Extremely Large Telescope

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:

Dynamical tracers provide key measurements of galaxies' total masses, complementing other methods of determining stellar and gas masses and also providing some of the only possible constraints on their dark matter content. Until recently, the deep spectrally-resolved observations necessary for these measurements were only accessible out to relatively low redshifts. Thanks to new observatories and instruments, particularly near infrared multi-object spectrographs as well as sub-mm interferometers, galaxy dynamical masses can now be measured out to the peak epoch of cosmic star formation and even earlier epochs. Here I give an overview of dynamical mass constraints of galaxy-scale dark matter fractions from the present day out to z ∼ 3. I also discuss comparisons between measurements from different dynamical tracers, and the modeling challenges and degeneracies that complicate our interpretations of observations.

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.

Abstract:

Time-delay cosmography leverages strongly lensed quasars to measure the Universe's current expansion rate, _ independently from other methods. The latest TDCOSMO milestone measurement primarily used quadruply lensed quasars for their mass profile constraints. However, doubly lensed quasars, being more abundant and offering precise time delays, could expand the sample by a factor of 5, significantly advancing towards a 1% precision measurement of We present the first TDCOSMO analysis of a doubly imaged source, ̋Eonze, including the measurement of the four necessary ingredients. First, by combining 17 years of data from the SMARTS, Euler, and WFI telescopes, we measured a time delay of 176.3 +11.4 -10.3 days. Second, using MUSE data, we extracted stellar velocity dispersion measurements in three radial bins with 5% to 13% precision. Third, employing F160W HST imaging for lens modelling and marginalising over various modelling choices, we measured the Fermat potential difference between the images. Fourth, using wide-field imaging, we measured the convergence added by objects not included in the lens modelling. By combining these four ingredients, we measured the time delay distance and the angular diameter distance to the deflector, favouring a power-law mass model over a baryonic and dark matter composite model. The measurement was performed blindly to prevent experimenter bias and resulted in a Hubble constant of hc = 64.2^ +5.8 _ -5.0 times łint ̨msmpc, where łint is the internal mass sheet degeneracy parameter. This is in agreement with the TDCOSMO-2025 milestone and its precision for łint=1 is comparable to that obtained with the best-observed quadruply lensed quasars (4-6%). This work is a stepping stone towards a precise measurement of using a large sample of doubly lensed quasars, supplementing the current sample. The next TDCOSMO milestone paper will include this system in its hierarchical analysis, constraining łint and jointly with multiple lenses.