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Effective Field Theories (EFTs) are the primary tool for interpreting precision collider data in the absence of new resonances. However, in the dimension-8 Standard Model Effective Field Theory (SMEFT), the utility of traditional…

High Energy Physics - Phenomenology · Physics 2026-03-05 Leonardo P. G. De Assis

Density functional theory (DFT) has transformed our ability to investigate and understand electronic ground states. In its original formulation, however, DFT is not suited to addressing (e.g.) degenerate ground states, mixed states with…

Chemical Physics · Physics 2025-10-30 Tim Gould , Leeor Kronik , Stefano Pittalis

Pretrained Language Models (PLMs) have become the de facto starting point for fine-tuning on downstream tasks. However, as model sizes continue to increase, traditional fine-tuning of all the parameters becomes challenging. To address this,…

Machine Learning · Computer Science 2024-07-16 Bharat Runwal , Tejaswini Pedapati , Pin-Yu Chen

In this work, we introduce a definition of the Discrete Fourier Transform (DFT) on Euclidean lattices in $\R^n$, that generalizes the $n$-th fold DFT of the integer lattice $\Z^n$ to arbitrary lattices. This definition is not applicable for…

Quantum Physics · Physics 2017-04-04 Lior Eldar , Peter Shor

We classify four-dimensional effective field theories (EFTs) with enhanced soft limits, which arise due to non-linearly realised symmetries on the Goldstone modes of such theories. We present an algorithm for deriving all possible algebras…

High Energy Physics - Theory · Physics 2019-09-04 Diederik Roest , David Stefanyszyn , Pelle Werkman

We consider the Standard Model extended by a heavy scalar singlet in different regions of parameter space and construct the appropriate low-energy effective field theories up to first nontrivial order. This top-down exercise in effective…

High Energy Physics - Phenomenology · Physics 2017-04-05 G. Buchalla , O. Cata , A. Celis , C. Krause

Mathematical morphology (MM) is a theory of non-linear operators used for the processing and analysis of images. Morphological neural networks (MNNs) are neural networks whose neurons compute morphological operators. Dilations and erosions…

Machine Learning · Computer Science 2020-11-13 Angelica Lourenço Oliveira , Marcos Eduardo Valle

We renormalize massless scalar effective field theories (EFTs) to higher loop orders and higher orders in the EFT expansion. To facilitate EFT calculations with the R* renormalization method, we construct suitable operator bases using…

High Energy Physics - Phenomenology · Physics 2025-07-11 Weiguang Cao , Franz Herzog , Tom Melia , Jasper Roosmale Nepveu

Fourier and related transforms is a family of algorithms widely employed in diverse areas of computational science, notoriously difficult to scale on high-performance parallel computers with large number of processing elements (cores). This…

Distributed, Parallel, and Cluster Computing · Computer Science 2019-05-09 Dmitry Pekurovsky

Recently we succeeded to make a reliable EFT prediction in a totally parameter-free manner for the $S$ factors for the solar $pp$ and $hep$ processes, $p+p\to d + e^+ +\nu_e$ and $\He3+p \to \He4 + e^+ + \nu_e$. The strategy used in there…

Nuclear Theory · Physics 2015-06-26 Tae-Sun Park

We present a novel approach to classify supersymmetric effective field theories (EFTs) whose scattering amplitudes exhibit enhanced soft limits. These enhancements arise due to non-linearly realised symmetries on the Goldstone modes of such…

High Energy Physics - Theory · Physics 2020-01-08 Diederik Roest , David Stefanyszyn , Pelle Werkman

Effective field theories (EFT) parameterize the long-distance effects of short-distance dynamics whose details may or may not be known. It is known that EFT coefficients must obey certain positivity constraints if causality and unitarity…

High Energy Physics - Theory · Physics 2021-06-16 Simon Caron-Huot , Vincent Van Duong

Orthogonal parameter-efficient fine-tuning (PEFT) adapts pretrained weights through structure-preserving multiplicative transformations, but existing methods often conflate two distinct design choices: the subspace in which adaptation…

Machine Learning · Computer Science 2026-05-13 Lanxin Zhao , Bamdev Mishra , Pratik Jawanpuria , Lequan Lin , Dai Shi , Junbin Gao , Andi Han

We develop an effective-field-theory (EFT) framework to analyze the spectra emerging from lattice simulations of a large class of confining gauge theories. Simulations of these theories, for which the light-fermion count is not far below…

High Energy Physics - Phenomenology · Physics 2017-08-02 Thomas Appelquist , James Ingoldby , Maurizio Piai

Differentiable programming has facilitated numerous methodological advances in scientific computing. Physics engines supporting automatic differentiation have simpler code, accelerating the development process and reducing the maintenance…

Computational Physics · Physics 2023-04-04 Chuin Wei Tan , Chris J. Pickard , William C. Witt

We reanalyze the effective field theory (EFT) approach for the scenario in which the particles that account for the dark matter (DM) in the universe are vector states that interact only through the Standard Model-like Higgs boson. These DM…

High Energy Physics - Phenomenology · Physics 2023-12-22 Giorgio Arcadi , Juan Carlos Criado , Abdelhak Djouadi

Density Functional Theory (DFT) is the de facto workhorse for large-scale electronic structure calculations in chemistry and materials science. While plane-wave DFT implementations remain the most widely used, real-space DFT provides…

Attempts to apply effective field theory (EFT) methods to nonrelativistic nucleon-nucleon (NN) scattering have raised questions about the nature and limitations of an EFT expansion when used nonperturbatively. We discuss the characteristics…

Nuclear Theory · Physics 2009-10-31 James V. Steele , R. J. Furnstahl

We discuss the implications of dimension-six operators of the Effective Field Theory (EFT) framework in the study of Vector Boson Scattering (VBS) in the $pp \to Z Z j j $ channel. We show that operators of dimension-six should not be…

High Energy Physics - Phenomenology · Physics 2019-05-22 Raquel Gomez-Ambrosio

Fine-tuning large language models (LLMs) is essential for enhancing their performance on specific tasks but is often resource-intensive due to redundant or uninformative data. To address this inefficiency, we introduce DELIFT (Data…

Computation and Language · Computer Science 2025-03-21 Ishika Agarwal , Krishnateja Killamsetty , Lucian Popa , Marina Danilevksy