Extremely Large Telescope

Abstract:

We present spatially resolved spectroscopic observations of the planetary nebula NGC 6720, the Ring Nebula, taken during the science verification phase of WEAVE, a new instrument mounted on the William Herschel Telescope on La Palma. We use the instrument’s Large Integral Field Unit (LIFU) to obtain spectra of the Ring Nebula, covering its entire optically bright inner regions as well as parts of its much fainter outer molecular halo. We report the discovery of emission from [Fe v] and [Fe vi] confined to a narrow ‘bar’ extending across the central regions of the nebula. No lines of other elements share this morphology or, at the spectral resolving power used (), the same radial velocity. The extent to which iron in this bar is depleted is presently unclear; comparison with JWST-detected dust continuum emission suggests that some dust grain destruction may be occurring in the region, but there is currently no observational evidence for the 50 km s shock waves or K X-ray emitting gas needed to enable this. Where the bar is located along the line of sight through the nebula, and how it was created, are new puzzles to be solved for this iconic planetary nebula.

Abstract:

The spectrum of an exoplanet reveals the physical, chemical, and biological processes that have shaped its history and govern its future. However, observations of exoplanet spectra are complicated by the overwhelming glare of their host stars. Here, we focus on high-resolution spectroscopy (HRS) (R∼5,000−140,000), which helps disentangle and isolate the exoplanet’s spectrum. HRS resolves molecular features into a dense forest of individual lines in a pattern that is unique for a given molecule. For close-in planets, the spectral lines undergo large Doppler shifts during the planet’s orbit, while the host star and Earth’s spectral features remain essentially stationary, enabling a velocity separation of the planet. For slower-moving, wide-orbit planets, HRS, aided by high contrast imaging, instead isolates their spectra using their spatial separation (high contrast spectroscopy; HCS). The planet’s spectral lines are compared with HRS model atmospheric spectra, typically using cross-correlation to sum their signals. It is essentially a form of fingerprinting for exoplanet atmospheres and works for both transiting and non-transiting planets. It measures their orbital velocity, true mass, and simultaneously characterizes their atmosphere. The unique sensitivity of HRS to the depth, shape, and position of the planet’s spectral lines allows it to measure atmospheric composition, structure, clouds, and dynamics, including day-to-night winds and equatorial jets, plus its rotation period and even its magnetic field. These are extracted using statistically robust log-likelihood frameworks and match space-based instruments in their precision. This chapter describes the HRS technique in detail and concludes with future prospects with Extremely Large Telescopes to identify biosignatures on nearby rocky worlds and map features in the atmospheres of giant exoplanets.

Abstract:

The advent of integral field data has revolutionised the study of galaxy evolution. A key component of this is dynamical modelling methods which have allowed for crucial insights to be made from kinematic data. Despite this importance, most dynamical models make a number of key assumptions which do not hold for real galaxies. These include assumptions about the geometry (axisymmetry or triaxiality), the shape of the velocity ellipsoid, and the shape of the underlying stellar distribution. At the same time, machine learning methods are becoming increasingly powerful, with many applications appearing in astronomy. As a first step towards building new dynamical modelling methods with machine learning, it is important to understand the types of machine learning architectures that are best fit for dynamical modelling. To investigate this, we construct a training set of dynamical models of early-type galaxies using Jeans Anisotropic Modelling (JAM). We then train a neural network on this data using the parameters of JAM and mock photometry as the input. We are able to accurately model JAM galaxies with relatively simple machine learning architectures, leading to a significant speed increase over traditional JAM modelling.