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DOE OSTI2015

Stochastic generation of hourly rainstorm events in Johor

Engineers and researchers in water-related studies are often faced with the problem of having insufficient and long rainfall record. Practical and effective methods must be developed to generate unavailable data from limited available data. Therefore, this paper presents a Monte-Carlo based stochastic hourly rainfall generation model to complement the unavailable data. The Monte Carlo simulation used in this study is based on the best fit of storm characteristics. Hence, by using the Maximum Likelihood Estimation (MLE) and Anderson Darling goodness-of-fit test, lognormal appeared to be the best rainfall distribution. Therefore, the Monte Carlo simulation based on lognormal distribution was used in the study. The proposed model was verified by comparing the statistical moments of rainstorm characteristics from the combination of the observed rainstorm events under 10 years and simulated rainstorm events under 30 years of rainfall records with those under the entire 40 years of observed rainfall data based on the hourly rainfall data at the station J1 in Johor over the period of 1972–2011. The absolute percentage error of the duration-depth, duration-inter-event time and depth-inter-event time will be used as the accuracy test. The results showed the first four product-moments of the observed rainstorm characteristics were close with the simulated rainstorm characteristics. The proposed model can be used as a basis to derive rainfall intensity-duration frequency in Johor.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Effect of excess superthermal hot electrons on finite amplitude ion-acoustic solitons and supersolitons in a magnetized auroral plasma

The effect of excess superthermal electrons is investigated on finite amplitude nonlinear ion-acoustic waves in a magnetized auroral plasma. The plasma model consists of a cold ion fluid, Boltzmann distribution of cool electrons, and kappa distributed hot electron species. The model predicts the evolution of negative potential solitons and supersolitons at subsonic Mach numbers region, whereas, in the case of Cairn's nonthermal distribution model for the hot electron species studied earlier, they can exist both in the subsonic and supersonic Mach number regimes. For the dayside auroral parameters, the model generates the super-acoustic electric field amplitude, speed, width, and pulse duration of about 18 mV/m, 25.4 km/s, 663 m, and 26 ms, respectively, which is in the range of the Viking spacecraft measurements.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2016

Nonlinear low frequency electrostatic structures in a magnetized two-component auroral plasma

Finite amplitude nonlinear ion-acoustic solitons, double layers, and supersolitons in a magnetized two-component plasma composed of adiabatic warm ions fluid and energetic nonthermal electrons are studied by employing the Sagdeev pseudopotential technique and assuming the charge neutrality condition at equilibrium. The model generates supersoliton structures at supersonic Mach numbers regime in addition to solitons and double layers, whereas in the unmagnetized two-component plasma case only, soliton and double layer solutions can be obtained. Further investigation revealed that wave obliqueness plays a critical role for the evolution of supersoliton structures in magnetized two-component plasmas. In addition, the effect of ion temperature and nonthermal energetic electron tends to decrease the speed of oscillation of the nonlinear electrostatic structures. The present theoretical results are compared with Viking satellite observations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

PolyODENet: Deriving mass-action rate equations from incomplete transient kinetics data

Kinetics of a reaction network that follows mass-action rate laws can be described with a system of ordinary differential equations (ODEs) with polynomial right-hand side. However, it is challenging to derive such kinetic differential equations from transient kinetic data without knowing the reaction network, especially when the data are incomplete due to experimental limitations. We introduce a program, PolyODENet, toward this goal. Based on the machine-learning method Neural ODE, PolyODENet defines a generative model and predicts concentrations at arbitrary time. As such, it is possible to include unmeasurable intermediate species in the kinetic equations. Importantly, we have implemented various measures to apply physical constraints and chemical knowledge in the training to regularize the solution space. Here, using simple catalytic reaction models, we demonstrate that PolyODENet can predict reaction profiles of unknown species and doing so even reveal hidden parts of reaction mechanisms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

A new synthetic correlation electron cyclotron emission diagnostic for validating nonlinear gyrokinetic simulations of electron temperature turbulence

To validate nonlinear gyrokinetic simulations of electron temperature turbulence, the experimental correlation electron cyclotron emission (CECE) measurements are to be compared using a synthetic CECE diagnostic, which generates modeled CECE measurement quantities by implementing realistic measurement parameters (e.g., spatial and wavenumber resolutions, radial location, etc.) to nonlinear gyrokinetic simulations. In this work, we calculate the radial and vertical spatial and wavenumber transfer functions, which are defined by the electron cyclotron emission emissivity radial profile and vertical probing antenna pattern, respectively. These transfer functions are applied to nonlinear gyrokinetic simulations of electron temperature turbulence using the continuum gyrokinetic code. A simultaneous comparison of the experimental electron temperature turbulence power spectrum and root-mean-square (RMS) level, as well as the radial correlation length with the new synthetic CECE diagnostic at a core location ρ ∼ 0.75 in an L-mode DIII-D tokamak plasma, is presented. Here, the preliminary result shows that the synthetic CECE output underestimates the RMS level by ∼42% and overestimates the radial correlation length by ∼40%.

Cyclotron radiation↗
DOE OSTI2024

Chaotic neural dynamics facilitate probabilistic computations through sampling

Cortical neurons exhibit highly variable responses over trials and time. Theoretical works posit that this variability arises potentially from chaotic network dynamics of recurrently connected neurons. Here, we demonstrate that chaotic neural dynamics, formed through synaptic learning, allow networks to perform sensory cue integration in a sampling-based implementation. We show that the emergent chaotic dynamics provide neural substrates for generating samples not only of a static variable but also of a dynamical trajectory, where generic recurrent networks acquire these abilities with a biologically plausible learning rule through trial and error. Furthermore, the networks generalize their experience in the stimulus-evoked samples to the inference without partial or all sensory information, which suggests a computational role of spontaneous activity as a representation of the priors as well as a tractable biological computation for marginal distributions. These findings suggest that chaotic neural dynamics may serve for the brain function as a Bayesian generative model.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

POPULATION EFFECTS ON THE METALLICITY DISTRIBUTION FUNCTION DERIVED FROM THE RED GIANT BRANCH

We have tested the reliability of the red giant branch (RGB) as a metallicity indicator accounting for observational errors as well as the complexity of star formation histories and chemical evolution histories observed in various stellar systems. We generate model color–magnitude diagrams (CMDs) produced with a variety of evolutionary histories and compare the resultant metallicity estimates from the colors and magnitudes of RGB stars to the true input metallicities. We include realistic models for photometric errors and completeness in our synthetic CMDs. As expected, for simple simple stellar populations dominated by old stars, the RGB provides a very accurate estimate of the modular metallicity value for a population. An error in the age of a system targeted for this type of study may produce metallicity errors of a few tenths of a dex. The size of this metallicity error depends linearly on the age error, and we find this dependence to be stronger with more precise photometry. If the population has experienced any significant star formation within the last ∼6 Gyr, the metallicity estimates, [M/H], derived from the RGB may be in error by up to ∼0.5 dex. Perhaps the most important consideration for this technique is an accurate, independent estimate of the average age for the target stellar system, especially if it is probable that a significant fraction of the population formed less than ∼6 Gyr ago.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

Physical properties of Gliese 229B based on newly determined carbon and oxygen abundances of Gliese 229A

Recently Tsuji and Nakajima and Tsuji et al. have developed a method of molecular line spectroscopy of M dwarfs with which carbon and oxygen abundances are derived respectively from CO and H{sub 2}O lines in the K band. They applied this method to Gliese 229A(Gl 229A), the primary star of the brown dwarf companion, Gliese 229B(Gl 229B). The derived abundances of Gl 229A are logA{sub C}=−3.27±0.07 and logA{sub O}=−3.10±0.02, which are close to the classical values of the solar abundances of carbon and oxygen. We generate model spectra of Gl 229B for the metallicity of Gl 229A as well as for the classical solar metallicity (logA{sub C}=−3.40 and logA{sub O}=−3.08). We find that the differences in the resulting spectra are not so large for differences in metallicity of 0.1 dex or so, but we now discuss the spectrum of Gl 229B on the basis of reliable metallicity. From the literature, the lower limit to the age of Gl 229A is found to be 0.3 Gyr. From the kinematics of Gl 229A, we evaluate the upper limit to the age of Gl 229A to be 3.0 Gyr. The observed and model spectra are compared and the goodness of fit is obtained in the range of model parameters 750⩽T{sub eff}⩽1000 K and 4.5⩽logg⩽5.5. Among the candidates that satisfy the age constraint, the best combinations of model parameters are (T{sub eff},logg) = (800 K, 4.75) and (850 K, 5.0), while acceptable combinations are (750 K, 4.75), (850 K, 4.75) and (900 K, 5.0).

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

TIDAL STREAM MORPHOLOGY AS AN INDICATOR OF DARK MATTER HALO GEOMETRY: THE CASE OF PALOMAR 5

This paper presents an example where the morphology of a single stellar stream can be used to rule out a specific galactic potential form without the need for velocity information. We investigate the globular cluster Palomar 5 (Pal 5), which is tidally disrupting into a cold, thin stream mapped over 22 deg on the sky with a typical width of 0.7 deg. We generate models of this stream by fixing Pal 5's present-day position, distance, and radial velocity via observations, while allowing its proper motion to vary. In a spherical dark matter halo we easily find models that fit the observed morphology. However, no plausible Pal 5 model could be found in the triaxial potential of Law and Majewski, which has been proposed to explain the properties of the Sagittarius stream. In this case, the long, thin, and curved morphology of the Pal 5 stream alone can be used to rule out such a potential configuration. Pal 5-like streams in this potential are either too straight, missing the curvature of the observations, or show an unusual morphology which we dub stream-fanning: a signature sensitive to the triaxiality of a potential. We conclude that the mere existence of other thin tidal streams must provide broad constraints on the orientation and shape of the dark matter halo they inhabit.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

Stability and coalescence of massive twin binaries

Massive stars are usually found in binaries, and binaries with periods less than 10 days may have a preference for near equal component masses (“twins”). In this paper we investigate the evolution of massive twin binaries all the way to contact and the possibility that these systems can be progenitors of double neutron star binaries. The small orbital separations of observed double neutron star binaries suggest that the progenitor systems underwent a common envelope phase at least once during their evolution. Bethe and Brown proposed that massive binary twins will undergo a common envelope evolution while both components are ascending the red giant branch (RGB) or asymptotic giant branch (AGB) simultaneously, also known as double-core evolution. Using models generated from the stellar evolution code EZ (evolve zero-age main sequence), we determine the range of mass ratios resulting in a contact binary with both components simultaneously ascending the RGB or AGB as a function of the difference in birth times, Δτ. We find that, even for a generous Δτ = 5 Myr, the minimum mass ratio q{sub min}=0.933 for an 8 M{sub ⊙} primary and increases for larger mass primaries. We use a smoothed particle hydrodynamics code, StarSmasher, to study specifically the evolution of q = 1 common envelope systems as a function of initial component mass, age, and orbital separation. We also consider a q = 0.997 system to test the effect of relaxing the constraint of strictly identical components. We find the dynamical stability limit, the largest orbital separation where the binary becomes dynamically unstable, as a function of the component mass and age. Finally, we calculate the efficiency of ejecting matter during the inspiral phase to extrapolate the properties of the remnant binary from our numerical results, assuming the common envelope is completely ejected. We find that for the nominal core masses, there is a minimum orbital separation for a given component mass such that the helium cores survive common envelope evolution in a tightly bound binary and are viable progenitors for double neutron stars.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

A NOVEL, FULLY AUTOMATED PIPELINE FOR PERIOD ESTIMATION IN THE EROS 2 DATA SET

We present a new method to discriminate periodic from nonperiodic irregularly sampled light curves. We introduce a periodic kernel and maximize a similarity measure derived from information theory to estimate the periods and a discriminator factor. We tested the method on a data set containing 100,000 synthetic periodic and nonperiodic light curves with various periods, amplitudes, and shapes generated using a multivariate generative model. We correctly identified periodic and nonperiodic light curves with a completeness of ∼90% and a precision of ∼95%, for light curves with a signal-to-noise ratio (S/N) larger than 0.5. We characterize the efficiency and reliability of the model using these synthetic light curves and apply the method on the EROS-2 data set. A crucial consideration is the speed at which the method can be executed. Using a hierarchical search and some simplification on the parameter search, we were able to analyze 32.8 million light curves in ∼18 hr on a cluster of GPGPUs. Using the sensitivity analysis on the synthetic data set, we infer that 0.42% of the sources in the LMC and 0.61% of the sources in the SMC show periodic behavior. The training set, catalogs, and source code are all available at http://timemachine.iic.harvard.edu.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2014

Supergravity inflation with broken shift symmetry and large tensor-to-scalar ratio

We propose a class of inflation models with potential V(φ) = αφ{sup n}exp(−β{sup m}φ{sup m}). We show that such kind of inflaton potentials can be realized in supergravity theory with a small shift symmetry breaking term in the K and quot;ahler potential. We find that the models with (m = 1,n = 1), (m = 1,n = 2), (m = 2,n = 1), and (m = 2,n = 3/2) can easily accommodate the Planck results. In particular, the tensor-to-scalar ratio is larger than 0.01 in the 1σ region. Thus, these models generate the typical large field inflation, and can be tested at the future Planck and QUBIC experiments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Field-level reconstruction from foreground-contaminated 21-cm maps

Current and upcoming 21-cm experiments will soon be able to map 21-cm spatial fluctuations in three dimensions for a wide range of redshifts. However, bright foreground contamination and the nature of radio interferometry create significant challenges, making it difficult to access rich cosmological information from the Fourier modes that lie within the “foreground wedge”. Here, in this work, we introduce two approaches aiming to reconstruct the full 21-cm density field, including the missing modes in the wedge: (a) a field-level inference under an effective field theory (EFT) framework; (b) a diffusion-based deep generative model trained on simulations. Under the EFT framework, we implement a fully differentiable forward model that maps the initial conditions of matter fluctuations to the observed, foreground-filtered 21-cm maps. This enables a gradient-based sampler to simultaneously sample the initial conditions and bias parameters, allowing a physically motivated mode reconstruction. Alternatively, we apply a variational diffusion model to perform 21-cm density reconstruction at the map level. Our model is trained on semi-numerical simulations over a wide range of astrophysical parameters. Our results from both approaches should provide improved cosmological constraints from the field level and also enable cross-correlation between experiments that have little or no overlapping modes.

cosmological perturbation theory↗
DOE OSTI2024

Robust anomaly detection for particle physics using multi-background representation learning

Abstract Anomaly, or out-of-distribution, detection is a promising tool for aiding discoveries of new particles or processes in particle physics. In this work, we identify and address two overlooked opportunities to improve anomaly detection (AD) for high-energy physics. First, rather than train a generative model on the single most dominant background process, we build detection algorithms using representation learning from multiple background types, thus taking advantage of more information to improve estimation of what is relevant for detection. Second, we generalize decorrelation to the multi-background setting, thus directly enforcing a more complete definition of robustness for AD. We demonstrate the benefit of the proposed robust multi-background AD algorithms on a high-dimensional dataset of particle decays at the Large Hadron Collider.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Discriminative versus generative approaches to simulation-based inference

Most of the fundamental, emergent, and phenomenological parameters of particle and nuclear physics are determined through parametric template fits. Simulations are used to populate histograms which are then matched to data. This approach is inherently lossy, since histograms are binned and low-dimensional. Deep learning has enabled unbinned and high-dimensional parameter estimation through neural likelihood(-ratio) estimation. We compare two approaches for neural simulation-based inference (NSBI): one based on discriminative learning (classification) and one based on generative modeling. These two approaches are directly evaluated on the same datasets, with a similar level of hyperparameter optimization in both cases. In addition to a Gaussian dataset, we study NSBI using a Higgs boson dataset from the FAIR Universe Challenge. We find that both the direct likelihood and likelihood ratio estimation are able to effectively extract parameters with reasonable uncertainties. For the numerical examples and within the set of hyperparameters studied, we found that the likelihood ratio method is more accurate and/or precise. Both methods have a significant spread from the network training and would require ensembling or other mitigation strategies in practice.

high energy physics↗
DOE OSTI2026

pop-cosmos : redshifts and physical properties of KiDS-1000 galaxies

ABSTRACT Principled Bayesian inference of galaxy properties has not previously been performed for wide-area weak-lensing surveys with millions of sources. We address this gap by applying the pop-cosmos generative model to perform spectral energy distribution (SED) fitting for 4 million KiDS (Kilo-Degree Survey)-1000 galaxies. Calibrated on deep COSMOS2020 photometric data, pop-cosmos specifies a physically motivated prior over the galaxy population up to $z \simeq 6$ in stellar population synthesis (SPS) parameter space. Using the Speculator SPS emulator with GPU (graphics processing unit)-accelerated Markov Chain Monte Carlo sampling, we perform full posterior inference at 8.2 GPU seconds per galaxy, obtaining joint constraints on galaxy redshifts and physical properties. We validate photometric redshifts against $\sim \!185\,\!000$ KiDS galaxies cross-matched to Dark Energy Spectroscopic Instrument Data Release 1 spectroscopic samples, achieving low bias ($2\times 10^{-3}$), scatter ($\sigma _{\mathrm{MAD}}=0.03$), and outlier fraction (3.2 per cent) for the Bright Galaxy Survey, with comparable performance (bias $3\times 10^{-2}$, $\sigma _{\mathrm{MAD}}=0.05$, 1.0 per cent outliers) for luminous red galaxies (LRGs). Within the LRG sample, we identify massive, dusty, star-forming contaminants at $z \simeq 0.4$ satisfying standard colour selections for quenched populations. We infer trends in stellar mass, star formation, metallicity, and dust across five tomographic redshift bins consistent with established scaling relations. Using specific star formation rate constraints, we identify $\sim$7 per cent of KiDS-1000 galaxies as quenched, versus 37 per cent implied by conservative colour cuts. This enables the construction of weak-lensing samples defined by physical properties while mitigating intrinsic alignment systematics and preserving statistical power. Our analysis validates pop-cosmos out of sample, establishing it as a scalable approach for galaxy evolution and cosmological analyses with photometric surveys.

Halder, Anik [Institute of Astronomy and Kavli Ins↗
DOE OSTI2019

Deblending galaxy superpositions with branched generative adversarial networks

Abstract Near-future large galaxy surveys will encounter blended galaxy images at a fraction of up to 50 per cent in the densest regions of the Universe. Current deblending techniques may segment the foreground galaxy while leaving missing pixel intensities in the background galaxy flux. The problem is compounded by the diffuse nature of galaxies in their outer regions, making segmentation significantly more difficult than in traditional object segmentation applications. We propose a novel branched generative adversarial network to deblend overlapping galaxies, where the two branches produce images of the two deblended galaxies. We show that generative models are a powerful engine for deblending given their innate ability to infill missing pixel values occluded by the superposition. We maintain high peak signal-to-noise ratio and structural similarity scores with respect to ground truth images upon deblending. Our model also predicts near-instantaneously, making it a natural choice for the immense quantities of data soon to be created by large surveys such as Large Synoptic Survey Telescope, Euclid, and Wide-Field Infrared Survey Telescope.

Reiman, David M.↗
DOE OSTI2020

Probing nuclear effects with neutrino-induced charged-current neutral pion production

We study neutrino-induced charged-current (CC) $\pi^0$ production on carbon nuclei using events with fully imaged final-state proton-$\pi^0$ systems. Novel use of final-state correlations based on transverse kinematic imbalance enable the first measurements of the struck nucleon's Fermi motion, of the intranuclear momentum transfer (IMT) dynamics, and of the final-state hadronic momentum configuration in neutrino pion production. Event distributions are presented for i) the momenta of neutrino-struck neutrons below the Fermi surface, ii) the direction of missing transverse momentum characterizing the strength of IMT, and iii) proton-pion momentum imbalance with respect to the lepton scattering plane. The observed Fermi motion and IMT strength are compared to the previous MINERvA measurement of neutrino CC quasielastic-like production. The measured shapes and absolute rates of these distributions, as well as the cross-section asymmetries show tensions with predictions from current neutrino generator models.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗