TESS asteroseismology of the known red-giant host stars HD 212771 and HD 203949

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AIMS - A new tool for stellar parameter determinations using asteroseismic constraints

A key aspect in the determination of stellar properties is the comparison of observational constraints with predictions from stellar models. Asteroseismic Inference on a Massive Scale (AIMS) is an open source code that uses Bayesian statistics and a Markov Chain Monte Carlo approach to find a representative set of models that reproduce a given set of classical and asteroseismic constraints. These models are obtained by interpolation on a pre-calculated grid, thereby increasing computational efficiency. We test the accuracy of the different operational modes within AIMS for grids of stellar models computed with the Li\`ege stellar evolution code (main sequence and red giants) and compare the results to those from another asteroseismic analysis pipeline, PARAM. Moreover, using artificial inputs generated from models within the grid (assuming the models to be correct), we focus on the impact on the precision of the code when considering different combinations of observational constraints (individual mode frequencies, period spacings, parallaxes, photospheric constraints,...). Our tests show the absolute limitations of precision on parameter inferences using synthetic data with AIMS, and the consistency of the code with expected parameter uncertainty distributions. Interpolation testing highlights the significance of the underlying physics to the analysis performance of AIMS and provides caution as to the upper limits in parameter step size. All tests demonstrate the flexibility and capability of AIMS as an analysis tool and its potential to perform accurate ensemble analysis with current and future asteroseismic data yields.Comment: Accepted for publication in MNRAS. 17 pages, 17 figure

AIMS - A new tool for stellar parameter determinations using asteroseismic constraints

A key aspect in the determination of stellar properties is the comparison of observational constraints with predictions from stellar models. Asteroseismic Inference on a Massive Scale (AIMS) is an open source code that uses Bayesian statistics and a Markov Chain Monte Carlo approach to find a representative set of models that reproduce a given set of classical and asteroseismic constraints. These models are obtained by interpolation on a pre-calculated grid, thereby increasing computational efficiency. We test the accuracy of the different operational modes within AIMS for grids of stellar models computed with the Li\`ege stellar evolution code (main sequence and red giants) and compare the results to those from another asteroseismic analysis pipeline, PARAM. Moreover, using artificial inputs generated from models within the grid (assuming the models to be correct), we focus on the impact on the precision of the code when considering different combinations of observational constraints (individual mode frequencies, period spacings, parallaxes, photospheric constraints,...). Our tests show the absolute limitations of precision on parameter inferences using synthetic data with AIMS, and the consistency of the code with expected parameter uncertainty distributions. Interpolation testing highlights the significance of the underlying physics to the analysis performance of AIMS and provides caution as to the upper limits in parameter step size. All tests demonstrate the flexibility and capability of AIMS as an analysis tool and its potential to perform accurate ensemble analysis with current and future asteroseismic data yields.Comment: Accepted for publication in MNRAS. 17 pages, 17 figure

Effect of birth weight, exclusive breastfeeding and growth in infancy on fat mass and fat free mass indices in early adolescence: an analysis of the Entebbe Mother and Baby Study (EMaBs) cohort [version 1; peer review: 1 approved, 2 approved with reservations]

Background: There is limited data from Africa on the effect of pre- and post-natal growth and infant feeding on later body composition. This study's aim was to investigate the effect of birth weight, exclusive breastfeeding and infant growth on adolescent body composition, using data from a Ugandan birth cohort. / Methods: Data was collected prenatally from pregnant women and prospectively from their resulting live offspring. Data on body composition (fat mass index [FMI] and fat free mass index [FFMI]) was collected from 10- and 11-year olds. Linear regression was used to assess the effect of birth weight, exclusive breastfeeding and infant growth on FMI and FFMI, adjusting for confounders. / Results: 177 adolescents with a median age of 10.1 years were included in analysis, with mean FMI 2.9 kg/m2 (standard deviation (SD) 1.2), mean FFMI 12.8 kg/m2 (SD 1.4) and mean birth weight 3.2 kg (SD 0.5). 90 (50.9%) were male and 110 (63.2%) were exclusively breastfeeding at six weeks of age. Birth weight was associated with FMI in adolescence (regression coefficient β= 0.66 per kg increase in birth weight, 95% confidence interval (CI) (0.04, 1.29), P=0.02), while exclusive breastfeeding (β= -0.43, 95% CI (-1.06, 0.19), P=0.12), growth 0-6 months (β= 0.24 95% CI (-0.43, 0.92), P=0.48) and growth 6-12 months (β= 0.61, 95% CI (-0.23, 1.46), P=0.11) were not associated with FMI among adolescents. Birth weight (β= 0.91, 95% CI (0.17, 1.65), P=0.01) was associated with FFMI in adolescence. Exclusive breastfeeding (β= 0.17, 95% CI (-0.60, 0.94), P=0.62), growth 0-6 months (β= 0.56, 95% CI (-0.20, 1.33), P= 0.10), and growth 6-12 months (β= -0.02, 95% CI (-1.02, 0.99), P=0.97) were not associated with FFMI. / Conclusions: Birth weight predicted body composition parameters in Ugandan early adolescents, however, exclusive breastfeeding at six weeks of age and growth in infancy did not

Effect of birth weight, exclusive breastfeeding and growth in infancy on fat mass and fat free mass indices in early adolescence: an analysis of the Entebbe Mother and Baby Study (EMaBs) cohort [version 2; peer review: 1 approved, 2 approved with reservations]

Background: There is limited data from Africa on the effect of pre- and post-natal growth and infant feeding on later body composition. This study's aim was to investigate the effect of birth weight, exclusive breastfeeding and infant growth on adolescent body composition, using data from a Ugandan birth cohort. / Methods: Data was collected prenatally from pregnant women and prospectively from their resulting live offspring. Data on body composition (fat mass index [FMI] and fat free mass index [FFMI]) was collected from 10- and 11-year olds. Linear regression was used to assess the effect of birth weight, exclusive breastfeeding and infant growth on FMI and FFMI, adjusting for confounders. / Results: 177 adolescents with a median age of 10.1 years were included in analysis, with mean FMI 2.9 kg/m 2 (standard deviation (SD) 1.2), mean FFMI 12.8 kg/m 2 (SD 1.4) and mean birth weight 3.2 kg (SD 0.5). 90 (50.9%) were male and 110 (63.2%) were exclusively breastfeeding at six weeks of age. Birth weight was associated with FMI in adolescence (regression coefficient β= 0.66 per kg increase in birth weight, 95% confidence interval (CI) (0.04, 1.29), P=0.02), while exclusive breastfeeding (β= -0.43, 95% CI (-1.06, 0.19), P=0.12), growth 0-6 months (β= 0.24 95% CI (-0.43, 0.92), P=0.48) and growth 6-12 months (β= 0.61, 95% CI (-0.23, 1.46), P=0.11) were not associated with FMI among adolescents. Birth weight (β= 0.91, 95% CI (0.17, 1.65), P=0.01) was associated with FFMI in adolescence. Exclusive breastfeeding (β= 0.17, 95% CI (-0.60, 0.94), P=0.62), growth 0-6 months (β= 0.56, 95% CI (-0.20, 1.33), P= 0.10), and growth 6-12 months (β= -0.02, 95% CI (-1.02, 0.99), P=0.97) were not associated with FFMI. / Conclusions: Birth weight predicted body composition parameters in Ugandan early adolescents, however, exclusive breastfeeding at six weeks of age and growth in infancy did not

AIMS - A new tool for stellar parameter determinations using asteroseismic constraints

A key aspect in the determination of stellar properties is the comparison of observational constraints with predictions from stellar models. Asteroseismic Inference on a Massive Scale (AIMS) is an open source code that uses Bayesian statistics and a Markov Chain Monte Carlo approach to find a representative set of models that reproduce a given set of classical and asteroseismic constraints. These models are obtained by interpolation on a pre-calculated grid, thereby increasing computational efficiency. We test the accuracy of the different operational modes within AIMS for grids of stellar models computed with the Liège stellar evolution code (main sequence and red giants) and compare the results to those from another asteroseismic analysis pipeline, PARAM. Moreover, using artificial inputs generated from models within the grid (assuming the models to be correct), we focus on the impact on the precision of the code when considering different combinations of observational constraints (individual mode frequencies, period spacings, parallaxes, photospheric constraints,⋯). Our tests show the absolute limitations of precision on parameter inferences using synthetic data with AIMS, and the consistency of the code with expected parameter uncertainty distributions. Interpolation testing highlights the significance of the underlying physics to the analysis performance of AIMS and provides caution as to the upper limits in parameter step size. All tests demonstrate the flexibility and capability of AIMS as an analysis tool and its potential to perform accurate ensemble analysis with current and future asteroseismic data yields