Browsing by Author "Tortora, Crescenzo"

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    Building the Largest Spectroscopic Sample of Ultracompact Massive Galaxies with the Kilo Degree Survey
    (IOP PUBLISHING LTD, 2020) Scognamiglio, Diana; Tortora, Crescenzo; Spavone, Marilena; Spiniello, Chiara; Napolitano, Nicola R.; D'Ago, Giuseppe; La Barbera, Francesco; Getman, Fedor; Roy, Nivya; Raj, Maria Angela; Radovich, Mario; Brescia, Massimo; Cavuoti, Stefano; Koopmans, Leon V. E.; Kuijken, Koen H.; Longo, Giuseppe; Petrillo, Carlo Enrico
    Ultracompact massive galaxies (UCMGs), i.e., galaxies with stellar masses M. > 8 ' 1010M. and effective radii Re < 1.5 kpc, are very rare systems, in particular at low and intermediate redshifts. Their origin as well as their number density across cosmic time are still under scrutiny, especially because of the paucity of spectroscopically confirmed samples. We have started a systematic census of UCMG candidates within the ESO Kilo Degree Survey, together with a large spectroscopic follow-up campaign to build the largest possible sample of confirmed UCMGs. This is the third paper of the series and the second based on the spectroscopic follow-up program. Here, we present photometrical and structural parameters of 33 new candidates at redshifts 0.15. z. 0.5 and confirm 19 of them as UCMGs, based on their nominal spectroscopically inferred M. and Re. This corresponds to a success rate of 58%, nicely consistent with our previous findings. The addition of these 19 newly confirmed objects allows us to fully assess the systematics on the system selection-and to finally reduce the number density uncertainties. Moreover, putting together the results from our current and past observational campaigns and some literature data, we build the largest sample of UCMGs ever collected, comprising 92 spectroscopically confirmed objects at 0.1. z. 0.5. This number raises to 116, allowing for a 3s tolerance on the M. and Re thresholds for the UCMG definition. For all these galaxies, we have estimated the velocity dispersion values at the effective radii, which have been used to derive a preliminary mass-velocity dispersion correlation.
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    Central velocity dispersion catalogue of LAMOST-DR7 galaxies
    (2020) Napolitano, Nicola R.; D'Ago, Giuseppe; Tortora, Crescenzo; Zhao, Gang; Luo, A-Li; Tang, Baitian; Zhang, Wei; Zhang, Yong; Li, Rui
    The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) is a major facility to carry out spectroscopic surveys for cosmology and galaxy evolution studies. The seventh data release of the LAMOST ExtraGAlactic Survey (LEGAS) is currently available and including redshifts of 193 361 galaxies. These sources are spread over similar to 11 500 deg(2) of the sky, largely overlapping with other imaging (SDSS and HSC) and spectroscopic (BOSS) surveys. The estimated depth of the galaxy sample, r similar to 17.8, the high signal-to-noise ratio, and the spectral resolution R = 1800, make the LAMOST spectra suitable for galaxy velocity dispersion (VD) measurements, which are invaluable to study the structure and formation of galaxies and to determine their central dark matter content. We present the first estimates of central VD of similar to 86 000 galaxies in LAMOST footprint. We have used a wrap-up procedure to perform the spectral fitting using PPXF, and derive VD measurements. Statistical errors are also assessed by comparing LAMOST VD estimates with the ones of SDSS and BOSS over a common sample of similar to 51 000 galaxies. The two data sets show a good agreement, within the statistical errors, in particular when VD values are corrected to 1 effective radius aperture. We also present a preliminary mass-sigma relation and find consistency with previous analyses based on local galaxy samples. These first results suggest that LAMOST spectra are suitable for galaxy VD measurements to complement the available catalogues of galaxy internal kinematics in the Northern hemisphere. We plan to expand this analysis to next LAMOST data releases.
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    Galaxy Spectra neural Network (GaSNet). II. Using deep learning for spectral classification and redshift predictions
    (2024) Zhong, Fucheng; Napolitano, Nicola R.; Heneka, Caroline; Li, Rui; Bauer, Franz Erik; Bouche, Nicolas; Comparat, Johan; Kim, Young-Lo; Krogager, Jens-Kristian; Longhetti, Marcella; Loveday, Jonathan; Roukema, Boudewijn F.; Rouse, Benedict L.; Salvato, Mara; Tortora, Crescenzo; Assef, Roberto J.; Cassara, Letizia P.; Costantin, Luca; Croom, Scott M.; Davies, Luke J. M.; Fritz, Alexander; Guiglion, Guillaume; Humphrey, Andrew; Pompei, Emanuela; Ricci, Claudio; Sifon, Cristobal; Tempel, Elmo; Zafar, Tayyaba
    The size and complexity reached by the large sky spectroscopic surveys require efficient, accurate, and flexible automated tools for data analysis and science exploitation. We present the Galaxy Spectra Network/GaSNet-II, a supervised multinetwork deep learning tool for spectra classification and redshift prediction. GaSNet-II can be trained to identify a customized number of classes and optimize the redshift predictions. Redshift errors are determined via an ensemble/pseudo-Monte Carlo test obtained by randomizing the weights of the network-of-networks structure. As a demonstration of the capability of GaSNet-II, we use 260k Sloan Digital Sky Survey spectra from Data Release 16, separated into 13 classes including 140k galactic, and 120k extragalactic objects. GaSNet-II achieves 92.4 per cent average classification accuracy over the 13 classes and mean redshift errors of approximately 0.23 per cent for galaxies and 2.1 per cent for quasars. We further train/test the pipeline on a sample of 200k 4MOST (4-metre Multi-Object Spectroscopic Telescope) mock spectra and 21k publicly released DESI (Dark Energy Spectroscopic Instrument) spectra. On 4MOST mock data, we reach 93.4 per cent accuracy in 10-class classification and mean redshift error of 0.55 per cent for galaxies and 0.3 per cent for active galactic nuclei. On DESI data, we reach 96 per cent accuracy in (star/galaxy/quasar only) classification and mean redshift error of 2.8 per cent for galaxies and 4.8 per cent for quasars, despite the small sample size available. GaSNet-II can process similar to 40k spectra in less than one minute, on a normal Desktop GPU. This makes the pipeline particularly suitable for real-time analyses and feedback loops for optimization of Stage-IV survey observations.