Imaging in the near-infrared is affected by a background signal coming from both the terrestrial atmosphere and the instrument itself, which plays an important role in limiting the instrument performances even when standard hardware solutions are applied – like the cryogenic cooling. Several extremely faint sources – which still produce relevant count levels – can therefore remain hidden under the noise, or else their weak characteristic peaks could be mistaken as residual noise peaks. In recent years, the development of increasingly sophisticated and performative deep learning techniques has been finding a number of applications in astronomical data handling and process. We present here a study aimed to identify below-the-noise (S/N⪅1) sources in near-infrared astronomical images. We used a dataset of images in the J (1.25-micron), H (1.65-micron) and K (2.2-micron) bands, acquired with the SWIRCAM near-infrared camera mounted at the AZT24 telescope in Campo Imperatore observatory in the decade 1999 – 2008. Each image from a first subset has been compared with the corresponding, photometrically deeper image from the 2MASS catalogue, producing a set of positions of the sources in 2MASS. After built a Denoising CNN with a paired catalog of 2MASS clean images and artificially added-noisy images with a GAN, the SWIRCAM images have then been fed as input to the CNN, with the aim of identifying a pattern in the background around the missed astronomical sources. The CNN has proven to be effective in removing IR image noise in a more efficient way with respect to classical analytical denoising algorithms, leading to detect extremely low S/N sources, which have also been compared to the validated catalog. The algorithm can be potentially applied to images coming from any telescope, identifying all the sources below the noise and above the intrinsic detectability threshold of the detector. As such, it represents a powerful way to push the limiting magnitude of a telescope beyond the classical paradigm based on the signal-to-noise ratio only.
The Low Frequency Array (LOFAR) is Europe’s largest radio telescope, originally designed, built and operated by ASTRON. It consists of an interferometric array of low band and high band antennas, distributed among 52 stations. Since 2018, a considerable upgrade of the main infrastructure has taken place both on the hardware and on the software side, the so-called LOFAR 2.0. The monitor and control software system of each LOFAR 2.0 station is based on the open-source TANGO-Controls framework, which manages the device architecture and the various functionalities of the station, including its states and transitions. Since each hardware device of the station is implemented as a software module, the startup of the station and its states transitions until a full operative state implies a non-trivial interaction and communication among the different device classes. The proposed design solution places each one of these devices in a specific hierarchical structure, which defines the parent-child relations and the allowed operations for its nodes. Besides that, the device hierarchy can be different according to the two main sequences that are involved in the station states transition: the power sequence and the control sequence. The whole set of sequential operations are entirely managed by the TANGO framework, in particular from a root device called Station Manager, which controls the children devices and the hierarchical sequences. In order to adhere to the TANGO architecture, the operations are mainly developed exploiting device attributes and properties, such that a potentially complex process is handled in a very straightforward, lightweight and maintainable way. The aforementioned software architecture has been already deployed and successfully tested on the LOFAR2 Test Station (L2TS) located in the Netherlands. Therefore, it is proving to be a primary feature for the whole LOFAR2 infrastructure, in view of a forthcoming fully operational phase within the next few years.
KEYWORDS: Telescopes, Data archive systems, Data modeling, Radio telescopes, Visualization, System integration, Telescope instrument control software, Efficient operations
The Low Frequency Array (LOFAR) is Europe’s largest radio telescope, designed, built and operated by ASTRON and international LOFAR partners. It is a complex instrument which had an expensive active human workflow and became difficult to adjust. The new Telescope Manager Specification System (TMSS) solves this by the introduction of a dynamic scheduler, a data-quality assessment workflow and a specification system that allows easy versioned specification of known observing setups but also detailed adjustments of observations and processing pipelines. In this presentation we will show the new optimized operations workflow and dynamic scheduling with TMSS.
The AZT24 is a 1.1m telescope installed at the Campo Imperatore observing station, in Central Italy, at an elevation of 2200 m a.s.l. Since the 2nd half of 1990s, its focal plane has been equipped with SWIRCAM, a 1-2.5 micron camera based on an LN2-cooled, HgCdTe detector, able to exploit the excellent observing conditions offered by the site, especially at those wavelengths. After almost 30 years of operation, this system will now be upgraded with a new IR imager, based on an InGaAs detector, TEC-cooled at around -80 °C. Even with a reduced spectral coverage, the NIR imager will cover a wider field-of-view and will benefit from the seeing-enhancement capability produced by a devoted Tip-Tilt (TT) corrector. The overall project is presented in this paper, with emphasis on a commercial InGaAs detector for astronomical applications. The opto-mechanical layout is optimized to reduce the instrumental thermal background, while the TT-correction system produces a significant narrowing of the PSF, increasing the signal-to-noise ratio of the detected sources. Simulations of the expected performances are reported: they show that the upgraded system is suitable for a number of science cases, ranging from extragalactic Astronomy to stellar Astrophysics and Solar System studies. In addition, it represents an interesting testbench for some technological investigations, both in the field of Adaptive Optics and in that of data acquisition and processing techniques.
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