Interferometry, essential in radio and infrared astronomy, faces a significant challenge: reconstructing images from sparsely sampled data. Current regularized minimization algorithms rely heavily on predefined priors and hyperparameters, leading to ambiguities and inaccuracies in the images. Here, we present a project to integrate Neural Networks into interferometric image reconstruction. By utilizing the principles of Compressed Sensing and generative Neural Networks, this approach can map infrared interferometric data to reconstruct images more accurately, reducing reliance on rigid priors. The adaptability of the Neural Network ensures that the reconstructions are more precise and less dependent on user input, which is a significant advancement over current methods that require extensive expertise. In this work, we present, as software demonstration, reconstructions obtained from the Event Horizon Telescope data of the black-hole shadow at the core of M87.
KEYWORDS: Calibration, James Webb Space Telescope, Stars, Point spread functions, Data modeling, Sensors, Astronomical interferometry, Equipment, Fourier optics, Astronomical interferometers
The multi-national James Webb Space Telescope (JWST) enables several new technologies, one of which is the first space-based infrared interferometer, the Aperture Masking Interferometry (AMI) mode of the Near Infrared Imager and Slitless Spectrograph (NIRISS). AMI is a niche but powerful tool for high resolution imaging of a variety of moderate- to high-contrast astronomical sources. The non-redundant mask (NRM) in the entrance pupil enables detection of structure below the classical Rayleigh diffraction limit, well inside the inner working angle of JWST’s coronagraphs. This explores a parameter space largely inaccessible to existing ground- and other space-based observatories. Early science observations leveraged the capabilities of this unique mode to observe dusty Wolf-Rayet binaries, spatially resolved solar system objects, massive exoplanet systems, and protoplanetary disks. The high quality of this space-based data demonstrated the need for improved analysis methods. We describe approaches to extracting interferometric observables, as well as pre- and post-extraction data cleaning routines we made available to the user community. We also discuss insights and unique challenges that were revealed during the commissioning, early calibration, and first science cycles of this promising observing mode: mitigation strategies for instrumental effects, lessons learned for optimizing observation configuration, and plans for ongoing calibration efforts. Knowledge gained from commissioning and calibration data – which are always non-proprietary – provide valuable insight into the capabilities and limitations of this mode, highlight areas that need improvement, and lay the groundwork for furthering JWST’s scientific objectives.
Since its introduction at the Very Large Telescope Interferometer (VLTI), the GRAVITY instrument has emerged as a key player in interferometry. Recognizing its substantial contributions, the European Southern Observatory and the GRAVITY consortium have embarked on an initiative to enhance the instrument's functions. This initiative, named GRAVITY+, aims to broaden the instrument's utility for the international astronomical community and foster new areas of research. GRAVITY+ incorporate advanced features such as a novel laser Adaptive Optics system and an improved fringe tracker to augment interferometric observation sky coverage. This paper details the development of a new Germanium (Ge) prism. This new technical capability of GRAVITY+ will serve to substantially increase the spectral resolution of the instrument to approximately R~15000. The design process, along with the scientific rationale underpinning this advancement, are thoroughly examined in this study.
Interferometry delivers the highest angular resolution. It is being used extensively in radio astronomy and, since about a decade, it is becoming an important player in infrared astronomy. However, infrared interferometry is restricted to sparse arrays and no full-phase information is recovered. While imaging is arguably the most intuitive way to analyze interferometric data, recovering images from sparsely sampled visibilities is an “ill-posed" problem. The current algorithms work under the framework of using regularized minimization techniques. These algorithms strongly depend on the priors and hyperparameters pre-defined. This gives rise to ambiguities and artifacts in the interpretation of the images and limits their accuracy/precision as well as their signal-to-noise ratio if the priors/regularizers are not well-defined. Also, it means that imaging is the domain of a handful of highly experienced astronomers, thus keeping the interferometric community small. CASSINI-AUTOMAP aims at disrupting this situation by creating a novel framework for interferometric image reconstruction. This project is based on the exploitation of the compressibility of a signal (following the principles of theory of Compressed Sensing) with a novel optimization scheme supported by Neural Networks. In particular, we focus our efforts in designing a Neural Network with adaptive activation functions to find an optimal mapping system between the infrared interferometric data and the reconstructed images. The online adaptability of the Neural Network frees us from having to rely on strong priors, making the reconstructions more accurate and less dependent on users' inputs. Our preliminary network architecture has been tested with Sparse Aperture Masking (SAM) data taken with the infrared camera NACO at the Very Large Telescope and it demonstrates the potential and reliability of the algorithm by recovering the interferometric observables. Future improvements on the software aims at analyzing data from instruments like GRAVITY at the Very Large Telescope Interferometer or the Sparse Aperture Masking mode of the James Webb Space Telescope.
Image reconstruction in optical interferometry has become an important asset for astrophysical studies during the last decades. This has been mainly due to improvements in the imaging capabilities of existing interferometers such as the second generation of beam combiners at the Very Large Telescope Interferometer; or the expected facilities like the Sparse Aperture Masking mode of the James Webb Space Telescope. Since 2004, the community has organized a biennial contest to formally test the different methods and algorithms for image reconstruction. In 2022, we celebrated the 9th edition of the ”Optical Interferometry Imaging Contest”. This initiative represents an open call for the different scientific groups to present their advances in the field of interferometric image reconstruction with sparse infrared arrays. This contest represents a unique opportunity to benchmark, in a systematic way, the current advances and limitations in the field, as well as to discuss possible future approaches. In this work, we summarize: (a) the rules of the 2022 contest; (b) the different data sets used and the selection procedure; (c) the methods and results obtained by each one of the participants; and (d) the metrics used to select the best reconstructed images. Finally, we named John Young as winner of this edition of the contest and Jacques Kluska as winner of an honorific mention for his participation in the contest.
Interferometry is a high resolution technique that enables us to study physical processes at the smallest spatial scales that we can probe with our telescopes. In the infrared and in (sub-)millimetric Very-Long-Baseline Interferometry, the technique is restricted to sparse arrays with only a few telescopes or antennas. While imaging would the most intuitive way to interpret interferometric data, recovering images from sparse arrays is an ill-posed" problem and Fourier inversion techniques are restricted. In this work, we explore a novel imaging scheme based on Compressed Sensing to recover interferometric images. For this purpose, simulated data from the Aperture Masking mode of the James Webb Space Telescope are presented. Our results suggest that reliable interferometric images can be recovered using this technique. In particular, we highlights the recovery of the source structure with high-contrast and low-level residuals.
Image reconstruction in optical interferometry has gained considerable importance for astrophysical studies during the last decade. This has been mainly due to improvements in the imaging capabilities of existing interferometers and the expectation of new facilities in the coming years. However, despite the advances made so far, image synthesis in optical interferometry is still an open field of research. Since 2004, the community has organized a biennial contest to formally test the different methods and algorithms for image reconstruction. In 2016, we celebrated the 7th edition of the "Interferometric Imaging Beauty Contest". This initiative represented an open call to participate in the reconstruction of a selected set of simulated targets with a wavelength-dependent morphology as they could be observed by the 2nd generation of VLTI instruments. This contest represents a unique opportunity to benchmark, in a systematic way, the current advances and limitations in the field, as well as to discuss possible future approaches. In this contribution, we summarize: (a) the rules of the 2016 contest; (b) the different data sets used and the selection procedure; (c) the methods and results obtained by each one of the participants; and (d) the metric used to select the best reconstructed images. Finally, we named Karl-Heinz Hofmann and the group of the Max-Planck-Institut fur Radioastronomie as winners of this edition of the contest.
KEYWORDS: Image restoration, Interferometry, Interferometers, Mid-IR, Visibility, Image processing, Spatial frequencies, Data modeling, Image quality, Signal to noise ratio
During the last decade, the first generation of beam combiners at the Very Large Telescope Interferometer has proved the importance of optical interferometry for high-angular resolution astrophysical studies in the nearand mid-infrared. With the advent of 4-beam combiners at the VLTI, the u - v coverage per pointing increases significantly, providing an opportunity to use reconstructed images as powerful scientific tools. Here, we present our ongoing studies to characterize the imaging capabilities of the Multi-AperTure mid-infrared SpectroScopic Experiment (MATISSE), a second-generation instrument for the Very Large Telescope Interferometer (VLTI). By providing simultaneous observations with 6 baselines and spectral resolutions up to R~5000. MATISSE will deliver, for the first time, thermal-IR interferometric data with enough u-v coverage and phase information for imaging. In this work, we report detailed image reconstruction studies carried out with the image reconstruction package SQUEEZE. For our studies, we use realistic simulated MATISSE data from radiative transfer simulations of a proto-planetary disk. In particular, we will discuss the role of the regularization function and of the initial brightness distribution. MATISSE will perform observations at three different mid-infrared bands: L, M and N. Hence, due to its large bandwidth, chromatic effects should be taken into account when image reconstruction is attempted. We also discuss the capabilities of SQUEEZE to perform multi-wavelength image reconstruction. Finally, we perform an analysis of the image quality and present our future line of research. The work here presented is being carried out within the Opticon FP7-2 joint research activity on interferometric imaging.
We present an overview of the VISIR instrument after its upgrade and return to science operations. VISIR is the midinfrared imager and spectrograph at ESO’s VLT. The project team is comprised of ESO staff and members of the original VISIR consortium: CEA Saclay and ASTRON. The project plan was based on input from the ESO user community with the goal of enhancing the scientific performance and efficiency of VISIR by a combination of measures: installation of improved hardware, optimization of instrument operations and software support. The cornerstone of the upgrade is the 1k by 1k Si:As AQUARIUS detector array manufactured by Raytheon. In addition, a new prism spectroscopic mode covers the whole N-band in a single observation. Finally, new scientific capabilities for high resolution and high-contrast imaging are offered by sub-aperture mask and coronagraphic modes. In order to make optimal use of favourable atmospheric conditions, a water vapour monitor has been deployed on Paranal, allowing for real-time decisions and the introduction of a user-defined constraint on water vapour. During the commissioning in 2012, it was found that the on-sky sensitivity of the AQUARIUS detector was significantly below expectations. Extensive testing of the detector arrays in the laboratory and on-sky enabled us to diagnose the cause for the shortcoming of the detector as excess low frequency noise. It is inherent to the design chosen for this detector and cannot be remedied by changing the detector set-up. Since this is a form of correlated noise, its impact can be limited by modulating the scene recorded by the detector. After careful analysis, we have implemented fast (up to 4 Hz) chopping with field stabilization using the secondary mirror of the VLT. During commissioning, the upgraded VISIR has been confirmed to be more sensitive than the old instrument, and in particular for low-resolution spectroscopy in the N-band, a gain of a factor 6 is realized in observing efficiency. After overcoming several additional technical problems, VISIR is back in Science Operations since April 2015. In addition an upgrade of the IT infrastructure related to VISIR has been conducted in order to support burst-mode operations. Science Verification of the new modes was performed in Feb 2016. The upgraded VISIR is a powerful instrument providing close to background limited performance for diffraction-limited observations at an 8-m telescope. It offers synergies with facilities such as ALMA, JWST, VLTI and SOFIA, while a wealth of targets is available from survey works like WISE. In addition, it will bring confirmation of the technical readiness and scientific value of several aspects for future mid-IR instrumentation at Extremely Large Telescopes. We also present several lessons learned during the project.
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