Dr. Stefano Di Frischia Profile

Dr. Stefano Di Frischia

at INAF - Osservatorio Astronomico d’Abruzzo

SPIE Involvement:

Author

Publications (4)

Proceedings Article | 25 July 2024 Poster + Paper

AI detection of S/N<1 sources in infrared images: a deep learning algorithm developed for the AZT24 facility at Campo Imperatore Observatory

Stefano Di Frischia, Mauro Dolci

Proceedings Volume 13101, 131012Y (2024) https://doi.org/10.1117/12.3018759

KEYWORDS: Astronomy, Denoising, Infrared imaging, Evolutionary algorithms, Algorithm development, Telescopes, Observatories, Infrared radiation, Deep learning, Infrared telescopes

Read Abstract +

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.

Proceedings Article | 25 July 2024 Paper

Management of LOFAR 2.0 station states and transitions through the TANGO-controls framework

Stefano Di Frischia, Hannes Feldt, Corné Lukken, Jan David Mol, Arno Schoenmakers

Proceedings Volume 13101, 131012S (2024) https://doi.org/10.1117/12.3018489

KEYWORDS: Control systems, Antennas, Design, Computer architecture, Control software, Process control, Interfaces, Software engineering

Read Abstract +

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.

Proceedings Article | 25 July 2024 Paper

Streamlining LOFAR operations with the new telescope manager specification system

Sander ter Veen, Jan David Mol, Jorrit Schaap, Jörn Künsemöller, Auke Klazema, Reinder Kraaij, Arno Schoenmakers, Fanna Lautenbach, Hannes Feldt, Corne Lukken, Stefano di Frischia

Proceedings Volume 13098, 130981Z (2024) https://doi.org/10.1117/12.3018969

KEYWORDS: Telescopes, Data archive systems, Data modeling, Radio telescopes, Visualization, System integration, Telescope instrument control software, Efficient operations

Read Abstract +

Proceedings Article | 18 July 2024 Poster + Paper

Upgrading the AZT24 telescope at the Campo Imperatore high-altitude observatory: design and installation of a new, seeing-enhanced NIR imager

M. Dolci, E. Brocato, G. Rodeghiero, S. Di Frischia, P. D'Incecco, M. Canzari, S. Benedetti, F. De Luise, M. Di Carlo, A. Di Cianno, N. Napoleone, A. Piersimoni, E. Portaluri, G. Raimondo, L. Tartaglia, A. Valentini, G. Valentini

Proceedings Volume 13096, 130968W (2024) https://doi.org/10.1117/12.3020225

KEYWORDS: Telescopes, Sensors, Near infrared, Indium gallium arsenide, Astronomy, Imaging systems, Point spread functions, Cameras

Read Abstract +

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Keywords/Phrases

Search In:

Publication Years