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Introduction to CAUSES: Description of weather and climate models and their near-surface temperature errors in 5-day hindcasts near the Southern Great Plains
We introduce the Clouds Above the United States and Errors at the Surface (CAUSES) project with its aim of better understanding the physical processes leading to warm screen-temperature biases over the American Midwest in many numerical models. In this first of four companion papers, 11 different models, from 9 institutes, perform a series of 5-day hindcasts, each initialised from reanalyses. After describing the common experimental protocol and detailing each model configuration, a gridded temperature data set is derived from observations and used to show that all the models have a warm bias over parts of the Midwest. Additionally, a strong diurnal cycle in the screen-temperature bias is found in most models. In some models the bias is largest around midday, while in others it is largest during the night. At the Department of Energy Atmospheric Radiation Measurement Southern Great Plains (SGP) site, the model biases are shown to extend several kilometers into the atmosphere. Finally, to provide context for the companion papers, in which observations from the SGP site are used to evaluate the different processes contributing to errors there, it is shown that there are numerous locations across the Midwest where the diurnal cycle of the error is highly correlated with the diurnal cycle of the error at SGP. This suggests that conclusions drawn from detailed evaluation of models using instruments located at SGP will be representative of errors that are prevalent over a larger spatial scale.
Scaling open-weight large language models for hydropower regulatory information extraction: A systematic analysis
Information extraction from regulatory and technical documents using large language models (LLMs) involves practical trade-offs between extraction quality and computational cost. We evaluate eight open-weight LLMs spanning 0.6B–70B parameters on hydropower licensing documents and report deployment-oriented evidence under a unified extraction schema and evaluation protocol. Across the model set, we observe clear scale-dependent trends in both baseline extraction quality and the effectiveness of reflective reasoning (self-checking) under our fixed-prompt, no-augmentation setting. Mid-scale models often provide a favorable balance of accuracy and efficiency, whereas the smallest models show limited or inconsistent gains from the reasoning variants tested. Larger models achieve the highest overall F1 scores but incur substantially greater compute and infrastructure requirements. We further find that reliability failure modes can distort conventional metrics in this domain: in particular, high recall can coincide with systematic extraction errors when models fabricate values for fields that are absent from the source text, underscoring the importance of conservative null handling and evidence-grounded evaluation. Overall, our study provides a reproducible resource–performance comparison for open-weight LLM-based extraction in hydropower regulatory documentation and offers practical guidance for model selection under different deployment constraints.
Machine learning based algorithms for uncertainty quantification in numerical weather prediction models
Complex numerical weather prediction models incorporate a variety of physical processes, each described by multiple alternative physical schemes with specific parameters. The selection of the physical schemes and the choice of the corresponding physical parameters during model configuration can significantly impact the accuracy of model forecasts. There is no combination of physical schemes that works best for all times, at all locations, and under all conditions. It is therefore of considerable interest to understand the interplay between the choice of physics and the accuracy of the resulting forecasts under different conditions. This paper demonstrates the use of machine learning techniques to study the uncertainty in numerical weather prediction models due to the interaction of multiple physical processes. The first problem addressed herein is the estimation of systematic model errors in output quantities of interest at future times, and the use of this information to improve the model forecasts. The second problem considered is the identification of those specific physical processes that contribute most to the forecast uncertainty in the quantity of interest under specified meteorological conditions. In order to address these questions we employ two machine learning approaches, random forests and artificial neural networks. The discrepancies between model results and observations at past times are used to learn the relationships between the choice of physical processes and the resulting forecast errors. Numerical experiments are carried out with the Weather Research and Forecasting (WRF) model. The output quantity of interest is the model precipitation, a variable that is both extremely important and very challenging to forecast. The physical processes under consideration include various micro-physics schemes, cumulus parameterizations, short wave, and long wave radiation schemes. The experiments demonstrate the strong potential of machine learning approaches to aid the study of model errors.
Design and expected performance of a fast neutron attenuation probe for light element density measurements
In this paper, we present the design and expected performance of a proof-of-concept 32 channel material identification system. Our system is based on the energy-dependent attenuation of fast neutrons for four elements: hydrogen, carbon, nitrogen and oxygen. We describe a new approach to obtaining a broad range of neutron energies to probe a sample, as well as our technique for reconstructing the molar densities within a sample. The system's performance as a function of time-of-flight energy resolution is explored using a Geant4-based Monte Carlo. Our results indicate that, with the expected detector response of our system, we will be able to determine the molar density of all four elements to within a 20–30% accuracy in a two hour scan time. In many cases this error is systematically low, thus the ratio between elements is more accurate. This degree of accuracy is enough to distinguish, for example, a sample of water from a sample of pure hydrogen peroxide: the ratio of oxygen to hydrogen is reconstructed to within 8±0.5% of the true value. Lastly, with future algorithm development that accounts for backgrounds caused by scattering within the sample itself, the accuracy of molar densities, not ratios, may improve to the 5–10% level for a two hour scan time.
Accurate Atomic Correlation and Total Energies for Correlation Consistent Effective Core Potentials
Very recently, we introduced a set of correlation consistent effective core potentials (ccECPs) constructed within full many-body approaches. By employing significantly more accurate correlated approaches, we were able to reach a new level of accuracy for the resulting effective core Hamiltonians. We also strived for simplicity of use and easy transferability into a variety of electronic structure methods in quantum chemistry and condensed matter physics. Here, as a reference for future use, we present exact or nearly exact total energy calculations for these ccECPs. The calculations cover H–Kr elements and are based on the state-of-the-art configuration interaction (CI), coupled-cluster (CC), and quantum Monte Carlo (QMC) calculations with systematically eliminated/improved errors. In particular, we carry out full CI/CCSD(T)/CCSDT(Q) calculations with cc-pVnZ with up to n = 6 basis sets and we estimate the complete basis set limits. Using combinations of these approaches, we achieved an accuracy of ≈1–10 mHa for K–Zn atoms and ≈0.1–0.3 mHa for all other elements—within about 1% or better of the ccECP total correlation energies. We also estimate the corresponding kinetic energies within the feasible limit of full CI calculations. In order to provide data for QMC calculations, we include fixed-node diffusion Monte Carlo energies for each element that give quantitative insights into the fixed-node biases for single-reference trial wave functions. The results offer a clear benchmark for future high-accuracy calculations in a broad variety of correlated wave function methods such as CI and CC as well is in stochastic approaches such as real space sampling QMC.
Bias-Adjustment Methods for Future Subdaily Precipitation Extremes Consistent Across Durations
Model output from climate projections often requires bias-adjustment to compensate for systematic model errors. A bias-adjustment method for extreme precipitation intensity is proposed that preserves the scaling equation for different accumulation levels from hourly to daily, using intensity-duration-frequency (IDF) modeling. A validation is performed within a pseudo-reality setting, based on hourly precipitation from 28 regional climate model projections of the EURO-CORDEX ensemble over Belgium. The scaling-based adjustment methods improve upon previous methods, an optimal method is identified, and, analytical quantile mapping methods must be avoided due to three identified problems. The ensemble mean of the adjusted extreme precipitation intensity obeys the above-mentioned scale-invariance property, which is consistent with observed extreme intensities. We thus show that IDF modeling provides added value in the context of bias-adjustment, and, that the particular IDF model proposed balances well between accuracy and the preservation of desired properties such as scale invariance and consistency among rainfall durations.
Electronic couplings for molecular charge transfer: Benchmarking CDFT, FODFT, and FODFTB against high-level ab initio calculations
We introduce a database (HAB11) of electronic coupling matrix elements (H{sub ab}) for electron transfer in 11 π-conjugated organic homo-dimer cations. High-level ab inito calculations at the multireference configuration interaction MRCI+Q level of theory, n-electron valence state perturbation theory NEVPT2, and (spin-component scaled) approximate coupled cluster model (SCS)-CC2 are reported for this database to assess the performance of three DFT methods of decreasing computational cost, including constrained density functional theory (CDFT), fragment-orbital DFT (FODFT), and self-consistent charge density functional tight-binding (FODFTB). We find that the CDFT approach in combination with a modified PBE functional containing 50% Hartree-Fock exchange gives best results for absolute H{sub ab} values (mean relative unsigned error = 5.3%) and exponential distance decay constants β (4.3%). CDFT in combination with pure PBE overestimates couplings by 38.7% due to a too diffuse excess charge distribution, whereas the economic FODFT and highly cost-effective FODFTB methods underestimate couplings by 37.6% and 42.4%, respectively, due to neglect of interaction between donor and acceptor. The errors are systematic, however, and can be significantly reduced by applying a uniform scaling factor for each method. Applications to dimers outside the database, specifically rotated thiophene dimers and larger acenes up to pentacene, suggests that the same scaling procedure significantly improves the FODFT and FODFTB results for larger π-conjugated systems relevant to organic semiconductors and DNA.
Critical analysis of fragment-orbital DFT schemes for the calculation of electronic coupling values
We present a critical analysis of the popular fragment-orbital density-functional theory (FO-DFT) scheme for the calculation of electronic coupling values. We discuss the characteristics of different possible formulations or “flavors” of the scheme which differ by the number of electrons in the calculation of the fragments and the construction of the Hamiltonian. In addition to two previously described variants based on neutral fragments, we present a third version taking a different route to the approximate diabatic state by explicitly considering charged fragments. In applying these FO-DFT flavors to the two molecular test sets HAB7 (electron transfer) and HAB11 (hole transfer), we find that our new scheme gives improved electronic couplings for HAB7 (−6.2% decrease in mean relative signed error) and greatly improved electronic couplings for HAB11 (−15.3% decrease in mean relative signed error). A systematic investigation of the influence of exact exchange on the electronic coupling values shows that the use of hybrid functionals in FO-DFT calculations improves the electronic couplings, giving values close to or even better than more sophisticated constrained DFT calculations. Comparing the accuracy and computational cost of each variant, we devise simple rules to choose the best possible flavor depending on the task. For accuracy, our new scheme with charged-fragment calculations performs best, while numerically more efficient at reasonable accuracy is the variant with neutral fragments.
Coupled-cluster based approach for core-level states in condensed phase: Theory and application to different protonated forms of aqueous glycine
A theoretical approach for calculating core-level states in condensed phase is presented. The approach is based on equation-of-motion coupled-cluster theory (EOMCC) and effective fragment potential (EFP) method. By introducing an approximate treatment of double excitations in the EOM-CCSD (EOM-CC with single and double substitutions) ansatz, we address poor convergence issues that are encountered for the core-level states and significantly reduce computational costs. While the approximations introduce relatively large errors in the absolute values of transition energies, the errors are systematic. Consequently, chemical shifts, changes in ionization energies relative to reference systems, are reproduced reasonably well. By using different protonation forms of solvated glycine as a benchmark system, we show that our protocol is capable of reproducing the experimental chemical shifts with a quantitative accuracy. The results demonstrate that chemical shifts are very sensitive to the solvent interactions and that explicit treatment of solvent, such as EFP, is essential for achieving quantitative accuracy.
Timing characterization of fast hCMOS sensors
We describe a method of analyzing gate profile data for ultrafast x-ray imagers that allows pixel-by-pixel determination of temporal sensitivity in the presence of substantial background oscillations. With this method, systematic timing errors in gate width and gate arrival time of up to 1 ns (in a 2 ns wide gate) can be removed. In-sensor variations in gate arrival and gate width are observed, with variations in each up to 0.5 ns. This method can be used to estimate the coarse timing of the sensor, even if errors up to several ns are present.
Many-body perturbation theory with hybrid density functional theory starting points accelerated by adaptively compressed exchange
We report on the use of the adaptively compressed exchange (ACE) operator to accelerate many-body perturbation theory (MBPT) calculations, including G 0 W 0 and the Bethe–Salpeter equation (BSE), for hybrid density functional theory starting points. We show that by approximating the exact exchange operator with the low-rank ACE operator, substantial computational savings can be achieved with systematically controllable errors in the quasiparticle energies computed with full-frequency G 0 W 0 and the optical absorption spectra and vertical excitation energies computed by solving the BSE within density matrix perturbation theory. Our implementation makes use of the ACE-accelerated electronic Hamiltonian to carry out both G 0 W 0 and BSE without explicitly computing empty states. We show the robustness of the approach and present the computational gains obtained on both the central processing unit and graphics processing unit nodes. In conclusion, our work will facilitate the exploration and evaluation of fine-tuned hybrid starting points aimed at enhancing the accuracy of MBPT calculations without involving computationally demanding self-consistency in Hedin’s equations.
The RR Lyrae stars: New perspectives
We demonstrate that the Oosterhoff II (Oo II) RR Lyrae ab variables are hotter by ∼270 K, at the same period, than Oo I variables. Or, at the same ((B) – (V)){sub 0} value the Oo II variables have larger radii than Oo I variables. This accounts for the reason Oo II variables are brighter (0.12-0.20 mag) than Oo I variables. The dependence of the light amplitude of RR Lyrae variables on temperature is independent of Oo type. This makes it possible to derive an accurate set of equations to relate intrinsic (B – V){sub 0} color indices to light amplitudes, which in turn can be used to determine the interstellar reddening (E (B – V)). With just a few variables (∼5), it is possible to determine the E (B – V) to an accuracy of <0.01 mag in the absence of systematic photometric errors. We discuss the errors introduced in color excess determinations by including the Blazhko stars in a solution. A comparison of color excess values of 23 globular clusters and two regions of the Large Magellanic Cloud (LMC), determined with the aid of our newly developed equations, are found to compare favorably (∼0.01 mag) with color excess values found in the literature. Four new Oo III variables, some found in metal-poor clusters, are discussed. An analysis of the galactic-field variables indicates the majority are Oo I and Oo II variables, but a few short-period (log P < –0.36) metal-strong variables, so far not found in galactic globular clusters are evidently ∼0.30 mag fainter than Oo I variables. Oo III variables may also be present in the field. We conclude that the RR Lyrae ab variables are primarily restricted to four sequences or groups. If we assume that the Oo I variables' mean absolute magnitude is Mv = 0.61, the mean absolute magnitudes of the other three sequences are: short-period variables Mv ∼ 0.89 mag, Oo II Mv ∼ 0.43 mag, and Oo III Mv ∼ 0.29 mag. The Oo I fundamental RR Lyrae ab red edge (FRE) and fundamental blue edge (FBE) occur at approximately the following temperatures: FRE T ∼ 6180 K and FBE T ∼ 6750 K. There is a strong dependence of Mv on [Fe/H] as we proceed from the short-period variables to the Oo I variables and to the Oo II variables, but there seems to be little or no dependence of Mv on [Fe/H] for stars within a group, at least for the Oo I and Oo II groups. The Oo II variables exhibit a weak period luminosity relation in V in many globular clusters unlike the Oo II-like variables in Oo I clusters which do not exhibit a P-L relation. The properties of some intermediate LMC clusters are discussed.
Single-epoch black hole mass estimators for broad-line active galactic nuclei: recalibrating Hβ with a new approach
Based on an updated Hβ reverberation mapping (RM) sample of 44 nearby active galactic nuclei (AGNs), we propose a novel approach for black hole (BH) mass estimation using two filtered luminosities computed from single-epoch (SE) AGN spectra around the Hβ region. We found that the two optimal-filter luminosities extract virial information (size and virial velocity of the broad-line region, BLR) from the spectra, justifying their usage in this empirical BH mass estimator. The major advantages of this new recipe over traditional SE BH mass estimators utilizing continuum luminosity and broad-line width are (1) it has a smaller intrinsic scatter of 0.28 dex calibrated against RM masses, (2) it is extremely simple to use in practice, without any need to decompose the spectrum, and (3) it produces unambiguous and highly repeatable results even with low signal-to-noise spectra. The combination of the two luminosities can also cancel out, to some extent, systematic luminosity errors potentially introduced by uncertainties in distance or flux calibration. In addition, we recalibrated the traditional SE mass estimators using broad Hβ FWHM and monochromatic continuum luminosity at 5100 Å (L {sub 5100}). We found that using the best-fit slopes on FWHM and L {sub 5100} (derived from fitting the BLR radius-luminosity relation and the correlation between rms line dispersion and SE FWHM, respectively) rather than simple assumptions (e.g., 0.5 for L {sub 5100} and 2 for FWHM) leads to more precise SE mass estimates, improving the intrinsic scatter from 0.41 dex to 0.36 dex with respect to the RM masses. We compared different estimators and discussed their applications to the Sloan Digital Sky Survey quasar sample. Due to the limitations of the current RM sample, application of any SE recipe calibrated against RM masses to distant quasars should be treated with caution.
THE MASS DISTRIBUTION OF SUBGIANT PLANET HOSTS
High mass stars are hostile to Doppler measurements due to rotation and activity on the main-sequence, so RV searches for planets around massive stars have relied on evolved stars. A large number of planets have been found around evolved stars with M > 1.5 M{sub Sun }. To test the robustness of mass determinations, Lloyd compared mass distributions of planet hosting subgiants with distributions from integrating isochrones and concluded that it is unlikely the subgiant planet hosts are this massive, but rather that the mass inferences are systematically in error. The conclusions of Lloyd have been called in to question by Johnson et al., who show TRILEGAL-based mass distributions that disagree with the mass distributions in Lloyd, which they attribute to Malmquist bias. Johnson et al. argue that the very small spectroscopic observational uncertainties favor high masses, and there are a large number of high mass sub giants in RV surveys. However, in this Letter, it is shown that Malmquist bias does not impact the mass distributions, but the mass distribution is sensitive to Galaxy model. The relationship needed to reconcile the subgiant planet host masses with any model of the Galactic stellar population is implausible, and the conclusion of Lloyd that spectroscopic mass determinations of subgiants are likely to have been overestimated is robust.
THE 2014 ALMA LONG BASELINE CAMPAIGN: AN OVERVIEW
A major goal of the Atacama Large Millimeter/submillimeter Array (ALMA) is to make accurate images with resolutions of tens of milliarcseconds, which at submillimeter (submm) wavelengths requires baselines up to ∼15 km. To develop and test this capability, a Long Baseline Campaign (LBC) was carried out from 2014 September to late November, culminating in end-to-end observations, calibrations, and imaging of selected Science Verification (SV) targets. This paper presents an overview of the campaign and its main results, including an investigation of the short-term coherence properties and systematic phase errors over the long baselines at the ALMA site, a summary of the SV targets and observations, and recommendations for science observing strategies at long baselines. Deep ALMA images of the quasar 3C 138 at 97 and 241 GHz are also compared to VLA 43 GHz results, demonstrating an agreement at a level of a few percent. As a result of the extensive program of LBC testing, the highly successful SV imaging at long baselines achieved angular resolutions as fine as 19 mas at ∼350 GHz. Observing with ALMA on baselines of up to 15 km is now possible, and opens up new parameter space for submm astronomy.
Analysis of the impact of broad absorption lines on quasar redshift measurements with synthetic observations
ABSTRACT Accurate quasar classifications and redshift measurements are increasingly important to precision cosmology experiments. Broad absorption line (BAL) features are present in 15–20 per cent of all quasars, and these features can introduce systematic redshift errors, and in extreme cases produce misclassifications. We quantitatively investigate the impact of BAL features on quasar classifications and redshift measurements with synthetic spectra that were designed to match observations by the Dark Energy Spectroscopic Instrument (DESI) survey. Over the course of 5 yr, DESI aims to measure spectra for 40 million galaxies and quasars, including nearly three million quasars. Our synthetic quasar spectra match the signal-to-noise ratio and redshift distributions of the first year of DESI observations, and include the same synthetic quasar spectra both with and without BAL features. We demonstrate that masking the locations of the BAL features decreases the redshift errors by about 1 per cent and reduces the number of catastrophic redshift errors by about 80 per cent. We conclude that identifying and masking BAL troughs should be a standard part of the redshift determination step for DESI and other large-scale spectroscopic surveys of quasars.
Dark Energy Survey Year 1 Results: Cross-Correlation Redshifts - Methods and Systematics Characterization
We use numerical simulations to characterize the performance of a clustering-based method to calibrate photometric redshift biases. In particular, we cross-correlate the weak lensing (WL) source galaxies from the Dark Energy Survey Year 1 (DES Y1) sample with redMaGiC galaxies (luminous red galaxies with secure photometric red- shifts) to estimate the redshift distribution of the former sample. The recovered redshift distributions are used to calibrate the photometric redshift bias of standard photo-z methods applied to the same source galaxy sample. We also apply the method to three photo-z codes run in our simulated data: Bayesian Photometric Redshift (BPZ), Directional Neighborhood Fitting (DNF), and Random Forest-based photo-z (RF). We characterize the systematic uncertainties of our calibration procedure, and find that these systematic uncertainties dominate our error budget. The dominant systematics are due to our assumption of unevolving bias and clustering across each redshift bin, and to differences between the shapes of the redshift distributions derived by clustering vs photo-z's. The systematic uncertainty in the mean redshift bias of the source galaxy sample is z ≲ 0.02, though the precise value depends on the redshift bin under consideration. Here, we discuss possible ways to mitigate the impact of our dominant systematics in future analyses.