Chasing the storm: Investigating the application of high-contrast imaging techniques in producing precise exoplanet light curves

TL;DR

This study quantifies how atmospheric and instrumental systematics affect ground-based differential spectrophotometry of high-contrast exoplanetary companions using a vAPP coronagraph. By injecting artificial companions into real LBTI/ALES+dgvAPP360 data and running HCIPy-based simulations, the authors dissect the contributions of AO residuals, non-common path aberrations, photon noise, and thermal background to light-curve precision. They demonstrate recoverability of injected variability for certain geometry but show significant limitations from short observational baselines and instrument-induced systematics, with typical precision in the few-percent range that depends on target brightness and cadence. The work highlights the potential of predictive control and focal-plane wavefront sensing to reduce systematics and guides future observing strategies and instrument development for precise exoplanet light curves from the ground.

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-stabilised 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.

Figures (12)

• Figure 1: The left-hand and centre panels are examples of the final processed LBT/ALES+dgvAPP360 images produced when the data were median-combined in both time and wavelength. Left: the case where no artificial companions were injected to the data, so only the bright host star HD 1160 A and its bona fide companion HD 1160 B is visible. Centre: similar to the left-hand panel, but three artificial companions have been injected at 90° intervals in position angle from HD 1160 B. All three artificial companions were injected with contrasts of $2.88$$\times$ 10$^{-3}$ (6.35 mag) relative to the host star. This image is a composite; for the purposes of the analysis, only one companion was injected at a time. Right: A single frame of data highlighting examples of the apertures (solid lines) and annuli (dashed lines) used to extract photometry and background measurements for the host star (in green) and artificial companions (in blue). The left-hand and centre panels use the same arbitrary logarithmic colour scale while the right-hand panel uses a different one, and all three panels are aligned to north, where north is up and east is to the left.
• Figure 2: The raw differential white-light curves for each of the injected artificial companions are shown in lighter colours in each panel, binned to 18 minutes of integration time per bin. The detrended differential white-light curves, after division by the multiple linear regression model to remove the modelled systematic trends, are then overplotted in darker colours. The left-hand panels show the light curves for the artificial companions injected without variability, whereas the right-hand panels are those injected with a simulated sinusoidal variability signal, which is overplotted in grey for comparison. The root mean square (RMS) shown are those of the detrended light curves.
• Figure 3: Simulated PSFs of a star and companion for a different Zernike mode are shown in each column at three different observing times. The data are in pupil-stabilised mode, so the aberrations remain static while the companion rotates over time. The symmetry in the Zernike mode and the location of the companion together determine the measured companion flux in an aperture.
• Figure 4: Simulated normalized flux of the star (black) and the companion (orange) for the first 100 Zernike modes, given by their Noll indices. The errors bars indicate the maximum and minimum retrieved companion flux over the observing sequence. The error bars for the star are too small to be visible. Top panel: all Zernike modes have the same 120 nm RMS wavefront error. Bottom panel: The power in the modes is scaled by a power law with a slope of -1.5, similar to expected NCPAs. In this more realistic scenario, we find that only the lowest-order aberrations are likely to significantly impact the average flux of the companion over the observing sequence.
• Figure 5: Simulated point-spread functions. The left-hand panel shows the PSF for the residual wavefront error after adaptive optics correction. The centre panel shows the same PSF simulated with photon noise, assuming a photon flux of 40.000 photons for a single image. This photon number is empirically matched to the counts in a single frame of the HD 1160 data. The right panel shows the same PSF as the left panel with photon noise and noise from the thermal background. This background noise is implemented as read noise and the amount is also empirically matched with the statistics in a single frame of the HD 1160 data.