy ellipticities measured with the Deep Lens Survey. Given interim posterior samples of lensing shear or convergence fields on the sky, we describe an algorithm to infer cosmological parameters via lens field marginalization. In the most general formulation of our algorithm we make no assumptions about weak shear or Gaussian-distributed shape noise or shears. Because we require solutions and matrix determinants of a linear system of dimension that scales with the number of galaxies, we expect our algorithm to require parallel high-performance computing resources for application to ongoing wide field lensing surveys.
79 ASTRONOMY AND ASTROPHYSICS↗
Relation between the Variations in the Mg iiλ2798 Emission Line and 3000 Å Continuum
We investigate the relationship between the Mg II λ2798 emission line and the 3000 Å continuum variations using a sample of 68 intermediate-redshift (z∼0.65--1.50) broad-line quasars spanning a bolometric luminosity range of 44.49 erg s{sup −1}⩽logL{sub bol}⩽46.31 erg s{sup −1} (Eddington ratio from ∼0.026 to 0.862). This sample is constructed from SDSS-DR7Q and BOSS-DR12Q, each with at least two spectroscopic epochs in SDSS-I/II/III surveys. In addition, we adopt the following signal-to-noise ratio (S/N) selection criteria: (a) for Mg II and the 3000 Å continuum, S/N ⩾ 10; and (b) for narrow lines, S/N ⩾ 5. All our quasar spectra are recalibrated based on the assumption of constant narrow emission-line fluxes. In an analysis of spectrum-to-spectrum variations, we find a fairly close correlation (Spearman ρ=0.593) between the variations in broad Mg II and in the continuum. This is consistent with the idea that Mg II is varying in response to the continuum emission variations. Adopting the modified weighted least squares regression method, we statistically constrain the slopes (i.e., the responsivity α of the broad Mg II) between the variations in both components for the sources in different luminosity bins after eliminating intrinsic biases introduced by the rescaling process itself. It is shown that the responsivity is quite small (average α-bar ≈0.464) and anti-correlates with the quasar luminosity. Our results indicate that high signal-to-noise flux measurements are required to robustly detect the intrinsic variability and the time lag of the Mg II line.
79 ASTRONOMY AND ASTROPHYSICS↗
Observational Constraints on the Tilted Spatially Flat and the Untilted Nonflat $\phi$CDM Dynamical Dark Energy Inflation Models
Herein we constrain spatially flat tilted and nonflat untilted scalar field ($\phi$) dynamical dark energy inflation ($\phi$CDM) models by using Planck 2015 cosmic microwave background (CMB) anisotropy measurements and recent baryonic acoustic oscillation distance observations, Type Ia supernovae apparent magnitude data, Hubble parameter measurements, and growth rate data. We assume an inverse power-law scalar field potential energy density $V(\phi )={V}_{0}{\phi }^{-\alpha }$. We find that the combination of the CMB data with the four non-CMB data sets significantly improves parameter constraints and strengthens the evidence for nonflatness in the nonflat untilted $\phi$CDM case from 1.8$σ$ for the CMB measurements only to more than 3.1$σ$ for the combined data. In the nonflat untilted $\phi$CDM model, current observations favor a spatially closed universe with spatial curvature contributing about two-thirds of a percent of the present cosmological energy budget. The flat tilted $\phi$CDM model is a 0.4$σ$ better fit to the data than is the standard flat tilted $Λ$CDM model: current data allow for the possibility that dark energy is dynamical. The nonflat tilted $\phi$CDM model is in better accord with the Dark Energy Survey bounds on the rms amplitude of mass fluctuations now ($σ_8$) as a function of the nonrelativistic matter density parameter now ($Ω_m$) but it does not provide as good a fit to the larger-multipole Planck 2015 CMB anisotropy data as does the standard flat tilted $Λ$CDM model. A few cosmological parameter value measurements differ significantly when determined using the tilted flat and the untilted nonflat $\phi$CDM models, including the cold dark matter density parameter and the reionization optical depth.
79 ASTRONOMY AND ASTROPHYSICS↗
The Aemulus Project. III. Emulation of the Galaxy Correlation Function
Using the N-body simulations of the Aemulus Project, we construct an emulator for the nonlinear clustering of galaxies in real and redshift space. We construct our model of galaxy bias using the halo occupation framework, accounting for possible velocity bias. The model includes 15 parameters, including both cosmological and galaxy bias parameters. We demonstrate that our emulator achieves ~1% precision at the scales of interest, 0.1 h -1 Mpc < r < 10 h -1 Mpc, and recovers the true cosmology when tested against independent simulations. Our primary parameters of interest are related to the growth rate of structure, f, and its degenerate combination, fσ 8 . Using this emulator, we show that the constraining power on these parameters monotonically increases as smaller scales are included in the analysis, all the way down to 0.1 h -1 Mpc. For a BOSS-like survey, the constraints on fσ 8 from r < 30 h -1 Mpc scales alone are nearly a factor of two tighter than those from the fiducial BOSS analysis of redshift-space clustering using perturbation theory at larger scales. The combination of real- and redshift-space clustering allows us to break the degeneracy between f and σ 8, yielding an 11% constraint on f alone for a BOSS-like analysis. The current Aemulus simulations limit this model to surveys of massive galaxies. Future simulations will allow this framework to be extended to all galaxy target types, including emission-line galaxies.
79 ASTRONOMY AND ASTROPHYSICS↗
Probing Blazar Emission Processes with Optical/Gamma-Ray Flare Correlations
We report that even with several thousand Fermi-Large Area Telescope (LAT) blazar detections, the γ-ray emission mechanism is poorly understood. We explore correlated optical/γ-ray flux variations for 178 Fermi-LAT blazars regularly monitored by the Katzman Automatic Imaging Telescope, the Small and Moderate Aperture Research Telescope System, and the Steward Observatory. Out of the 178 sources, 121 show a measurable (>1σ) discrete correlation function peak. Using the derived time lags and Bayesian block light-curve decompositions, we measure the fraction of common and orphan flares between the two bands. After accounting for sampling and sensitivity limitations we quantify for the first time the true orphan flare rates of optical and γ-ray flares: 54.5% of optical and 20% of γ-ray flares are orphan events. Both the intraband temporal relation and the small orphan γ-ray flare fraction point toward leptonic processes as the likely mechanisms for the high-energy emission. Motivated to discriminate between synchrotron self-Compton (SSC) and external-Compton dominance in individual sources, we use the flux–flux variations to determine the slope m of the log f opt –log f γ dependence. The slope distribution suggests a bimodal population with high and intermediate synchrotron peak objects showing larger m than low synchrotron peak objects. We find that m is naturally decreased through pollution from the orphan (typically optical) flares and develop a method to statistically recover, given the sources' measured orphan flare rate, the intrinsic m. Finally, while source classes show composite behavior, the majority of BL Lac objects favor m = 2, indicating an SSC origin for the γ-rays. No preference for either m is found in flat spectrum radio quasars.
79 ASTRONOMY AND ASTROPHYSICS↗
Using the Tilted flat-ΛCDM and the Untilted Non-flat ΛCDM Inflation Models to Measure Cosmological Parameters from a Compilation of Observational Data
We use the physically consistent tilted spatially flat and untilted non-flat ΛCDM inflation models to constrain cosmological parameter values with the Planck 2015 cosmic microwave background (CMB) anisotropy data and recent SNe Ia measurements, baryonic acoustic oscillations (BAO) data, growth rate observations, and Hubble parameter measurements. The most dramatic consequence of including the four non-CMB data sets is the significant strengthening of the evidence for non-flatness in the non-flat ΛCDM model, from 1.8σ for the CMB data alone to 5.1σ for the full data combination. The BAO data is the most powerful of the non-CMB data sets in more tightly constraining model-parameter values and in favoring a spatially closed universe in which spatial curvature contributes about a percent to the current cosmological energy budget. The untilted non-flat ΛCDM model better fits the large-angle CMB temperature anisotropy angular spectrum and is more consistent with the Dark Energy Survey constraints on the current value of the rms amplitude of mass fluctuations (σ 8 ) as a function of the current value of the nonrelativistic matter-density parameter (Ω m ) but does not provide as good a fit to the smaller-angle CMB temperature anisotropy data, as does the tilted flat-ΛCDM model. Some measured cosmological parameter values differ significantly between the two models, including the reionization optical depth and the baryonic matter density parameter, both of whose 2σ ranges (in the two models) are disjointed or almost so.
79 ASTRONOMY AND ASTROPHYSICS↗
Clustering of LRGs in the DECaLS DR8 Footprint: Distance Constraints from Baryon Acoustic Oscillations Using Photometric Redshifts
A photometric redshift sample of luminous red galaxies (LRGs) obtained from the DECam Legacy Survey (DECaLS) is analyzed to probe cosmic distances by exploiting the wedge approach of the two-point correlation function. Although the cosmological information is highly contaminated by the uncertainties existing in the photometric redshifts from the galaxy map, an angular diameter distance can be probed at the perpendicular configuration in which the measured correlation function is minimally contaminated. An ensemble of wedged correlation functions selected up to a given threshold based on having the least contamination was studied in previous work (Sridhar & Song 2019) using simulations, and the extracted cosmological information was unbiased within this threshold. Furthermore, we apply the same methodology for analyzing the LRG sample from DECaLS, which will provide the optical imaging for targeting two-thirds of the Dark Energy Spectroscopic Instrument footprint and measure the angular diameter distances at z = 0.69 and z = 0.87 to be D A (0.697) = (1529 ± 73 Mpe)(r d /r d.fid ) and D A (0.874) = (1674 ± 102 Mpe)(r d /r d.fid ) with a fractional error of 4.77% and 6.09%, respectively. We obtain a value of H 0 = 66.58 ± 5.31 km s -1 Mpc -1 , which supports the H 0 measured by all other baryon acoustic oscillation results and is consistent with the ΛCDM model.
79 ASTRONOMY AND ASTROPHYSICS↗
Statistical Validation of Multiple Related Data Sets—Case Study Using Interstellar Boundary Explorer Satellite Data
Abstract Space scientists often face the question of whether data collected by different instruments are measurements of the same source population. This paper proposes a statistical validation method for evaluating the agreement between such related data sets. It offers a detailed case study focused on validating a new data set from the Interstellar Boundary Explorer (IBEX) mission, which serves as a practical how-to guide for similar analyses. Since 2008, the IBEX satellite has been gathering data on heliospheric energetic neutral atoms (ENAs) while being exposed to various sources of background noise, such as cosmic rays and solar energetic particles. The IBEX mission initially released only a qualified triple-coincidence (qABC) data product, which was designed to provide observations of ENAs free of background contamination. Further measurements revealed that the qABC data were in fact susceptible to contamination, having relatively low ENA counts and high background rates. To mitigate this issue, the mission team recently considered releasing a certain qualified double-coincidence (qBC) data product, which has roughly twice the detection rate of the qABC data product. This paper presents a simulation-based validation of the new qBC data product against the already-released qABC data product. The results show that the qBCs can plausibly be said to be measuring the same source population as the qABCs up to an average absolute deviation of 3.6%. Visual diagnostics provide additional confirmation of source rate coherence across data products. The framework introduced here is general and can be applied to other validation problems both within and outside the field of space physics.
79 ASTRONOMY AND ASTROPHYSICS↗
Electron Energy Partition across Interplanetary Shocks. I. Methodology and Data Product
Analyses of 15,314 electron velocity distribution functions (VDFs) within ±2 hr of 52 interplanetary (IP) shocks observed by the Wind spacecraft near 1 au are introduced. The electron VDFs are fit to the sum of three model functions for the cold dense core, hot tenuous halo, and field-aligned beam/strahl component. The best results were found by modeling the core as either a bi-kappa or a symmetric (or asymmetric) bi-self-similar VDF, while both the halo and beam/strahl components were best fit to bi-kappa VDF. This is the first statistical study to show that the core electron distribution is better fit to a self-similar VDF than a bi-Maxwellian under all conditions. The self-similar distribution deviation from a Maxwellian is a measure of inelasticity in particle scattering from waves and/or turbulence. The ranges of values defined by the lower and upper quartiles for the kappa exponents are κ ec ~ 5.40–10.2 for the core, κ eh ~ 3.58–5.34 for the halo, and κ eb ~ 3.40–5.16 for the beam/strahl. The lower-to-upper quartile range of symmetric bi-self-similar core exponents is s ec ~ 2.00–2.04, and those of asymmetric bi-self-similar core exponents are p ec ~ 2.20–4.00 for the parallel exponent and q ec ~ 2.00–2.46 for the perpendicular exponent. The nuanced details of the fit procedure and description of resulting data product are also presented. The statistics and detailed analysis of the results are presented in Paper II and Paper III of this three-part study.
79 ASTRONOMY AND ASTROPHYSICS↗