Bayesian peak bagging analysis of 19 low-mass low-luminosity red giants observed with Kepler

The currently available Kepler light curves contain an outstanding amount of information but a detailed analysis of the individual oscillation modes in the observed power spectra, also known as peak bagging, is computationally demanding and challenging to perform on a large number of targets. Our intent is to perform for the first time a peak bagging analysis on a sample of 19 low-mass low-luminosity red giants observed by Kepler for more than four years. This allows us to provide high-quality asteroseismic measurements that can be exploited for an intensive testing of the physics used in stellar structure models, stellar evolution and pulsation codes, as well as for refining existing asteroseismic scaling relations in the red giant branch regime. For this purpose, powerful and sophisticated analysis tools are needed. We exploit the Bayesian code Diamonds, using an efficient nested sampling Monte Carlo algorithm, to perform both a fast fitting of the individual oscillation modes and a peak detection test based on the Bayesian evidence. We find good agreement for the parameters estimated in the background fitting phase with those given in the literature. We extract and characterize a total of 1618 oscillation modes, providing the largest set of detailed asteroseismic mode measurements ever published. We report on the evidence of a change in regime observed in the relation between linewidths and effective temperatures of the stars occurring at the bottom of the RGB. We show the presence of a linewidth depression or plateau around $\nu_\mathrm{max}$ for all the red giants of the sample. Lastly, we show a good agreement between our measurements of maximum mode amplitudes and existing maximum amplitudes from global analyses provided in the literature, useful as empirical tools to improve and simplify the future peak bagging analysis on a larger sample of evolved stars.Comment: 78 pages, 46 figures, 22 tables. Accepted for publication in A&

High-precision acoustic helium signatures in 18 low-mass low-luminosity red giants. Analysis from more than four years of Kepler observations

High-precision frequencies of acoustic modes in red giant stars are now available thanks to the long observing length and high-quality of the light curves provided by the NASA Kepler mission, thus allowing to probe the interior of evolved cool low-mass stars with unprecedented level of detail. We characterize the acoustic signature of the helium second ionization zone in a sample of 18 low-mass low-luminosity red giants by exploiting new mode frequency measurements derived from more than four years of Kepler observations. We analyze the second frequency differences of radial acoustic modes in all the stars of the sample by using the Bayesian code Diamonds. We find clear acoustic glitches due to the signature of helium second ionization in all the stars of the sample. We measure the acoustic depth and the characteristic width of the acoustic glitches with a precision level on average around $\sim$2% and $\sim$8%, respectively. We find good agreement with theoretical predictions and existing measurements from the literature. Lastly, we derive the amplitude of the glitch signal at $\nu_\mathrm{max}$ for the second differences and for the frequencies with an average precision of $\sim$6%, obtaining values in the range 0.14-0.24 $\mu$Hz, and 0.08-0.33 $\mu$Hz, respectively, which can be used to investigate the helium abundance in the stars.Comment: 12 pages, 19 figures, 3 tables. Accepted for publication in A&

Profit efficiency among Kenyan smallholders milk producers: A case study of Meru-South district, Kenya

Production inefficiency is usually analyzed by economical efficiency, which is composed of two components-technical and allocative efficiencies. This study provided a direct measure of production efficiency of the smallholder milk producers in Kenya using a stochastic profit frontier and inefficiency model. The primary data were collected, using IMPACT (intergrated modeling platform for mixed animal crops systems) structured questionnaire and includes four conventional inputs and socio-economic factors affecting production. The result showed that profit efficiencies of the sampled farmers varied widely between 26% and 73% with a mean of 60% suggesting that an estimated 40% of the profit is lost due to a combination of both technical and allocative inefficiencies in the smallholder dairy milk production. This study further observed that level of education, experience, and the size of the farm influenced profit efficiency positively while profit efficiency decreased with age. This implies that profit inefficiency among smallholder dairy milk producers can be reduced significantly with improvement in the level of education of sampled farmer