HARMONI is the first light visible and near-IR integral field spectrograph for the ELT. It covers a large spectral range from 450 nm to 2450 nm with resolving powers from 3500 to 18000 and spatial sampling from 60 mas to 4 mas. It can operate in two Adaptive Optics modes - SCAO (including a High Contrast capability) and LTAO - or with NOAO. The project is preparing for Final Design Reviews. HARMONI is a work-horse instrument that provides efficient, spatially resolved spectroscopy of extended objects or crowded fields of view. The gigantic leap in sensitivity and spatial resolution that HARMONI at the ELT will enable promises to transform the landscape in observational astrophysics in the coming decade. The project has undergone some key changes to the leadership and management structure over the last two years. We present the salient elements of the project restructuring, and modifications to the technical specifications. The instrument design is very mature in the lead up to the final design review. In this paper, we provide an overview of the instrument's capabilities, details of recent technical changes during the red flag period, and an update of sensitivities.
KEYWORDS: Signal to noise ratio, Exoplanets, Detection and tracking algorithms, Planets, Exoplanetary science, Stars, Sensors, Error analysis, Data modeling, Astrophysics
Exoplanets detection by direct imaging remains one of the most challenging field of modern astronomy. The signal of the star can prevent the detection of orbiting companions in single datasets, but combining information from several observations helps boost the detection limits. We propose a new algorithm named PACOME, based on PACO’s approach, which optimally combines, in a maximum likelihood sense, multi-epoch datasets and improves the detection sensitivity of potential exoplanets by taking into account their orbital motions. The efficiency of the algorithm is tested on the well-known exoplanetary system 51 Eridani.
Direct imaging is an active research topic in astronomy for the detection and the characterization of young substellar objects. The very high contrast between the host star and its companions makes detection particularly challenging. In addition to the use of an extreme adaptive optics system and a coronagraph to strongly attenuate the starlight contamination, dedicated post-processing methods combining several images recorded with the pupil tracking mode of the telescope are needed. In previous works, we have presented the PACO algorithm capturing the spatial correlations of the data with a multi-variate Gaussian model whose parameters are estimated in a data-driven fashion at the scale of a patch of a few tens of pixels. PACO is parameter free and delivers reliable detection confidences with an improved sensitivity compared to the standard methods of the field (e.g., cADI, PCA, TLOCI ). However, there is a room for improvement in the detection sensitivity due to the approximate fidelity of the PACO statistical model with respect to the observations. We propose 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 is trained in a supervised fashion to detect the signature of synthetic sources in the preprocessed science data. The network is trained from scratch with a custom data augmentation strategy allowing to generate a large training set from a single spatio-temporal dataset. Finally, the trained network is applied to the preprocessed observations and delivers a detection map. We apply our method on eleven datasets from the VLT/SPHERE-IRDIS instrument and compare our method with PACO and other baselines of the field (cADI, PCA). Our results show that the proposed method performs on-par with or better than these algorithms, with a contrast improvement up to half a magnitude with respect to PACO.
We recently proposed REXPACO, an algorithm for imaging circumstellar environments from high-contrast angular differential imaging (ADI) data. In the context of high-contrast imaging where the signal of interest is largely dominated by a nuisance term due to the stellar light leakages and the noise, our algorithm amounts to jointly estimating the object of interest and the statistics (mean and covariance matrix) of the nuisance component. In this contribution, we first extend the REXPACO algorithm by refining the statistical model of the nuisance component it embeds. Capitalizing on the improved robustness of this new method named robust REXPACO, we then show how it can be modified to deal with angular plus spectral differential imaging (ASDI) datasets. We apply our methods on several ADI and ASDI datasets from the IRDIS and IFS imagers of the VLT/SPHERE instrument and we show that the proposed algorithms significantly reduce the typical artifacts produced by state-of-the-art algorithms. By also taking into account the instrumental point spread function (PSF), our algorithms yield a deblurred estimate of the object of interest without the artifacts observed with other methods.
The Exoplanet Imaging Data Challenge is a community-wide effort meant to offer a platform for a fair and common comparison of image processing methods designed for exoplanet direct detection. For this purpose, it gathers on a dedicated repository (Zenodo), data from several high-contrast ground-based instruments worldwide in which we injected synthetic planetary signals. The data challenge is hosted on the CodaLab competition platform, where participants can upload their results. The specifications of the data challenge are published on our website https://exoplanet-imaging-challenge.github.io/. The first phase, launched on the 1st of September 2019 and closed on the 1st of October 2020, consisted in detecting point sources in two types of common data-set in the field of high-contrast imaging: data taken in pupil-tracking mode at one wavelength (subchallenge 1, also referred to as ADI) and multispectral data taken in pupil-tracking mode (subchallenge 2, also referred to as ADI+mSDI). In this paper, we describe the approach, organisational lessons-learnt and current limitations of the data challenge, as well as preliminary results of the participants’ submissions for this first phase. In the future, we plan to provide permanent access to the standard library of data sets and metrics, in order to guide the validation and support the publications of innovative image processing algorithms dedicated to high-contrast imaging of planetary systems.
In in-line digital holography, the background of the recorded images is sometimes much higher than the signal of interest. It can originates, for example, from the diffraction of dusts or fringes coming from multiple reflexions in the optical components. It is often correlated, nonstationary and not constant over time. Detecting a weak signal superimposed over such a background is challenging. Detection of the pattern then requires a statistical modeling of the background. In this work, spatial correlations are locally estimated based on several background images. A fast algorithm that computes detection maps is derived. The proposed approach is evaluated on images obtained from experimental data recorded with a holographic microscope.
KEYWORDS: Exoplanets, Signal to noise ratio, Statistical analysis, Stars, Detection and tracking algorithms, Exoplanetary science, Point spread functions, Binary data, Spectrographs, Imaging systems
The search for new exoplanets by direct imaging is a very active research topic in astronomy. The detection is particularly challenging because of the very high contrast between the host star and the companions. They thus remain hidden by a nonstationary background displaying strong spatial correlations. We propose a new algorithm named PACO (for PAtch COvariances) for reduction of differential imaging datasets. Contrary to existing approaches, we model the background correlations using a local Gaussian distribution that locally captures the spatial correlations at the scale of a patch of a few tens of pixels. The decision in favor of the presence or the absence of an exoplanet in then performed by a binary hypothesis test. The method is completely parameter-free and produces both stationary and statistically grounded detection maps so that the false alarm rate, the probability of detection and the contrast can be directly assessed without post-processing and/or Monte-Carlo simulations. We describe in a forthcoming paper its detailed principle and implementation. In this paper, we recall the principle of the PACO algorithm and we give new illustrations of its benefits in terms of detection capabilities on datasets from the VLT/SPHERE-IRDIS instrument. We also apply our algorithm on multi-spectral datasets from the VLT/SPHERE-IFS spectrograph. The performance of PACO is compared to state-of-the-art algorithms such as TLOCI and KLIP-PCA.
Among the various configurations that may be used in digital holography, the original in-line “Gabor” configuration is the simplest setup, with a single beam. It requires sparsity of the sample but it is free from beam separation device and associated drawbacks. This option is particularly suited when cost, compact design or stability are important. This configuration is also easier to adapt on a traditional microscope. Finally, from the metrological point of view, this configuration, combined with parametric inverse reconstructions using Lorenz-Mie Theory, has proven to make possible highly accurate estimation of spherical particles parameters (3D location, radius and refractive index) with sub-micron accuracy. Experimental parameters such as the defocus distance, the choice of the objective, or the coherence of the source have a strong influence on the accuracy of the estimation. They are often studied experimentally on specific setups. We previously demonstrated the benefit of using statistical signal processing tools as the Cram´er-Rao Lower Bounds to predict best theoretical accuracy reachable for opaque object. This accuracy depends on the image/hologram formation model, the noise model and the signal to noise ratio in the holograms. In a co-design framework, we propose here to investigate the influence of experimental parameters on the estimation of the radius and refractive index of micrometer-sized transparent spherical objects. In this context, we use Lorenz-Mie Theory to simulate spherical object holograms, to compute Cram´er-Rao Lower bounds, and to numerically reconstruct the objects parameters using an inverse problem approach. Then, these theoretical studies are used to challenge our digital holographic microscopy setup and conclude about accuracy, limitations and possible enhancements.
Lensless color microscopy is a recent 3D quantitative imaging method allowing to retrieve physical parameters characterizing microscopic objects spread in a volume. The main advantages of this technique are related to its simplicity, compactness, low sensitivity of the setup to vibrations and the possibility to accurately characterize objects. The cost-effectiveness of the method can be further increased using low-end laser diodes as coherent sources and CMOS color sensor equipped with a Bayer filter array. However, the central wavelength delivered by this type of laser is generally known only with a limited precision and can evolve because of its dependence on temperature and power supply voltage. In addition, Bayer-type filters of conventional color sensors are not very selective, resulting in spectral mixing (crosstalk phenomenon) of signals from each color channel. Ignoring these phenomena leads to significant errors in holographic reconstructions. We have proposed a maximum likelihood estimation method to calibrate the setup (central wavelength of the laser sources and spectral mixing introduced by the Bayer filters) using spherical objects naturally present in the field of view or added (calibration objects). This calibration method provides accurate estimates of the wavelengths and of the crosstalk, with an uncertainty comparable to that of a high-resolution spectrometer. To perform the image reconstruction from color holograms following the self-calibration of the setup, we describe a regularized inversion method that includes a linear hologram formation model, sparsity constraints and an edge-preserving regularization. We show on holograms of calibrated objects that the self-calibration of the setup leads to an improvement of the reconstructions.
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