arXiv subjects

Maud Langlois

Publications and source records attributed to Maud Langlois.

At least 19 recordsLinked to original sources

arXiv2026

SAXO+, the second-stage adaptive optics for SPHERE: NCPA compensation and dark-hole loop with a pyramid wavefront sensor

The SAXO+ upgrade of the VLT/SPHERE adaptive optics system introduces a second-stage near-infrared pyramid wavefront sensor to improve high-contrast imaging, making accurate calibration of non-common path aberrations (NCPAs) essential to fully exploit its performance. This work refines the expected level of NCPAs in SAXO+ and presents the calibration procedures developed for static NCPA compensation and focal-plane dark-hole control. Monte Carlo simulations based on an updated Zemax optical model were used to estimate the NCPA error budget. These simulations are in good agreement with previous measurements on SPHERE and with the assumptions adopted in earlier performance studies. We also propose a calibration strategy that offloads most static aberration correction to the first-stage deformable mirror while preserving the second-stage mirror stroke for high-speed adaptive optics correction. These results validate the expected SAXO+ optical quality and establish the calibration framework required for efficient NCPA compensation and focal-plane wavefront control during future on-sky operations.

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arXiv2026

Destructuring the disk of AB Aurigae: Dynamics and accretion

In this paper, we present near-IR polarized images of the AB Aur disk at three epochs spanning 3.85 years with SPHERE/IRDIS, as well as Halpha images obtained with SPHERE/ZIMPOL at a single epoch. The purpose of this study is to analyze the dynamics of the entire disk and of the various structures in near-IR polarimetry, and to identify sources of Halpha emission to derive constraints on their mass accretion rate. The dynamical study in the near-IR shows that the disk globally follows Keplerian rotation, but we observe a departure from this behavior at radii smaller than ~60au. At the smallest radius of ~25au, we measure a deviation from Keplerian rotation as large as ~12deg over 3.85 years, demonstrating sub-Keplerian rotation. The two bright spirals within the millimeter cavity have different dynamic trends, and we discuss their possible link with the identified planet candidates. We also discuss the implications of the non-Keplerian behavior, and we posit that it could be related to interactions with multiple protoplanets orbiting out of the disk plane on elliptical orbits. Furthermore, the orbital analysis of the compact sources (labeled f1, f2, and f3) suggests that their orbital planes are significantly inclined with respect to the disk plane by several tens of degrees. The variability of the shadows suggests that they are produced by optically thick regions located within ~60au. For the photometric analysis in Halpha, we derive a flux of about 8.22x10^{-15} erg/s/cm^2 for the entire feature f1, but only 6.46x10^{-16} erg/s/cm^2 at the location of AB Aur b, consistent with non-detection. If f1 were a point source and the accretion remained constant for 1Myr, it would correspond to ~5-20 Jupiter masses according to the magnetospheric accretion model or ~6-10 Jupiter masses according to the boundary layer accretion model.

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arXiv2025

Diffusion models for polarimetric reconstruction of circumstellar environments

In this paper, we propose an approach combining diffusion models and inverse problems for the reconstruction of circumstellar disk images. Our method builds upon the Rhapsodie framework for polarimetric imaging, substituting its classical prior with a diffusion model trained on synthetic data. Our formulation explicitly incorporates stellar leakage while efficiently handling missing data and high level noise inherent to high-contrast polarimetric imaging. Experiments show significant improvement over conventional methods within our framework of assumptions, opening new perspectives for studying circumstellar environments.

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arXiv2025

Deep learning for exoplanet detection and characterization by direct imaging at high contrast

Exoplanet imaging is a major challenge in astrophysics due to the need for high angular resolution and high contrast. We present a multi-scale statistical model for the nuisance component corrupting multivariate image series at high contrast. Integrated into a learnable architecture, it leverages the physics of the problem and enables the fusion of multiple observations of the same star in a way that is optimal in terms of detection signal-to-noise ratio. Applied to data from the VLT/SPHERE instrument, the method significantly improves the detection sensitivity and the accuracy of astrometric and photometric estimation.

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arXiv2025

A New Statistical Model of Star Speckles for Learning to Detect and Characterize Exoplanets in Direct Imaging Observations

The search for exoplanets is an active field in astronomy, with direct imaging as one of the most challenging methods due to faint exoplanet signals buried within stronger residual starlight. Successful detection requires advanced image processing to separate the exoplanet signal from this nuisance component. This paper presents a novel statistical model that captures nuisance fluctuations using a multi-scale approach, leveraging problem symmetries and a joint spectral channel representation grounded in physical principles. Our model integrates into an interpretable, end-to-end learnable framework for simultaneous exoplanet detection and flux estimation. The proposed algorithm is evaluated against the state of the art using datasets from the SPHERE instrument operating at the Very Large Telescope (VLT). It significantly improves the precision-recall trade-off, notably on challenging datasets that are otherwise unusable by astronomers. The proposed approach is computationally efficient, robust to varying data quality, and well suited for large-scale observational surveys.

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arXiv2024

Blind and robust reconstruction of adaptive optics point spread functions for asteroid deconvolution and moon detection

Initially designed to detect and characterize exoplanets, extreme adaptive optics systems (AO) open a new window on the solar system by resolving its small bodies. Nonetheless, despite the always increasing performances of AO systems, the correction is not perfect, degrading their image and producing a bright halo that can hide faint and close moons. Using a reference point spread function (PSF) is not always sufficient due to the random nature of the turbulence. In this work, we present our method to overcome this limitation. It blindly reconstructs the AO-PSF directly in the data of interest, without any prior on the instrument nor the asteroid's shape. This is done by first estimating the PSF core parameters under the assumption of a sharp-edge and flat object, allowing the image of the main body to be deconvolved. Then, the PSF faint extensions are reconstructed with a robust penalization optimization, discarding outliers on-the-fly such as cosmic rays, defective pixels and moons. This allows to properly model and remove the asteroid's halo. Finally, moons can be detected in the residuals, using the reconstructed PSF and the knowledge of the outliers learned with the robust method. We show that our method can be easily applied to different instruments (VLT/SPHERE, Keck/NIRC2), efficiently retrieving the features of AO-PSFs. Compared with state-of-the-art moon enhancement algorithms, moon signal is greatly improved and our robust detection method manages to discriminate faint moons from outliers.

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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

REXPACO ASDI: Joint unmixing and deconvolution of the circumstellar environment by angular and spectral differential imaging

Angular and spectral differential imaging is an observational technique of choice to investigate the immediate vicinity of stars. The relative angular motion and spectral scaling between on-axis and off-axis sources are exploited by post-processing techniques to separate two components: the residual star light and the light coming from surrounding objects such as circumstellar disks or point-like objects. This paper introduces a new algorithm to jointly unmix these two components and deconvolve disk images. The proposed algorithm is based on a statistical modeling of the residual star light, accounting for its spatial and spectral correlations. While critical, these correlations are not modeled and compensated for by existing reconstruction algorithms. We leverage dedicated shrinkage techniques to estimate the large number of parameters of our model of correlations in a data-driven fashion. We show that the resulting separable model of the spatial and spectral covariances captures very accurately the star light, thus allowing its efficient suppression. We apply our method on several datasets from the VLT/SPHERE instrument and compare the performances against standard algorithms of the field (cADI, PCA). We show that accounting for the multiple correlations of the data significantly enhances the reconstruction quality, leading to better preservation of both the disk morphology and photometry. Thanks to its unique joint spectral modeling of the data, the proposed algorithm can reconstruct disks having a circular symmetry (e.g., rings, spirals) at intensities one million times fainter than the star, without resorting to additional reference datasets free from the off-axis objects.

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arXiv2024

MODEL&CO: Exoplanet detection in angular differential imaging by learning across multiple observations

Direct imaging of exoplanets is particularly challenging due to the high contrast between the planet and the star luminosities, and their small angular separation. In addition to tailored instrumental facilities implementing adaptive optics and coronagraphy, post-processing methods combining several images recorded in pupil tracking mode are needed to attenuate the nuisances corrupting the signals of interest. Most of these post-processing methods build a model of the nuisances from the target observations themselves, resulting in strongly limited detection sensitivity at short angular separations due to the lack of angular diversity. To address this issue, we propose to build the nuisance model from an archive of multiple observations by leveraging supervised deep learning techniques. The proposed approach casts the detection problem as a reconstruction task and captures the structure of the nuisance from two complementary representations of the data. Unlike methods inspired by reference differential imaging, the proposed model is highly non-linear and does not resort to explicit image-to-image similarity measurements and subtractions. The proposed approach also encompasses statistical modeling of learnable spatial features. The latter is beneficial to improve both the detection sensitivity and the robustness against heterogeneous data. We apply the proposed algorithm to several datasets from the VLT/SPHERE instrument, and demonstrate a superior precision-recall trade-off compared to the PACO algorithm. Interestingly, the gain is especially important when the diversity induced by ADI is the most limited, thus supporting the ability of the proposed approach to learn information across multiple observations.

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arXiv2024

Combining statistical learning with deep learning for improved exoplanet detection and characterization

In direct imaging at high contrast, the bright glare produced by the host star makes the detection and the characterization of sub-stellar companions particularly challenging. In spite of the use of an extreme adaptive optics system combined with a coronagraphic mask to strongly attenuate the starlight contamination, dedicated post-processing methods combining several images recorded with the pupil tracking mode of the telescope are needed to reach the required contrast. In that context, we recently proposed to combine the statistics-based model of PACO with a deep learning approach in a three-step algorithm. First, the data are centered and whitened locally using the PACO framework to improve the stationarity and the contrast in a preprocessing step. Second, a convolutional neural network (CNN) is trained in a supervised fashion to detect the signature of synthetic sources in the preprocessed science data. Finally, the trained network is applied to the preprocessed observations and delivers a detection map. A second network is trained to infer locally the photometry of detected sources. Both deep models are trained from scratch with a custom data augmentation strategy allowing to generate a large training set from a single spatio-temporo-spectral dataset. This strategy can be applied to process jointly the images of observations conducted with angular, and eventually spectral, differential imaging (A(S)DI). In this proceeding, we present in a unified framework the key ingredients of the deep PACO algorithm both for ADI and ASDI. We apply our method on several datasets from the the IRDIS imager of the VLT/SPHERE instrument. Our method reaches, in average, a better trade-off between precision and recall than the comparative algorithms.

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arXiv2024

Shrinkage MMSE estimators of covariances beyond the zero-mean and stationary variance assumptions

We tackle covariance estimation in low-sample scenarios, employing a structured covariance matrix with shrinkage methods. These involve convexly combining a low-bias/high-variance empirical estimate with a biased regularization estimator, striking a bias-variance trade-off. Literature provides optimal settings of the regularization amount through risk minimization between the true covariance and its shrunk counterpart. Such estimators were derived for zero-mean statistics with i.i.d. diagonal regularization matrices accounting for the average sample variance solely. We extend these results to regularization matrices accounting for the sample variances both for centered and non-centered samples. In the latter case, the empirical estimate of the true mean is incorporated into our shrinkage estimators. Introducing confidence weights into the statistics also enhance estimator robustness against outliers. We compare our estimators to other shrinkage methods both on numerical simulations and on real data to solve a detection problem in astronomy.

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arXiv2024

Upgrading SPHERE with the second stage AO system SAXO+: non-common path aberrations estimation and correction

SAXO+ is a planned enhancement of the existing SAXO, the VLT/ SPHERE adaptive optics system, deployed on ESO's Very Large Telescope. This upgrade is designed to significantly enhance the instrument's capacity to detect and analyze young Jupiter-like planets. The pivotal addition in SAXO+ is a second-stage adaptive optics system featuring a dedicated near-infrared pyramid wavefront sensor and a second deformable mirror. This secondary stage is strategically integrated to address any residual wavefront errors persisting after the initial correction performed by the current primary AO loop, SAXO. However, several recent studies clearly showed that in good conditions, even in the current system SAXO, non-common path aberrations (NCPAs) are the limiting factor of the final normalized intensity in focal plane, which is the final metric for ground-based high-contrast instruments. This is likely to be even more so the case with the new AO system, with which the AO residuals will be minimized. Several techniques have already been extensively tested on SPHERE in internal source and/or on-sky and will be presented in this paper. However, the use of a new type of sensor for the second stage, a pyramid wavefront sensor, will likely complicate the correction of these aberrations. Using an end-to-end AO simulation tool, we conducted simulations to gauge the effect of measured SPHERE NCPAs in the coronagraphic image on the second loop system and their correction using focal plane wavefront sensing systems. We finally analyzed how the chosen position of SAXO+ in the beam will impact the evolution of the NCPAs in the new instrument.

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arXiv2024

Numerical simulations for the SAXO+ upgrade: Performance analysis of the adaptive optics system

SPHERE, operating at the VLT since 2014, is currently one of the high-contrast instruments with a higher performance. Its adaptive optics system, known as SAXO, will be upgraded to SAXO+, which features the addition of a second stage of adaptive optics. This stage will use a near-infrared pyramid wavefront sensor to record images of fainter exoplanets around redder stars. In this work, we compare the performance of SAXO and SAXO+. We look for the optimal values of the key system parameters of SAXO+ for various science cases and turbulence conditions. We performed numerical simulations using COMPASS, an end-to-end adaptive optics simulation tool. We simulated perfect coronagraph images of an on-axis point source, and we minimized the residual starlight intensity between 3 and 5 $λ/D$ as a performance criterion. The explored parameter space includes science cases, turbulence conditions, and key system parameters. In every science case and turbulence condition, SAXO+ reduces the residual starlight intensity inside the correction zone of the second stage by a factor of ten compared to SAXO. The optimal first stage gain is lower for SAXO+ than for SAXO alone. We quantified the gain in performance of SAXO+ when changing the second stage frequency from 2 kHz to 3 kHz, and we conclude that 2 kHz may be sufficient for most realistic conditions. We give the optimal first stage gain as well as the first and second stage frequencies for every seeing, coherence time, and science case. Finally, we find that a 2 ${λ_{\mathrm{WFS}}}/D$ pyramid modulation radius is a good trade-off between performance and robustness against varying turbulence conditions. This study shows that the future SAXO+ system will outperform the current SAXO system in all studied cases.

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arXiv2024

Full orbital solutions in pre-main sequence high-order multiple systems: GG Tau Ab and UX Tau B

High-order multiple (triple and beyond) systems are relatively common. Their interaction with circumstellar and circumbinary material can have a large impact on the formation and evolution of planetary systems and depends on their orbital properties. GG\,Tau and UX\,Tau are two pre-main sequence high-order multiple systems in which the tightest pair has a projected separation of $\approx5$--20\,au. Characterizing precisely their orbits is crucial to establish their long-term stability, to predict the dynamics and evolution of circumstellar matter, and to evaluate the potential for planet formation in such systems. We combine existing astrometric measurements with previously unpublished high-resolution observations of the GG\,Tau\,Ab and UX\,Tau\,B pairs and perform Keplerian orbital fits. For GG\,Tau\,Ab the data presented here represent the first detection of orbital motion. For both systems they yield dramatic increases in orbital coverage ($\gtrsim60\%$ and $\approx100\%$ for UX\,Tau\,B and GG\,Tau\,Ab, for orbital periods of $\approx32$ and $\approx8$\,yr, respectively) and allow us to obtain well-constrained orbital fits, including dynamical masses with $\lesssim10\%$ and $\lesssim7\%$ random and systematic uncertainties. We find that both GG\,Tau\,A and UX\,Tau\,A--B likely form stable hierarchical systems, although one possible deprojection solution for GG\,Tau is strongly misaligned and could experience von Zeipel-Lidov-Kozai oscillations. We further find that the UX\,Tau\,B orbit is much more eccentric than the GG\,Tau\,Ab one, possibly explaining the lack of circumstellar material in the former. The newly-determined orbits revive the question of the dynamical fate of gas and dust in these two hierarchical systems and should spur new dedicated simulations to assess the long-term evolution of the systems and the dynamical perturbations imposed by the close binaries they host.

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arXiv2023

The polarisation properties of the HD 181327 debris ring. Evidence for sub-micron particles from scattered light observations

Polarisation is a powerful remote-sensing tool to study the nature of particles scattering the starlight. It is widely used to characterise interplanetary dust particles in the Solar System and increasingly employed to investigate extrasolar dust in debris discs' systems. We aim to measure the scattering properties of the dust from the debris ring around HD 181327 at near-infrared wavelengths. We obtained high-contrast polarimetric images of HD 181327 in the H band with the SPHERE / IRDIS instrument on the Very Large Telescope (ESO). We complemented them with archival data from HST / NICMOS in the F110W filter reprocessed in the context of the Archival Legacy Investigations of Circumstellar Environments (ALICE) project. We developed a combined forward-modelling framework to simultaneously retrieve the scattering phase function in polarisation and intensity. We detected the debris disc around HD 181327 in polarised light and total intensity. We measured the scattering phase function and the degree of linear polarisation of the dust at 1.6 micron in the birth ring. The maximum polarisation is 23.6% +/- 2.6% and occurs between a scattering angle of 70 deg and 82 deg. We show that compact spherical particles made of a highly refractive and relatively absorbing material in a differential power-law size distribution of exponent $-3.5$ can simultaneously reproduce the polarimetric and total intensity scattering properties of the dust. This type of material cannot be obtained with a mixture of silicates, amorphous carbon, water ice, and porosity, and requires a more refracting component such as iron-bearing minerals. We reveal a striking analogy between the near-infrared polarisation of comets and that of HD 181327. The methodology developed here combining VLT/SPHERE and HST/NICMOS may be applicable in the future to combine the polarimetric capabilities of SPHERE with the sensitivity of JWST.

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arXiv2023

Closing the loop as an inverse problem: the real-time control of THEMIS adaptive optics

We have taken advantage of the implementation of an adaptive optics system on the Themis solar telescope to implement innovative strategies based on an inverse problem formulation for the control loop. Such an approach encompassing the whole system implies the estimation of the pixel variances of the Shack-Hartmann wavefront sensor, a novel real-time method to extract the wavefront slopes as well as their associated noise covariance, and the computation of pseudo-open loop data. The optimal commands are computed by iteratively solving a regularized inverse problem with spatio-temporal constraints including Kolmogorov statistics. The latency of the dedicated real-time control software with conventional CPU is shorter than 300 $μ$s from the acquisition of the raw 400 x 400 pixel wavefront sensor image to the sending of the commands.

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arXiv2023

SAXO+ upgrade : second stage AO system end-to-end numerical simulations

SAXO+ is a proposed upgrade to SAXO, the AO system of the SPHERE instrument on the ESO Very Large Telescope. It will improve the capabilities of the instrument for the detection and characterization of young giant planets. It includes a second stage adaptive optics system composed of a dedicated near-infrared wavefront sensor and a deformable mirror. This second stage will remove the residual wavefront errors left by the current primary AO loop (SAXO). This paper focuses on the numerical simulations of the second stage (SAXO+) and concludes on the impact of the main AO parameters used to build the design strategy. Using an end-to-end AO simulation tool (COMPASS), we investigate the impact of several parameters on the performance of the AO system. We measure the performance in minimizing the star residuals in the coronagraphic image. The parameters that we study are : the second stage frequency, the photon flux on each WFS, the first stage gain and the DM number of actuators of the second stage. We show that the performance is improved by a factor 10 with respect to the current AO system (SAXO). The optimal second stage frequency is between 1 and 2 kHz under good observing conditions. In a red star case, the best SAXO+ performance is achieved with a low first stage gain of 0.05, which reduces the first stage rejection.

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arXiv2023

deep PACO: Combining statistical models with deep learning for exoplanet detection and characterization in direct imaging at high contrast

Direct imaging is an active research topic in astronomy for the detection and the characterization of young sub-stellar objects. The very high contrast between the host star and its companions makes the observations particularly challenging. In this context, post-processing methods combining several images recorded with the pupil tracking mode of telescope are needed. In previous works, we have presented a data-driven algorithm, PACO, capturing locally the spatial correlations of the data with a multi-variate Gaussian model. PACO delivers better detection sensitivity and confidence than the standard post-processing methods of the field. However, there is room for improvement due to the approximate fidelity of the PACO statistical model to the time evolving observations. In this paper, we propose to combine the statistical model of PACO with supervised deep learning. The data are first pre-processed with the PACO framework to improve the stationarity and the contrast. A convolutional neural network (CNN) is then trained in a supervised fashion to detect the residual signature of synthetic sources. Finally, the trained network delivers a detection map. The photometry of detected sources is estimated by a second CNN. We apply the proposed approach to several datasets from the VLT/SPHERE instrument. Our results show that its detection stage performs significantly better than baseline methods (cADI, PCA), and leads to a contrast improvement up to half a magnitude compared to PACO. The characterization stage of the proposed method performs on average on par with or better than the comparative algorithms (PCA, PACO) for angular separation above 0.5".

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