arXiv subjects

Markus Kasper

Publications and source records attributed to Markus Kasper.

At least 19 recordsLinked to original sources

arXiv2026

Exploring reinforcement learning to enhance focal-plane wavefront control for vortex coronagraphs

High Contrast Imaging (HCI) on ground-based telescopes suffers from phase aberrations on the observed wavefront caused by atmospheric turbulence. Adaptive Optics (AO) systems are adept at correcting these aberrations, but fall short in the correction of non-common path aberrations (NCPAs). NCPAs arise because the wavefront sensor (WFS) measures and corrects a wavefront that is different from that affecting the science images, thus requiring additional intervention. This work makes use of focal-plane wavefront sensing and reinforcement learning (RL) to address the wavefront aberrations caused by NCPAs. The PO4NCPA algorithm utilizes sequential phase diversity to address phase ambiguities and is tested on a simulation designed for the Mid-infrared ELT Imager and Spectrograph (METIS) instrument. In this paper, we present the performance of PO4NCPA with scalar and vector vortex coronagraphs to demonstrate its flexibility.

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arXiv2026

Development of an adaptive optics testbed at MPIA for the ELT/Planetary Camera and Spectrograph (PCS)

The Planetary Camera and Spectrograph (PCS) is a proposed second-generation instrument for the Extremely Large Telescope (ELT), dedicated to the direct imaging and characterization of exoplanets. To meet its demanding science requirements, PCS will incorporate an extreme adaptive optics (AO) system, building upon the heritage of existing ELT AO instruments such as ELT/METIS, as well as high-contrast AO systems at the ELT and the VLT, including SPHERE and its upcoming upgrade, SAXO+. PCS development requires extensive research and development to advance critical AO technologies. In this work, we present the Max Planck Institute for Astronomy (MPIA) plan for a modular testbed to validate key components and control strategies. This testbed will integrate two deformable mirrors, including a DM prototype developed by Bertin-ALPAO in collaboration with ESO, with an estimated delivery in 2029. The facility will enable testing of different Fourier filtering wavefront sensors, including novel mask designs, while exploring different control architectures, such as woofer-tweeter configurations with a single wavefront sensor for both deformable mirrors or fully independent AO stages. Additionally, the testbed will leverage MPIA's expertise in real-time computer development to experiment with advanced control strategies, including predictive control and machine learning-enhanced AO techniques. This contribution presents the current status of PCS development at MPIA, highlighting the ongoing R\&D efforts to mature its AO system for high-contrast imaging with the ELT.

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arXiv2026

Upgrading LBTI/NOMIC with a quadruple annular groove phase mask and GeoSnap detector for imaging nearby, habitable-zone exoplanets

The Large Binocular Telescope Interferometer (LBTI)'s Nulling-Optimized Mid-Infrared Camera (NOMIC) is among the most capable thermal-infrared imaging systems available for high-contrast, high-angular-resolution astronomical observations. Here we describe two in-progress upgrades to LBTI/NOMIC: (1) the design, fabrication, and installation of a quadruple annular groove phase mask (Q-AGPM) coronagraph, and (2) the installation of a 13 micron-cutoff Teledyne GeoSnap array. The Q-AGPM is the first coronagraph to be installed within NOMIC and one of the first optimized for N-band (~11 micron) observations. It places four annular groove phase masks on a single diamond substrate so that, in the LBTI dual-aperture imaging mode, each of the two telescope beams can be chopped between a pair of masks without loss of observing efficiency. The GeoSnap array will replace NOMIC's original AQUARIUS array, delivering higher quantum efficiency, larger well depth, faster and more linear readout, and freedom from the excess low-frequency noise that requires aggressive chopping. Together these upgrades substantially improve the achievable contrast and sensitivity at small angular separations. We also present a high-contrast Fizeau imaging sequence obtained with LBTI's new FFTCam fringe tracker, which confirms the interferometric gain over a single aperture through injection/recovery tests: relative to an equal-time single aperture exposure, the S/N = 3 contrast is a factor of ~2-4 deeper across 0.2-1 arcsec, spanning the contrast- and background-limited regimes. Finally, we describe the role of the upgraded LBTI/NOMIC instrument within the Breakthrough Watch program at the University of Arizona, which aims to perform the deepest observations yet of the habitable zones of the nearest single Sun-like stars.

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arXiv2026

Direct Imaging Discovery of Giant Exoplanet $\beta$ Pictoris d: A Decade-Long Game of Hide-and-Seek

We report the direct imaging discovery of a third exoplanet in the $\beta$ Pictoris system. We detect $\beta$ Pictoris d ($\beta$ Pic d) in non-coronagraphic observations obtained with VLT/ERIS as well as multi-epoch archival datasets from JWST/NIRCam and VLT/SPHERE. Astrometric measurements over an 11-year baseline demonstrate that it is consistent with a gravitationally-bound source with orbital motion. Joint multi-planet orbit fits of all three planets in the system yield a semi-major axis of $26.0^{+2.2}_{-6.1}$ au and inclination $89.0^{+0.7}_{-0.6}$ deg for planet d. $\beta$ Pic d has a larger orbital semi-major axis than the other known planets in the system, but is coplanar with the inner two planets, and its orbit is consistent with sculpting the inner edge of the debris disk. $\beta$ Pic d has a contrast of $\Delta L^{\prime}=12.11\pm0.15$ mag, with colors and luminosity that closely match those of 51 Eri b, another exoplanet in the $\beta$ Pictoris moving group. Its VLT/ERIS and JWST/NIRCam colors are distinct from those of free-floating planetary-mass objects of a similar age and temperature. Its red $F410M-F444W$ color indicates strong CO$_2$ absorption in its atmosphere and suggests significant enhancement in metals compared to free-floating objects. From the ATMO hot-start evolutionary models, we estimate an effective temperature of $600^{+45}_{-60}$ K and mass of $2.4\pm0.6$ $M_{\rm Jup}$, which also closely matches similar estimates for 51 Eri b. $\beta$ Pic d is among the lowest-mass exoplanets imaged from the ground. This discovery highlights the deep sensitivity achievable with ground-based imaging in the mid-infrared and the discovery potential of future high-contrast observations with the Extremely Large Telescope.

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arXiv2026

On-sky demonstration of reinforcement learning for adaptive optics control

Reinforcement learning (RL)-based algorithms have recently emerged as a promising approach for adaptive optics (AO) control. In simulations and laboratory experiments, they have demonstrated robustness to real-world effects such as photon and detector noise, misregistration, vibrations, and rapid variations in seeing conditions. However, their performance has not yet been validated on sky. We report the first on-sky demonstration of a reinforcement learning controller for adaptive optics, named Policy Optimization for AO (PO4AO). We further analyze its on-sky behavior and identify directions for improving the algorithm and its implementation.PO4AO was implemented and deployed on the Papyrus adaptive optics system installed at the Coud\'e focus of the 1.52 m telescope (T152) at the OHP. A Python-based implementation was interfaced with the existing real-time controller (DAO RTC) via shared-memory buffers. The performance of PO4AO was compared to that of a standard integrator controller over several nights, covering a range of flux levels and atmospheric conditions. PO4AO consistently outperformed the standard integrator in all tested configurations. The controller successfully learned and compensated for vibration patterns and demonstrated strong robustness to measurement noise. Once tuned for Papyrus, PO4AO operated in a turnkey fashion, using a single set of hyperparameters across varying observing conditions and science targets. These performance gains were achieved despite a non-optimized Python implementation introducing approximately $750\,\mu\text{s}$ of additional latency, along with control jitter and occasional frame drops. When properly implemented and optimized, PO4AO constitutes a robust and high-performance turnkey controller for single-conjugate adaptive optics systems, paving the way for broader adoption of reinforcement learning strategies in on-sky AO operations.

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arXiv2026

Continuing to Advance European High Contrast Imaging Research and Development towards HWO and LIFE

The European Research and Development for Space based High Contrast Imaging II Workshop, held at MPIA in May 2025, advanced Europe strategic coordination in support of future exoplanet imaging missions such as the Habitable Worlds Observatory and the Large Interferometer for Exoplanets mission. Building on the first 2024 workshop, this meeting defined concrete priorities across eight technical areas, including wavefront sensing, coronagraphs, post processing, nulling interferometry, deformable mirrors, detectors, and telescope design. Discussions emphasized Europe strengths in adaptive optics, ground-based facilities, and interferometry, while identifying key gaps, particularly the need for a dedicated European vacuum testbed for high contrast imaging. The community highlighted near infrared or UV coronagraphy as a promising domain for European leadership and called for joint development of advanced data reduction algorithms, detectors, and cross-mission coordination with HWO and LIFE. The workshop outcomes establish a collaborative roadmap to strengthen Europe technological readiness, foster agency partnerships, and ensure its continued leadership in the next generation of space-based exoplanet exploration.

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arXiv2026

Focal plane wavefront control with model-based reinforcement learning

The direct imaging of potentially habitable exoplanets is one prime science case for high-contrast imaging instruments on extremely large telescopes. Most such exoplanets orbit close to their host stars, where their observation is limited by fast-moving atmospheric speckles and quasi-static non-common-path aberrations (NCPA). Conventional NCPA correction methods often use mechanical mirror probes, which compromise performance during operation. This work presents machine-learning-based NCPA control methods that automatically detect and correct both dynamic and static NCPA errors by leveraging sequential phase diversity. We extend previous work in reinforcement learning for AO to focal plane control. A new model-based RL algorithm, Policy Optimization for NCPAs (PO4NCPA), interprets the focal-plane image as input data and, through sequential phase diversity, determines phase corrections that optimize both non-coronagraphic and post-coronagraphic PSFs without prior system knowledge. Further, we demonstrate the effectiveness of this approach by numerically simulating static NCPA errors on a ground-based telescope and an infrared imager affected by water-vapor-induced seeing (dynamic NCPAs). Simulations show that PO4NCPA robustly compensates static and dynamic NCPAs. In static cases, it achieves near-optimal focal-plane light suppression with a coronagraph and near-optimal Strehl without one. With dynamics NCPA, it matches the performance of the modal least-squares reconstruction combined with a 1-step delay integrator in these metrics. The method remains effective for the ELT pupil, vector vortex coronagraph, and under photon and background noise. PO4NCPA is model-free and can be directly applied to standard imaging as well as to any coronagraph. Its sub-millisecond inference times and performance also make it suitable for real-time low-order correction of atmospheric turbulence beyond HCI.

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arXiv2024

The GPU-based High-order adaptive OpticS Testbench

The GPU-based High-order adaptive OpticS Testbench (GHOST) at the European Southern Observatory (ESO) is a new 2-stage extreme adaptive optics (XAO) testbench at ESO. The GHOST is designed to investigate and evaluate new control methods (machine learning, predictive control) for XAO which will be required for instruments such as the Planetary Camera and Spectrograph of ESOs Extremely Large Telescope. The first stage corrections are performed in simulation, with the residual wavefront error at each iteration saved. The residual wavefront errors from the first stage are then injected into the GHOST using a spatial light modulator. The second stage correction is made with a Boston Michromachines Corporation 492 actuator deformable mirror and a pyramid wavefront sensor. The flexibility of the bench also opens it up to other applications, one such application is investigating the flip-flop modulation method for the pyramid wavefront sensor.

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arXiv2024

Upgrading SPHERE with the second stage AO system SAXO+: frequency-based data-driven controller for adaptive optics

This study introduces a novel frequency-based data-driven controller for adaptive optics, using power spectral density for optimization while ensuring stability criteria. It addresses disturbance rejection, command amplitude constraints and system transfer functions through convex optimization to obtain an optimal control in an infinite input response filter form. Evaluated within the SAXO+ project, it demonstrates efficacy under diverse atmospheric conditions and operational scenarios. The proposed controller is tested in both standard and disentangled adaptive optics schemes, showcasing its adaptability and performance. Experimental validation is conducted using the COMPASS simulation tool, affirming the controller's promise for enhancing adaptive optics systems in real-world applications.

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arXiv2024

The power of prediction: spatiotemporal Gaussian process modeling for predictive control in slope-based wavefront sensing

Time-delay error is a significant error source in adaptive optics (AO) systems. It arises from the latency between sensing the wavefront and applying the correction. Predictive control algorithms reduce the time-delay error, providing significant performance gains, especially for high-contrast imaging. However, the predictive controller's performance depends on factors such as the WFS type, the measurement noise, the AO system's geometry, and the atmospheric conditions. This work studies the limits of prediction under different imaging conditions through spatiotemporal Gaussian process models. The method provides a predictive reconstructor that is optimal in the least-squares sense, conditioned on the fixed times series of WFS data and our knowledge of the atmosphere. We demonstrate that knowledge is power in predictive AO control. With an SHS-based extreme AO instrument, perfect knowledge of Frozen Flow evolution (wind and Cn2 profile) leads to a reduction of the residual wavefront phase variance up to a factor of 3.5 compared to a non-predictive approach. If there is uncertainty in the profile or evolution models, the gain is more modest. Still, assuming that only effective wind speed is available (without direction) led to reductions in variance by a factor of 2.3. We also study the value of data for predictive filters by computing the experimental utility for different scenarios to answer questions such as: How many past data frames should the prediction filter consider, and is it always most advantageous to use the most recent data? We show that within the scenarios considered, more data consistently increases prediction accuracy. Further, we demonstrate that given a computational limitation on how many past frames we can use, an optimized selection of $n$ past frames leads to a 10-15% additional improvement in RMS over using the n latest consecutive frames of data.

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arXiv2024

Use the 4S (Signal-Safe Speckle Subtraction): Explainable Machine Learning reveals the Giant Exoplanet AF Lep b in High-Contrast Imaging Data from 2011

The main challenge of exoplanet high-contrast imaging (HCI) is to separate the signal of exoplanets from their host stars, which are many orders of magnitude brighter. HCI for ground-based observations is further exacerbated by speckle noise originating from perturbations in Earth's atmosphere and imperfections in the telescope optics. Various data post-processing techniques are used to remove this speckle noise and reveal the faint planet signal. Often, however, a significant part of the planet signal is accidentally subtracted together with the noise. In the present work, we use explainable machine learning to investigate the reason for the loss of the planet signal for one of the most used post-processing methods: principal component analysis (PCA). We find that PCA learns the shape of the telescope point spread function for high numbers of PCA components. This representation of the noise captures not only the speckle noise but also the characteristic shape of the planet signal. Building on these insights, we develop a new post-processing method (4S) that constrains the noise model to minimize this signal loss. We apply our model to 11 archival HCI datasets from the VLT-NACO instrument in the L'-band and find that our model consistently outperforms PCA. The improvement is largest at close separations to the star ($\leq 4 \lambda /D$) providing up to 1.5 magnitudes deeper contrast. This enhancement enables us to detect the exoplanet AF Lep b in data from 2011, 11 years before its subsequent discovery. We present updated orbital parameters for this object.

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arXiv2024

Bioverse: GMT and ELT Direct Imaging and High-Resolution Spectroscopy Assessment $\unicode{x2013}$ Surveying Exo-Earth O$_{\mathrm{2}}$ and Testing the Habitable Zone Oxygen Hypothesis

Biosignature detection in the atmospheres of Earth-like exoplanets is one of the most significant and ambitious goals for astronomy, astrobiology, and humanity. Molecular oxygen is among the strongest indicators of life on Earth, but it will be extremely difficult to detect via transmission spectroscopy. We used the Bioverse statistical framework to assess the ability to probe Earth-like O$_{\mathrm{2}}$ levels on hypothetical nearby habitable zone exo-Earth candidates (EECs) using direct imaging and high-resolution spectroscopy on the Giant Magellan Telescope (GMT) and the Extremely Large Telescope (ELT). Assuming continued improvement in instruments and data processing, our analysis highlights the best-case scenarios. Earth-like O$_{\mathrm{2}}$ levels could be probed on up to $\sim$7 and $\sim$19 EECs orbiting bright M dwarfs within 20 pc in a hypothetical 10-year survey on the GMT and ELT, respectively. Four known super-Earth candidates, including Proxima Centauri b, could be probed for O$_{\mathrm{2}}$ within about one week of observations on the ELT and a few months on the GMT. We also assessed the ability of the ELT to test the habitable zone oxygen hypothesis $\unicode{x2013}$ that habitable zone Earth-sized planets are more likely to have O$_{\mathrm{2}}$ $\unicode{x2013}$ within a 10-year survey using Bioverse. Testing this hypothesis requires either $\sim$1/2 of the EECs to have O$_{\mathrm{2}}$ or $\sim$1/3 if $\eta_{\oplus}$ is large. A northern hemisphere large-aperture telescope, such as the Thirty Meter Telescope (TMT), would expand the target star pool by about 25%, reduce the time to probe biosignatures on individual targets, and provide an additional independent check on potential biosignature detections.

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arXiv2023

Laboratory Experiments of Model-based Reinforcement Learning for Adaptive Optics Control

Direct imaging of Earth-like exoplanets is one of the most prominent scientific drivers of the next generation of ground-based telescopes. Typically, Earth-like exoplanets are located at small angular separations from their host stars, making their detection difficult. Consequently, the adaptive optics (AO) system's control algorithm must be carefully designed to distinguish the exoplanet from the residual light produced by the host star. A new promising avenue of research to improve AO control builds on data-driven control methods such as Reinforcement Learning (RL). RL is an active branch of the machine learning research field, where control of a system is learned through interaction with the environment. Thus, RL can be seen as an automated approach to AO control, where its usage is entirely a turnkey operation. In particular, model-based reinforcement learning (MBRL) has been shown to cope with both temporal and misregistration errors. Similarly, it has been demonstrated to adapt to non-linear wavefront sensing while being efficient in training and execution. In this work, we implement and adapt an RL method called Policy Optimization for AO (PO4AO) to the GHOST test bench at ESO headquarters, where we demonstrate a strong performance of the method in a laboratory environment. Our implementation allows the training to be performed parallel to inference, which is crucial for on-sky operation. In particular, we study the predictive and self-calibrating aspects of the method. The new implementation on GHOST running PyTorch introduces only around 700 microseconds in addition to hardware, pipeline, and Python interface latency. We open-source well-documented code for the implementation and specify the requirements for the RTC pipeline. We also discuss the important hyperparameters of the method, the source of the latency, and the possible paths for a lower latency implementation.

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arXiv2023

Exoplanet imaging with ELTs: exploring a second-stage AO with a Zernike wavefront sensor on the ESO/GHOST testbed

We propose to explore a cascade extreme Adaptive optics (ExAO) approach with a second stage based on a Zernike wavefront sensor (ZWFS) for exoplanet imaging and spectroscopy. Most exoplanet imagers currently use a single-stage ExAO to correct for the effects of atmospheric turbulence and produce high-Strehl images of observed stars in the near-infrared. While such systems enable the observation of warm gaseous companions around nearby stars, adding a second-stage AO enables to push the wavefront correction further and possibly observe colder or smaller planets. This approach is currently investigated in different exoplanet imagers (VLT/SPHERE, Mag-AOX, Subaru/SCExAO) by considering a Pyramid wavefront sensor (PWFS) in the second arm to measure the residual atmospheric turbulence left from the first stage. Since these aberrations are expected to be very small (a few tens of nm in the near-infrared domain), we propose to investigate an alternative approach based on the ZWFS. This sensor is a promising concept with a small capture range to estimate residual wavefront errors thanks to its large sensitivity, simple phase reconstruction and easiness of implementation. In this contribution, we perform preliminary tests on the GHOST testbed at ESO to validate this approach experimentally. Additional experiments with petalling effects are also showed, giving promising wavefront correction results. Finally, we briefly discuss a first comparison between PWFS-based and ZWFS-based second-stage AO to draw preliminary conclusions on the interests of both schemes for exoplanet imaging and spectroscopy with the upgrade of the current exoplanet imagers and the envisioned ExAO instruments for ELTs.

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arXiv2023

The beta Pictoris system: Setting constraints on the planet and the disk structures at mid-IR wavelengths with NEAR

[abridged] We analyzed mid-infrared high-contrast coronagraphic images of the beta Pictoris system, taking advantage of the NEAR experiment using the VLT/VISIR instrument. The goal of our analysis is to investigate both the detection of the planet beta Pictoris b and of the disk features at mid-IR wavelengths. In addition, by combining several epochs of observation, we expect to constrain the position of the known clumps and improve our knowledge on the dynamics of the disk. To evaluate the planet b flux contribution, we extracted the photometry and compared it to the flux published in the literature. In addition, we used previous data from T-ReCS and VISIR, to study the evolution of the position of the southwest clump that was initially observed in the planetary disk back in 2003. While we did not detect the planet b, we were able to put constraints on the presence of circumplanetary material, ruling out the equivalent of a Saturn-like planetary ring around the planet. The disk presents several noticeable structures, including the known southwest clump. Using a 16-year baseline, sampled with five epochs of observations, we were able to examine the evolution of the clump: the clump orbits in a Keplerian motion with an sma of 56.1+-0.4 au. In addition to the known clump, the images clearly show the presence of a second clump on the northeast side of the disk and fainter and closer structures that are yet to be confirmed. We found correlations between the CO clumps detected with ALMA and the mid-IR images. If the circumplanetary material were located at the Roche radius, the maximum amount of dust determined from the flux upper limit around beta Pictoris b would correspond to the mass of an asteroid of 5 km in diameter. Finally, the Keplerian motion of the southwestern clump is possibly indicative of a yet-to-be-detected planet or signals the presence of a vortex.

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arXiv2023

Comparing Apples with Apples: Robust Detection Limits for Exoplanet High-Contrast Imaging in the Presence of non-Gaussian Noise

Over the past decade, hundreds of nights have been spent on the worlds largest telescopes to search for and directly detect new exoplanets using high-contrast imaging (HCI). Thereby, two scientific goals are of central interest: First, to study the characteristics of the underlying planet population and distinguish between different planet formation and evolution theories. Second, to find and characterize planets in our immediate Solar neighborhood. Both goals heavily rely on the metric used to quantify planet detections and non-detections. Current standards often rely on several explicit or implicit assumptions about the noise. For example, it is often assumed that the residual noise after data post-processing is Gaussian. While being an inseparable part of the metric, these assumptions are rarely verified. This is problematic as any violation of these assumptions can lead to systematic biases. This makes it hard, if not impossible, to compare results across datasets or instruments with different noise characteristics. We revisit the fundamental question of how to quantify detection limits in HCI. We focus our analysis on the error budget resulting from violated assumptions. To this end, we propose a new metric based on bootstrapping that generalizes current standards to non-Gaussian noise. We apply our method to archival HCI data from the NACO-VLT instrument and derive detection limits for different types of noise. Our analysis shows that current standards tend to give detection limit that are about one magnitude too optimistic in the speckle-dominated regime. That is, HCI surveys may have excluded planets that can still exist.

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arXiv2023

The ESO's Extremely Large Telescope Working Groups

Since 2005 ESO has been working with its community and industry to develop an extremely large optical/infrared telescope. ESO's Extremely Large Telescope, or ELT for short, is a revolutionary ground-based telescope that will have a 39-metre main mirror and will be the largest visible and infrared light telescope in the world. To address specific topics that are needed for the science operations and calibrations of the telescope, thirteen specific working groups were created to coordinate the effort between ESO, the instrument consortia, and the wider community. We describe here the goals of these working groups as well as their achievements so far.

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arXiv2023

Lessons learned from the NEAR experiment and prospects for the upcoming mid-IR HCI instruments

The mid-infrared (IR) regime is well suited to directly detect the thermal signatures of exoplanets in our solar neighborhood. The NEAR experiment: demonstration of high-contrast imaging (HCI) capability at ten microns, can reach sub-mJy detection sensitivity in a few hours of observation time, which is sufficient to detect a few Jupiter mass planets in nearby systems. One of the big limitations for HCI in the mid-IR is thermal sky-background. In this work, we show that precipitate water vapor (PWV) is the principal contributor to thermal sky background and science PSF quality. In the presence of high PWV, the HCI performance is significantly degraded in the background limited regime.

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