Early spectral evolution of classical novae: consistent evidence for multiple distinct outflows

The physical mechanism driving mass ejection during a nova eruption is still poorly understood. Possibilities include ejection in a single ballistic event, a common envelope interaction, a continuous wind, or some combination of these processes. Here we present a study of 12 Galactic novae, for which we have pre-maximum high-resolution spectroscopy. All 12 novae show the same spectral evolution. Before optical peak, they show a slow P Cygni component. After peak a fast component quickly arises, while the slow absorption remains superimposed on top of it, implying the presence of at least two physically distinct flows. For novae with high-cadence monitoring, a third, intermediate-velocity component is also observed. These observations are consistent with a scenario where the slow component is associated with the initial ejection of the accreted material and the fast component with a radiation-driven wind from the white dwarf. When these flows interact, the slow flow is swept up by the fast flow, producing the intermediate component. These colliding flows may produce the gamma-ray emission observed in some novae. Our spectra also show that the transient heavy element absorption lines seen in some novae have the same velocity structure and evolution as the other lines in the spectrum, implying an association with the nova ejecta rather than a pre-existing circumbinary reservoir of gas or material ablated from the secondary. While this basic scenario appears to qualitatively reproduce multi-wavelength observations of classical novae, substantial theoretical and observational work is still needed to untangle the rich diversity of nova properties.Comment: 39 pages, 35 figures, submitted to Ap

Variations on bayesian optimization applied to numerical flow simulations

Bayesian Optimization (BO) has recently regained interest in optimization problems involving expensive black-box objective functions. Several variants have been proposed in the literature, such as including gradient and/or multi-fidelity information, and it has been extended to multi-objective optimization problems. Despite its recent applications to numerical flow simulations, the efficiency of this method and its variants remains to be characterized in typical applications involving canonical flows. In this work, the efficiency of classical BO and alternative derivative-free methods is compared on a simplified flow case, i.e. drag reduction in the two-dimensional flow around a cylinder. The application of BO to complex flows is then showcased by considering a three-dimensional case at Reynolds number Re = 3900. Next, the performance of BO with gradient and/or multi-fidelity information is investigated for global modelling and optimization on typical benchmark objective functions and on the cylinder case at Re = 200. Finally, an algorithm combining dimension reduction and Multi-objective Bayesian Optimization (MOBO) is proposed. It is found that BO was more efficient than other derivative-free alternatives and showed promising results on the three-dimensional cylinder at Re = 3900 by reducing drag by 23 %. The performance of the algorithm was further improved when multi-fidelity and/or gradient information was included, both for modelling and optimization. Including gradient information on the low-fidelity model was useful for global modelling and to decrease rapidly the objective function in a BO framework. On the contrary, adding derivative information on the high-fidelity model generally gave the most accurate approximation of the minimum but was inefficient for global modelling when the computational cost of the gradient was high. Finally, the developed algorithm combining dimension reduction and MOBO enabled us to obtain more precise and diverse minima.136 página

Sobre dinamica caotica e convergencia em algoritmos de equalização autodidata

Orientador : João Marcos Travassos RomanoDissertação (mestrado) - Universidade de Campinas, Faculdade de Engenharia Eletrica e de ComputaçãoMestrad

Geometric singular perturbation analysis of mixed-mode dynamics in pituitary cells

Pseudo-plateau bursting is a type of oscillatory waveform associated with mixed mode dynamics in slow/fast systems and commonly found in neural bursting models. In a recent model for the electrical activity in a pituitary lactotroph, two types of pseudo-plateau bursts were discovered: one in which the calcium drives the bursts and another in which the calcium simply follows them. Multiple methods from dynamical systems theory have been used to understand the bursting. The classic 2-timescale approach treats the calcium concentration as a slowly varying parameter and considers a parametrized family of fast subsystems. A more novel and successful 2-timescale approach divides the system so that there is only one fast variable and shows that the bursting arises from canard dynamics. Both methods can be effective analytic tools but there has been little justification for one approach over the other. In the first part of this thesis, we demonstrate that the two analysis techniques are different unfoldings of a 3-timescale system. We show that elementary applications of geometric singular perturbation theory and bifurcation theory in the 2-timescale and 3- timescale methods provides us with substantial predictive power. We use that predictive power to explain the transient and long-term dynamics of the pituitary lactotroph model. The canard phenomenon occurs generically in singular perturbation problems with at least two slow variables. Canards are closely associated with folded singularities and in the case of folded nodes, lead to a local twisting of invariant manifolds. Folded node canards and folded saddle canards (and their bifurcations) have been studied extensively in 3 dimensions. The folded saddle-node (FSN) is the codimension-1 bifurcation that gives rise to folded nodes and folded saddles. There are two types of FSN. In the FSN type I, the center manifold of the FSN is tangent to the curve of fold bifurcations of the fast subsystem. In the FSN II, the center manifold of the FSN is transverse to the curve of fold bifurcations of the fast subsystem. Both types of FSN bifurcation are ubiquitous in applications and are typically the organizing centers for delay phenomena. In particular, the FSN I and FSN II demarcate the bursting regions in parameter space. Their dynamics however, are not completely understood. Recent studies have unravelled the local dynamics of the FSN II. In the second part of this thesis, we extend canard theory into the FSN I regime by combining methods from geometric singular perturbation theory (blow-up), and the theory of dynamic bifurcations (analytic continuation into the plane of complex time). We prove the existence of canards and faux canards near the FSN I, and study the associated delayed loss of stability

Luminous Stars in Nearby Galaxies

Studies on the populations of luminous stars in nearby resolved galaxies have revealed a complex distribution in the luminosity–temperature plane (the HR diagram). The fundamentals of massive star evolution are mostly understood, but the roles of mass loss, episodic mass loss, rotation, and binarity are still in question. Moreover, the final stages of these stars of different masses and their possible relation to each other are not understood. The purpose of this volume is to provide a current review of the different populations of evolved massive stars. The emphasis is on massive stars in the Local Group, the Magellanic Clouds, and the nearby spirals M31 and M33

Hydrogeological engineering approaches to investigate and characterize heterogeneous aquifers

This dissertation presents a compilation of five stand-alone manuscripts (Chapters 2 through 5 and Appendix A). Chapters 2 through 5 present hydrogeological analysis approaches, while Appendix A is utilized within the dissertation introduction as an example of a non-physically based modeling approach, albeit demonstrated on a non-hydrogeologically based application. Chapter 2 presents an inverse approach to decompose pumping influences from water-level fluctuations observed at a monitoring location. Chapter 3 presents an inferencing approach to identify effective aquifer properties at the interwell scale that can be applied to highly transient datasets. Chapter 4 introduces the use of a Markov-chain model of spatial correlation to an automated geostatistical inverse framework, demonstrating the approach on a 2-D two-stratigraphic-unit synthetic aquifer. Chapter 5 utilizes the inverse framework introduced in Chapter 4 to develop a stochastic analysis approach to identify the most plausible geostatistical model given the available data. The dissertation introduction reconciles these hydrogeological engineering approaches within the context of the current hydrogeological perspective, discussing where these approaches within the often conflicting goals of providing operational decision support based on modeling and advancing the science of hydrogeology beyond its current limitations

Intelligence artificielle et optimisation avec parallélisme

This document is devoted to artificial intelligence and optimization. This part will bedevoted to having fun with high level ideas and to introduce the subject. Thereafter,Part II will be devoted to Monte-Carlo Tree Search, a recent great tool for sequentialdecision making; we will only briefly discuss other tools for sequential decision making;the complexity of sequential decision making will be reviewed. Then, part IIIwill discuss optimization, with a particular focus on robust optimization and especiallyevolutionary optimization. Part IV will present some machine learning tools, useful ineveryday life, such as supervised learning and active learning. A conclusion (part V)will come back to fun and to high level ideas.On parlera ici de Monte-Carlo Tree Search, d'UCT, d'algorithmes évolutionnaires et d'autres trucs et astuces d'IA;l'accent sera mis sur la parallélisation