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At least 55 records · Page 3

Flow annealed importance sampling bootstrap meets differentiable particle physics

High-energy physics requires the generation of large numbers of simulated data samples from complex but analytically tractable distributions called matrix elements. Surrogate models, such as normalizing flows, are gaining popularity for this task due to their computational efficiency. We adopt an approach based on flow annealed importance sampling bootstrap (FAB) that evaluates the differentiable target density during training and helps avoid the costly generation of training data in advance. We show that FAB reaches higher sampling efficiency with fewer target evaluations in high dimensions in comparison to other methods.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS

Enhancing Solar Power Forecasting with Regularized Constrained Quantile Regression Averaging and Bootstrapping Techniques

Probabilistic solar power forecasting (SPF) plays an essential role in optimizing power-grid operations by quantifying the forecast uncertainty. To improve the accuracy and robustness of probabilistic SPF, this paper introduces the regularized constrained quantile regression averaging (rCQRA) method to combine outputs from multiple PSPF models. In addition, a bootstrapping method was used to quantify model uncertainty, providing insights into the reliability and significance of each ensemble component. To evaluate its efficacy, the proposed rCQRA method is used to integrate four PSPF methods. The resulting SPF models are trained and validated using a real-world six-year dataset from a rooftop solar plant in the USA. The performance of the proposed rCQRA method is evaluated and compared with two benchmark methods under three categories of weather conditions. It is shown that the rCQRA method has superior performance in its forecast reliability, sharpness, and accuracy.

Ensemble learning, probabilistic solar power forec

Bootstrap unloader

Circuit can sample a number of transducers in sequence without drawing from them. This bootstrap unloader uses a differential amplifier with one input connected to a circuit which is the equivalent of the circuit to be unloaded, and the other input delivering the proper unloading currents.

Pfiffner, H. J.

Bootstrap Unloader-Patent

Bootstrap unloading circuits for sampling transducer voltage sources without drawing current

Pfiffner, H. J.

'Bootstrap' charging of surfaces composed of multiple materials

The paper examines the charging of a checkerboard array of two materials, only one of which tends to acquire a negative potential alone, using the NASA Charging Analyzer Program (NASCAP). The influence of the charging material's field causes the otherwise 'non-charging' material to acquire a negative potential due to the suppression of its secondary emission ('bootstrap' charging). The NASCAP predictions for the equilibrium potential difference between the two materials are compared to results based on an analytical model.

Stannard, P. R.

A bootstrap approach to bump hunting

An important question in cluster analysis and pattern recognition is the determination of the number of clusters into which a given population should be divided. Frequently, particularly when certain specific clustering methods are being used, the number of clusters is taken to be equal to the number of modes, or local maxima, in the probability density function underlying the given data set. The use of kernal density estimates in mode estimation is discussed. The test statistic to be used is defined and a bootstrap technique for assessing significance is given. An illustrative application is followed by an examination of the asymptotic behavior of the test statistic.

Silverman, B. W.

A Bootstrap Approach to Martian Manufacturing

In-Situ Resource Utilization (ISRU) is an essential element of any affordable strategy for a sustained human presence on Mars. Ideally, Martian habitats would be extremely massive to allow plenty of room to comfortably live and work, as well as to protect the occupants from the environment. Moreover, transportation and power generation systems would also require significant mass if affordable. For our approach to ISRU, we use the industrialization of the U.S. as a metaphor. The 19th century started with small blacksmith shops and ended with massive steel mills primarily accomplished by blacksmiths increasing their production capacity and product size to create larger shops, which produced small mills, which produced the large steel mills that industrialized the country. Most of the mass of a steel mill is comprised of steel in simple shapes, which are produced and repaired with few pieces of equipment also mostly made of steel in basic shapes. Due to this simplicity, we expect that the 19th century manufacturing growth can be repeated on Mars in the 21st century using robots as the primary labor force. We suggest a "bootstrap" approach to manufacturing on Mars that uses a "seed" manufacturing system that uses regolith to create major structural components and spare parts. The regolith would be melted, foamed, and sintered as needed to fabricate parts using casting and solid freeform fabrication techniques. Complex components, such as electronics, would be brought from Earth and integrated as needed. These parts would be assembled to create additional manufacturing systems, which can be both more capable and higher capacity. These subsequent manufacturing systems could refine vast amounts of raw materials to create large components, as well as assemble equipment, habitats, pressure vessels, cranes, pipelines, railways, trains, power generation stations, and other facilities needed to economically maintain a sustained human presence on Mars.

Dorais, Gregory A.

Improving the five-point bootstrap

We present a new algorithm for the numerical evaluation of five-point conformal blocks in d-dimensions, greatly improving the efficiency of their computation. To do this we use an appropriate ansatz for the blocks as a series expansion in radial coordinates, derive a set of recursion relations for the unknown coefficients in the ansatz, and evaluate the series using a Padé approximant to accelerate its convergence. We then study the 〈σσϵσσ〉 correlator in the 3d critical Ising model by truncating the operator product expansion (OPE) and only including operators with conformal dimension below a cutoff ∆ ⩽ ∆cutoff. We approximate the contributions of the operators above the cutoff by the corresponding contributions in a suitable disconnected five-point correlator. Using this approach, we compute a number of OPE coefficients with greater accuracy than previous methods.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS

Transforming the bootstrap: using Transformers to compute scattering amplitudes in planar N = 4 Super Yang-Mills theory

Abstract We pursue the use of deep learning methods to improve state-of-the-art computations in theoretical high-energy physics. Planar N = 4 Super Yang-Mills theory is a close cousin to the theory that describes Higgs boson production at the Large Hadron Collider; its scattering amplitudes are large mathematical expressions containing integer coefficients. In this paper, we apply Transformers to predict these coefficients. The problem can be formulated in a language-like representation amenable to standard cross-entropy training objectives. We design two related experiments and show that the model achieves high accuracy (> 98%) on both tasks. Our work shows that Transformers can be applied successfully to problems in theoretical physics that require exact solutions.

Cai, Tianji (ORCID:0000000232359486)

Exact solution of the frustrated Potts model with next-nearest-neighbor interactions in one dimension via AI bootstrapping

The one-dimensional (1D) 𝐽 1 −𝐽 2 𝑞-state Potts model is solved exactly for arbitrary 𝑞 by analytically block-diagonalizing the original 𝑞 2 ×𝑞 2 transfer matrix into a simple 2 × 2 maximally symmetric subspace, based on using OpenAI's reasoning model o3-mini-high to exactly solve the 𝑞 = 3 case. Furthermore, by matching relevant subspaces, we map the Potts model onto a simpler effective 1D 𝑞-state Potts model, where 𝐽 2 acts as the nearest-neighbor interaction and 𝐽 1 as an effective magnetic field, nontrivially generalizing a 56-year-old theorem previously limited to the simplest case (𝑞 = 2, the Ising model). Our exact results provide insights to phenomena such as atomic or electronic order stacking in layered materials and the emergence of dome-shaped phases in complex phase diagrams. In conclusion, this work is anticipated to fuel both research in 1D frustrated magnets for recently discovered finite-temperature application potentials and the fast moving topic area of AI in science.

1-dimensional spin chains

Double Bootstrapping

This code performs a simulation experiment that involves (i) drawing random numbers from the normal distribution, (ii) resampling elements from arrays with replacement, and (iii) computing various quantities like mean, standard deviation, etc. Further information is available in section 4 of FERMILAB-FN-1273-ETD [https://inspirehep.net/literature/2925453].

Shyamsundar, Prasanth [Fermi National Accelerator

Bell-State Bootstrapping via Spot-Checking

We present a spot-checking method to monitor and mitigate errors in imperfect entangled photon sources from semi-trusted third parties. We demonstrate that our method effectively mitigates polarization drifts and yields high-fidelity polarization-entangled Bell-state generation.

Zhang, Yanbao [ORNL] (ORCID:0000000245530561)

Allan Variance is Bootstrap Aggregation for Spectral Estimation

Characterization of clocks and inertial sensors, such as accelerometers and gyroscopes, typically includes Allan variance analysis. Allan variance is ubiquitous in timing and navigation communities which may appear niche compared with generalized spectral analysis. This note provides some motivation for Allan Variance for audiences more familiar with spectral analysis.

Walker, Michael Ray [Sandia National Laboratories