On the Brightest Horizontal Branch Population II Star γ Piscium

The bright star γ Psc is among the nearest Population II giants located in the red clump region. Here we demonstrate that γ Psc is actually a core-helium burning horizontal branch star. As such, the τ ≃ 12 Gyr old γ Psc is found to be slightly over-massive at M HB = 0.97 ± 0.12 M ⊙, which suggests that it is possibly a rejuvenated source.

Precision measurements on oxygen formation in stellar helium burning with gamma-ray beams and a Time Projection Chamber

The carbon/oxygen (C/O) ratio at the end of stellar helium burning is the single most important nuclear input to stellar evolution theory. However, it is not known with sufficient accuracy, due to large uncertainties in the cross-section for the fusion of helium with 12C to form 16O, denoted as 12C(α, γ)16O. Here we present results based on a method that is significantly different from the experimental efforts of the past four decades. With data measured inside one detector and with vanishingly small background, angular distributions of the 12C(α, γ)16O reaction were obtained by measuring the inverse 16O(γ, α)12C reaction with gamma-beams and a Time Projection Chamber (TPC) detector. We agree with current world data for the total reaction cross-section and further evidence the strength of our method with accurate angular distributions measured over the 1− resonance at Ecm ~ 2.4 MeV. Our technique promises to yield results that will surpass the quality of the currently available data.

Conformable Fractional Models of the Stellar Helium Burning via Artificial Neural Networks

The helium burning phase represents the second stage that the star used to consume nuclear fuel in its interior. In this stage, the three elements, carbon, oxygen, and neon, are synthesized. The present paper is twofold: firstly, it develops an analytical solution to the system of the conformable fractional differential equations of the helium burning network, where we used, for this purpose, the series expansion method and obtained recurrence relations for the product abundances, that is, helium, carbon, oxygen, and neon. Using four different initial abundances, we calculated 44 gas models covering the range of the fractional parameter α = 0.5 − 1 with step Δ α = 0.05 . We found that the effects of the fractional parameter on the product abundances are small which coincides with the results obtained by a previous study. Secondly, we introduced the mathematical model of the neural network (NN) and developed a neural network algorithm to simulate the helium burning network using a feed-forward process. A comparison between the NN and the analytical models revealed very good agreement for all gas models. We found that NN could be considered as a powerful tool to solve and model nuclear burning networks and could be applied to the other nuclear stellar burning networks.

Precision measurements on oxygen formation in stellar helium burning with gamma-ray beams and a Time Projection Chamber

Stellar Evolution theory relies on our knowledge of nuclear reactions, with the carbon/oxygen (C/O) ratio, at the end of helium burning, being the single most important input. However, the C/O ratio is still not known with sufficient accuracy, due to large uncertainties in the cross section for the fusion of helium with 12C to form 16O, denoted as the 12C(α,γ)16O reaction. We present initial results at moderately low energies using a novel method, which is significantly different from the experimental efforts of the past four decades. Precise angular distributions of the 12C(α,γ)16O reaction were obtained by measuring the inverse 16O(γ,α)12C reaction with gamma-beams and a Time Projection Chamber detector. These allowed us to measure, for the first time, the interference angle of the l = 1 and 2 partial waves contributing to this reaction (φ12), which agrees with predictions based on the unitarity of the scattering matrix.

Testing the intrinsic scatter of the asteroseismic scaling relations with Kepler red giants

Asteroseismic scaling relations are often used to derive stellar masses and radii, particulaly for stellar, exoplanet, and Galactic studies. It is therefore important that their precisions are known. Here we measure the intrinsic scatter of the underlying seismic scaling relations for Δν and νmax, using two sharp features that are formed in the H–R diagram (or related diagrams) by the red giant populations. These features are the edge near the zero-age core-helium-burning phase, and the strong clustering of stars at the so-called red giant branch bump. The broadening of those features is determined by factors including the intrinsic scatter of the scaling relations themselves, and therefore it is capable of imposing constraints on them. We modelled Kepler stars with a Galaxia synthetic population, upon which we applied the intrinsic scatter of the scaling relations to match the degree of sharpness seen in the observation. We found that the random errors from measuring Δν and νmax provide the dominating scatter that blurs the features. As a consequence, we conclude that the scaling relations have intrinsic scatter of $\sim 0.5\%$ (Δν), $\sim 1.1\%$ (νmax), $\sim 1.7\%$ (M) and $\sim 0.4\%$ (R), for the SYD pipeline measured Δν and νmax. This confirms that the scaling relations are very powerful tools. In addition, we show that standard evolution models fail to predict some of the structures in the observed population of both the HeB and RGB stars. Further stellar model improvements are needed to reproduce the exact distributions.

Conformable Fractional Models of the Stellar Helium Burning via Artificial Neural Networks

The helium burning phase represents the second stage that the star used to consume nuclear fuel in its interior. In this stage, the three elements carbon, oxygen, and neon are synthesized. The present paper has two folds, the first is to develop an analytical solution to the system of the conformable fractional differential equations of the helium burning network, where we used for this purpose the series expansion method and obtained recurrence relations for the product abundances i.e. helium, carbon, oxygen, and neon. Using four different initial abundances, we calculated 44 gas models covering the range of the fractional parameter with step . We found that the effects of the fractional parameter on the product abundances are small which coincides with the results obtained by a previous study. Second, we introduced the mathematical model of the neural network (NN) and developed a neural network algorithm to simulate the helium burning network using its feed-forward model that is trained by the back propagation (BP) gradient descent delta rule algorithm. A comparison between the NN and the analytical models revealed very good agreement for all gas models. We found that NN could be considered as a powerful tool to solve and model nuclear burning networks and could be applied to the other nuclear stellar burning networks.

Fast and Automated Peak Bagging with DIAMONDS (FAMED)

Stars of low and intermediate mass that exhibit oscillations may show tens of detectable oscillation modes each. Oscillation modes are a powerful tool to constrain the internal structure and rotational dynamics of the star, hence allowing one to obtain an accurate stellar age. The tens of thousands of solar-like oscillators that have been discovered thus far are representative of the large diversity of fundamental stellar properties and evolutionary stages available. Because of the wide range of oscillation features that can be recognized in such stars, it is particularly challenging to properly characterize the oscillation modes in detail, especially in light of large stellar samples. Overcoming this issue requires an automated approach, which has to be fast, reliable, and flexible at the same time. In addition, this approach should not only be capable of extracting the oscillation mode properties of frequency, linewidth, and amplitude from stars in different evolutionary stages, but also able to assign a correct mode identification for each of the modes extracted. Here we present the new freely available pipeline FAMED (Fast and AutoMated pEak bagging with DIAMONDS), which is capable of performing an automated and detailed asteroseismic analysis in stars ranging from the main sequence up to the core-helium-burning phase of stellar evolution. This, therefore, includes subgiant stars, stars evolving along the red giant branch (RGB), and stars likely evolving toward the early asymptotic giant branch. In this paper, we additionally show how FAMED can detect rotation from dipolar oscillation modes in main sequence, subgiant, low-luminosity RGB, and core-helium-burning stars.