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

Cantero, C.; Absil, O.; Dahlqvist, C.-H.; Van Droogenbroeck, M.

doi:10.1051/0004-6361/202346085

A&A

2023

DOI: 10.1051/0004-6361/202346085

|View full text |Cite

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

C. Cantero,

O. Absil,

C.-H. Dahlqvist

et al.

Abstract: Context. Supervised deep learning was recently introduced in high-contrast imaging (HCI) through the SODINN algorithm, a con-volutional 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. Aims. This work aims to… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article3

Relationship

Self Cite0

Independent3

Authors

Journals

Cited by 3 publications

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Machine learning for exoplanet detection in high-contrast spectroscopy

Nath-Ranga,

Absil,

Christiaens

et al. 2024

A&A

View full text Add to dashboard Cite

The advent of high-contrast imaging instruments combined with medium-resolution spectrographs allows spectral and temporal dimensions to be combined with spatial dimensions to detect and potentially characterize exoplanets with higher sensitivity. We developed a new method to effectively leverage the spectral and spatial dimensions in integral-field spectroscopy (IFS) datasets using a supervised deep-learning algorithm to improve the detection sensitivity to high-contrast exoplanets. We began by applying a data transform whereby the four-dimensional (two spatial dimensions, one spectral dimension, and one temporal dimension) IFS datasets are replaced by four-dimensional cross-correlation coefficient tensors obtained by cross-correlating our data with young gas giant spectral template spectra. Thus, the spectral dimension is replaced by a radial velocity dimension and the rest of the dimensions are retained `as is'. This transformed data is then used to train machine learning (ML) algorithms. We trained a 2D convolutional neural network with temporally averaged spectral cubes as input, and a convolutional long short-term memory memory network that uses the temporal data as well. We compared these two models with a purely statistical (non-ML) exoplanet detection algorithm, which we developed specifically for four-dimensional datasets, based on the concept of the standardized trajectory intensity mean (STIM) map. We tested our algorithms on simulated young gas giant s inserted into a SINFONI dataset that contains no known exoplanet, and explored the sensitivity of algorithms to detect these exoplanets at contrasts ranging from $10^ $ to $10^ $ for different radial separations. We quantify the relative sensitivity of the algorithms by using modified receiver operating characteristic curves (mROCs). We discovered that the ML algorithms produce fewer false positives and have a higher true positive rate than the STIM-based algorithm. We also show that the true positive rate of ML algorithms is less impacted by changing radial separation than the STIM-based algorithm. Finally, we show that preserving the velocity dimension of the cross-correlation coefficients in the training and inference plays an important role in ML algorithms being more sensitive to the simulated young gas giant s. black In this paper we demonstrate that ML techniques have the potential to improve the detection limits and reduce false positives for directly imaged planets in IFS datasets, after transforming the spectral dimension into a radial velocity dimension through a cross-correlation operation and that the presence of the temporal dimension does not lead to increased sensitivity.

show abstract

Machine learning for exoplanet detection in high-contrast spectroscopy

Nath-Ranga,

Absil,

Christiaens

et al. 2024

A&A

View full text Add to dashboard Cite

show abstract

An alternating minimization algorithm with trajectory for direct exoplanet detection

Daglayan,

Vary,

Absil

et al. 2024

A&A

View full text Add to dashboard Cite

Effective image post-processing algorithms are vital for the successful direct imaging of exoplanets. Standard point spread function (PSF) subtraction methods use techniques based on a low-rank approximation to separate the rotating planet signal from the quasi-static speckles and rely on signal-to-noise ratio maps to detect the planet. These steps do not interact or feed each other, leading to potential limitations in the accuracy and efficiency of exoplanet detection. We aim to develop a novel approach that iteratively finds the flux of the planet and the low-rank approximation of quasi-static signals in an attempt to improve upon current PSF subtraction techniques. In this study, we extend the standard L2 norm minimization paradigm to an L1 norm minimization framework in order to better account for noise statistics in the high contrast images. Then, we propose a new method, referred to as the alternating minimization algorithm with trajectory (AMAT), that makes more advanced use of estimating the low-rank approximation of the speckle field and the planet flux by alternating between them and utilizing both L1 and L2 norms. For the L1 norm minimization, we propose using L1 norm low-rank approximation (L1-LRA), a low-rank approximation computed using an exact block-cyclic coordinate descent method, while we use randomized singular value decomposition for the L2 norm minimization. Additionally, we enhance the visibility of the planet signal using a likelihood ratio as a post-processing step. Numerical experiments performed on a VLT/SPHERE-IRDIS dataset show the potential of AMAT to improve upon the existing approaches in terms of higher S/N, sensitivity limits (contrast curves), and receiver operating characteristic curves. Moreover, for a systematic comparison, we used datasets from the exoplanet data challenge to compare our algorithm with other algorithms in the challenge, and we find AMAT with a likelihood ratio map performs better than most algorithms tested on the exoplanet data challenge.

show abstract

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

Bodrito,

Flasseur,

Mairal

et al. 2024

Monthly Notices of the Royal Astronomical Society

View full text Add to dashboard Cite

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.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

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

Cited by 3 publications

References 53 publications

Machine learning for exoplanet detection in high-contrast spectroscopy

Machine learning for exoplanet detection in high-contrast spectroscopy

An alternating minimization algorithm with trajectory for direct exoplanet detection

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

Contact Info

Product

Resources

About