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1527 Publications visible to you, out of a total of 1527
Towards a self-consistent model of the convective core boundary in upper main sequence stars

Abstract (Expand)

There is strong observational evidence that the convective cores of intermediate-mass and massive main sequence stars are substantially larger than those predicted by standard stellar-evolution models. … However, it is unclear what physical processes cause this phenomenon or how to predict the extent and stratification of stellar convective boundary layers. Convective penetration is a thermal-timescale process that is likely to be particularly relevant during the slow evolution on the main sequence. We use our low-Mach-number SEVEN-LEAGUE HYDRO code to study this process in 2.5D and 3D geometries. Starting with a chemically homogeneous model of a 15  M⊙ zero-age main sequence star, we construct a series of simulations with the luminosity increased and opacity decreased by the same factor, ranging from 10^3 to 10^6. After reaching thermal equilibrium, all of our models show a clear penetration layer; its thickness becomes statistically constant in time and it is shown to converge upon grid refinement. The penetration layer becomes nearly adiabatic with a steep transition to a radiative stratification in simulations at the lower end of our luminosity range. This structure corresponds to the adiabatic ‘step overshoot’ model often employed in stellar-evolution calculations. The simulations with the highest and lowest luminosity differ by less than a factor of two in the penetration distance. The high computational cost of 3D simulations makes our current 3D data set rather sparse. Depending on how we extrapolate the 3D data to the actual luminosity of the initial stellar model, we obtain penetration distances ranging from 0.09 to 0.44 pressure scale heights, which is broadly compatible with observations.

Authors: R. Andrassy, G. Leidi, J. Higl, P. V. F. Edelmann, F. R. N. Schneider, F. K. Röpke

Date Published: 1st Mar 2024

Publication Type: Journal

Deep learning for postprocessing global probabilistic forecasts on subseasonal time scales

Abstract

Not specified

Authors: Nina Horat, Sebastian Lerch

Date Published: 1st Mar 2024

Publication Type: Journal

Non-crossing quantile regression neural network as a calibration tool for ensemble weather forecasts

Abstract

Not specified

Authors: Mengmeng Song, Dazhi Yang, Sebastian Lerch, Xiang’ao Xia, Gokhan Mert Yagli, Jamie M. Bright, Yanbo Shen, Bai Liu, Xingli Liu, Martin János Mayer

Date Published: 1st Mar 2024

Publication Type: Journal

Effect of nuclear reactions rates and core boundary mixing on the seismology of red clump stars

Abstract (Expand)

Context: Modeling of the stars in the red clump (RC), that is, core helium-burning stars that have gone through a He flash, is challenging because of the uncertainties associated with the physical … processes in their core and during the helium flash. By probing the internal stellar structure, asteroseismology allows us to constrain the core properties of RC stars and eventually, to improve our understanding of this evolutionary phase. Aims: We aim to quantify the impact on the seismic properties of the RC stars of the two main core modeling uncertainties: core boundary mixing, and helium-burning nuclear reaction rates. Methods: Using the MESA stellar evolution code, we computed models with different core boundary mixing as well as different 3α and 12C(α, γ)16O nuclear reaction rates. We investigated the impact of these parameters on the period spacing ΔΠ, which is a probe of the region around the core. Results: We find that different core boundary mixing schemes yield significantly different period spacings, with differences of 30 s between the maximum ΔΠ value computed with semiconvection and maximal overshoot. We show that an increased rate of 12C(α, γ)16O lengthens the core helium-burning phase, which extends the range of ΔΠ covered by the models during their evolution. This results in a difference of 10 s between the models computed with a nominal rate and a rate multiplied by 2, which exceeds the observational uncertainties. The effect of changing the 3α reaction rate is comparatively small. Conclusions: The core boundary mixing is the main source of uncertainty in the seismic modeling of RC stars. Moreover, the effect of the 12C(α, γ)16O is non-negligible, even though it is difficult to distinguish from the effect of the mixing. This degeneracy could be seen more frequently in the future in the new seismic data from the PLATO mission and through theoretical constraints from numerical simulations.

Authors: Anthony Noll, Sarbani Basu, Saskia Hekker

Date Published: 1st Mar 2024

Publication Type: Journal

Substituting density functional theory in reaction barrier calculations for hydrogen atom transfer in proteins

Abstract (Expand)

Hydrogen atom transfer (HAT) reactions are important in many biological systems. As these reactions are hard to observe experimentally, it is of high interest to shed light on them using simulations. … Here, we present a machine learning model based on graph neural networks for the prediction of energy barriers of HAT reactions in proteins. As input, the model uses exclusively non-optimized structures as obtained from classical simulations. It was trained on more than 17 000 energy barriers calculated using hybrid density functional theory. We built and evaluated the model in the context of HAT in collagen, but we show that the same workflow can easily be applied to HAT reactions in other biological or synthetic polymers. We obtain for relevant reactions (small reaction distances) a model with good predictive power (R2 ∼ 0.9 and mean absolute error of <3 kcal mol−1). As the inference speed is high, this model enables evaluations of dozens of chemical situations within seconds. When combined with molecular dynamics in a kinetic Monte-Carlo scheme, the model paves the way toward reactive simulations.

Authors: Kai Riedmiller, Patrick Reiser, Elizaveta Bobkova, Kiril Maltsev, Ganna Gryn’ova, Pascal Friederich, Frauke Gräter

Date Published: 14th Feb 2024

Publication Type: Journal

Easy uncertainty quantification (EasyUQ): Generating predictive distributions from single-valued model output

Abstract

Not specified

Authors: Eva-Maria Walz, Alexander Henzi, Johanna Ziegel, Tilmann Gneiting

Date Published: 8th Feb 2024

Publication Type: Journal

Testing quantile forecast optimality

Abstract

Not specified

Authors: Jack Fosten, Daniel Gutknecht, Marc-Oliver Pohle

Date Published: 8th Feb 2024

Publication Type: Journal

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