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$$R\,{\sim }\,5{,}000-140{,}000$$), 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:

Hydrocarbons play a key role in shaping the chemistry of the interstellar medium, but their enrichment and relation with carbonaceous grains and polycyclic aromatic hydrocarbons still lack clear observational constraints. Here we report on JWST NIRSpec + MIRI/MRS infrared observations (~3–28 μm) of the local ultra-luminous infrared galaxy (ULIRG) IRAS 07251−0248, which revealed the extragalactic detection of small gas-phase hydrocarbons, such as benzene (C6H6), triacetylene (C6H2), diacetylene (C4H2), acetylene (C2H2), methane (CH4) and methyl radical (CH3), as well as deep amorphous C–H absorptions in the solid phase. The unexpectedly high abundance of these molecules indicates an extremely rich hydrocarbon chemistry not explained by high-temperature gas-phase chemistry, ice desorption or oxygen depletion. Instead, the most plausible explanation is the erosion and fragmentation of carbonaceous grains and polycyclic aromatic hydrocarbons. This scenario is supported by the correlation between the abundance of one of their main fragmentation products, C2H2, and the cosmic-ray ionization rate for a sample of local ULIRGs. These hydrocarbons are outflowing at ~160 km s⁻¹, which may represent a potential formation pathway for hydrogenated amorphous grains. Our results indicate that IRAS 07251−0248 might not be unique but represents an extreme example of the commonly rich hydrocarbon chemistry prevalent in deeply obscured galactic nuclei.

Abstract:

ABSTRACT Substellar companions such as exoplanets and brown dwarfs exhibit changes in brightness arising from top-of-atmosphere inhomogeneities, providing insights into their atmospheric structure and dynamics. This variability can be measured in the light curves of high-contrast companions from the ground by combining differential spectrophotometric monitoring techniques with high-contrast imaging. However, ground-based observations are sensitive to the effects of turbulence in Earth’s atmosphere, and while adaptive optics (AO) systems and bespoke data processing techniques help to mitigate these, residual systematics can limit photometric precision. Here, we inject artificial companions to data obtained with an AO system and a vector Apodizing Phase Plate coronagraph to test the level to which telluric and other systematics contaminate such light curves, and thus how well their known variability signals can be recovered. We find that varying companions are distinguishable from non-varying companions, but that variability amplitudes and periods cannot be accurately recovered when observations cover only a small number of periods. Residual systematics remain above the photon noise in the light curves but have not yet reached a noise floor. We also simulate observations to assess how specific systematic sources, such as non-common path aberrations and AO residuals, can impact aperture photometry as a companion moves through pupil-stabilized data. We show that only the lowest order aberrations are likely to affect flux measurements, but that thermal background noise is the dominant source of scatter in raw companion photometry. Predictive control and focal-plane wavefront sensing techniques will help to further reduce systematics in data of this type.

Abstract:

Constraining the mass-sheet degeneracy (MSD) is crucial for improving the precision and accuracy of time-delay cosmography. Joint analyses based on lensing and stellar kinematics have been widely adopted to break the MSD. A three-dimensional (3D) mass and stellar tracer population is required to accurately interpret the kinematics data. Our forward-modeling procedure is aimed at evaluating the projection effects using strong lensing and kinematics observables and to determine an optimal model assumption for the stellar kinematics analysis leading to an unbiased interpretation of the MSD and H_0. We numerically simulated the projection and selection effects for both a triaxial early-type galaxy (ETG) sample from the TNG100 simulation and an axisymmetric sample that matches the properties of slow-rotator galaxies representative of the strong lens galaxy population. Using the axisymmetric sample, we generated mock kinematics observables with spherically aligned axisymmetric Jeans anisotropic modeling (JAM) and assessed the kinematic recovery under different model assumptions. Using the triaxial sample, we quantified the random uncertainty introduced by modeling triaxial galaxies with axisymmetric JAM. We show that spherical JAM analysis of spatially unresolved kinematic data introduces a bias of up to 2%-4% (depending on the intrinsic shape of the lens) in the inferred MSD. Our model largely corrects this bias, resulting in a residual random uncertainty in the range of 0-2.2% in the stellar velocity dispersion (0-4.4% in H_0), depending on the projected ellipticity and the anisotropy of the stellar orbits. This residual uncertainty can be further mitigated by the use of spatially resolved kinematic data, which constrain the intrinsic axis ratio. We also show that the random uncertainty in the kinematics recovery using axisymmetric JAM for axisymmetric galaxies is at the level of 0.24% in the velocity dispersion, and the uncertainty using axisymmetric JAM for triaxial galaxies is at the level of 0.17% in the velocity dispersion.