Prospects for SNIa Explosion Mechanism Identification Through Supernova Remnants

We present the first results from an ongoing work aimed to use supernovae remnants to discriminate among different type Ia supernovae explosion models. We have computed the hydrodynamic interaction of supernova ejecta with the interstellar medium, obtaining the evolution of the density, temperature and ionization structure of the remnant. We have used ejecta profiles obtained from 1D hydrodynamic calculations of the different explosion mechanisms that are currently under debate. We have analyzed the best indicators that allow to discriminate among the different explosion mechanisms, taking into account the diversity of scenarios proposed for the presupernova evolution of the binary system, and the uncertain amount of electron heating in collisionless shocks.Comment: 4 pages, 3 figures. Proceedings of the ESO/MPA/MPE Workshop 'From Twilight to Highlight', the Physics of Supernovae. Garching July 29 - 31, 200

Bounded Error Identification of Systems With Time-Varying Parameters

This note presents a new approach to guaranteed system identification for time-varying parameterized discrete-time systems. A bounded description of noise in the measurement is considered. The main result is an algorithm to compute a set that contains the parameters consistent with the measured output and the given bound of the noise. This set is represented by a zonotope, that is, an affine map of a unitary hypercube. A recursive procedure minimizes the size of the zonotope with each noise corrupted measurement. The zonotopes take into account the time-varying nature of the parameters in a nonconservative way. An example has been provided to clarify the algorithm

16O(p,α)13N^{16}O(p,\alpha)^{13}N makes explosive oxygen burning sensitive to the metallicity of the progenitors of type Ia supernovae

Even though the main nucleosynthetic products of type Ia supernovae belong to the iron-group, intermediate-mass alpha-nuclei (silicon, sulfur, argon, and calcium) stand out in their spectra up to several weeks past maximum brightness. Recent measurements of the abundances of calcium, argon, and sulfur in type Ia supernova remnants have been interpreted in terms of metallicity-dependent oxygen burning, in accordance with previous theoretical predictions. It is known that $\alpha$-rich oxygen burning results from $^{16}$O$\rightarrow^{12}$C followed by efficient $^{12}$C+$^{12}$C fusion reaction, as compared to oxygen consumption by $^{16}$O fusion reactions, but the precise mechanism of dependence on the progenitor metallicity has remained unidentified so far. I show that the chain $^{16}$O(p,$\alpha$)$^{13}$N($\gamma$,p)$^{12}$C boosts $\alpha$-rich oxygen burning when the proton abundance is large, increasing the synthesis of argon and calcium with respect to sulfur and silicon. For high-metallicity progenitors, the presence of free neutrons leads to a drop in the proton abundance and the above chain is not efficient. Although the rate of $^{16}$O(p,$\alpha$)$^{13}$N can be found in astrophysical reaction rate libraries, its uncertainty is unconstrained. Assuming that all reaction rates other than $^{16}$O(p,$\alpha$)$^{13}$N retain their standard values, an increase by a factor of approximately seven of the $^{16}$O(p,$\alpha$)$^{13}$N rate at temperatures in the order $3-5\times10^9$ K is enough to explain the whole range of calcium-to-sulfur mass ratios measured in Milky Way and LMC supernova remnants. These same measurements provide a lower limit to the $^{16}$O(p,$\alpha$)$^{13}$N rate in the mentioned temperature range, on the order of a factor of 0.5 with respect to the rate reported in widely used literature tabulations.Comment: 5 pages, 6 figures, accepted for Astronomy & Astrophysic

Thermal X-ray emission from shocked ejecta in Type Ia Supernova Remnants. Prospects for explosion mechanism identification

The explosion mechanism behind Type Ia supernovae is a matter of continuing debate. The diverse attempts to identify or at least constrain the physical processes involved in the explosion have been only partially successful so far. In this paper we propose to use the thermal X-ray emission from young supernova remnants originated in Type Ia events to extract relevant information concerning the explosions themselves. We have produced a grid of thermonuclear supernova models representative of the paradigms currently under debate: pure deflagrations, delayed detonations, pulsating delayed detonations and sub-Chandrasekhar explosions, using their density and chemical composition profiles to simulate the interaction with the surrounding ambient medium and the ensuing plasma heating, non-equilibrium ionization and thermal X-ray emission of the ejecta. Key observational parameters such as electron temperatures, emission measures and ionization time scales are presented and discussed. We find that not only is it possible to identify the explosion mechanism from the spectra of young Type Ia Supernova Remnants, it is in fact necessary to take the detailed ejecta structure into account if such spectra are to be modeled in a self-consistent way. Neither element line flux ratios nor element emission measures are good estimates of the true ratios of ejected masses, with differences of as much as two or three orders of magnitude for a given model. Comparison with observations of the Tycho SNR suggests a delayed detonation as the most probable explosion mechanism. Line strengths, line ratios, and the centroid of the Fe Kalpha line are reasonably well reproduced by a model of this kind.Comment: 11 pages, 8 figures (5 of them color), accepted for publication by the Ap

Interval model predictive control

6TH INTERNATIONAL WORKSHOP ON ALGORITHMS AND ARCHITECTURES FOR REAL TIME CONTROL (6) (6.2000.PALMA DE MALLORCA. ESPAÑA)Model Predictive Control is one of the most popular control strategy in the process industry. One of the reason for this success can be attributed to the fact that constraints and uncertainties can be handled. There are many techniques based on interval mathematics that are used in a wide range of applications. These interval techniques can mean an important contribution to Model Predictive Control giving algorithms to achieve global optimization and constraint satisfaction