arXiv subjects

Antonio Vanzanella

Publications and source records attributed to Antonio Vanzanella.

5 recordsLinked to original sources

arXiv2026

Rubin J122659.4+090236: An Extremely Low Surface Brightness Galaxy Candidate Discovered in the Rubin LSST Early Data Preview 2

We report the serendipitous discovery of an exceptionally low surface brightness galaxy (LSBG) candidate, Rubin J122659.4+090236, in Rubin Observatory imaging of the interacting NGC 4410 system, identified in the Cosmic Treasure Chest public release. 2D Sérsic modelling of the Rubin g, r, and i images reveals a nearly round system with a shallow profile (n ~ 0.4), an effective radius of R_e ~ 6'', and central surface brightnesses of $μ_{0,g}=27.52\pm0.04$, $μ_{0,r}=27.62\pm0.07$, and $μ_{0,i}=27.04\pm0.08$ mag arcsec$^{-2}$. EAZY photo-z fitting favours an intermediate-z solution at z~0.3, while a low-redshift solution at z~0.028, consistent with the NGC 4410 system, is also permitted by a restricted EAZY fit over 0<z<0.1 without imposing a redshift prior. These alternatives imply substantially different physical interpretations, ranging from a diffuse dwarf-like system to an exceptionally extended background LSBG. This discovery demonstrates Rubin's sensitivity to extremely diffuse galaxies and highlights the potential of the LSST survey to uncover large samples of such elusive systems across wide areas, enabling systematic studies of the LSBG population and its role in galaxy evolution.

astro-ph.GA↗

arXiv2026

From DES to KiDS: Domain adaptation for cross-survey detection of low-surface-brightness galaxies

Low-surface-brightness galaxies (LSBGs) are vital for understanding galaxy formation, but their diffuse nature makes them challenging to detect. Upcoming large-scale surveys are expected to uncover large numbers of LSBGs, requiring robust automated methods to identify them across heterogeneous datasets. As a precursor to the Legacy Survey of Space and Time (LSST) and Euclid, we explore domain adaptation techniques for cross-survey LSBG identification. Using models trained on the Dark Energy Survey (DES), we search for LSBGs in the Kilo-Degree Survey Data Release 5 (KiDS DR5). We used an ensemble consisting of one convolutional neural network (CNN) and two transformer models trained on DES cutouts and applied to KiDS DR5 imaging data. Structural parameters were estimated with galfitm, and photometric redshifts and stellar population properties were estimated through spectral energy distribution fitting with CIGALE. We identify 20,180 LSBGs and 434 ultra-diffuse galaxies (UDGs) in KiDS DR5. Their structural parameters are similar to known LSBGs from DES and the Hyper Suprime-Cam SSP Survey (HSC-SSP). The KiDS-LSBGs follow a continuous size-luminosity relation connecting classical dwarf galaxies and UDGs, and their colours are bimodal ($\sim73\%$ blue, $\sim27\%$ red). Cross-matching with spectroscopic and cluster catalogues provides redshifts for 4,913 systems, enabling a systematic characterisation of the star-forming main sequence of LSBGs. Strong environmental trends are evident, with cluster LSBGs and UDGs exhibiting redder colours and reduced star formation compared to non-cluster systems. We demonstrate that domain adaptation enables robust cross-survey LSBG identification with deep learning models, providing a scalable pathway for constructing homogeneous LSBG catalogues for the LSST and Euclid era.

astro-ph.GA↗

arXiv2025

DART-Vetter: A Deep LeARning Tool for automatic triage of exoplanet candidates

In the identification of new planetary candidates in transit surveys, the employment of Deep Learning models proved to be essential to efficiently analyse a continuously growing volume of photometric observations. To further improve the robustness of these models, it is necessary to exploit the complementarity of data collected from different transit surveys such as NASA's Kepler, Transiting Exoplanet Survey Satellite (TESS), and, in the near future, the ESA PLAnetary Transits and Oscillation of stars (PLATO) mission. In this work, we present a Deep Learning model, named DART-Vetter, able to distinguish planetary candidates (PC) from false positives signals (NPC) detected by any potential transiting survey. DART-Vetter is a Convolutional Neural Network that processes only the light curves folded on the period of the relative signal, featuring a simpler and more compact architecture with respect to other triaging and/or vetting models available in the literature. We trained and tested DART-Vetter on several dataset of publicly available and homogeneously labelled TESS and Kepler light curves in order to prove the effectiveness of our model. Despite its simplicity, DART-Vetter achieves highly competitive triaging performance, with a recall rate of 91% on an ensemble of TESS and Kepler data, when compared to Exominer and Astronet-Triage. Its compact, open source and easy to replicate architecture makes DART-Vetter a particularly useful tool for automatizing triaging procedures or assisting human vetters, showing a discrete generalization on TCEs with Multiple Event Statistic (MES) > 20 and orbital period < 50 days.

astro-ph.EP↗

arXiv2024

How much earlier would LSST have discovered currently known long-period comets?

Among solar system objects, comets coming from the Oort Cloud are an elusive population, intrinsically rare and difficult to detect. Nonetheless, as the more pristine objects we can observe, they encapsulate critical cues on the formation of planetary systems and are the focus of many scientific investigations and science missions. The Legacy Survey of Space and Time (LSST), which will start to operate from the Vera C. Rubin Observatory in 2025, is expected to dramatically improve our detection ability of these comets by performing regular monitoring of the Southern sky deep down to magnitude 24.5 with excellent astrometry. However, making straightforward predictions on future LSST detection rates is challenging due to our biased knowledge of the underlying population. This is because identifications to date have been conducted by various surveys or individual observers, often without detailed information on their respective selection functions. Recent efforts to predict incoming flux of Long Period Comets still suffer of the lack of systematic, well-characterized, homogeneous cometary surveys. Here, we adopt a different point of view by asking how much earlier~on known comets on long-period or hyperbolic orbits would have been discovered by a LSST-like survey if it was already in place 10 years prior to their perihelion epoch. In this case, we are not simulating a real flux of incoming comet, as all comets in our sample reach the perihelion simultaneously, but we can analyze the impact of a LSST-like survey on individual objects. We find that LSST would have found about 40% of comets in our sample at least 5 years prior to their perihelion epoch, and at double (at least) the distance at which they were actually discovered. Based on this approach, we find that LSST has the potentiality to at least twofold the current discovery rate of long-period and hyperbolic comets.

astro-ph.EP↗

arXiv2023

The Data Processor of the SPB2 Fluorescence Telescope: in flight performance

EUSO-SPB2 (Extreme Universe Space Observatory on a Super Pressure Balloon II) is a precursor mission for a future space observatory for multi-messenger astrophysics, planned to be launched in Spring 2023 with a flight duration target of 100 days. The Fluorescence Telescope (FT) hosted on board is designed to detect Ultra High Energy Cosmic Rays via the UV fluorescence emission of the Extensive Air Showers in the atmosphere. The Data Processor (DP) of the FT is the component of the electronics system that performs data management and instrument control for the telescope. The DP controls front-end electronics, tags events with arrival time and payload position through a GPS system, provides signals for time synchronization of the event and measures the live and dead time of the telescope. Furthermore, it manages mass memory for data storage, performs housekeeping monitoring, and controls the power-on and power-off sequences. Finally, the data processor combines the data from the PDMs and onboard differential GPS and prioritizes data for download. The long duration of the flight poses strict requirements on electronics and data handling. The operations at high altitude in an unpressurized environment represent a technological challenge for heat dissipation. This contribution will provide an overview of the innovative elements developed and the results of the integration and field test campaigns. We will also present some preliminary analysis of the performance during the flight.

astro-ph.IM↗