12,637 research outputs found
On recent SFR calibrations and the constant SFR approximation
Star Formation Rate (SFR) inferences are based in the so-called constant SFR
approximation, where synthesis models are require to provide a calibration; we
aims to study the key points of such approximation to produce accurate SFR
inferences. We use the intrinsic algebra used in synthesis models, and we
explore how SFR can be inferred from the integrated light without any
assumption about the underling Star Formation history (SFH). We show that the
constant SFR approximation is actually a simplified expression of more deeper
characteristics of synthesis models: It is a characterization of the evolution
of single stellar populations (SSPs), acting the SSPs as sensitivity curve over
different measures of the SFH can be obtained. As results, we find that (1) the
best age to calibrate SFR indices is the age of the observed system (i.e. about
13Gyr for z=0 systems); (2) constant SFR and steady-state luminosities are not
requirements to calibrate the SFR; (3) it is not possible to define a SFR
single time scale over which the recent SFH is averaged, and we suggest to use
typical SFR indices (ionizing flux, UV fluxes) together with no typical ones
(optical/IR fluxes) to correct the SFR from the contribution of the old
component of the SFH, we show how to use galaxy colors to quote age ranges
where the recent component of the SFH is stronger/softer than the older
component.
Particular values of SFR calibrations are (almost) not affect by this work,
but the meaning of what is obtained by SFR inferences does. In our framework,
results as the correlation of SFR time scales with galaxy colors, or the
sensitivity of different SFR indices to sort and long scale variations in the
SFH, fit naturally. In addition, the present framework provides a theoretical
guide-line to optimize the available information from data/numerical
experiments to improve the accuracy of SFR inferences.Comment: A&A accepted, 13 pages, 4 Figure
Non-Kramers Freezing and Unfreezing of Tunneling in the Biaxial Spin Model
The ground state tunnel splitting for the biaxial spin model in the magnetic
field, H = -D S_{x}^2 + E S_{z}^2 - g \mu_B S_z H_z, has been investigated
using an instanton approach. We find a new type of spin instanton and a new
quantum interference phenomenon associated with it: at a certain field, H_2 =
2SE^{1/2}(D+E)^{1/2}/(g \mu_B), the dependence of the tunneling splitting on
the field switches from oscillations to a monotonic growth. The predictions of
the theory can be tested in Fe_8 molecular nanomagnets.Comment: 7 pages, minor changes, published in EP
Entropy production for coarse-grained dynamics
Systems out of equilibrium exhibit a net production of entropy. We study the
dynamics of a stochastic system represented by a Master Equation that can be
modeled by a Fokker-Planck equation in a coarse-grained, mesoscopic
description. We show that the corresponding coarse-grained entropy production
contains information on microscopic currents that are not captured by the
Fokker-Planck equation and thus cannot be deduced from it. We study a
discrete-state and a continuous-state system, deriving in both the cases an
analytical expression for the coarse-graining corrections to the entropy
production. This result elucidates the limits in which there is no loss of
information in passing from a Master Equation to a Fokker-Planck equation
describing the same system. Our results are amenable of experimental
verification, which could help to infer some information about the underlying
microscopic processes
Adapting to Unknown Disturbance Autocorrelation in Regression with Long Memory
We show that it is possible to adapt to nonparametric disturbance auto-correlation in time series regression in the presence of long memory in both regressors and disturbances by using a smoothed nonparametric spectrum estimate in frequency-domain generalized least squares. When the collective memory in regressors and disturbances is sufficiently strong, ordinary least squares is not only asymptotically inefficient but asymptotically non-normal and has a slow rate of convergence, whereas generalized least squares is asymptotically normal and Gauss-Markov efficient with standard convergence rate. Despite the anomalous behaviour of nonparametric spectrum estimates near a spectral pole, we are able to justify a standard construction of frequency-domain generalized least squares, earlier considered in case of short memory disturbances. A small Monte Carlo study of finite sample performance is included.Time series regression, long memory, adaptive estimation.
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