arXiv subjects

Carl-Henrik Dahlqvist

Publications and source records attributed to Carl-Henrik Dahlqvist.

7 recordsLinked to original sources

arXiv2026

Enhanced detection limits in the SHINE F150 survey through the Regime Switching Model. Optimizing thresholds and investigating environmental noise

In high-contrast imaging, a novel detection algorithm for angular differential imaging (ADI) sequences has recently been introduced: the Regime Switching Model (RSM). In this study, we apply the RSM algorithm to analyze the F150 sample from the SHINE high-contrast imaging survey carried out with VLT/SPHERE, aiming to enhance detection limits and identify new exoplanet candidates. Additionally, we investigate how environmental conditions influence post-processed noise distributions and detection thresholds. We generate detection maps and contrast curves for 213 observations in the F150 SHINE sample using the RSM algorithm. A clustering approach based on environmental parameters is used to group observations with similar noise characteristics. We propose two methods for defining radial detection thresholds in the RSM maps: fitting a log-normal distribution to the post-processed noise and maximizing the F1 score. We also assess the performance of various combinations of post-processing techniques within the RSM framework to identify optimal configurations. This study demonstrates the utility of clustering based on observational parameters, effectively distinguishing features like wind-driven halos and low-wind effects. Detection thresholds vary significantly across clusters, differing by up to a factor of 10, highlighting the importance of considering observational environments. Log-normal thresholds provide conservative, noise-aware limits, while F1 score-based thresholds offer observation-specific results, both showing compatibility overall. RSM improves detection limits by an average factor of two at 1arcsec and five at inner working angles compared to standard PCA processing. This study reports more than 30 newly detected signals, including one promising candidate awaiting second-epoch confirmation.

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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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arXiv2023

NA-SODINN: a deep learning algorithm for exoplanet image detection based on residual noise regimes

Supervised deep learning was recently introduced in high-contrast imaging (HCI) through the SODINN algorithm, a convolutional neural network designed for exoplanet detection in angular differential imaging (ADI) datasets. The benchmarking of HCI algorithms within the Exoplanet Imaging Data Challenge (EIDC) showed that (i) SODINN can produce a high number of false positives in the final detection maps, and (ii) algorithms processing images in a more local manner perform better. This work aims to improve the SODINN detection performance by introducing new local processing approaches and adapting its learning process accordingly. We propose NA-SODINN, a new deep learning binary classifier based on a convolutional neural network (CNN) that better captures image noise correlations in ADI-processed frames by identifying noise regimes. Our new approach was tested against its predecessor, as well as two SODINN-based hybrid models and a more standard annular-PCA approach, through local receiving operating characteristics (ROC) analysis of ADI sequences from the VLT/SPHERE and Keck/NIRC-2 instruments. Results show that NA-SODINN enhances SODINN in both sensitivity and specificity, especially in the speckle-dominated noise regime. NA-SODINN is also benchmarked against the complete set of submitted detection algorithms in EIDC, in which we show that its final detection score matches or outperforms the most powerful detection algorithms.Throughout the supervised machine learning case, this study illustrates and reinforces the importance of adapting the task of detection to the local content of processed images.

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arXiv2021

Auto-RSM: an automated parameter-selection algorithm for the RSM map exoplanet detection algorithm

Most of the high-contrast imaging (HCI) data-processing techniques used over the last 15 years have relied on the angular differential imaging (ADI) observing strategy, along with subtraction of a reference point spread function (PSF) to generate exoplanet detection maps. Recently, a new algorithm called regime switching model (RSM) map has been proposed to take advantage of these numerous PSF-subtraction techniques; RSM uses several of these techniques to generate a single probability map. Selection of the optimal parameters for these PSF-subtraction techniques as well as for the RSM map is not straightforward, is time consuming, and can be biased by assumptions made as to the underlying data set. We propose a novel optimisation procedure that can be applied to each of the PSF-subtraction techniques alone, or to the entire RSM framework. The optimisation procedure consists of three main steps: (i) definition of the optimal set of parameters for the PSF-subtraction techniques using the contrast as performance metric, (ii) optimisation of the RSM algorithm, and (iii) selection of the optimal set of PSF-subtraction techniques and ADI sequences used to generate the final RSM probability map. The optimisation procedure is applied to the data sets of the exoplanet imaging data challenge (EIDC), which provides tools to compare the performance of HCI data-processing techniques. The data sets consist of ADI sequences obtained with three state-of-the-art HCI instruments: SPHERE, NIRC2, and LMIRCam. The results of our analysis demonstrate the interest of the proposed optimisation procedure, with better performance metrics compared to the earlier version of RSM, as well as to other HCI data-processing techniques.

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arXiv2021

Constraints on the nearby exoplanet $ε$ Ind Ab from deep near/mid-infrared imaging limits

The past decade has seen increasing efforts in detecting and characterising exoplanets by high contrast imaging in the near/mid-infrared, which is the optimal wavelength domain for studying old, cold planets. In this work, we present deep AO imaging observations of the nearby Sun-like star $ε$ Ind A with NaCo ($L^{\prime}$) and NEAR (10-12.5 microns) instruments at VLT, in an attempt to directly detect its planetary companion whose presence has been indicated from radial velocity (RV) and astrometric trends. We derive brightness limits from the non-detection of the companion with both instruments, and interpret the corresponding sensitivity in mass based on both cloudy and cloud-free atmospheric and evolutionary models. For an assumed age of 5 Gyr for the system, we get detectable mass limits as low as 4.4 $M_{\rm J}$ in NaCo $L^{\prime}$ and 8.2 $M_{\rm J}$ in NEAR bands at 1.5$\arcsec$ from the central star. If the age assumed is 1 Gyr, we reach even lower mass limits of 1.7 $M_{\rm J}$ in NaCo $L^{\prime}$ and 3.5 $M_{\rm J}$ in NEAR bands, at the same separation. However, based on the dynamical mass estimate (3.25 $M_{\rm J}$) and ephemerides from astrometry and RV, we find that the non-detection of the planet in these observations puts a constraint of 2 Gyr on the lower age limit of the system. NaCo offers the highest sensitivity to the planetary companion in these observations, but the combination with the NEAR wavelength range adds a considerable degree of robustness against uncertainties in the atmospheric models. This underlines the benefits of including a broad set of wavelengths for detection and characterisation of exoplanets in direct imaging studies.

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arXiv2020

Improving the RSM map exoplanet detection algorithm: PSF forward modelling and optimal selection of PSF subtraction techniques

High-contrast imaging (HCI) is one of the most challenging techniques for exoplanet detection. It relies on sophisticated data processing to reach high contrasts at small angular separations. Most data processing techniques of this type are based on the angular differential imaging (ADI) observing strategy to perform the reference PSF subtraction, and generally make use of signal-to-noise (S/N) maps to infer the existence of planetary signals via thresholding. An alternative method for generating the final detection map was recently proposed with the regime-switching model (RSM) map, which uses a regime-switching framework to generate a probability map based on cubes of residuals generated by different PSF subtraction techniques. In this paper, we present several improvements to the original RSM map, focusing on novel PSF subtraction techniques and their optimal combinations, as well as a new procedure for estimating the probabilities involved. We started by implementing two forward-model versions of the RSM map algorithm based on the LOCI and KLIP PSF subtraction techniques. We then addressed the question of optimally selecting the PSF subtraction techniques to optimise the overall performance of the RSM map. A new forward-backward approach was also implemented to take into account both past and future observations to compute the RSM map probabilities, leading to improved precision in terms of astrometry and lowering the background speckle noise. We tested the ability of these various improvements to increase the performance of the RSM map based on different data sets via a computation of ROC curves. These results demonstrate the benefits of these proposed improvements. Finally, we present a new framework to generate contrast curves based on probability maps. The contrast curves highlight the higher performance of the RSM map compared to a standard S/N map at small angular separations.

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arXiv2019

RSM detection map for direct exoplanet detection in ADI sequences

Beyond the choice of wavefront control systems or coronographs, advanced data processing methods play a crucial role in disentangling potential planetary signals from bright quasi-static speckles. Among these methods, angular differential imaging (ADI) for data sets obtained in pupil tracking mode (ADI sequences) is one of the foremost research avenues, considering the many observing programs performed with ADI-based techniques and the associated discoveries. Inspired by the field of econometrics, here we propose a new detection algorithm for ADI sequences, deriving from the regime-switching model first proposed in the 1980s. The proposed model is very versatile as it allows the use of PSF-subtracted data sets (residual cubes) provided by various ADI-based techniques, separately or together, to provide a single detection map. The temporal structure of the residual cubes is used for the detection as the model is fed with a concatenated series of pixel-wise time sequences. The algorithm provides a detection probability map by considering two possible regimes for concentric annuli, the first one accounting for the residual noise and the second one for the planetary signal in addition to the residual noise. The algorithm performance is tested on data sets from two instruments, VLT/NACO and VLT/SPHERE. The results show an overall better performance in the receiver operating characteristic space when compared with standard signal-to-noise-ratio maps for several state-of-the-art ADI-based post-processing algorithms.

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