Imaging Earth-like exoplanets with coronagraphs on future large segmented space telescopes such as the Habitable Worlds Observatory requires contrasts down to 10^-10 at separations below 100 mas, imposing segment phasing control down to a few picometers. We evaluate how this constraint can be relaxed by optimizing telescope and instrument design, quantifying the impact on performance stability under segment phasing aberrations. We propose a system-level approach, adjusting the primary mirror segmentation and the focal-plane mask size (inner working angle). We compare the passive robustness to segment phasing errors across systems, and the ability of a Zernike low-order wavefront sensor to reconstruct aberrations and recover target performance. Increasing the focal-plane mask radius or decreasing segment count improves both passive robustness and sensor reconstruction: Increasing the mask radius from 3.5 to 6.5λ/D relaxes phasing constraints by up to a factor 4 near the IWA, and reducing the segment count from 85 (5 rings) to 7 (1 ring) relaxes them by up to a factor 2; The same mask radius increase also doubles, on average, the sensor's sensitivity to photon noise across segment piston, tip, and tilt modes. To conclude, jointly optimizing the segmentation scheme and mask size, so the low-order PSD envelope is blocked by the mask, can significantly relax phasing requirements, complementing active correction, with direct implications for HWO.

Deformable mirrors (DMs) are a key component of coronagraph instruments performing adaptive optics on the ground, and for future space observatories such as HWO and Roman CGI. Here, they will be used as part of the wavefront sensing and control system to "dig a dark zone” - remove residual stellar light to create a high-contrast region in the focal plane where faint companions can be detected. To reach the deep contrasts needed to directly image cool or reflected light planets (<1e-8) accurate calibration of the DM actuator gain is essential as picometer differences between the expected and realized DM surface can significantly degrade dark zone (DZ) digging efficiency. This increases the overheads needed to achieve a DZ and critically places more stringent requirements on observatory stability. Furthermore, DM gain varies with actuator stroke, necessitating rapid, in situ gain map recalculations to maintain DZ digging efficiency over time. Zernike wavefront sensors (ZWFS) are well-suited for this task as they efficiently provide picometer-level sensitivity and will likely already be included on board as part of a low order wavefront sensor for HWO. Here we present results from the HiCAT testbed at STScI where we calibrated gain maps for our Boston Micromachines 952-actuator micro electromechanical (MEMS) DMs using both a Fizeau interferometer and a ZWFS to compare performance. With the ZWFS we compute a gain map using both local linear fits around a given DM solution, and present a formalism for deriving more complex quadratic solutions which are more computationally intensive, but accurate over most of the dynamic range of each actuator. We then use these techniques to calibrate the DMs on the HiCAT testbed and show increased DZ digging efficiency with the new gain map and better contrast performance moving from 14 to 16 bit control electronics as enabled by these calibrations.

We provide a quantification of the technological gap between the James Webb Space Telescope (JWST) and the Habitable Worlds Observatory (HWO) for the goal of exo-Earth imaging around Sun-like stars at the 10^-10 raw contrast level. We use JWST's in-flight telemetry of the primary segmented mirror to simulate a JWST-like telescope equipped with a modern coronagraph instrument, inspired by the Roman Space Telescope (RST) Coronagraphic Instrument (CGI), featuring an Apodized Pupil Lyot Coronagraph and active deformable mirror wavefront control on a segmented, unobstructed, off-axis telescope. We show that it can achieve around 10^-10 raw contrast for very bright stars (brighter than magnitude 4) for fast control frequencies of 100 Hz, but that this level of control still lacks sufficient signal to correct JWST-amplitude errors for fainter stars. We show that an improvement of a factor of ten in wavefront stability is sufficient to extend this capability to a 10^-10 raw contrast across all considered control frequencies (1 Hz to 100 Hz), assuming no reaction wheel vibrations, for stars up to a magnitude of 11. These results establish a new quantitative benchmark linking JWST's demonstrated thermo-mechanical stability to HWO's requirements, showing that active wavefront control relaxes the structural stability demands on the observatory, and identifying wavefront stability as the critical technological gap that must be closed for HWO to achieve its exo-Earth imaging goals.

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.

We investigate the stability of a segmented deformable mirror (DM) on high-contrast testbeds and its impact on the images produced with coronagraphs. Segmented apertures are promising to obtain large primary mirrors for future missions with starlight suppression capabilities. Cophased at the subnanometer level, segments can be slightly misaligned by small drifts, proving harmful for exoplanet observations. We study the impact of misalignments on contrast using the High-contrast imager for Complex Aperture Telescopes (HiCAT), a testbed that includes a 37-segment DM and produces coronagraphic images with 2.5 & times;10(-8) contrast in narrowband light. Temporal wavefront errors due to the segmented DM are estimated with a Zernike wavefront sensor. Our in-lab results show aberrations at the subnanometer level, proving encouraging for contrast stability studies. We then use a digital twin of HiCAT to simulate coronagraphic images with an initial 0.5 & times;10(-8) contrast and the segments in flat position. By injecting known perturbations on the segments, we observe a contrast degradation by a factor of 2.5, nearly corresponding to the typical contrast observed on HiCAT. These results highlight the importance of segment cophasing sensing and control strategies to ensure the required contrasts for exo-Earth imaging with a large segmented aperture for the Habitable Worlds Observatory mission.

High-contrast exoplanet imaging requires dedicated laboratory testbeds for the development and validation of coronagraph architectures, wavefront sensing and control methods, calibration strategies, and system-level observing concepts. These testbeds often share similar software needs, yet many tools are developed independently at each institution. The CATKit2-High-Contrast-Imaging collaboration, or CATKit2-HCI, addresses this gap by providing a shared software framework for reusable HCI infrastructure. Built on top of CATKit2, an open-source hardware control and synchronization framework originally developed for the High-contrast Imager for Complex Aperture Telescopes (HiCAT) testbed at the Space Telescope Science Institute, CATKit2-HCI provides the collaborative layer for HCI-specific algorithms, calibration tools, diagnostics, visualization, and performance metrics. The collaboration currently includes multiple coronagraph testbeds in the United States and Europe. Its goals are to reduce duplicated software development, improve code quality through shared review, enable more direct comparison of results across facilities, and facilitate the movement of students, postdoctoral researchers, and collaborators between laboratories. We describe the motivation, architecture, collaboration model, shared technical capabilities, and early cross-testbed examples of CATKit2-HCI as a framework for accelerating coronagraph technology development.

Direct imaging of exoplanets involves collecting photons emitted or reflected by a planet orbiting a host star. This approach is challenging due to the extreme brightness difference between exoplanets and their host stars as well as the small angular separation between a star and its orbiting exoplanet. Coronagraphs and deformable mirrors are used to block and redistribute the starlight to enable imaging of the exoplanets. Speckle discrimination algorithms aim to find potential exoplanets but struggle to distinguish actual planetary signals from residual starlight speckles, which can lead to false positives. Existing speckle discrimination methods rely on binary classification rather than probabilistic outputs, limiting their ability to differentiate between exoplanets and noise based on subtle patterns. These algorithms operate in the post-processing stage and do not autonomously follow up on detected points of interest by refining observational parameters, such as spectral bands, exposure time, or region of interest. In this work, we apply deep learning models, specifically Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), to high-contrast exoplanet detection. CNNs are effective at detecting local spatial features, making them well-suited for identifying small, well-defined planetary signals. ViTs leverage self-attention mechanisms to capture long-range dependencies, which may improve their ability to distinguish exoplanets from complex noise patterns. The models are trained and tested using images from the High-contrast imager for Complex Aperture Telescopes (HiCAT) simulator developed by the Space Telescope Science Institute, with synthetic exoplanets injected into raw testbed images. By comparing the performance of CNNs and ViTs, we assess their suitability for future exoplanet detection efforts. This study highlights how AI-driven approaches can address the growing demands of next-generation observatories by enhancing detection sensitivity, reducing false positives, and enabling real-time follow-up actions to refine imaging parameters.

The next few years will be critical for technology development for Habitable Worlds Observatory (HWO) in its mission to search for and characterize extrasolar planets. To achieve its stated goals with contrasts of one part in ten billion, HWO will require outstanding stability and precision, particularly in measuring and controlling the wavefront of the light propagate through the telescope and coronagraph system. We present simulations for the Photonic-Enabled ExoPlanet Spectroscopic Sensor (PEEPSS), which uses a set of photonic lanterns to efficiently couple light from the "dark hole" in the coronograph focal plane (where the exoplanets are expected to lie) into single-mode fibers and the main spectrograph. PEEPSS uses rejected host star light from the region interior to the dark hole to aid in the wavefront sensing; this has the advantage of doing the sensing in the coronograph focal plane, eliminating non-common-path errors between the wavefront sensing and science channels. The photonics lanterns allow us to combine our science channel and wavefront sensor into a single system. PEEPSS will be particularly advantageous provide in the near-infrared (NIR) bandpass, which is of particular interest for HWO. Because the limiting inner working angle (IWA) of a coronagraph scales as wavelength over diameter, exoplanet imaging in the NIR becomes a major challenge as the IWA can exceed the exoplanet orbital radius. PEEPSS will enable NIR coronagraphic observations at smaller IWA than other approaches, increasing the observational parameter space HWO can probe in the search for exoplanets.

Directly imaging and detecting exoplanets with a high-contrast imaging instrument must overcome residual starlight and quasi-static wavefront errors which pose significant challenges for real-time detection. In this work, we combine an Extended Kalman Filter (EKF) and a Convolutional Neural Network (CNN) to enhance exoplanet detection in high-contrast images. The EKF estimates the real and imaginary components of the open-loop electric field as well as the incoherent intensity for each pixel. The EKF is assembled using blocks or cells of pixels in the dark zone where each cell is the size of a potential planet point spread function (PSF). This aids the estimator as the properties of the planet PSF pixels should be related. The cell mapping for the EKF is initialized randomly assuming no prior knowledge of the potential planet location. The CNN is trained and tested on images from the High-contrast imager for Complex Aperture Telescopes (HiCAT) testbed at the Space Telescope Science Institute. HiCAT does not have an off-axis light source to emulate a planet on the testbed, so planets are injected via software into the raw testbed images. The CNN is trained to distinguish planetary signals from noise, such as starlight leakage, and to identify potential planets within specific grid cells in an image. Once the CNN identifies a possible planetary signal, the cell mapping and all appropriate EKF components are adjusted to center the detected planet within a single cell. This enables the EKF to refine the estimation of coherent and incoherent light at the updated grid coordinates, allowing for a more dynamic adaptation to changing wavefront errors and helping to ensure optimal suppression of residual starlight. This CNN-EKF approach provides a pathway for integrating machine learning and statistical modeling for real-time, autonomous exoplanet detection. It highlights the potential for advancing the performance of future space-based high-contrast imaging missions. The scope of this paper demonstrated that all major components of the autonomous detection pipeline are functional without causing delays or contrast degradation.

Stellar coronagraphs use closed-loop focal-plane wavefront sensing and control algorithms to create high-contrast dark zones suitable for imaging exoplanets and exozodiacal dust clouds around nearby stars. Model-based algorithms are susceptible to model mismatch, wherein a departure of the coronagraph's true optical characteristics from the assumed model causes reduced control loop performance. We describe a simple technique for empirically tuning the wavefront control Jacobian matrix using applied deformable mirror commands and observed images. This mitigates model mismatch and recovers near-optimal control loop performance, and additionally provides a simple means for verifying model quality on-orbit or in a laboratory setting. We demonstrate the proposed least-squares system identification method experimentally using the High-contrast Imager for Complex Aperture Telescopes (HiCAT) testbed, as well as via simulations with a Habitable Worlds Observatory-like system configuration.

The performance of high-contrast imaging systems such as the proposed Habitable Worlds Observatory relies on extremely accurate wavefront sensing and control to control diffracted starlight down to the 10-10 flux ratio level. Least-squares system identification (LSID) has been proposed to mitigate model mismatch and improve the convergence speed of wavefront control algorithms such as electric field conjugation (EFC). We present our implementation and experimental validation of LSID on the High-contrast imager for Complex Aperture Telescopes (HiCAT) testbed at the Space Telescope Science Institute (Baltimore, Maryland, United States), extending the monochromatic results of previous work to multiwavelength light. We find that LSID identifies Jacobian (control) matrices that improve EFC convergence speed and depth compared with model-based Jacobians. Most notably, we see an improvement in final contrast from similar to 3.2x10(-7 ) using a model-based Jacobian to similar to 1.8x10(-7 )using an LSID Jacobian over a 60 nm (similar to 9%) bandpass centered at 640 nm.

We study a mid-order wavefront sensor (MOWFS) to address fine cophasing errors in exoplanet imaging with future large segmented aperture space telescopes. Observing Earth analogs around Sun-like stars requires contrasts down to 10-10 in visible light. One promising solution consists of producing a high-contrast dark zone in the image of an observed star. In a space observatory, this dark region will be altered by several effects, and among them, the small misalignments of the telescope mirror segments due to fine thermo-mechanical drifts. To correct for these errors in real time, we investigate a wavefront control loop based on a MOWFS with a Zernike sensor. Such a MOWFS was installed on the high-contrast imager for complex aperture telescopes (HiCAT) testbed in Baltimore in June 2023. The bench uses a 37-segment Iris-AO deformable mirror to mimic telescope segmentation and some wavefront control strategies to produce a dark zone with such an aperture. In this contribution, we first use the MOWFS to characterize the Iris-AO segment discretization steps. For the central segment, we find a minimal step of 125 +/- 31 pm. This result will help us to assess the contribution of the Iris-AO DM on the contrast in HiCAT. We then determine the detection limits of the MOWFS, estimating wavefront error amplitudes of 119 and 102pm for 10 s and 1 min exposure time with a SNR of 3. These values inform us about the measurement capabilities of our wavefront sensor on the testbed. These preliminary results will be useful to provide insights on metrology and stability for exo-Earth observations with the Habitable Worlds Observatory.

One of the primary science goals of the HabitableWorlds Observatory (HWO) as defined by the Astro2020 decadal survey is the imaging of the first Earth-like planet around a Sun-like star. A key technology gap towards reaching this goal are the development of ultra-low-noise photon counting detectors capable of measuring the incredibly low count rates coming from these planets which are at contrasts of similar to 1 x 10(-10). Superconducting energy-resolving detectors (ERDs) are a promising technology for this purpose as, despite their technological challenges, needing to be cooled below their superconducting transition temperature (< 1K), they have essentially zero read noise, dark current, or clock-induced charge, and can get the wavelength of each incident photon without the use of additional throughput-reducing filters or gratings that spread light over many pixels. The use of these detectors on HWO will not only impact the science of the mission by decreasing the required exposure times for exo-Earth detection and characterization, but also in a wavefront sensing and control context when used for starlight suppression to generate a dark zone. We show simulated results using both an EMCCD and an ERD to "dig a dark zone" demonstrating that ERDs can achieve the same final contrast as an EMCCD in about half of the total time. We also perform a simple case study using an exposure time calculator tool called the Error Budget Software (EBS) to determine the required integration times to detect water for HWO targets of interest using both EMCCDs and ERDs. This shows that once a dark zone is achieved, using an ERD can decrease these exposure times by factors of 1.5-2 depending on the specific host star properties.

With the commencement of the development of the Habitable Worlds Observatory, it is imperative that the community has an understanding of (1) the stability requirements for the observatory to inform the design and (2) the gains expected from post-processing to inform observing scenarios and science yield estimates. We demonstrate that a previously developed, photon-efficient dark-zone maintenance (DZM) algorithm, that corrects quasi-static wavefront error drifts by using only science images, is compatible with traditional post-processing techniques. Further, we augment the DZM algorithm to estimate the coherent and incoherent light separately and introduce three novel post-processing techniques that leverage the concurrent estimation of coherent and incoherent light. With the DZM algorithm implemented on the High-contrast imager for Complex Aperture Telescopes (HiCAT) testbed at the Space Telescope Science Institute (STScI), artificial drifts are injected as a random walk on a set of deformable mirrors (DMs) and are corrected with DZM. An injected fake planet is recovered in post-processing using a variety of techniques, such as angular differential imaging (ADI), and three novel techniques presented in this paper: incoherent accumulated imaging (IAI), software-based coherent differential imaging (CDI), and coherent reference differential imaging (CoRDI). All post-processing techniques can recover an injected planet at the same contrast level as the dark-zone background contrast (similar to 8x10(-8)), and the ADI technique is shown to recover a 4x10(-8) planet in a 8x10(-8) dark zone. For a space-based observatory, this would mean that if the instrument can reach a contrast level, we can maintain it and recover a planet that is undetectable in a single frame.

A major endeavor of this decade is the direct characterization of young giant exoplanets at high spectral resolution to determine the composition of their atmosphere and infer their formation processes and evolution. Such a goal represents a major challenge owing to their small angular separation and luminosity contrast with respect to their parent stars. Instead of designing and implementing completely new facilities, it has been proposed to leverage the capabilities of existing instruments that offer either high-contrast imaging or high-dispersion spectroscopy by coupling them using optical fibers. In this work, we present the implementation and first on-sky results of the High-Resolution Imaging and Spectroscopy of Exoplanets (HiRISE) instrument at the Very Large Telescope (VLT), which combines the exoplanet imager SPHERE with the recently upgraded high-resolution spectrograph using single-mode fibers. The goal of HiRISE is to enable the characterization of known companions in the $H$ band at a spectral resolution on the order of $R = = 100\,000$ in a few hours of observing time. We present the main design choices and the technical implementation of the system, which is constituted of three major parts: the fiber injection module inside of SPHERE, the fiber bundle around the telescope, and the fiber extraction module at the entrance of We also detail the specific calibrations required for HiRISE and the operations of the instrument for science observations. Finally, we detail the performance of the system in terms of astrometry, temporal stability, optical aberrations, and transmission, for which we report a peak value of sim 3.9 based on sky measurements in median observing conditions. Finally, we report on the first astrophysical detection of HiRISE to illustrate its potential.

Maintaining wavefront stability while directly imaging exoplanets over long exposure times is an ongoing problem in the field of high-contrast imaging. Robust and efficient high-order wavefront sensing and control systems are required for maintaining wavefront stability to counteract mechanical and thermal instabilities. Dark zone maintenance (DZM) has been proposed to address quasi-static optical aberrations and maintain high levels of contrast for coronagraphic space telescopes. To further experimentally test this approach for future missions, such as the Habitable Worlds Observatory, this paper quantifies the differences between the theoretical closedloop contrast bounds and DZM performance on the High-contrast Imager for Complex Aperture Telescopes (HiCAT) testbed. The quantification of DZM is achieved by traversing important parameters of the system, specifically the total direct photon rate entering the aperture of the instrument, ranging from 1.85 x 10(6) to 1.85x10(8) photons per second, and the wavefront error drift rate, ranging from sigma(drift) = 0.3- 3 nm/root iteration, injected via the deformable mirror actuators. This is tested on the HiCAT testbed by injecting random walk drifts using two Boston Micromachines kilo deformable mirrors (DMs). The parameter scan is run on the HiCAT simulator and the HiCAT testbed where the corresponding results are compared to the model-based theoretical contrast bounds to analyze discrepancies. The results indicate an approximate one and a half order of magnitude difference between the theoretical bounds and testbed results.