arXiv subjects

Rico Landman

Publications and source records attributed to Rico Landman.

32 records · Page 2Linked to original sources

arXiv2024

Revealing H$_2$O dissociation in WASP-76~b through combined high- and low-resolution transmission spectroscopy

Numerous chemical constraints have been possible for exoplanetary atmospheres thanks to high-resolution spectroscopy (HRS) from ground-based facilities as well as low-resolution spectroscopy (LRS) from space. These two techniques have complementary strengths, and hence combined HRS and LRS analyses have the potential for more accurate abundance constraints and increased sensitivity to trace species. In this work we retrieve the atmosphere of the ultra-hot Jupiter WASP-76~b, using high-resolution CARMENES/CAHA and low-resolution HST WFC3 and Spitzer observations of the primary eclipse. As such hot planets are expected to have a substantial fraction of H$_2$O dissociated, we conduct retrievals including both H$_2$O and OH. We explore two retrieval models, one with self-consistent treatment of H$_2$O dissociation and another where H$_2$O and OH are vertically-homogeneous. Both models constrain H$_2$O and OH, with H$_2$O primarily detected by LRS and OH through HRS, highlighting the strengths of each technique and demonstrating the need for combined retrievals to fully constrain chemical compositions. We see only a slight preference for the H$_2$O-dissociation model given that the photospheric constraints for both are very similar, indicating $\log(\mathrm{OH/H_2O}) = 0.7^{+0.3}_{-0.3}$ at 1.5~mbar, showing that the majority of the H$_2$O in the photosphere is dissociated. However, the bulk O/H and C/O ratios inferred from the models differs significantly, and highlights the challenge of constraining bulk compositions from photospheric abundances with strong vertical chemical gradients. Further observations with JWST and ground-based facilities may help shed more light on these processes.

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arXiv2024

Making the unmodulated Pyramid wavefront sensor smart. Closed-loop demonstration of neural network wavefront reconstruction with MagAO-X

Almost all current and future high-contrast imaging instruments will use a Pyramid wavefront sensor (PWFS) as a primary or secondary wavefront sensor. The main issue with the PWFS is its nonlinear response to large phase aberrations, especially under strong atmospheric turbulence. Most instruments try to increase its linearity range by using dynamic modulation, but this leads to decreased sensitivity, most prominently for low-order modes, and makes it blind to petal-piston modes. In the push toward high-contrast imaging of fainter stars and deeper contrasts, there is a strong interest in using the PWFS in its unmodulated form. Here, we present closed-loop lab results of a nonlinear reconstructor for the unmodulated PWFS of the Magellan Adaptive Optics eXtreme (MagAO-X) system based on convolutional neural networks (CNNs). We show that our nonlinear reconstructor has a dynamic range of >600 nm root-mean-square (RMS), significantly outperforming the linear reconstructor that only has a 50 nm RMS dynamic range. The reconstructor behaves well in closed loop and can obtain >80% Strehl at 875 nm under a large variety of conditions and reaches higher Strehl ratios than the linear reconstructor under all simulated conditions. The CNN reconstructor also achieves the theoretical sensitivity limit of a PWFS, showing that it does not lose its sensitivity in exchange for dynamic range. The current CNN's computational time is 690 microseconds, which enables loop speeds of >1 kHz. On-sky tests are foreseen soon and will be important for pushing future high-contrast imaging instruments toward their limits.

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arXiv2023

$β$ 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 $β$ 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 $β$ 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.

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arXiv2023

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.

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arXiv2023

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.

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arXiv2023

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.

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arXiv2023

Chasing rainbows and ocean glints: Inner working angle constraints for the Habitable Worlds Observatory

NASA is engaged in planning for a Habitable Worlds Observatory (HabWorlds), a coronagraphic space mission to detect rocky planets in habitable zones and establish their habitability. Surface liquid water is central to the definition of planetary habitability. Photometric and polarimetric phase curves of starlight reflected by an exoplanet can reveal ocean glint, rainbows and other phenomena caused by scattering by clouds or atmospheric gas. Direct imaging missions are optimised for planets near quadrature, but HabWorlds' coronagraph may obscure the phase angles where such optical features are strongest. The range of accessible phase angles for a given exoplanet will depend on the planet's orbital inclination and/or the coronagraph's inner working angle (IWA). We use a recently-created catalog relevant to HabWorlds of 164 stars to estimate the number of exo-Earths that could be searched for ocean glint, rainbows, and polarization effects due to Rayleigh scattering. We find that the polarimetric Rayleigh scattering peak is accessible in most of the exo-Earth planetary systems. The rainbow due to water clouds at phase angles of ${\sim}20-60^\circ$ would be accessible with HabWorlds for a planet with an Earth equivalent instellation in ${\sim}{46}$ systems, while the ocean glint signature at phase angles of ${\sim}130-170^\circ$ would be accessible in ${\sim}{16}$ systems, assuming an IWA${=}62$ mas ($3λ/D$). Improving the IWA${=}41$ mas ($2λ/D$) increases accessibility to rainbows and glints by factors of approximately 2 and 3, respectively. By observing these scattering features, HabWorlds could detect a surface ocean and water cycle, key indicators of habitability.

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arXiv2023

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.

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arXiv2023

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.

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arXiv2022

Joint optimization of wavefront sensing and reconstruction with automatic differentiation

High-contrast imaging instruments need extreme wavefront control to directly image exoplanets. This requires highly sensitive wavefront sensors which optimally make use of the available photons to sense the wavefront. Here, we propose to numerically optimize Fourier-filtering wavefront sensors using automatic differentiation. First, we optimize the sensitivity of the wavefront sensor for different apertures and wavefront distributions. We find sensors that are more sensitive than currently used sensors and close to the theoretical limit, under the assumption of monochromatic light. Subsequently, we directly minimize the residual wavefront error by jointly optimizing the sensing and reconstruction. This is done by connecting differentiable models of the wavefront sensor and reconstructor and alternatingly improving them using a gradient-based optimizer. We also allow for nonlinearities in the wavefront reconstruction using Convolutional Neural Networks, which extends the design space of the wavefront sensor. Our results show that optimization can lead to wavefront sensors that have improved performance over currently used wavefront sensors. The proposed approach is flexible, and can in principle be used for any wavefront sensor architecture with free design parameters.

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arXiv2021

Detection of OH in the ultra-hot Jupiter WASP-76b

Ultra-hot Jupiters have dayside temperatures at which most molecules are expected to thermally dissociate. The dissociation of water vapour results in the production of the hydroxyl radical (OH). While OH absorption is easily observed in near-infrared spectra of M dwarfs, which have similar effective temperatures as ultra-hot Jupiters, it is often not considered when studying the atmospheres of ultra-hot Jupiters. We use high-resolution spectroscopic observations of a transit of WASP-76b obtained using CARMENES to study the presence of OH. After validating the OH line list, we generate model transit spectra of WASP-76b with petitRADTRANS. The data are corrected for stellar and telluric contamination and cross-correlated with the model spectra. After combining all cross-correlation functions from the transit, a detection map is constructed. Constraints on the planet properties from the OH absorption are obtained from a Markov chain Monte Carlo analysis. OH is detected in the atmosphere of WASP-76b with a peak signal-to-noise ratio of 6.1. From the retrieval we obtain $K_p=232 \pm 12$ km/s and a blueshift of $-13.2 \pm 1.6$ km/s, which are offset from the expected velocities. Considering the fast spin rotation of the planet, the blueshift is best explained with the signal predominantly originating from the evening terminator and the presence of a strong dayside-to-nightside wind. The increased $K_p$ over its expected value (196.5 km/s) is, however, a bit puzzling. The signal is found to be broad, with a full width at half maximum of $16.8^{+4.6}_{-4.0}$ km/s. The retrieval results in a weak constraint on the mean temperature of 2700-3700 K at the pressure range of the OH signal. We show that OH is readily observable in the transit spectra of ultra-hot Jupiters. Studying this molecule can provide insights into the molecular dissociation processes in the atmospheres of such planets.

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arXiv2021

Self-optimizing adaptive optics control with Reinforcement Learning for high-contrast imaging

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. One way to reduce these effects is to use predictive control. We describe how model-free Reinforcement Learning can be used to optimize a Recurrent Neural Network controller for closed-loop predictive control. First, 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 mitigate vibrations and reduce the residuals for power-law input turbulence as compared to an optimal gain integrator. We also show that the controller can learn to minimize random vibrations without requiring online updating of the control law. Next, we show in simulations that our algorithm can also be applied to the control of a high-order deformable mirror. We demonstrate that our controller can provide two orders of magnitude improvement in contrast at small separations under stationary turbulence. Furthermore, we show more than an order of magnitude improvement in contrast for different wind velocities and directions without requiring online updating of the control law.

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arXiv2020

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 mirrors

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arXiv2020

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.

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