arXiv subjects

Markus J. Bonse

Publications and source records attributed to Markus J. Bonse.

16 recordsLinked to original sources

arXiv2026

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

We report the direct imaging discovery of a third exoplanet in the $β$ Pictoris system. We detect $β$ Pictoris d ($β$ 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. $β$ 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. $β$ Pic d has a contrast of $ΔL^{\prime}=12.11\pm0.15$ mag, with colors and luminosity that closely match those of 51 Eri b, another exoplanet in the $β$ 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. $β$ 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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arXiv2025

Consequences of Non-Gaussian Instrumental Noise in Perturbed Nulling Interferometers

With the astrophysics community working towards the first observations and characterizations of Earth-like exoplanets, interest in space-based nulling interferometry has been renewed. This technique promises unique scientific and technical advantages by enabling direct mid-infrared observations. However, concept studies of nulling interferometers often overlook the impact of systematic noise caused by instrument perturbations. Earlier research introduced analytical and numerical models to address instrumental noise and, building on these results, we reproduce key simulations and report that the noise in the differential output of nulling interferometers follows a non-Gaussian distribution. The presence of non-Gaussian noise challenges the validity of classical hypothesis tests in detection performance estimates, as their reliance on Gaussian assumptions leads to overconfidence in detection thresholds. For the first time, we derive the true noise distribution of the differential output of a dual Bracewell nulling interferometer, demonstrating that it follows iterative convolutions of Bessel functions. Understanding this noise distribution enables a refined formulation of hypothesis testing in nulling interferometry, leading to a semi-analytical prediction of detection performance. This computationally efficient instrument model, implemented in a publicly available codebase, is designed for integration into science yield predictions for nulling interferometry mission concepts. It will play a key role in refining key mission parameters for the Large Interferometer For Exoplanets (LIFE).

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arXiv2025

Keplerian motion of a compact source orbiting the inner disc of PDS 70: a third protoplanet in resonance with b and c?

The disc around PDS 70 hosts two directly imaged protoplanets in a gap. Previous VLT/SPHERE and recent James Webb Space Telescope/NIRCam observations have hinted at the presence of a third compact source in the same gap at ~13 au, interior to the orbit of PDS 70 b. We reduce seven published and one unpublished VLT/SPHERE datasets in YJH and K bands, as well as an archival VLT/NaCo dataset in L' band, and an archival VLT/SINFONI dataset in H+K band. We combine angular-, spectral- and reference star differential imaging to search for protoplanet candidates. We recover the compact source in all epochs, consistent with the JWST detection, moving on an arc that can be fit by Keplerian motion of a protoplanet which could be in a resonance with PDS 70 b & c. We find that the spectral slope is overall consistent with the unresolved star and inner disc emission at 0.95--1.65$μ$m, which suggests a dust scattering dominated spectrum. An excess beyond 2.3$μ$m could be thermal emission from either a protoplanet or heated circumplanetary dust, variability, or inner disc contamination, and requires confirmation. While we currently cannot rule out a moving inner disc feature or a dust clump associated with an unseen planet, the data supports the hypothesis of a third protoplanet in this remarkable system.

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arXiv2025

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 ($\leq 4 λ/D$) 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

VLTI/GRAVITY Observations of AF Lep b: Preference for Circular Orbits, Cloudy Atmospheres, and a Moderately Enhanced Metallicity

Direct imaging observations are biased towards wide-separation, massive companions that have degenerate formation histories. Although the majority of exoplanets are expected to form via core accretion, most directly imaged exoplanets have not been convincingly demonstrated to follow this formation pathway. We obtained new interferometric observations of the directly imaged giant planet AF Lep b with the VLTI/GRAVITY instrument. We present three epochs of 50$μ$as relative astrometry and the K-band spectrum of the planet for the first time at a resolution of R=500. Using only these measurements, spanning less than two months, and the Hipparcos-Gaia Catalogue of Accelerations, we are able to significantly constrain the planet's orbit; this bodes well for interferometric observations of planets discovered by Gaia DR4. Including all available measurements of the planet, we infer an effectively circular orbit ($e<0.02, 0.07, 0.13$ at $1, 2, 3 σ$) in spin-orbit alignment with the host, and a measure a dynamical mass of $M_\mathrm{p}=3.75\pm0.5\,M_\mathrm{Jup}$. Models of the spectrum of the planet show that it is metal rich ([M/H]$=0.75\pm0.25$), with a C/O ratio encompassing the solar value. This ensemble of results show that the planet is consistent with core accretion formation.

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arXiv2024

Exoplanet Imaging Data Challenge, phase II: Comparison of algorithms in terms of characterization capabilities

In this communication, we report on the results of the second phase of the Exoplanet Imaging Data Challenge started in 2019. This second phase focuses on the characterization of point sources (exoplanet signals) within multispectral high-contrast images from ground-based telescopes. We collected eight data sets from two high-contrast integral field spectrographs (namely Gemini-S/GPI and VLT/SPHERE-IFS) that we calibrated homogeneously, and in which we injected a handful of synthetic planetary signals (ground truth) to be characterized by the data challenge participants. The tasks of the participants consist of (1) extracting the precise astrometry of each injected planetary signals, and (2) extracting the precise spectro-photometry of each injected planetary signal. Additionally, the participants may provide the 1-sigma uncertainties on their estimation for further analyses. When available, the participants can also provide the posterior distribution used to estimate the position/spectrum and uncertainties. The data are permanently available on a Zenodo repository and the participants can submit their results through the EvalAI platform. The EvalAI submission platform opened on April 2022 and closed on the 31st of May 2024. In total, we received 4 valid submissions for the astrometry estimation and 4 valid submissions for the spectrophotometry (each submission, corresponding to one pipeline, has been submitted by a unique participant). In this communication, we present an analysis and interpretation of the results.

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arXiv2024

The VLT/ERIS vortex coronagraph: design, pointing control, and on-sky performance

The Enhanced Resolution Imager and Spectrograph (ERIS) is the new near-infrared instrument at the VLT-UT4. ERIS replaces and extends the observational capabilities formerly provided by SINFONI and NACO: integral field spectroscopy at 1 - 2.5 $μ$m, imaging at 1 - 5 $μ$m with several options for high-contrast imaging, and long-slit spectroscopy. In particular, a vortex coronagraph is now available for high contrast observations at L and M band. It is implemented using annular groove (or vortex) phase masks (one for each of the L and M bands) in a focal plane, and a Lyot stop in a downstream pupil plane. The vortex coronagraph has a discovery space starting already at $\sim$1$λ/D$, and works well in broadbands. However, to reach its optimal performance, it is critical to correct for slow pointing errors onto the vortex phase mask, which mandates a dedicated pointing control strategy. To do so, a control loop based on the QACITS algorithm has been developed and commissioned for ERIS. Good pointing stability is now regularly achieved with errors between 0.01 and 0.02 $λ/D$ and a correction rate of 0.2 Hz. In this contribution, we first review the design of the ERIS vortex coronagraph. We then detail the implementation of the QACITS algorithm describing the entire observing sequence, including the calibration steps, the initial centering, and the stabilization during the observing template. We then discuss performance based on commissioning data in terms of pointing accuracy and stability. Finally, we present post-processed contrast curves obtained during commissioning and compare them with NACO vortex data, showing a significant improvement of about 1 mag at all separations.

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arXiv2024

Machine Learning for Exoplanet Detection in High-Contrast Spectroscopy: Revealing Exoplanets by Leveraging Hidden Molecular Signatures in Cross-Correlated Spectra with Convolutional Neural Networks

The new generation of observatories and instruments (VLT/ERIS, JWST, ELT) motivate the development of robust methods to detect and characterise faint and close-in exoplanets. Molecular mapping and cross-correlation for spectroscopy use molecular templates to isolate a planet's spectrum from its host star. However, reliance on signal-to-noise ratio (S/N) metrics can lead to missed discoveries, due to strong assumptions of Gaussian independent and identically distributed noise. We introduce machine learning for cross-correlation spectroscopy (MLCCS); the method aims to leverage weak assumptions on exoplanet characterisation, such as the presence of specific molecules in atmospheres, to improve detection sensitivity for exoplanets. MLCCS methods, including a perceptron and unidimensional convolutional neural networks, operate in the cross-correlated spectral dimension, in which patterns from molecules can be identified. We test on mock datasets of synthetic planets inserted into real noise from SINFONI at K-band. The results from MLCCS show outstanding improvements. The outcome on a grid of faint synthetic gas giants shows that for a false discovery rate up to 5%, a perceptron can detect about 26 times the amount of planets compared to an S/N metric. This factor increases up to 77 times with convolutional neural networks, with a statistical sensitivity shift from 0.7% to 55.5%. In addition, MLCCS methods show a drastic improvement in detection confidence and conspicuity on imaging spectroscopy. Once trained, MLCCS methods offer sensitive and rapid detection of exoplanets and their molecular species in the spectral dimension. They handle systematic noise and challenging seeing conditions, can adapt to many spectroscopic instruments and modes, and are versatile regarding atmospheric characteristics, which can enable identification of various planets in archival and future data.

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arXiv2023

CROCODILE \\ Incorporating medium-resolution spectroscopy of close-in directly imaged exoplanets into atmospheric retrievals via cross-correlation

The investigation of the atmospheres of closely separated, directly imaged gas giant exoplanets is challenging due to the presence of stellar speckles that pollute their spectrum. To remedy this, the analysis of medium- to high-resolution spectroscopic data via cross-correlation with spectral templates (cross-correlation spectroscopy) is emerging as a leading technique. We aim to define a robust Bayesian framework combining, for the first time, three widespread direct-imaging techniques, namely photometry, low-resolution spectroscopy, and medium-resolution cross-correlation spectroscopy in order to derive the atmospheric properties of close-in directly imaged exoplanets. Our framework CROCODILE (cross-correlation retrievals of directly imaged self-luminous exoplanets) naturally combines the three techniques by adopting adequate likelihood functions. To validate our routine, we simulated observations of gas giants similar to the well-studied $β$~Pictoris~b planet and we explored the parameter space of their atmospheres to search for potential biases. We obtain more accurate measurements of atmospheric properties when combining photometry, low- and medium-resolution spectroscopy into atmospheric retrievals than when using the techniques separately as is usually done in the literature. We find that medium-resolution ($R \approx 4000$) K-band cross-correlation spectroscopy alone is not suitable to constrain the atmospheric properties of our synthetic datasets; however, this problem disappears when simultaneously fitting photometry and low-resolution ($R \approx 60$) spectroscopy between the Y and M bands. Our framework allows the atmospheric characterisation of directly imaged exoplanets using the high-quality spectral data that will be provided by the new generation of instruments such as VLT/ERIS, JWST/MIRI, and ELT/METIS.

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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

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

First on-sky results of ERIS at VLT

ERIS (Enhanced Resolution Imager and Spectrograph) is a new adaptive optics instrument installed at the Cassegrain focus of the VLT-UT4 telescope at the Paranal Observatory in Chile. ERIS consists of two near-infrared instruments: SPIFFIER, an integral field unit (IFU) spectrograph covering J to K bands, and NIX, an imager covering J to M bands. ERIS has an adaptive optics system able to work with both LGS and NGS. The Assembly Integration Verification (AIV) phase of ERIS at the Paranal Observatory was carried out starting in December 2021, followed by several commissioning runs in 2022. This contribution will describe the first preliminary results of the on-sky performance of ERIS during its commissioning and the future perspectives based on the preliminary scientific results.

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arXiv2022

Half-sibling regression meets exoplanet imaging: PSF modeling and subtraction using a flexible, domain knowledge-driven, causal framework

High-contrast imaging of exoplanets hinges on powerful post-processing methods to denoise the data and separate the signal of a companion from its host star, which is typically orders of magnitude brighter. Existing post-processing algorithms do not use all prior domain knowledge that is available about the problem. We propose a new method that builds on our understanding of the systematic noise and the causal structure of the data-generating process. Our algorithm is based on a modified version of half-sibling regression (HSR), a flexible denoising framework that combines ideas from the fields of machine learning and causality. We adapt the method to address the specific requirements of high-contrast exoplanet imaging data obtained in pupil tracking mode. The key idea is to estimate the systematic noise in a pixel by regressing the time series of this pixel onto a set of causally independent, signal-free predictor pixels. We use regularized linear models in this work; however, other (non-linear) models are also possible. In a second step, we demonstrate how the HSR framework allows us to incorporate observing conditions such as wind speed or air temperature as additional predictors. When we apply our method to four data sets from the VLT/NACO instrument, our algorithm provides a better false-positive fraction than PCA-based PSF subtraction, a popular baseline method in the field. Additionally, we find that the HSR-based method provides direct and accurate estimates for the contrast of the exoplanets without the need to insert artificial companions for calibration in the data sets. Finally, we present first evidence that using the observing conditions as additional predictors can improve the results. Our HSR-based method provides an alternative, flexible and promising approach to the challenge of modeling and subtracting the stellar PSF and systematic noise in exoplanet imaging data.

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arXiv2020

Physically constrained causal noise models for high-contrast imaging of exoplanets

The detection of exoplanets in high-contrast imaging (HCI) data hinges on post-processing methods to remove spurious light from the host star. So far, existing methods for this task hardly utilize any of the available domain knowledge about the problem explicitly. We propose a new approach to HCI post-processing based on a modified half-sibling regression scheme, and show how we use this framework to combine machine learning with existing scientific domain knowledge. On three real data sets, we demonstrate that the resulting system performs clearly better (both visually and in terms of the SNR) than one of the currently leading algorithms. If further studies can confirm these results, our method could have the potential to allow significant discoveries of exoplanets both in new and archival data.

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arXiv2019

PynPoint: a modular pipeline architecture for processing and analysis of high-contrast imaging data

The direct detection and characterization of planetary and substellar companions at small angular separations is a rapidly advancing field. Dedicated high-contrast imaging instruments deliver unprecedented sensitivity, enabling detailed insights into the atmospheres of young low-mass companions. In addition, improvements in data reduction and PSF subtraction algorithms are equally relevant for maximizing the scientific yield, both from new and archival data sets. We aim at developing a generic and modular data reduction pipeline for processing and analysis of high-contrast imaging data obtained with pupil-stabilized observations. The package should be scalable and robust for future implementations and in particular well suitable for the 3-5 micron wavelength range where typically (ten) thousands of frames have to be processed and an accurate subtraction of the thermal background emission is critical. PynPoint is written in Python 2.7 and applies various image processing techniques, as well as statistical tools for analyzing the data, building on open-source Python packages. The current version of PynPoint has evolved from an earlier version that was developed as a PSF subtraction tool based on PCA. The architecture of PynPoint has been redesigned with the core functionalities decoupled from the pipeline modules. Modules have been implemented for dedicated processing and analysis steps, including background subtraction, frame registration, PSF subtraction, photometric and astrometric measurements, and estimation of detection limits. The pipeline package enables end-to-end data reduction of pupil-stabilized data and supports classical dithering and coronagraphic data sets. As an example, we processed archival VLT/NACO L' and M' data of beta Pic b and reassessed the planet's brightness and position with an MCMC analysis, and we provide a derivation of the photometric error budget.

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arXiv2018

Wavelet based speckle suppression for exoplanet imaging - Application of a de-noising technique in the time domain

Context. High-contrast exoplanet imaging is a rapidly growing field as can be seen through the significant resources invested. In fact, the detection and characterization of exoplanets through direct imaging is featured at all major ground-based observatories. Aims. We aim to improve the signal-to-noise ratio (SNR) achievable for ground-based, adaptive-optics assisted exoplanet imaging by applying sophisticated post-processing algorithms. In particular, we investigate the benefits of including time domain information. Methods. We introduce a new speckle-suppression technique in data post-processing based on wavelet transformation. This technique explicitly considers the time domain in a given data set (specifically the frequencies of speckle variations and their time dependence) and allows us to filter-out speckle noise. We combine our wavelet-based algorithm with state-of-the-art principal component analysis (PCA) based PSF subtraction routines and apply it to archival data sets of known directly imaged exoplanets. The data sets were obtained in the L filter where the short integration times allow for a sufficiently high temporal sampling of the speckle variations. Results. We demonstrate that improvements in the peak SNR of up to forty to sixty percent can be achieved. We also show that, when combined with wavelet-denoising, the PCA PSF model requires systematically smaller numbers of components for the fit to achieve the highest SNR. The improvement potential is, however, data set dependent or, more specifically, closely linked to the field rotation available in a given data set: larger amounts of rotation allow for a better suppression of the speckle noise. Conclusions. We have demonstrated that by applying advanced data post-processing techniques, the contrast performance in archival high-contrast imaging data sets can be improved.

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