X-Shooting ULLYSES: Massive stars at low metallicity: I. Project description

Observations of individual massive stars, super-luminous supernovae, gamma-ray bursts, and gravitational wave events involving spectacular black hole mergers indicate that the low-metallicity Universe is fundamentally different from our own Galaxy. Many transient phenomena will remain enigmatic until we achieve a firm understanding of the physics and evolution of massive stars at low metallicity (Z). The Hubble Space Telescope has devoted 500 orbits to observing ∼250 massive stars at low Z in the ultraviolet (UV) with the COS and STIS spectrographs under the ULLYSES programme. The complementary X-Shooting ULLYSES (XShootU) project provides an enhanced legacy value with high-quality optical and near-infrared spectra obtained with the wide-wavelength coverage X-shooter spectrograph at ESOa's Very Large Telescope. We present an overview of the XShootU project, showing that combining ULLYSES UV and XShootU optical spectra is critical for the uniform determination of stellar parameters such as effective temperature, surface gravity, luminosity, and abundances, as well as wind properties such as mass-loss rates as a function of Z. As uncertainties in stellar and wind parameters percolate into many adjacent areas of astrophysics, the data and modelling of the XShootU project is expected to be a game changer for our physical understanding of massive stars at low Z. To be able to confidently interpret James Webb Space Telescope spectra of the first stellar generations, the individual spectra of low-Z stars need to be understood, which is exactly where XShootU can deliver

Data-driven reduction for a class of multiscale fast-slow stochastic dynamical systems

© 2016 Society for Industrial and Applied Mathematics. Multi-time-scale stochastic dynamical systems are ubiquitous in science and engineering, and the reduction of such systems and their models to only their slow components is often essential for scientific computation and further analysis. Rather than being available in the form of an explicit analytical model, often such systems can only be observed as a data set which embodies dynamics on several time scales. We focus on applying and adapting data-mining and manifold learning techniques to detect the slow components in a class of such multiscale data. Traditional data-mining methods are based on metrics (and thus, geometries) which are not informed of the multiscale nature of the underlying system dynamics; such methods cannot successfully recover the slow variables. Here, we present an approach which utilizes both the local geometry and the local noise dynamics within the data set through a metric which is both insensitive to the fast variables and more general than simple statistical averaging. Our analysis of the approach provides conditions for successfully recovering the underlying slow variables, as well as an empirical protocol guiding the selection of the method parameters. Interestingly, the recovered underlying variables are gauge invariant - they are insensitive to the measuring instrument/observation function