Nonlinear wavefront reconstruction with convolutional neural networks for Fourier-based wavefront sensors

Fourier-based wavefront sensors, such as the Pyramid Wavefront Sensor (PWFS), are the current preference for high contrast imaging due to their high sensitivity. However, these wavefront sensors have intrinsic nonlinearities that constrain the range where conventional linear reconstruction methods can be used to accurately estimate the incoming wavefront aberrations. We propose to use Convolutional Neural Networks (CNNs) for the nonlinear reconstruction of the wavefront sensor measurements. It is demonstrated that a CNN can be used to accurately reconstruct the nonlinearities in both simulations and a lab implementation. We show that solely using a CNN for the reconstruction leads to suboptimal closed loop performance under simulated atmospheric turbulence. However, it is demonstrated that using a CNN to estimate the nonlinear error term on top of a linear model results in an improved effective dynamic range of a simulated adaptive optics system. The larger effective dynamic range results in a higher Strehl ratio under conditions where the nonlinear error is relevant. This will allow the current and future generation of large astronomical telescopes to work in a wider range of atmospheric conditions and therefore reduce costly downtime of such facilities.Comment: 14 pages, 7 figure

Tempestas ex machina: A review of machine learning methods for wavefront control

As we look to the next generation of adaptive optics systems, now is the time to develop and explore the technologies that will allow us to image rocky Earth-like planets; wavefront control algorithms are not only a crucial component of these systems, but can benefit our adaptive optics systems without requiring increased detector speed and sensitivity or more effective and efficient deformable mirrors. To date, most observatories run the workhorse of their wavefront control as a classic integral controller, which estimates a correction from wavefront sensor residuals, and attempts to apply that correction as fast as possible in closed-loop. An integrator of this nature fails to address temporal lag errors that evolve over scales faster than the correction time, as well as vibrations or dynamic errors within the system that are not encapsulated in the wavefront sensor residuals; these errors impact high contrast imaging systems with complex coronagraphs. With the rise in popularity of machine learning, many are investigating applying modern machine learning methods to wavefront control. Furthermore, many linear implementations of machine learning methods (under varying aliases) have been in development for wavefront control for the last 30-odd years. With this work we define machine learning in its simplest terms, explore the most common machine learning methods applied in the context of this problem, and present a review of the literature concerning novel machine learning approaches to wavefront control.Comment: SPIE Proceeding: 2023 / 12680-1

Trade-offs in high-contrast integral field spectroscopy for exoplanet detection and characterisation: Young gas giants in emission

Context: Combining high-contrast imaging with medium- or high-resolution integral field spectroscopy has the potential to boost the detection rate of exoplanets, especially at small angular separations. Furthermore, it immediately provides a spectrum of the planet that can be used to characterise its atmosphere. The achievable spectral resolution, wavelength coverage, and FOV of such an instrument are limited by the number of available detector pixels. Methods: The trade-offs are studied through end-to-end simulations of a typical high-contrast imaging instrument, analytical considerations, and atmospheric retrievals. The results are then validated with archival VLT/SINFONI data of the planet beta Pictoris b. Results: We show that molecular absorption spectra generally have decreasing power towards higher spectral resolution and that molecule mapping is already powerful for moderate resolutions (R>300). When choosing between wavelength coverage and spectral resolution for a given number of spectral bins, it is best to first increase the spectral resolution until R~2,000 and then maximise the bandwidth within an observing band. We find that T-type companions are most easily detected in the J/H band through methane and water features, while L-type companions are best observed in the H/K band through water and CO features. Such an instrument does not need to have a large FOV, as most of the gain in contrast is obtained in the speckle-limited regime close to the star. We show that the same conclusions are valid for the constraints on atmospheric parameters such as the C/O ratio, metallicity, surface gravity, and temperature, while higher spectral resolution (R~10,000) is required to constrain the radial velocity and spin of the planet.Comment: Accepted for publication in A&

β\beta Pictoris b through the eyes of the upgraded CRIRES+

Context: High-resolution spectrographs fed by adaptive optics (AO) provide a unique opportunity to characterize directly imaged exoplanets. Observations with such instruments allow us to probe the atmospheric composition, spin rotation, and radial velocity of the planet, thereby helping to reveal information on its formation and migration history. The recent upgrade of the Cryogenic High-Resolution Infrared Echelle Spectrograph (CRIRES+) at the VLT makes it a highly suitable instrument for characterizing directly imaged exoplanets. Aims: In this work, we report on observations of $\beta$ Pictoris b with CRIRES+ and use them to constrain the planets atmospheric properties and update the estimation of its spin rotation. Methods: The data were reduced using the open-source \textit{pycrires} package. We subsequently forward-modeled the stellar, planetary, and systematic contribution to the data to detect molecules in the planet's atmosphere. We also used atmospheric retrievals to provide new constraints on its atmosphere. Results: We confidently detected water and carbon monoxide in the atmosphere of $\beta$ Pictoris b and retrieved a slightly sub-solar carbon-to-oxygen ratio, which is in agreement with previous results. The interpretation is hampered by our limited knowledge of the C/O ratio of the host star. We also obtained a much improved constraint on its spin rotation of $19.9 \pm 1.0$ km/s, which gives a rotation period of $8.7 \pm 0.8$ hours, assuming no obliquity. We find that there is a degeneracy between the metallicity and clouds, but this has minimal impact on the retrieved C/O, $v\sin{i}$, and radial velocity. Our results show that CRIRES+ is performing well and stands as a highly useful instrument for characterizing directly imaged planets.Comment: Accepted for publication in A&

Retrieval survey of metals in six ultra-hot Jupiters: Trends in chemistry, rain-out, ionisation and atmospheric dynamics

Ground-based high-resolution spectroscopy (HRS) has detected numerous chemical species and atmospheric dynamics in exoplanets, most notably ultra-hot Jupiters (UHJs). However, quantitative estimates on abundances have been challenging but are essential for accurate comparative characterisation and to determine formation scenarios. In this work we retrieve the atmospheres of six UHJs (WASP-76~b, MASCARA-4~b, MASCARA-2~b, WASP-121~b, HAT-P-70~b and WASP-189~b) with ESPRESSO and HARPS-N/HARPS observations, exploring trends in eleven neutral species and dynamics. While Fe abundances agree well with stellar values, Mg, Ni, Cr, Mn and V show more variation, highlighting the difficulty in using a single species as a proxy for metallicity. We find that Ca, Na, Ti and TiO are under-abundant, potentially due to ionisation and/or night-side rain-out. Our retrievals also show that relative abundances between species are more robust, consistent with previous works. We perform spatially- and phase-resolved retrievals for WASP-76~b and WASP-121~b given their high signal-to-noise observations, and find the chemical abundances in each of the terminator regions are broadly consistent. We additionally constrain dynamics for our sample through Doppler shifts and broadening of the planetary signals during the primary eclipse, with median blue shifts between $\sim$0.9-9.0~km/s due to day-night winds. Furthermore, we constrain spectroscopic masses for MASCARA-2~b and HAT-P-70~b consistent with their known upper limits, but we note that these may be biased due to degeneracies. This work highlights the importance of future HRS studies to further probe differences and trends between exoplanets.Comment: 26 pages, 11 figures, 5 tables, published in A

Integrated photonic-based coronagraphic systems for future space telescopes

The detection and characterization of Earth-like exoplanets around Sun-like stars is a primary science motivation for the Habitable Worlds Observatory. However, the current best technology is not yet advanced enough to reach the 10^-10 contrasts at close angular separations and at the same time remain insensitive to low-order aberrations, as would be required to achieve high-contrast imaging of exo-Earths. Photonic technologies could fill this gap, potentially doubling exo-Earth yield. We review current work on photonic coronagraphs and investigate the potential of hybridized designs which combine both classical coronagraph designs and photonic technologies into a single optical system. We present two possible systems. First, a hybrid solution which splits the field of view spatially such that the photonics handle light within the inner working angle and a conventional coronagraph that suppresses starlight outside it. Second, a hybrid solution where the conventional coronagraph and photonics operate in series, complementing each other and thereby loosening requirements on each subsystem. As photonic technologies continue to advance, a hybrid or fully photonic coronagraph holds great potential for future exoplanet imaging from space.Comment: Conference Proceedings of SPIE: Techniques and Instrumentation for Detection of Exoplanets XI, vol. 12680 (2023

Visible extreme adaptive optics on extremely large telescopes: Towards detecting oxygen in Proxima Centauri b and analogs

Looking to the future of exo-Earth imaging from the ground, core technology developments are required in visible extreme adaptive optics (ExAO) to enable the observation of atmospheric features such as oxygen on rocky planets in visible light. UNDERGROUND (Ultra-fast AO techNology Determination for Exoplanet imageRs from the GROUND), a collaboration built in Feb. 2023 at the Optimal Exoplanet Imagers Lorentz Workshop, aims to (1) motivate oxygen detection in Proxima Centauri b and analogs as an informative science case for high-contrast imaging and direct spectroscopy, (2) overview the state of the field with respect to visible exoplanet imagers, and (3) set the instrumental requirements to achieve this goal and identify what key technologies require further development.Comment: SPIE Proceeding: 2023 / 12680-6

Mechanisms and in vivo functions of contact inhibition of locomotion

Contact inhibition of locomotion (CIL) is a process whereby a cell ceases motility or changes its trajectory upon collision with another cell. CIL was initially characterized more than half a century ago and became a widely studied model system to understand how cells migrate and dynamically interact. Although CIL fell from interest for several decades, the scientific community has recently rediscovered this process. We are now beginning to understand the precise steps of this complex behaviour and to elucidate its regulatory components, including receptors, polarity proteins and cytoskeletal elements. Furthermore, this process is no longer just in vitro phenomenology; we now know from several different in vivo models that CIL is essential for embryogenesis and in governing behaviours such as cell dispersion, boundary formation and collective cell migration. In addition, changes in CIL responses have been associated with other physiological processes, such as cancer cell dissemination during metastasis

Self-optimizing adaptive optics control with Reinforcement Learning

Current and future high-contrast imaging instruments require extreme Adaptive Optics (XAO) systems to reach contrasts necessary to directly image exoplanets. Telescope vibrations and the temporal error induced by the latency of the control loop limit the performance of these systems. Optimization of the (predictive) control algorithm is crucial in reducing these effects. We describe how model-free Reinforcement Learning can be used to optimize a Recurrent Neural Network controller for closed-loop adaptive optics control. We verify our proposed approach for tip-tilt control in simulations and a lab setup. The results show that this algorithm can effectively learn to suppress a combination of tip-tilt vibrations. Furthermore, we report decreased residuals for power-law input turbulence compared to an optimal gain integrator. Finally, we demonstrate that the controller can learn to identify the parameters of a varying vibration without requiring online updating of the control law. We conclude that Reinforcement Learning is a promising approach towards data-driven predictive control; future research will apply this approach to the control of high-order deformable mirrorsComment: 15 pages, 6 figures, submitted to SPIE Astronomical Telescopes + Instrumentation (2020

Into the red: An M-band study of the chemistry and rotation of β Pictoris b at high spectral resolution

High-resolution cross-correlation spectroscopy (HRCCS) combined with adaptive optics has been enormously successful in advancing our knowledge of exoplanet atmospheres, from chemistry to rotation and atmospheric dynamics. This powerful technique now drives major science cases for ELT instrumentation including METIS/ELT, GMTNIRS/GMT and MICHI/TMT, targeting biosignatures on rocky planets at 3–5 μm, but remains untested beyond 3.5 μm where the sky thermal background begins to provide the dominant contribution to the noise. We present 3.51–5.21 μm M-band CRIRES+/VLT observations of the archetypal young directly imaged gas giant β Pictoris b, detecting CO absorption at S/N = 6.6 at 4.73 μm and H2O at S/N = 5.7, and thus extending the use of HRCCS into the thermal background noise dominated infrared. Using this novel spectral range to search for more diverse chemistry we report marginal evidence of SiO at S/N = 4.3, potentially indicative that previously proposed magnesium-silicate clouds in the atmosphere are either patchy, transparent at M-band wavelengths, or possibly absent on the planetary hemisphere observed. The molecular detections are rotationally broadened by the spin of β Pic b, and we infer a planetary rotation velocity of vsin(i) = 22 ± 2 km s−1 from the cross-correlation with the H2O model template, consistent with previous K-band studies. We discuss the observational challenges posed by the thermal background and telluric contamination in the M-band, the custom analysis procedures required to mitigate these issues, and the opportunities to exploit this new infrared window for HRCCS using existing and next-generation instrumentation