Chemical abundances in Galactic planetary nebulae with Spitzer spectra

We present new low-resolution (R~800) optical spectra of 22 Galactic PNe with Spitzer spectra. These data are combined with recent optical spectroscopic data available in the literature to construct representative samples of compact (and presumably young) Galactic disc and bulge PNe with Spitzer spectra. Attending to the nature of the dust features seen in their Spitzer spectra, Galactic disc and bulge PNe are classified according to four major dust types (oxygen chemistry or OC, carbon chemistry or CC, double chemistry or DC, featureless or F) and subtypes (amorphous and crystalline, and aliphatic and aromatic). Nebular gas abundances of He, N, O, Ne, S, Cl, and Ar, as well as plasma parameters (e.g. Ne, Te) are homogeneously derived and we study the median chemical abundances and nebular properties in Galactic disc and bulge PNe depending on their Spitzer dust types and subtypes. A comparison of the derived median abundance patterns with AGB nucleosynthesis predictions show mainly that i) DC PNe, both with amorphous and crystalline silicates, display high-metallicity (solar/supra-solar) and the highest He abundances and N/O ratios, suggesting relatively massive (~3-5 M_sun) hot bottom burning AGB stars as progenitors; ii) PNe with O-rich and C-rich unevolved dust (amorphous and aliphatic) seem to evolve from subsolar metallicity (z~0.008) and lower mass (<3 M_sun) AGB stars; iii) a few O-rich PNe and a significant fraction of C-rich PNe with more evolved dust (crystalline and aromatic, respectively) display chemical abundances similar to DC PNe, suggesting that they are related objects. A comparison of the derived nebular properties with predictions from models combining the theoretical central star evolution with a simple nebular model is also presented. Finally, a possible link between the Spitzer dust properties, chemical abundances, and evolutionary status is discussed.Comment: Accepted for publication in Astronomy & Astrophysics (45 pages, 17 figures, and 14 tables); final version (language corrected

Analysis of the acoustic cut-off frequency and HIPs in six Kepler stars with stochastically excited pulsations

Gravito-acoustic modes in the Sun and other stars propagate in resonant cavities with a frequency below a given limit known as the cut-off frequency. At higher frequencies, waves are no longer trapped in the stellar interior and become traveller waves. In this article we study six pulsating solar-like stars at different evolutionary stages observed by the NASA Kepler mission. These high signal-to-noise targets show a peak structure that extends at very high frequencies and are good candidates for studying the transition region between the modes and the interference peaks or pseudo-modes. Following the same methodology successfully applied on Sun-as-a-star measurements, we uncover the existence of pseudo-modes in these stars with one or two dominant interference patterns depending on the evolutionary stage of the star. We also infer their cut-off frequency as the midpoint between the last eigenmode and the first peak of the interference patterns. By using ray theory we show that, while the period of one of the interference pattern is very close to half the large separation the other, one depends on the time phase of mixed waves, thus carrying additional information on the stellar structure and evolution.Comment: Accepted for publication in A&A. 14 pages, 28 figure

Preface "Nonlinear processes in oceanic and atmospheric flows"

Nonlinear phenomena are essential ingredients in many oceanic and atmospheric processes, and successful understanding of them benefits from multidisciplinary collaboration between oceanographers, meteorologists, physicists and mathematicians. The present Special Issue on ``Nonlinear Processes in Oceanic and Atmospheric Flows'' contains selected contributions from attendants to the workshop which, in the above spirit, was held in Castro Urdiales, Spain, in July 2008. Here we summarize the Special Issue contributions, which include papers on the characterization of ocean transport in the Lagrangian and in the Eulerian frameworks, generation and variability of jets and waves, interactions of fluid flow with plankton dynamics or heavy drops, scaling in meteorological fields, and statistical properties of El Ni\~no Southern Oscillation.Comment: This is the introductory article to a Special Issue on "Nonlinear Processes in Oceanic and Atmospheric Flows'', published in the journal Nonlinear Processes in Geophysics, where the different contributions are summarized. The Special Issue itself is freely available from http://www.nonlin-processes-geophys.net/special_issue103.htm

Multi-step Reinforcement Learning: A Unifying Algorithm

Unifying seemingly disparate algorithmic ideas to produce better performing algorithms has been a longstanding goal in reinforcement learning. As a primary example, TD($\lambda$) elegantly unifies one-step TD prediction with Monte Carlo methods through the use of eligibility traces and the trace-decay parameter $\lambda$. Currently, there are a multitude of algorithms that can be used to perform TD control, including Sarsa, $Q$-learning, and Expected Sarsa. These methods are often studied in the one-step case, but they can be extended across multiple time steps to achieve better performance. Each of these algorithms is seemingly distinct, and no one dominates the others for all problems. In this paper, we study a new multi-step action-value algorithm called $Q(\sigma)$ which unifies and generalizes these existing algorithms, while subsuming them as special cases. A new parameter, $\sigma$, is introduced to allow the degree of sampling performed by the algorithm at each step during its backup to be continuously varied, with Sarsa existing at one extreme (full sampling), and Expected Sarsa existing at the other (pure expectation). $Q(\sigma)$ is generally applicable to both on- and off-policy learning, but in this work we focus on experiments in the on-policy case. Our results show that an intermediate value of $\sigma$, which results in a mixture of the existing algorithms, performs better than either extreme. The mixture can also be varied dynamically which can result in even greater performance.Comment: Appeared at the Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18