3,713 research outputs found
Phase diagram of an extended Agassi model
Background: The Agassi model is an extension of the Lipkin-Meshkov-Glick
model that incorporates the pairing interaction. It is a schematic model that
describes the interplay between particle-hole and pair correlations. It was
proposed in the 1960's by D. Agassi as a model to simulate the properties of
the quadrupole plus pairing model.
Purpose: The aim of this work is to extend a previous study by Davis and
Heiss generalizing the Agassi model and analyze in detail the phase diagram of
the model as well as the different regions with coexistence of several phases.
Method: We solve the model Hamiltonian through the Hartree-Fock-Bogoliubov
(HFB) approximation, introducing two variational parameters that play the role
of order parameters. We also compare the HFB calculations with the exact ones.
Results: We obtain the phase diagram of the model and classify the order of
the different quantum phase transitions appearing in the diagram. The phase
diagram presents broad regions where several phases, up to three, coexist.
Moreover, there is also a line and a point where four and five phases are
degenerated, respectively.
Conclusions: The phase diagram of the extended Agassi model presents a rich
variety of phases. Phase coexistence is present in extended areas of the
parameter space. The model could be an important tool for benchmarking novel
many-body approximations.Comment: Accepted for publication in PR
An extended Agassi model: algebraic structure, phase diagram, and large size limit
The Agassi model is a schematic two-level model that involves pairing and
monopole-monopole interactions. It is, therefore, an extension of the well
known Lipkin-Meshkov-Glick (LMG) model. In this paper we review the algebraic
formulation of an extension of the Agassi model as well as its bosonic
realization through the Schwinger representation. Moreover, a mean-field
approximation for the model is presented and its phase diagram discussed.
Finally, a analysis, with proportional to the degeneracy of each
level, is worked out to obtain the thermodynamic limit of the ground state
energy and some order parameters from the exact Hamiltonian diagonalization for
finite.Comment: Accepted in Physica Scripta. Focus on SSNET 201
Neogene to recent contraction and basin inversion along the Nubia-Iberia boundary in SW Iberia
The SW of Iberia is currently undergoing compression related to the convergence between Nubia and Iberia. Multiple compressive structures, and their related seismic activity, have been documented along the diffuse Nubia-Iberia plate boundary, including the Gorringe bank west of the Gulf of Cadiz, and the Betic-Rif orogen to the east. Despite seismic activity indicating a dominant compressive stress along the Algarve margin in the Gulf of Cadiz, the structures at the origin of this seismicity remain elusive. This paper documents the contractional structures that provide linkage across the Gulf of Cadiz and play a major role in defining the present-day seismicity and bathymetry of this area. The structures described in this paper caused the Neogene inversion of the Jurassic oblique passive margin that formed between the central Atlantic and the Ligurian Tethys. This example of a partially inverted margin provides insights into the factors that condition the inversion of passive margins
Nuclear Physics in the Era of Quantum Computing and Quantum Machine Learning
In this paper, the application of quantum simulations and quantum machine
learning to solve low-energy nuclear physics problems is explored. The use of
quantum computing to deal with nuclear physics problems is, in general, in its
infancy and, in particular, the use of quantum machine learning in the realm of
nuclear physics at low energy is almost nonexistent. We present here three
specific examples where the use of quantum computing and quantum machine
learning provides, or could provide in the future, a possible computational
advantage: i) the determination of the phase/shape in schematic nuclear models,
ii) the calculation of the ground state energy of a nuclear shell model-type
Hamiltonian and iii) the identification of particles or the determination of
trajectories in nuclear physics experiments.Comment: Submitted to the special issue "Quantum Machine Learning" of the
journal Advanced Quantum Technologie
Predicting the spread of epidemiological diseases by using a multi-objective algorithm
The epidemiological models are able to predict the spread of diseases, but a previous work on calibrating some involved parameters must be done. In this work, we propose a methodology to adjust those parameters based on solving a multi-objective optimization problem whose objective functions measure the accuracy of the model. More precisely, we have considered the Between-Countries Disease Spread model because it involves a set of countries taking into account the migratory movements among them. As a result, using some real data about the number of detected cases and the number of deaths for the Ebola virus
disease, we have shown that the methodology is able to find a set of values for the parameters so that the model fits the outbreak spread for a set of countries
Quantum quench influenced by an excited-state phase transition
We analyze excited-state quantum phase transitions (ESQPTs) in three
schematic (integrable and nonintegrable) models describing a single-mode
bosonic field coupled to a collection of atoms. It is shown that the presence
of the ESQPT in these models affects the quantum relaxation processes following
an abrupt quench in the control parameter. Clear cut evidence of the ESQPT
effects is presented in integrable models, while in the nonintegrable model the
evidence is blurred due to chaotic behavior of the system in the region around
the critical energy.Comment: submitted to Physical Review
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