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At least 37 records · Page 2

DES Y3 + KiDS-1000: Consistent cosmology combining cosmic shear surveys

We present a joint cosmic shear analysis of the Dark Energy Survey (DES Y3) and the Kilo-Degree Survey (KiDS-1000) in a collaborative effort between the two survey teams. We find consistent cosmological parameter constraints between DES Y3 and KiDS-1000 which, when combined in a joint-survey analysis, constrain the parameter $S_8 = \sigma_8 \sqrt{\Omega_{\rm m}/0.3}$ with a mean value of $0.790^{+0.018}_{-0.014}$. The mean marginal is lower than the maximum a posteriori estimate, $S_8=0.801$, owing to skewness in the marginal distribution and projection effects in the multi-dimensional parameter space. Our results are consistent with $S_8$ constraints from observations of the cosmic microwave background by Planck, with agreement at the $1.7\sigma$ level. We use a Hybrid analysis pipeline, defined from a mock survey study quantifying the impact of the different analysis choices originally adopted by each survey team. We review intrinsic alignment models, baryon feedback mitigation strategies, priors, samplers and models of the non-linear matter power spectrum.

79 ASTRONOMY AND ASTROPHYSICS↗

Time Dilated Bundt Cake Analysis of PV Output [Poster]

We present a novel method for modeling time-dependent statistics in the power signal generated by a photovoltaic (PV) system. Our white-box machine learning method is interpretable and auditable, based on principles of multiperiodic basis functions and convex optimization. Our proposed method of time dilating the daily signal to remove night time values results in a novel representation of PV power signals, evocative of a ‘Bundt cake’. The proposed model describes the marginal distribution of power output as a function of date and time. The resulting probabilistic model of a PV system can be used to perform a variety of tasks, and here, we demonstrate the application of clear sky detection.

14 SOLAR ENERGY↗

Score-based deterministic density sampling

We propose a deterministic sampling framework using Score-Based Transport Modeling for sampling an unnormalized target density π given only its score ∇ log π. Our method approximates the Wasserstein gradient flow on KL($f_t$∥π) by learning the time-varying score ∇ log $f_t$ on the fly using score matching. While having the same marginal distribution as Langevin dynamics, our method produces smooth deterministic trajectories, resulting in monotone noise-free convergence. We prove that our method dissipates relative entropy at the same rate as the exact gradient flow, provided sufficient training. Numerical experiments validate our theoretical findings: our method converges at the optimal rate, has smooth trajectories, and is often more sample efficient than its stochastic counterpart. Experiments on high-dimensional image data show that our method produces high-quality generations in as few as 15 steps and exhibits natural exploratory behavior. The memory and runtime scale linearly in the sample size.

97 MATHEMATICS AND COMPUTING↗

Statistical wind profile gust model

A statistical wind profile gust model for the Space Transportation Operations and Trade Studies is developed by using 1800 Jimsphere wind profile data collected at Cape Kennedy during 1965 to 1972. Wind profiles from the surface to 20 km in component form, i.e., zonal and meridional are processed through the digital filters of different wave length ranges bases on the Martin-Graham cosine rolloff model. The residuals obtained from the filtering processes for the data base for the statistical analysis. For each wind component the gust and gust length at a specified reference altitude in a residual profile are defined. A two parameter gamma probability marginal distribution seems to fit the component gust amplitude and the gust length when redefined. The problem of finding an appropriate bivariate joint distribution of the gust amplitude and length remains to be solved. The probability distribution of the modulus of the gust amplitudes was derived under the assumption that they are independently distributed as gamma variates.

Doss, D. C.↗

Theoretical studies of the effects of grain noise on photographic stellar astrometry and photometry

A method of incorporating the effects of photographic emulsion grain noise into digital image centering algorithms is presented which improves the accuracy of the derived stellar positions and magnitudes. Theoretical formulae are then derived for the limiting error of the center, and the photometric parameters. For IIIa-J, this error is 0.2-0.3 mu for bright unsaturated images, which agrees quite well with measurements made with the Yale PDS microdensitometer. It is expected that, with further improvements in the positional accuracy of the PDS, it should be possible to reach the emulsion grain noise limit, providing that emulsion shifts or other large scale errors do not dominate. It is also shown that, with appropriate trimming, marginal distribution image centering algorithms can yield an accuracy only slightly poorer than that obtained with two-dimensional distributions.

Lee, J.-F.↗

Isotropic probability measures in infinite dimensional spaces: Inverse problems/prior information/stochastic inversion

Let R be the real numbers, R(n) the linear space of all real n-tuples, and R(infinity) the linear space of all infinite real sequences x = (x sub 1, x sub 2,...). Let P sub n :R(infinity) approaches R(n) be the projection operator with P sub n (x) = (x sub 1,...,x sub n). Let p(infinity) be a probability measure on the smallest sigma-ring of subsets of R(infinity) which includes all of the cylinder sets P sub n(-1) (B sub n), where B sub n is an arbitrary Borel subset of R(n). Let p sub n be the marginal distribution of p(infinity) on R(n), so p sub n(B sub n) = p(infinity)(P sub n to the -1(B sub n)) for each B sub n. A measure on R(n) is isotropic if it is invariant under all orthogonal transformations of R(n). All members of the set of all isotropic probability distributions on R(n) are described. The result calls into question both stochastic inversion and Bayesian inference, as currently used in many geophysical inverse problems.

Backus, George↗

Isotropic probability measures in infinite-dimensional spaces

Let R be the real numbers, R(n) the linear space of all real n-tuples, and R(infinity) the linear space of all infinite real sequences x = (x sub 1, x sub 2,...). Let P sub in :R(infinity) approaches R(n) be the projection operator with P sub n (x) = (x sub 1,...,x sub n). Let p(infinity) be a probability measure on the smallest sigma-ring of subsets of R(infinity) which includes all of the cylinder sets P sub n(-1) (B sub n), where B sub n is an arbitrary Borel subset of R(n). Let p sub n be the marginal distribution of p(infinity) on R(n), so p sub n(B sub n) = p(infinity) (P sub n to the -1 (B sub n)) for each B sub n. A measure on R(n) is isotropic if it is invariant under all orthogonal transformations of R(n). All members of the set of all isotropic probability distributions on R(n) are described. The result calls into question both stochastic inversion and Bayesian inference, as currently used in many geophysical inverse problems.

Backus, George↗

Comparison of laser transit anemometry data analysis techniques

Two techniques for the extraction of two-dimensional flow information from laser transit anemometry (LTA) data sets are presented and compared via a simulation study and experimental investigation. The methods are a probability density function (PDF) estimation technique and a marginal distribution analysis technique. The simulation study builds on the results of previous work and provides a quantification of the accuracy of both techniques for various LTA data acquisition scenarios. The experimental comparison consists of using an LTA system to survey the flow downstream of a turbulence generator in a small low-speed wind tunnel. The collected data sets are analyzed and compared.

Humphreys, William M., Jr.↗

A simple test of independence for truncated data with applications to redshift surveys

This paper presents an easily applied permutation test for H0, closely related to Lyden-Bell's (1971) estimate of the marginal distribution of truncated data. The test is applied to two redshift-magnitude surveys, one of galaxies and one of quasars. Assuming statistical independence, testing H0 amounts to testing validity of the cosmological model. Segal's (1986) chronomatic cosmological model is rejected under H0. On the other hand, for the quasar sample H0 is rejected strongly in a conventional cosmological model (and in a chronomatic model as well) indicating either incorrectness of the models or, as is more commonly assumed, indicating strong luminosity evolution.

Efron, Bradley↗

DES Y3 + KiDS-1000: Consistent Cosmology Combining Cosmic Shear Surveys

We present a joint cosmic shear analysis of the Dark Energy Survey (DES Y3) and the Kilo-Degree Survey (KiDS-1000) in a collaborative effort between the two survey teams. We find consistent cosmological parameter constraints between DES Y3 and KiDS-1000 which, when combined in a joint-survey analysis, constrain the parameter S 8 =σ 8 √(Ω m /0.3) with a mean value of 0.790 +0.018 −0.014. The mean marginal is lower than the maximum a posteriori estimate, S 8 =0.801, owing to skewness in the marginal distribution and projection effects in the multi-dimensional parameter space. Our results are consistent with S 8 constraints from observations of the cosmic microwave background by Planck, with agreement at the 1.7 σ level. We use a Hybrid analysis pipeline, defined from a mock survey study quantifying the impact of the different analysis choices originally adopted by each survey team. We review intrinsic alignment models, baryon feedback mitigation strategies, priors, samplers and models of the non-linear matter power spectrum.

cosmology↗

Decarbonizing the grid: Utilizing demand-side flexibility for carbon emission reduction through locational marginal emissions in distribution networks

Decarbonization of the electric grid has become an important world-wide priority and is actively happening in many ways by introducing innovations and new technologies from the generation sectors to the demand sectors. In particular, one promising pathway toward such net-zero carbon emissions is to utilize the demand-side flexibility with the increasing number of flexible loads in distribution networks. In this paper, we explore a load shifting strategy with the emerging concept of location marginal emissions (LMEs) to reduce carbon emissions. LMEs measure the impact of carbon emissions including the locational aspect in more granular way and thus provide a novel mechanism for the system operator and load aggregators to design the LME-based load shifting strategy, which can efficiently guide consumers and thus adjust their consumption behaviors. Simulation case studies on the IEEE test networks are performed to validate the capability of the proposed load shifting method to reduce carbon emissions. We also compare this with other relevant strategies to discuss multiple scenarios and corresponding results. Finally, while each provides a different level of flexibility, all the explored strategies tested have led to solutions that have lower carbon emissions, indicating the great potential of demand-side flexibility in reducing carbon emissions for future distribution networks.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Marginal stability constraint on runaway electron distribution

High-frequency kinetic instabilities of the strongly anisotropic runaway electrons (RE) can enhance the pitch-angle scattering of the runaways significantly. This wave-induced scattering can easily prevail over runaway scattering on high-Z impurities. In a steady state, collisional damping balances the kinetic drive of the unstable waves, keeping the RE distribution function at marginal stability. The marginal stability constraint limits the achievable RE densities and the shape of the RE distribution function. In this study, we consider whistler and compressional Alfvén waves as the primary source of enhanced elastic scattering of the runaways. By balancing the anomalous Doppler resonance drive with the collisional wave damping, we find the RE distribution function in the ultra-relativistic range of the phase space. We also derive an expression for the spectral energy density of the waves. We show that the power needed to compensate for the wave dissipation is negligible compared to the work of the electric field. The latter is in balance with the synchrotron losses of the runaway electrons.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Digital image centering. II

Digital image centering algorithms were compared in a test involving microdensitometer raster scans of a refractor parallax series consisting of 22 stars on 26 plates. The highest accuracy in determining stellar image positions was provided by an algorithm which involved fitting of a symmetric Gaussian curve and a flat background to the image marginal density distributions. Algorithms involving transmission marginals instead of density marginals were found to be less accurate. The repeatability and computational efficiency of the digital image centering technique were also studied.

Auer, L. H.↗

Interchange stability of a rapidly rotating magnetosphere

A rotation-dominated magnetosphere is unstable to magnetic flux-tube interchange motions if and only if the plasma content of a unit magnetic flux tube is a decreasing function of distance from the spin axis. For a spin-aligned dipole field, an approximate expression is obtained for the marginally stable distribution. Plasma filling the Jovian magnetosphere from internal sources would initially violate this stability criterion so that interchange motions would act to establish the marginally stable distribution.

Hill, T. W.↗

Robust Distribution State Estimation for Reliable Locational Marginal Pricing under Cyber-Attacks

Here this paper examines the impact of false data injection (FDI) cyber-attacks on distribution system state estimation (DSSE) and the resulting distribution locational marginal price (DLMP) in power markets. Two robust high-breakdown regression estimators, namely S- and MM- estimators, are implemented to provide resistance against FDI attacks targeting measurements and grid topology, creating leverage points. The introduced estimators are compared to the weighted least squares (WLS) with a bad data detection and rejection module (BDD) and the robust Huber M-estimator. The proposed estimators are shown to be effective and compare favorably to both existing Huber M- and the WLS with BDD in the presence of topology FDI attacks. Both the S- and MM-estimators provide good performance in the case of clean and corrupted measurements. Their performance is comparable in this case to the Huber M- and the WLS, followed by a BDD module. The simulation considered a modified distribution IEEE 13 and 34-bus systems where the impact of FDI attack scenarios is shown on the state and the DLMP pricing in the presence of distributed Generation.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Enhanced deep neural networks with transfer learning for distribution LMP considering load and PV uncertainties

As the flexibility of generation and demand increases in distribution systems, the residential loads are emerging as a promising means to participate in demand response and the transactive energy market. Market pricing is an instrumental mechanism for the distribution system operator to exploit the full potential of the flexible resources. The distribution locational marginal price (DLMP) can be used to guide the residential load consumption. This type of market signal helps the distribution system operator to optimize the scheduling of all resources while satisfying related network constraints through a day-ahead market. However, solving the optimization problem for large-scale systems can be computationally expensive. To address the scalability and practicability limitations of the DLMP framework, a learning-based approach is proposed in this paper to complement the day-ahead distribution market framework. Here, the proposed approach combines long short-term memory and transfer learning to develop deep neural network that can capture the spatial–temporal correlation of the input data. The model can determine the optimal DLMP for each node in a distribution system without the system parameters required to formulate the optimization problem. Testing results on IEEE 33-bus and 123-bus systems show that the proposed approach can generate a comparable DLMP against the optimization solutions.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Bivariate extreme value distributions

In certain engineering applications, such as those occurring in the analyses of ascent structural loads for the Space Transportation System (STS), some of the load variables have a lower bound of zero. Thus, the need for practical models of bivariate extreme value probability distribution functions with lower limits was identified. We discuss the Gumbel models and present practical forms of bivariate extreme probability distributions of Weibull and Frechet types with two parameters. Bivariate extreme value probability distribution functions can be expressed in terms of the marginal extremel distributions and a 'dependence' function subject to certain analytical conditions. Properties of such bivariate extreme distributions, sums and differences of paired extremals, as well as the corresponding forms of conditional distributions, are discussed. Practical estimation techniques are also given.

Elshamy, M.↗

Drift-cyclotron loss-cone instability in 3-D simulations of a sloshing-ion simple mirror

The kinetic stability of collisionless, sloshing beam-ion (45° pitch angle) plasma is studied in a three-dimensional (3-D) simple magnetic mirror, mimicking the Wisconsin high-temperature superconductor axisymmetric mirror experiment. The collisional Fokker–Planck code CQL3D-m provides a slowing-down beam-ion distribution to initialize the kinetic-ion/fluid-electron code Hybrid-VPIC, which then simulates free plasma decay without external heating or fuelling. Over 1 – 10 μs, drift-cyclotron loss-cone (DCLC) modes grow and saturate in amplitude. The DCLC scatters ions to a marginally stable distribution with gas-dynamic rather than classical-mirror confinement. Sloshing ions can trap cool (low-energy) ions in an electrostatic potential well to stabilize DCLC, but DCLC itself does not scatter sloshing beam-ions into the said well. Instead, cool ions must come from external sources such as charge-exchange collisions with a low-density neutral population. Manually adding cool ∼1keV ions improves beam-ion confinement several-fold in Hybrid-VPIC simulations, which qualitatively corroborates prior measurements from real mirror devices with sloshing ions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗