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We give an approach for characterizing interference by lower bounding the number of units whose outcome depends on selected groups of treated individuals, such as depending on the treatment of others, or others who are at least a certain…

Methodology · Statistics 2025-11-04 David Choi

Two-neutron transfer reactions are studied within the interacting boson model based on the nuclear energy density functional theory. Constrained self-consistent mean-field calculations with the Skyrme energy density functional are performed…

Nuclear Theory · Physics 2019-03-11 K. Nomura , Y. Zhang

In this work we study binary classification problems where we assume that our training data is subject to uncertainty, i.e. the precise data points are not known. To tackle this issue in the field of robust machine learning the aim is to…

Machine Learning · Computer Science 2022-03-04 Jannis Kurtz

Multiscale correlation functions in high Reynolds number experimental turbulence and synthetic signals are investigated. Fusion Rules predictions as they arise from multiplicative, almost uncorrelated, random processes for the energy…

chao-dyn · Physics 2009-10-31 R. Benzi , L. Biferale , F. Toschi

A quantum thermodynamic system can conserve non-commuting observables, but the consequences of this phenomenon on relaxation are still not fully understood. We investigate this problem by leveraging an observable-dependent approach to…

The isospin-invariant interacting boson model IBM-3 is analyzed in situations where $SU_T(3)$ charge symmetry [or, equivalently, $U_L(6)$ $sd$ symmetry] is conserved. Analytic expressions for energies, electromagnetic transitions,…

Nuclear Theory · Physics 2009-10-31 J. E. Garcia--Ramos , P. Van Isacker

Pairing correlations play a very important role in atomic nuclei. Although several effective pairing interactions have been used in mean field calculations for nucleons, little is known about effective pairing interactions for hyperons.…

Nuclear Theory · Physics 2021-08-03 Yu-Ting Rong , Pengwei Zhao , Shan-Gui Zhou

Multiple high precision $\beta$-decay measurements are being carried out these days on various nuclei, in search of beyond the Standard Model signatures. These measurements necessitate accurate standard model theoretical predictions to be…

Nuclear Theory · Physics 2022-09-28 Ayala Glick-Magid , Doron Gazit

The partition function of nonequilibrium distribution which we recently obtained [arXiv:0802.0259] in the framework of the maximum isotropization model (MIM) is exploited to extract physical information from experimental data on the proton…

Nuclear Theory · Physics 2008-04-11 Dmitry Anchishkin , Stanislav Yezhov

There is an increasing interest in quantifying the predictive power in nuclear structure calculations. We discuss how both experimental and systematic errors at the NN-level can be used to estimate the theoretical uncertainties by rather…

Nuclear Theory · Physics 2012-06-18 R. Navarro Perez , J. E. Amaro , E. Ruiz Arriola

Bayesian inference is a widely used technique for real-time characterization of quantum systems. It excels in experimental characterization in the low data regime, and when the measurements have degrees of freedom. A decisive factor for its…

Quantum Physics · Physics 2025-07-10 Alexandra Ramôa , Raffaele Santagati , Nathan Wiebe

Nuclear structure at short $NN$-distances is still poorly understood. In particular, the full quantum structure of the nucleus with a correlated $NN$-pair is a challenge to theory. So far, model descriptions have been limited to the average…

Nuclear Theory · Physics 2024-05-15 A. B. Larionov , Yu. N. Uzikov

We present an overview of our studies on the nonequilibrium dynamics of quantum systems that have many interacting particles. Our emphasis is on systems that show strong level repulsion, referred to as chaotic systems. We discuss how full…

Statistical Mechanics · Physics 2019-05-01 Lea F. Santos , E. J. Torres-Herrera

In the field of quantum metrology and sensing, a collection of quantum systems (e.g. spins) are used as a probe to estimate some physical parameter (e.g. magnetic field). It is usually assumed that there are no interactions between the…

Quantum Physics · Physics 2018-05-16 Shane Dooley , Michael Hanks , Shojun Nakayama , William J. Munro , Kae Nemoto

We study notions of robustness of Markov kernels and probability distribution of a system that is described by $n$ input random variables and one output random variable. Markov kernels can be expanded in a series of potentials that allow to…

Commutative Algebra · Mathematics 2011-10-07 Johannes Rauh , Nihat Ay

Current and future electron and neutrino scattering experiments will be greatly aided by a better understanding of the role played by short-range correlations in nuclei. Two-body physics, including nucleon-nucleon correlations and two-body…

Nuclear Theory · Physics 2023-02-01 M. Piarulli , S. Pastore , R. B. Wiringa , S. Brusilow , R. Lim

In this talk I discuss two types of experiments with exotic nuclei which could be performed at the forthcoming Italian INFN facilities with radioactive beams. First I will discuss nuclear and Coulomb breakup experiments which involve heavy…

Nuclear Theory · Physics 2017-08-23 Angela Bonaccorso

For a better understanding of the dynamics of exotic nuclei it is of crucial importance to develop a practical microscopic theory easy to be applied to a wide range of masses. Theoretically the basic task consists in formulating an easy…

Nuclear Theory · Physics 2009-11-11 M. Tomaselli , T. Kuehl , D. Ursescu

The strongly repulsive core of the short-range nucleon-nucleon interaction leads to the existence of high-momentum nucleons in nuclei. Inclusive electron scattering can be used to probe these high-momentum nucleons and study the nature of…

High Energy Physics - Phenomenology · Physics 2009-11-10 J. Arrington

Robust Bayesian inference is the calculation of posterior probability bounds given perturbations in a probabilistic model. This paper focuses on perturbations that can be expressed locally in Bayesian networks through convex sets of…

Artificial Intelligence · Computer Science 2013-02-08 Fabio Gagliardi Cozman