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Analytic boosted boson discrimination
Observables which discriminate boosted topologies from massive QCD jets are of great importance for the success of the jet substructure program at the Large Hadron Collider. Such observables, while both widely and successfully used, have been studied almost exclusively with Monte Carlo simulations. In this paper we present the first all-orders factorization theorem for a two-prong discriminant based on a jet shape variable, D 2 , valid for both signal and background jets. Our factorization theorem simultaneously describes the production of both collinear and soft subjets, and we introduce a novel zero-bin procedure to correctly describe the transition region between these limits. By proving an all orders factorization theorem, we enable a systematically improvable description, and allow for precision comparisons between data, Monte Carlo, and first principles QCD calculations for jet substructure observables. Using our factorization theorem, we present numerical results for the discrimination of a boosted Z boson from massive QCD background jets. We compare our results with Monte Carlo predictions which allows for a detailed understanding of the extent to which these generators accurately describe the formation of two-prong QCD jets, and informs their usage in substructure analyses. In conclusion, our calculation also provides considerable insight into the discrimination power and calculability of jet substructure observables in general.
Estimating Higher-Order Moments Using Symmetric Tensor Decomposition
In this paper, we consider the problem of decomposing higher-order moment tensors, i.e., the sum of symmetric outer products of data vectors. Such a decomposition can be used to estimate the means in a Gaussian mixture model and for other applications in machine learning. The dth-order empirical moment tensor of a set of p observations of n variables is a symmetric d-way tensor. Our goal is to nd a low-rank tensor approximation comprising r $\ll$ p symmetric outer products. The challenge is that forming the empirical moment tensor costs O(pn d ) operations and O(n d ) storage, which may be prohibitively expensive; additionally, the algorithm to compute the low-rank approximation costs O(n d ) per iteration. Our contribution is avoiding formation of the moment tensor, computing the low-rank tensor approximation of the moment tensor implicitly using O(pnr) operations per iteration and no extra memory. This advance opens the door to more applications of higher-order moments since they can now be efficiently computed. We present numerical evidence of the computational savings and show an example of estimating the means for higher-order moments.
A new member of ferrous sulfates, FeSO{sub 4}·2H{sub 2}O with PtS topology showing spin-canted long-range antiferromagnetic ordering
A sanderite ferrous sulfate FeSO{sub 4}·2H{sub 2}O has been synthesized by the hydro/solvothermal method. Its crystal structure (Pccn, a=6.3160 Å, b=7.7550 Å, c=8.9880 Å, V=440.2 Å{sup 3}, Z=4) can be regarded as the condensation of alternately corner-shared FeO{sub 4}(H{sub 2}O){sub 2} octahedra and SO{sub 4} tetrahedra with a similar topology of PtS. By structural comparison with the known hydrated ferrous sulfates, the structural relation among them has been noted and discussed in detail. A variable temperature magnetic study shows a spin-canted long-range antiferromagnetic ordering in the low temperature regime, which might result from a possible phase transition during the cooling from the high temperature. - Graphical abstract: As a new number of ferrous sulfates, sanderite FeSO{sub 4}·2H{sub 2}O has been synthesized under hydro/solvothermal conditions, which exhibits a similar topology of PtS. - Highlights: • Sanderite ferrous sulfate has been synthesized. • The topology of its structure is similar to that of PtS. • A structural relation between these hydrated ferrous sulfates is discovered.
Alteration of natural 37 Ar activity concentration in the subsurface by gas transport and water infiltration
High 37 Ar activity concentration in soil gas is proposed as a key evidence for the detection of underground nuclear explosion by the Comprehensive Nuclear Test-Ban Treaty. However, such a detection is challenged by the natural background of 37 Ar in the subsurface, mainly due to Ca activation by cosmic rays. A better understanding and improved capability to predict 37 Ar activity concentration in the subsurface and its spatial and temporal variability is thus required. A numerical model integrating 37 Ar production and transport in the subsurface is developed, including variable soil water content and water infiltration at the surface. A parameterized equation for 37 Ar production in the first 15 m below the surface is studied, taking into account the major production reactions and the moderation effect of soil water content. Using sensitivity analysis and uncertainty quantification, a realistic and comprehensive probability distribution of natural 37 Ar activity concentrations in soil gas is proposed, including the effects of water infiltration. Site location and soil composition are identified as the parameters allowing for a most effective reduction of the possible range of 37 Ar activity concentrations. The influence of soil water content on 37Ar production is shown to be negligible to first order, while 37 Ar activity concentration in soil gas and its temporal variability appear to be strongly influenced by transient water infiltration events. Overall, these results will be used as a basis for practical CTBTO concepts of operation during an OSI.
Estimating Cohort Survival through Tributaries for Salmonid Populations with Variable Ages at Migration
Abstract Survival of juvenile anadromous Pacific salmonids from their earliest age of seaward movement (“out-migration”) through the tributaries that connect their rearing grounds to larger-order rivers (“cohort survival”) is an important yet often unmonitored factor in the complex life history of these species. Populations with variable age at out-migration (e.g., steelhead Oncorhynchus mykiss) or multiple juvenile rearing strategies (e.g., Chinook Salmon O. tshawytscha) raise particular challenges in survival monitoring. A multiple-state release–recapture model is presented to estimate cohort survival for salmonid species with variable age at out-migration and demonstrated in two case studies. Annual releases of fish tagged with passive integrated transponder (PIT) tags from the same brood year (cohort) and their detections by instream PIT-tag detection arrays in tributaries and at hydroelectric dams are used to estimate survival through tributaries and the age composition of juvenile migrants reaching downstream sites. The 2010 cohort of Chinook Salmon from the Chiwawa River in Washington State had estimated survival of 0.59 to the middle Wenatchee River array in Tumwater Dam Reservoir (river distance of 30 km) and 0.12 to McNary Dam (364 km) on the Columbia River. The age composition shifted from 60.1% subyearlings at the Wenatchee River array to 100% yearlings at McNary Dam, reflecting the use of the Wenatchee River as a nonnatal rearing area, whereas McNary Dam is in the migratory corridor. The 2010 cohort of steelhead from the Twisp River in Washington State had estimated survival from the yearling stage of 0.30 to Rocky Reach Dam (146 km from the Twisp River mouth) on the Columbia River and 0.17 to McNary Dam (439 km). The low survival to the Columbia River resulted from both mortality and adoption of a resident life history and reflects the diverse population structure in the Twisp River. Received February 4, 2015; accepted June 11, 2015
MINING NUCLEAR TRANSIENT DATA THROUGH SYMBOLIC CONVERSION
Dynamic Probabilistic Risk Assessment (DPRA) methodologies generate enormous amounts of data for a very large number of simulations. The data contain temporal information of both the state variables of the simulator and the temporal status of specific systems/components. In order to measure system performances, limitations and resilience, such data need to be carefully analyzed with the objective of discovering the correlations between sequence/timing of events and system dynamics. A first approach toward discovering these correlations from data generated by DPRA methodologies has been performed by organizing scenarios into groups using classification or clustering based algorithms. The identification of the correlations between system dynamics and timing/sequencing of events is performed by observing the temporal distribution of these events in each group of scenarios. Instead of performing “a posteriori” analysis of these correlations, this paper shows how it is possible to identify the correlations implicitly by performing a symbolic conversion of both continuous (temporal profiles of simulator state variables) and discrete (status of systems and components) data. Symbolic conversion is performed for each simulation by properly quantizing both continuous and discrete data and then converting them as a series of symbols. After merging both series together, a temporal phrase is obtained. This phrase preserves duration, coincidence and sequence of both continuous and discrete data in a uniform and consistent manner. In this paper it is also shown that by using specific distance measures, it is still possible to post-process such symbolic data using clustering and classification techniques but in considerably less time since the memory needed to store the data is greatly reduced by the symbolic conversion.
A Least-Squares-Based Weak Galerkin Finite Element Method for Second Order Elliptic Equations
Here, in this article, we introduce a least-squares-based weak Galerkin finite element method for the second order elliptic equation. This new method is shown to provide very accurate numerical approximations for both the primal and the flux variables. In contrast to other existing least-squares finite element methods, this new method allows us to use discontinuous approximating functions on finite element partitions consisting of arbitrary polygon/polyhedron shapes. We also develop a Schur complement algorithm for the resulting discretization problem by eliminating all the unknowns that represent the solution information in the interior of each element. Optimal order error estimates for both the primal and the flux variables are established. An extensive set of numerical experiments are conducted to demonstrate the robustness, reliability, flexibility, and accuracy of the least-squares-based weak Galerkin finite element method. Finally, the numerical examples cover a wide range of applied problems, including singularly perturbed reaction-diffusion equations and the flow of fluid in porous media with strong anisotropy and heterogeneity.
A simple introduction to the SiMPL method for density-based topology optimization
We introduce a novel method for solving density-based topology optimization problems: Sigmoidal Mirror descent with a Projected Latent variable (SiMPL). The SiMPL method (pronounced as “the simple method”) optimizes a design using only first-order derivative information of the objective function. The bound constraints on the density field are enforced with the help of the (negative) Fermi–Dirac entropy, which is also used to define a non-symmetric distance function called a Bregman divergence on the set of admissible designs. This Bregman divergence leads to a simple update rule that is further simplified with the help of a so-called latent variable. Because the SiMPL method involves discretizing the latent variable, it produces a sequence of pointwise-feasible iterates, even when high-order finite elements are used in the discretization. Numerical experiments demonstrate that the method outperforms other popular first-order optimization algorithms. In conclusion, to outline the general applicability of the technique, we include examples with (self-load) compliance minimization and compliant mechanism optimization problems.
ZENN: A thermodynamics-inspired computational framework for heterogeneous data–driven modeling
Traditional entropy-based methods—such as cross-entropy loss in classification problems—have long been essential tools for representing the information uncertainty and physical disorder in data and for developing artificial intelligence algorithms. However, the rapid growth of data across various domains has introduced new challenges, particularly the integration of heterogeneous datasets with intrinsic disparities. To address this, we introduce a zentropy-enhanced neural network (ZENN), extending zentropy theory into the data science domain via intrinsic entropy, enabling more effective learning from heterogeneous data sources. ZENN simultaneously learns both energy and intrinsic entropy components, capturing the underlying structure of multisource data. To support this, we redesign the neural network architecture to better reflect the intrinsic properties and variability inherent in diverse datasets. We demonstrate the effectiveness of ZENN on classification tasks and energy landscape reconstructions, showing its superior generalization capabilities and robustness-particularly in predicting high-order derivatives. In image and text classification tasks, ZENN demonstrates superior generalization by introducing a learnable temperature variable that models latent multisource heterogeneity, allowing it to surpass state-of-the-art models on CIFAR-10/100, BBC News, and AG News. As a practical application in materials science, we employ ZENN to reconstruct the Helmholtz energy landscape of Fe3Pt using data generated from density functional theory and capture key material behaviors, including negative thermal expansion and the critical point in the temperature–pressure space. Overall, this work presents a zentropy-grounded framework for data-driven machine learning, positioning ZENN as a versatile and robust approach for scientific problems involving complex, heterogeneous datasets.
Adaptive Interface-PINNs (AdaI-PINNs) for inverse problems: Determining material properties for heterogeneous systems
Here, we determine spatially varying discontinuous material properties using a domain-decomposition based physics-informed neural networks (PINNs) framework named the Adaptive Interface-PINNs or AdaI-PINNs (Roy et al., 2024). We propose the use of distinct neural networks for the field variables and material properties within each material, utilizing adaptive activation functions. While the neural networks across different materials share the same weights and biases, their activation functions are uniquely tailored using a hyperparameter that influences the slope of the activation function. The proposed framework is tested on several one-dimensional and two-dimensional benchmark examples, and its performance is compared with conventional PINNs and existing domain-decomposition PINNs frameworks, namely, the Multi-domain physics-informed neural network (M-PINN), and the eXtended physics-informed neural networks (XPINNs). The results demonstrate that the proposed approach can determine randomly distributed discontinuous material properties with an L 2 error of $\mathscr{O}$ (10 -3 ) for the material property and the root-mean-square error of $\mathscr{O}$ (10 -3 ) for the primary variable while the other approaches yield errors that are approximately two orders of magnitude larger (that is, $\mathscr{O}$ (10 -1 )). Moreover, the spatial distribution of material properties obtained using the proposed framework is in close agreement with the true distribution, whereas the other approaches fare much worse. Additionally, the proposed approach is approximately 40% faster than its competitors, indicating its potential as a robust alternative for solving inverse problems in heterogeneous materials.
Sulfur Biogeochemical Cycling and Redox Dynamics in a Shale-Dominated Mountainous Watershed
Sulfur (S) is an essential macronutrient and important component of the earth’s crust, and its cycling has critical impacts on trace metal mobility, water quality, and human health. Pyrite weathering is the primary pathway by which sulfur enters surface waters. However, biogeochemical cycling of sulfur in soils and the river corridor mediates sulfate exports. In this study, we identified the major forms of sulfur across multiple compartments and scales in a pristine mountainous watershed, including shale bedrock weathering profiles, hillslope soils, and alluvial floodplain sediments, in order to provide insight into biogeochemical sulfur cycling in a hydrologically variable alpine system. X-ray absorption near-edge spectroscopy (XANES) analysis of shale weathering profiles showed clear evidence of pyrite oxidation to sulfate, with large accumulations of intermediate S(0) (20%–53%). Micro-scale XANES showed evidence of reprecipitation of pyrite at fracture surfaces within the permanently saturated zone. Organic sulfur dominated S speciation in shallow hillslope soil and floodplain sediment, with little evidence of reduced inorganic S. However, mackinawite formation, representing active sulfate reduction, was observed in saturated oxbow sediments and saturated weathered shale underlying floodplain sediments. Further evidence of sulfate reduction from aqueous sulfur isotopic analysis was observed in shallow groundwater transects across an Fe-reducing meander, whereas increases in pore water sulfate concentrations implied sulfur oxidation at other locations. Furthermore, the data present an integrated picture of sulfur cycling in a shale-dominated watershed, where riverine sulfate exports are mediated by biological cycling, particularly in redox-stratified and temporally dynamic hyporheic zone sediments.
Learning to Correct Climate Projection Biases
The fidelity of climate projections is often undermined by biases in climate models due to their simplification or misrepresentation of unresolved climate processes. While various bias correction methods have been developed to post-process model outputs to match observations, existing approaches usually focus on limited, low-order statistics, or break either the spatiotemporal consistency of the target variable, or its dependency upon model resolved dynamics. We develop a Regularized Adversarial Domain Adaptation (RADA) methodology to overcome these deficiencies, and enhance efficient identification and correction of climate model biases. Instead of pre-assuming the spatiotemporal characteristics of model biases, we apply discriminative neural networks to distinguish historical climate simulation samples and observation samples. The evidences based on which the discriminative neural networks make distinctions are applied to train the domain adaptation neural networks to bias correct climate simulations. We regularize the domain adaptation neural networks using cycle-consistent statistical and dynamical constraints. An application to daily precipitation projection over the contiguous United States shows that our methodology can correct all the considered moments of daily precipitation at approximately $1^\circ$ resolution, ensures spatiotemporal consistency and inter-field correlations, and can discriminate between different dynamical conditions. Our methodology offers a powerful tool for disentangling model parameterization biases from their interactions with the chaotic evolution of climate dynamics, opening a novel avenue toward big-data enhanced climate predictions.
Deep learning on the 2-dimensional Ising model to extract the crossover region with a variational autoencoder
The 2-dimensional Ising model on a square lattice is investigated with a variational autoencoder in the non-vanishing field case for the purpose of extracting the crossover region between the ferromagnetic and paramagnetic phases. The encoded latent variable space is found to provide suitable metrics for tracking the order and disorder in the Ising configurations that extends to the extraction of a crossover region in a way that is consistent with expectations. The extracted results achieve an exceptional prediction for the critical point as well as agreement with previously published results on the configurational magnetizations of the model. The performance of this method provides encouragement for the use of machine learning to extract meaningful structural information from complex physical systems where little a priori data is available.
High dimensional predictions of suicide risk in 4.2 million US Veterans using ensemble transfer learning
We present an ensemble transfer learning method to predict suicide from Veterans Affairs (VA) electronic medical records (EMR). A diverse set of base models was trained to predict a binary outcome constructed from reported suicide, suicide attempt, and overdose diagnoses with varying choices of study design and prediction methodology. Each model used twenty cross-sectional and 190 longitudinal variables observed in eight time intervals covering 7.5 years prior to the time of prediction. Ensembles of seven base models were created and fine-tuned with ten variables expected to change with study design and outcome definition in order to predict suicide and combined outcome in a prospective cohort. The ensemble models achieved c-statistics of 0.73 on 2-year suicide risk and 0.83 on the combined outcome when predicting on a prospective cohort of ~4.2 M veterans. The ensembles rely on nonlinear base models trained using a matched retrospective nested case-control (Rcc) study cohort and show good calibration across a diversity of subgroups, including risk strata, age, sex, race, and level of healthcare utilization. In addition, a linear Rcc base model provided a rich set of biological predictors, including indicators of suicide, substance use disorder, mental health diagnoses and treatments, hypoxia and vascular damage, and demographics. Similar content being viewed by others
Towards accurate cosmological predictions for rapidly oscillating scalar fields as dark matter
As we are entering the era of precision cosmology, it is necessary to count on accurate cosmological predictions from any proposed model of dark matter. In this paper we present a novel approach to the cosmological evolution of scalar fields that eases their analytic and numerical analysis at the background and at the linear order of perturbations. The new method makes use of appropriate angular variables that simplify the writing of the equations of motion, and which also show that the usual field variables play a secondary role in the cosmological dynamics. We apply the method to a scalar field endowed with a quadratic potential and revisit its properties as dark matter. Some of the results known in the literature are recovered, and a better understanding of the physical properties of the model is provided. It is confirmed that there exists a Jeans wavenumber k {sub J} , directly related to the suppression of linear perturbations at wavenumbers k > k {sub J} , and which is verified to be k {sub J} = a √ mH . We also discuss some semi-analytical results that are well satisfied by the full numerical solutions obtained from an amended version of the CMB code CLASS. Finally we draw some of the implications that this new treatment of the equations of motion may have in the prediction of cosmological observables from scalar field dark matter models.
AREPO-RT : radiation hydrodynamics on a moving mesh
We introduce AREPO-RT , a novel radiation hydrodynamic (RHD) solver for the unstructured moving-mesh code AREPO . Our method solves the moment-based radiative transfer equations using the M1 closure relation. We achieve second-order convergence by using a slope-limited linear spatial extrapolation and a first-order time prediction step to obtain the values of the primitive variables on both sides of the cell interface. A Harten–Lax–van Leer flux function, suitably modified for moving meshes, is then used to solve the Riemann problem at the interface. The implementation is fully conservative and compatible with the individual time-stepping scheme of AREPO . It incorporates atomic hydrogen (H) and helium (He) thermochemistry, which is used to couple the ultraviolet radiation field to the gas. Additionally, infrared (IR) radiation is coupled to the gas under the assumption of local thermodynamic equilibrium between the gas and the dust. We successfully apply our code to a large number of test problems, including applications such as the expansion of HII regions, radiation pressure-driven outflows, and the levitation of optically thick layer of gas by trapped IR radiation. The new implementation is suitable for studying various important astrophysical phenomena, such as the effect of radiative feedback in driving galactic scale outflows, radiation-driven dusty winds in high-redshift quasars, or simulating the reionization history of the Universe in a self-consistent manner.
Deuteron-α scattering: Separable versus nonseparable Faddeev approach
Deuteron-induced reactions are widely used to probe nuclear structure and astrophysical information. Those (d,p) reactions may be viewed as three-body reactions and described with Faddeev techniques. Faddeev-AGS equations in momentum space have a long tradition of utilizing separable interactions in order to arrive at sets of coupled integral equations in one variable. However, it needs to be demonstrated that observables calculated based on separable interactions agree exactly with those based on nonseparable forces. Momentum space AGS equations are solved with separable and nonseparable forces as coupled integral equations. Deuteron-α scattering is calculated via momentum space AGS equations using the CD-Bonn neutron-proton force and a Woods-Saxon type neutron(proton)- 4 He force, for which the Pauli-forbidden S-wave bound state is projected out. Elastic as well as breakup observables are calculated and compared to results in which the interactions in the two-body sub-systems are represented by separable interactions derived in the Ernst-Shakin-Thaler (EST) framework. We find that the calculations based on the separable representation of the interactions and the original interactions give results that are in excellent agreement. Specifically, integrated cross sections and angular distributions for elastic scattering agree within ≈ 1 %, which is well below typical experimental errors. In addition, the fivefold differential cross sections corresponding to breakup of the deuteron agree extremely well.