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Register a new userMeasurement of low signal-to-noise-ratio solar p modes in spatially-resolved helioseismic data
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We present an adaptation of the rotation-corrected, m-averaged spectrum technique designed to observe low signal-to-noise-ratio, low-frequency solar p modes. The frequency shift of each of the 2l+1 m spectra of a given (n,l) multiplet is chosen that maximizes the likelihood of the m-averaged spectrum. A high signal-to-noise ratio can result from combining individual low signal-to-noise-ratio, individual-m spectra, none of which would yield a strong enough peak to measure. We apply the technique to GONG and MDI data and show that it allows us to measure modes with lower frequencies than those obtained with classic peak-fitting analysis of the individual-m spectra. We measure their central frequencies, splittings, asymmetries, lifetimes, and amplitudes. The low-frequency, low- and intermediate-angular degrees rendered accessible by this new method correspond to modes that are sensitive to the deep solar interior down to the core and to the radiative interior. Moreover, the low-frequency modes have deeper upper turning points, and are thus less sensitive to the turbulence and magnetic fields of the outer layers, as well as uncertainties in the nature of the external boundary condition. As a result of their longer lifetimes (narrower linewidths) at the same signal-to-noise ratio the determination of the frequencies of lower-frequency modes is more accurate, and the resulting
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Helioseismic holography is an imaging technique used to study heterogeneities and flows in the solar interior from observations of solar oscillations at the surface. Holograms contain noise due to the stochastic nature of solar oscillations. We provide a theoretical framework for modeling signal and noise in Porter-Bojarski helioseismic holography. The wave equation may be recast into a Helmholtz-like equation, so as to connect with the acoustics literature and define the holography Greens function in a meaningful way. Sources of wave excitation are assumed to be stationary, horizontally homogeneous, and spatially uncorrelated. Using the first Born approximation we calculate holograms in the presence of perturbations in sound-speed, density, flows, and source covariance, as well as the noise level as a function of position. This work is a direct extension of the methods used in time-distance helioseismology to model signal and noise. To illustrate the theory, we compute the hologram intensity numerically for a buried sound-speed perturbation at different depths in the solar interior. The reference Greens function is obtained for a spherically-symmetric solar model using a finite-element solver in the frequency domain. Below the pupil area on the surface, we find that the spatial resolution of the hologram intensity is very close to half the local wavelength. For a sound-speed perturbation of size comparable to the local spatial resolution, the signal-to-noise ratio is approximately constant with depth. Averaging the hologram intensity over a number $N$ of frequencies above 3 mHz increases the signal-to-noise ratio by a factor nearly equal to the square root of $N$. This may not be the case at lower frequencies, where large variations in the holographic signal are due to the individual contributions of the long-lived modes of oscillation.
The PICsIT detector onboard the INTEGRAL satellite was designed to provide information about emission in the soft gamma-ray band for many bright sources. Due to strong and variable instrumental background, only 4 objects have been detected so far using standard software. The moderate sensitivity of PICsIT can be compensated for in the case of many objects by adopting a long exposure time, thanks to INTEGRALs large field of view. With angular resolution far higher than that of all other instruments operating in a similar energy band, PICsIT is suitable for fields too crowded or too significantly affected by Galactic diffuse emission. Therefore, it is desirable to improve the spectral extraction software to both obtain more reliable results and enlarge the number of objects that can be studied. The new PICsIT spectral extraction method is based on three elements: careful modelling of the background, an energy-dependent pixel-illumination function, and the computation of the probability density of the source count rate. The most important element is the proper treatment of the Poisson-distributed data, developed within a Bayesian framework. The new method was tested extensively on both a large true data set and simulated data. Results assumed in simulations were reproduced perfectly, without any bias and with high precision. Count rates measured for Crab were far more stable than those obtained with the standard software. For weaker sources, the new method produced spectra of far higher quality and allows us to detect at least 8 additional objects. Comparison with other INTEGRAL instruments demonstrated that PICsIT is well calibrated and provides valuable information about the continuum emission in the 250 keV -- 1 MeV band.
We examine the constraints imposed by helioseismic data on the solar heavy element abundances. In prior work we argued that the measured depth of the surface convection zone R_CZ and the surface helium abundance Y_surf were good metallicity indicators which placed separable constraints on light metals (CNONe) and the heavier species with good relative meteoritic abundances. The resulting interiors-based abundance scale was higher than some published studies based on 3D model atmospheres at a highly significant level. In this paper we explore the usage of the solar sound speed in the radiative interior as an additional diagnostic, and find that it is sensitive to changes in the Ne/O ratio even for models constructed to have the same R_CZ and Y_surf. Three distinct helioseismic tests (opacity in the radiative core, ionization in the convection zone, and the core mean molecular weight) yield consistent results. Our preferred O, Ne and Fe abundances are 8.86 +/-0.04, 8.15 +/-0.17 and 7.50 +/-0.05 respectively. They are consistent with the midrange of recently published 3D atmospheric abundances measurements. The values for O, Ne and Fe which combine interiors and atmospheric inferences are 8.83 +/-0.04, 8.08 +/-0.09 and 7.49 +/-0.04 respectively.
Here we report a theoretical model based on Greens functions and averaging techniques that gives ana- lytical estimates to the signal to noise ratio (SNR) near the first parametric instability zone in parametrically- driven oscillators in the presence of added ac drive and added thermal noise. The signal term is given by the response of the parametrically-driven oscillator to the added ac drive, while the noise term has two dif- ferent measures: one is dc and the other is ac. The dc measure of noise is given by a time-average of the statistically-averaged fluctuations of the position of the parametric oscillator due to thermal noise. The ac measure of noise is given by the amplitude of the statistically-averaged fluctuations at the frequency of the parametric pump. We observe a strong dependence of the SNR on the phase between the external drive and the parametric pump, for some range of the phase there is a high SNR, while for other values of phase the SNR remains flat or decreases with increasing pump amplitude. Very good agreement between analytical estimates and numerical results is achieved.
We present a new and up-to-date analysis of the solar low-degree $p$-mode parameter shifts from the Birmingham Solar-Oscillations Network (BiSON) over the past 22 years, up to the end of 2014. We aim to demonstrate that they are not dominated by changes in the asymmetry of the resonant peak profiles of the modes and that the previously published results on the solar-cycle variations of mode parameters are reliable. We compare the results obtained using a conventional maximum likelihood estimation algorithm and a new one based on the Markov Chain Monte Carlo (MCMC) technique, both taking into account mode asymmetry. We assess the reliability of the solar-cycle trends seen in the data by applying the same analysis to artificially generated spectra. We find that the two methods are in good agreement. Both methods accurately reproduce the input frequency shifts in the artificial data and underestimate the amplitude and width changes by a small amount, around 10 per cent. We confirm earlier findings that the frequency and line width are positively correlated, and the mode amplitude anticorrelated, with the level of solar activity, with the energy supplied to the modes remaining essentially unchanged. For the mode asymmetry the correlation with activity is marginal, but the MCMC algorithm gives more robust results than the MLE. The magnitude of the parameter shifts is consistent with earlier work. There is no evidence that the frequency changes we see arise from changes in the asymmetry, which would need to be much larger than those observed in order to give the observed frequency shift.
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