No Arabic abstract
Convection plays a key role in the evolution of stars due to energy transport and mixing of composition. Despite its importance, this process is still not well understood. One longstanding conundrum in all 1D stellar evolution codes is the treatment of convective boundaries. In this study we compare two convective uncertainties, the boundary location (Ledoux versus Schwarzschild) and the amount of extra mixing, and their impact on the early evolution of massive stars. With increasing convective boundary mixing (CBM), we find a convergence of the two different boundary locations, a decreasing blue to red super giant ratio and a reduced importance of semiconvection.
In this work, we investigate the impact of uncertainties due to convective boundary mixing (CBM), commonly called `overshoot, namely the boundary location and the amount of mixing at the convective boundary, on stellar structure and evolution. For this we calculated two grids of stellar evolution models with the MESA code, each with the Ledoux and the Schwarzschild boundary criterion, and vary the amount of CBM. We calculate each grid with the initial masses $15$, $20$ and $25,rm{M}_odot$. We present the stellar structure of the models during the hydrogen and helium burning phases. In the latter, we examine the impact on the nucleosynthesis. We find a broadening of the main-sequence with more CBM, which is more in agreement with observations. Furthermore during the core hydrogen burning phase there is a convergence of the convective boundary location due to CBM. The uncertainties of the intermediate convective zone remove this convergence. The behaviour of this convective zone strongly affects the surface evolution of the model, i.e. how fast it evolves red-wards. The amount of CBM impacts the size of the convective cores and the nucleosynthesis, e.g. the $^{12}$C to $^{16}$O ratio and the weak s-process. Lastly, we determine the uncertainty that the range of parameter values investigated introduce and we find differences of up to $70%$ for the core masses and the total mass of the star.
As part of a larger program aimed at better quantifying the uncertainties in stellar computations, we attempt to calibrate the extent of convective overshooting in low to intermediate mass stars by means of eclipsing binary systems. We model 12 such systems, with component masses between 1.3 and 6.2 solar masses, using the detailed binary stellar evolution code STARS, producing grids of models in both metallicity and overshooting parameter. From these, we determine the best fit parameters for each of our systems. For three systems, none of our models produce a satisfactory fit. For the remaining systems, no single value for the convective overshooting parameter fits all the systems, but most of our systems can be well described with an overshooting parameter between 0.09 and 0.15, corresponding to an extension of the mixed region above the core of about 0.1-0.3 pressure scale heights. Of the nine systems where we are able to obtain a good fit, seven can be reasonably well fit with a single parameter of 0.15. We find no evidence for a trend of the extent of overshooting with either mass or metallicity, though the data set is of limited size. We repeat our calculations with a second evolution code, MESA, and we find general agreement between the two codes. For the extension of the mixed region above the convective core required by the MESA models is about 0.15-0.4 pressure scale heights. For the system EI Cep, we find that MESA gives an overshooting region that is larger than the STARS one by about 0.1 pressure scale heights for the primary, while for the secondary the difference is only 0.05 pressure scale heights.
Interpretability is an important area of research for safe deployment of machine learning systems. One particular type of interpretability method attributes model decisions to input features. Despite active development, quantitative evaluation of feature attribution methods remains difficult due to the lack of ground truth: we do not know which input features are in fact important to a model. In this work, we propose a framework for Benchmarking Attribution Methods (BAM) with a priori knowledge of relative feature importance. BAM includes 1) a carefully crafted dataset and models trained with known relative feature importance and 2) three complementary metrics to quantitatively evaluate attribution methods by comparing feature attributions between pairs of models and pairs of inputs. Our evaluation on several widely-used attribution methods suggests that certain methods are more likely to produce false positive explanations---features that are incorrectly attributed as more important to model prediction. We open source our dataset, models, and metrics.
Using a new s-nucleosynthesis code, coupled with the stellar evolution code Star2003, we performed simulations to study the impact of the convection treatment on the s-process during core He-burning of a 25 Msun star (ZAMS mass) with an initial metallicity of Z=0.02. Particular attention was devoted to the impact of the extent of overshooting on the s-process efficiency. The results show enhancements of about a factor 2-3 in s-process efficiency (measured as the average overproduction factor of the 6 s-only nuclear species with $60lesssim Alesssim 90$) with overshooting parameter values in the range 0.01-0.035, compared to results obtained with the same model but without overshooting. The impact of these results on the p-process model based on type II supernovae is discussed.
The last decade has seen a rapid development in asteroseismology thanks to the CoRoT and Kepler missions. With more detailed asteroseismic observations available, it is becoming possible to infer exactly how oscillations are driven and dissipated in solar-type stars. We have carried out three-dimensional (3D) stellar atmosphere simulations together with one-dimensional (1D) stellar structural models of key benchmark turn-off and subgiant stars to study this problem from a theoretical perspective. Mode excitation and damping rates are extracted from 3D and 1D stellar models based on analytical expressions. Mode velocity amplitudes are determined by the balance between stochastic excitation and linear damping, which then allows the estimation of the frequency of maximum oscillation power, $ u_{max}$, for the first time based on ab initio and parameter-free modelling. We have made detailed comparisons between our numerical results and observational data and achieved very encouraging agreement for all of our target stars. This opens the exciting prospect of using such realistic 3D hydrodynamical stellar models to predict solar-like oscillations across the HR-diagram, thereby enabling accurate estimates of stellar properties such as mass, radius and age.