1,721,085 research outputs found
The θ-dependence of the SU(N) critical temperature at large N
No full textWe investigate, by means of numerical lattice simulations, the θ-dependence of the critical deconfinement temperature of SU(N) gauge theories at large N : Tc(θ) = Tc(0)[1 – Rθ2 + O(θ4)], with R ~ O(1/N2). We follow two different strategies to determine R, one based on the calculation of the latent heat of the transition and on the jump of the topological susceptibility at the θ = 0 critical point, the other relying on a direct probe of Tc(θ) by means of imaginary-θ Monte Carlo simulations. Our results show that R follows the expected large-N scaling
θ dependence in the small- N limit of 2d CPN-1 models
No full textWe present a systematic numerical study of θ dependence around θ=0 in the small-N limit of 2d CPN-1 models, aimed at clarifying the possible presence of a divergent topological susceptibility in the continuum limit. We follow a twofold strategy, based on one side on direct simulations for N=2 and N=3 on lattices with correlation lengths up to O(102) and, on the other side, on the small-N extrapolation of results obtained for N up to 9. Based on that, we provide conclusive evidence for a finite topological susceptibility at N=3, with a continuum estimate ζ2χ=0.110(5). On the other hand, results obtained for N=2 are still inconclusive: They are consistent with a logarithmically divergent continuum extrapolation but do not yet exclude a finite continuum value, ζ2χ∼0.4, with the divergence taking place for N slightly below 2 in this case. Finally, results obtained for the nonquadratic part of θ dependence, in particular, for the so-called b2 coefficient, are consistent with a θ dependence, matching that of the dilute instanton gas approximation at the point where ζ2χ diverges
Large-N expansion and θ-dependence of 2D CPN-1 models beyond the leading order
No full textWe investigate the θ-dependence of two-dimensional CPN-1 models in the large-N limit by lattice simulations. Thanks to a recent algorithm proposed by M. Hasenbusch to improve the critical slowing down of topological modes, combined with simulations at imaginary values of θ, we manage to determine the vacuum energy density up the sixth order in θ and up to N=51. Our results support analytic predictions, which are known up to the next-to-leading term in 1/N for the quadratic term in θ (topological susceptibility), and up to the leading term for the quartic coefficient b2. Moreover, we give a numerical estimate of further terms in the 1/N expansion for both quantities, pointing out that the 1/N convergence for the θ-dependence of this class of models is particularly slow
Physiological noise: a comprehensive review on informative randomness in neural systems
No full textNoise is often regarded as mere interference in the analysis of biomedical signals. Nonetheless, stochasticity plays a critical and informative role in the dynamics of complex systems, particularly in neurocardiovascular and neural systems. This review provides a comprehensive exploration on informative randomness in physiological contexts, tracing the evolution of noise research from its foundations on Brownian motion to its applications in neural systems, including the neuroautonomic regulation of cardiovascular dynamics. Key distinctions are made between output (measurement) noise and dynamic (intrinsic) noise, which directly influence the system behaviors at various levels. Several physiological noise identification techniques, such as stochastic differential equations, Bayesian methods, and Kalman filters, are evaluated in real-world scenarios. Special emphasis is placed on the role of physiological noise in multiscale neural systems, such as brain dynamics, neuronal communication, and heart-brain interactions, highlighting how it shapes complex functions. Furthermore, physiological noise is presented as a potential clinical biomarker, offering insights into the underlying structure and health of neural systems. Future research is encouraged to investigate multivariate noise estimation methods and their implications for understanding causality and systemic interactions in neurocardiovascular networks
- …
