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COSTANZA ARGIROFFI

Deep DECam Survey of Carina Nebula: Unveiling YSO Short-Term Variability

  • Authors: Tramuto, A.S.; Bonito, R.; Venuti, L.; Miceli, M.; Argiroffi, C.; Hartigan, P.
  • Publication year: 2025
  • Type: Audiovisual
  • OA Link: http://hdl.handle.net/10447/700254

Abstract

We aim to study the process of accretion in Young Stellar Objects (YSOs) thanks to the release of the deepest catalogue of the Carina Nebula star-forming region (Tramuto et al., in prep.) made with Dark Energy Camera (DECam) in the 'z' band, whose survey (Hartigan et al., in prep.) has managed to retrieve time-domain data of variable sources like the YSOs in this very rich stellar nursery. The study of large samples of young star cluster members involving many different kinds of parameters is critical for understanding the evolution of accretion on an individual vs. statistical basis. In particular, using our Carina Nebula dataset, containing YSOs, we are able to retrieve information on the physical phenomena behind the different cases of short-term variability in YSOs (timescale: hours/days), most of them caused by the accretion of matter from the circumstellar disk, like bursters, dippers (due to the presence of warped disks), flares (due to magnetic reconnection). Here we discuss statistical investigation performed on the catalogue to infer its capability to discriminate YSOs in this star-forming region (SFR) and to reconstruct the pace of fast-changing flux of these sources and the physics behind them.Moreover, with the arrival of the Rubin Legacy Survey of Space and Time (LSST), there will be unprecedented opportunities to extract large catalogues of YSOs and study their distinct physical processes thanks to its deep filters and multi-band information. Using our data from DECam — a significant precursor to the Rubin LSST — as well as insights from its time-series data, we will present the most comprehensive and deep survey of the Carina Nebula SFR, so that this study will be crucial in preparation for the future Rubin LSST data (Bonito & Hartigan et al. 2018 and Bonito & Venuti et al. 2023)