On the Iron content in rich nearby Clusters of Galaxies

In this paper we study the iron content of a sample of 22 nearby hot clusters observed with BeppoSAX. We find that the global iron mass of clusters is tightly related to the cluster luminosity and that the relatively loose correlation between the iron mass and the cluster temperature follows from the combination of the iron mass vs. luminosity and luminosity vs. temperature correlations. The iron mass is found to scale linearly with the intracluster gas mass, implying that the global iron abundance in clusters is roughly constant. This result suggests that enrichment mechanisms operate at a similar rate in all clusters. By employing population synthesis and chemical enrichment models, we show that the iron mass associated to the abundance excess which is always found in the centre of cool core clusters can be entirely produced by the brightest cluster galaxy (BCG), which is always found at the centre of cool core clusters. The iron mass associated to the excess, the optical magnitude of the BCG and the temperature of the cluster are found to correlate with one another suggesting a link between the properties of the BCG and the hosting cluster. These observational facts lends strength to current formation theories which envisage a strong connection between the formation of the giant BCG and its hosting cluster.Comment: 12 pages, tables and figures included. Accepted for publication in A&

Old age and supersolar metallicity in a massive z ∼ 1.4 early-type galaxy from VLT/X-Shooter spectroscopy

We present the first estimate of age, stellar metallicity and chemical abundance ratios, for an individual early-type galaxy at high-redshift (z = 1.426) in the COSMOS (Cosmological Evolution Survey) field. Our analysis is based on observations obtained with the X-Shooter instrument at the Very Large Telescope (VLT), which cover the visual and near-infrared spectrum at high (R > 5000) spectral resolution. We measure the values of several spectral absorptions tracing chemical species, in particular magnesium and iron, besides determining the age-sensitive D4000 break. We compare the measured indices to stellar population models, finding good agreement. We find that our target is an old (t > 3 Gyr), high-metallicity ([Z/H] > 0.5) galaxy which formed its stars at zform >5 within a short time-scale ∼0.1 Gyr, as testified by the strong [α/Fe] ratio (>0.4), and has passively evolved in the first >3-4 Gyr of its life. We have verified that this result is robust against the choice and number of fitted spectral features, and stellar population model. The result of an old age and high-metallicity has important implications for galaxy formation and evolution confirming an early and rapid formation of the most massive galaxies in the Universe

Stellar metallicity from optical and UV spectral indices: Test case for WEAVE-StePS

Context. The upcoming generation of optical spectrographs on four meter-class telescopes, with their huge multiplexing capabilities, excellent spectral resolution, and unprecedented wavelength coverage, will provide high-quality spectra for thousands of galaxies. These data will allow us to examine of the stellar population properties at intermediate redshift, an epoch that remains unexplored by large and deep surveys. Aims. We assess our capability to retrieve the mean stellar metallicity in galaxies at different redshifts and signal-to-noise ratios (S/N), while simultaneously exploiting the ultraviolet (UV) and optical rest-frame wavelength coverage. Methods. The work is based on a comprehensive library of spectral templates of stellar populations, covering a wide range of age and metallicity values and built assuming various star formation histories, to cover an observable parameter space with diverse chemical enrichment histories and dust attenuation. We took into account possible observational errors, simulating realistic observations of a large sample of galaxies carried out with WEAVE at the William Herschel Telescope at different redshifts and S/N values. We measured all the available and reliable indices on the simulated spectra and on the comparison library. We then adopted a Bayesian approach to compare the two sets of measurements in order to obtain the probability distribution of stellar metallicity with an accurate estimate of the uncertainties. Results. The analysis of the spectral indices has shown how some mid-UV indices, such as BL3580 and Fe3619, can provide reliable constraints on stellar metallicity, along with optical indicators. The analysis of the mock observations has shown that even at S/N = 10, the metallicity can be derived within 0.3 dex, in particular, for stellar populations older than 2 Gyr. The S/N value plays a crucial role in the uncertainty of the estimated metallicity and so, the differences between S/N = 10 and S/N = 30 are quite large, with uncertainties of ~0.15 dex in the latter case. On the contrary, moving from S/N = 30 to S/N = 50, the improvement on the uncertainty of the metallicity measurements is almost negligible. Our results are in good agreement with other theoretical and observational works in the literature and show how the UV indicators, coupled with classic optical ones, can be advantageous in constraining metallicities. Conclusions. We demonstrate that a good accuracy can be reached on the spectroscopic measurements of the stellar metallicity of galaxies at intermediate redshift, even at low S/N, when a large number of indices can be employed, including some UV indices. This is very promising for the upcoming surveys carried out with new, highly multiplexed, large-field spectrographs, such as StePS at the WEAVE and 4MOST, which will provide spectra of thousands of galaxies covering large spectral ranges (between 3600 and 9000 Å in the observed frame) at relatively high S/N (>10 Å -1)F.R.D., A.I., M.L, S.Z., A.G., F.L.B. acknowledge financial support from grant 1.05.01.86.16 – Mainstream 2020. A.F.M. acknowledges support from RYC2021-031099-I and PID2021-123313NAI00 of MICIN/AEI/10.13039/501100011033/FEDER,UE. L.C. acknowledges financial support from Comunidad de Madrid under Atraccion de Talento grant 2018-T2/TIC-11612 and Spanish Ministerio de Ciencia e Innovacion MCIN/AEI/10.13039/501100011033 through grant PGC2018-093499-BI00. R.G.B. acknowledges financial support from the grants CEX2021-001131-S funded by MCIN/AEI/10.13039/501100011033 and to PID2019-109067-GB100. A.V. acknowledges support from grant PID2019-107427GB-C32 and PID2021-123313NA-I00 from the Spanish Ministry of Science, Innovation and Universities MCIU. This work has also been supported through the IAC project TRACES, which is partially supported through the state budget and the regional budget of the Consejería de Economía, Industria, Comercio y Conocimiento of the Canary Islands Autonomous Community. A.V. also acknowledges support from the ACIISI, Consejería de Economía, Conocimiento y Empleo del Gobierno de Canarias and the European Regional Development Fund (ERDF) under grant with reference ProID202101007

The wide-field, multiplexed, spectroscopic facility WEAVE : survey design, overview, and simulated implementation

Funding for the WEAVE facility has been provided by UKRI STFC, the University of Oxford, NOVA, NWO, Instituto de Astrofísica de Canarias (IAC), the Isaac Newton Group partners (STFC, NWO, and Spain, led by the IAC), INAF, CNRS-INSU, the Observatoire de Paris, Région Île-de-France, CONCYT through INAOE, Konkoly Observatory (CSFK), Max-Planck-Institut für Astronomie (MPIA Heidelberg), Lund University, the Leibniz Institute for Astrophysics Potsdam (AIP), the Swedish Research Council, the European Commission, and the University of Pennsylvania.WEAVE, the new wide-field, massively multiplexed spectroscopic survey facility for the William Herschel Telescope, will see first light in late 2022. WEAVE comprises a new 2-degree field-of-view prime-focus corrector system, a nearly 1000-multiplex fibre positioner, 20 individually deployable 'mini' integral field units (IFUs), and a single large IFU. These fibre systems feed a dual-beam spectrograph covering the wavelength range 366-959 nm at R ∼ 5000, or two shorter ranges at R ∼ 20,000. After summarising the design and implementation of WEAVE and its data systems, we present the organisation, science drivers and design of a five- to seven-year programme of eight individual surveys to: (i) study our Galaxy's origins by completing Gaia's phase-space information, providing metallicities to its limiting magnitude for ∼ 3 million stars and detailed abundances for ∼ 1.5 million brighter field and open-cluster stars; (ii) survey ∼ 0.4 million Galactic-plane OBA stars, young stellar objects and nearby gas to understand the evolution of young stars and their environments; (iii) perform an extensive spectral survey of white dwarfs; (iv) survey  ∼ 400 neutral-hydrogen-selected galaxies with the IFUs; (v) study properties and kinematics of stellar populations and ionised gas in z 1 million spectra of LOFAR-selected radio sources; (viii) trace structures using intergalactic/circumgalactic gas at z > 2. Finally, we describe the WEAVE Operational Rehearsals using the WEAVE Simulator.PostprintPeer reviewe

The wide-field, multiplexed, spectroscopic facility WEAVE: Survey design, overview, and simulated implementation

WEAVE, the new wide-field, massively multiplexed spectroscopic survey facility for the William Herschel Telescope, will see first light in late 2022. WEAVE comprises a new 2-degree field-of-view prime-focus corrector system, a nearly 1000-multiplex fibre positioner, 20 individually deployable 'mini' integral field units (IFUs), and a single large IFU. These fibre systems feed a dual-beam spectrograph covering the wavelength range 366$-$959\,nm at $R\sim5000$, or two shorter ranges at $R\sim20\,000$. After summarising the design and implementation of WEAVE and its data systems, we present the organisation, science drivers and design of a five- to seven-year programme of eight individual surveys to: (i) study our Galaxy's origins by completing Gaia's phase-space information, providing metallicities to its limiting magnitude for $\sim$3 million stars and detailed abundances for $\sim1.5$ million brighter field and open-cluster stars; (ii) survey $\sim0.4$ million Galactic-plane OBA stars, young stellar objects and nearby gas to understand the evolution of young stars and their environments; (iii) perform an extensive spectral survey of white dwarfs; (iv) survey $\sim400$ neutral-hydrogen-selected galaxies with the IFUs; (v) study properties and kinematics of stellar populations and ionised gas in $z<0.5$ cluster galaxies; (vi) survey stellar populations and kinematics in $\sim25\,000$ field galaxies at $0.3\lesssim z \lesssim 0.7$; (vii) study the cosmic evolution of accretion and star formation using $>1$ million spectra of LOFAR-selected radio sources; (viii) trace structures using intergalactic/circumgalactic gas at $z>2$. Finally, we describe the WEAVE Operational Rehearsals using the WEAVE Simulator.Comment: 41 pages, 27 figures, accepted for publication by MNRA

Scaling relations of early-type galaxies at 1 < z

We studied the scaling properties of a sample of 65 ETGs at 1 < z < 2 with spectroscopic confirmation of their redshift and spectral type. The sample collects proprietary (Longhetti et al. 2007) and archival HST data and it is composed of 30 ETGs with HST-NICMOS observations (see Saracco et al. 2009) and of 35 ETGs from the GOODS-South field covered by HST-ACS observations. The whole sample is covered also by ground-based optical and near-IR observations while complementary mid-IR data (Spitzer or AKARI) are available for 45 galaxies. The study of the Kormendy, the size-luminosity and the size-mass relations of these ETGs shows that a large fraction (~50%) of them follows the local relations. These 'normal' ETGs are not smaller (denser) than their local counterparts with comparable stellar mass, luminosity and surface brightness and no size evolution is required for them. On the contrary, the remaining half of the sample is composed of very compact ETGs with sizes (densities) 2.5-3 (15-30) times smaller (higher) than the other ETGs and than local ETGs. Thus, not all the high-z ETGs are superdense and, consequently, only some of them must experience size evolution showing that the evolutionary path of ETGs at 0 < z < 2 is not univocal. We also find that the stellar population of normal ETGs formed at 1.5 < zform < 3 while it formed at 2 < zform < 9 in compact ETGs. This suggests that different histories of mass assembly must take place at high-z to produce both the normal and the superdense ETGs seen at 1 < z < 2 (Saracco et al. 2010)

Galaxy Spectra neural Network (GaSNet). II. Using deep learning for spectral classification and redshift predictions

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 ∼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.</p

Galaxy Spectra neural Network (GaSNet). II. Using Deep Learning for Spectral Classification and Redshift Predictions

23 pages and 31 figures. The draft has been submitted to MNRASLarge sky spectroscopic surveys have reached the scale of photometric surveys in terms of sample sizes and data complexity. These huge datasets require efficient, accurate, and flexible automated tools for data analysis and science exploitation. We present the Galaxy Spectra Network/GaSNet-II, a supervised multi-network 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 for classified objects in each of them. It also provides redshift errors, using a network-of-networks that reproduces a Monte Carlo test on each spectrum, by randomizing their weight initialization. As a demonstration of the capability of the deep learning pipeline, 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% average classification accuracy over the 13 classes (larger than 90% for the majority of them), and an average redshift error of approximately 0.23% for galaxies and 2.1% for quasars. We further train/test the same pipeline to classify spectra and predict redshifts for a sample of 200k 4MOST mock spectra and 21k publicly released DESI spectra. On 4MOST mock data, we reach 93.4% accuracy in 10-class classification and an average redshift error of 0.55% for galaxies and 0.3% for active galactic nuclei. On DESI data, we reach 96% accuracy in (star/galaxy/quasar only) classification and an average redshift error of 2.8% for galaxies and 4.8% for quasars, despite the small sample size available. GaSNet-II can process ~40k spectra in less than one minute, on a normal Desktop GPU. This makes the pipeline particularly suitable for real-time analyses of Stage-IV survey observations and an ideal tool for feedback loops aimed at night-by-night survey strategy optimization

Galaxy Spectra neural Network (GaSNet). II. Using Deep Learning for Spectral Classification and Redshift Predictions

23 pages and 31 figures. The draft has been submitted to MNRASInternational audienceLarge sky spectroscopic surveys have reached the scale of photometric surveys in terms of sample sizes and data complexity. These huge datasets require efficient, accurate, and flexible automated tools for data analysis and science exploitation. We present the Galaxy Spectra Network/GaSNet-II, a supervised multi-network 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 for classified objects in each of them. It also provides redshift errors, using a network-of-networks that reproduces a Monte Carlo test on each spectrum, by randomizing their weight initialization. As a demonstration of the capability of the deep learning pipeline, 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% average classification accuracy over the 13 classes (larger than 90% for the majority of them), and an average redshift error of approximately 0.23% for galaxies and 2.1% for quasars. We further train/test the same pipeline to classify spectra and predict redshifts for a sample of 200k 4MOST mock spectra and 21k publicly released DESI spectra. On 4MOST mock data, we reach 93.4% accuracy in 10-class classification and an average redshift error of 0.55% for galaxies and 0.3% for active galactic nuclei. On DESI data, we reach 96% accuracy in (star/galaxy/quasar only) classification and an average redshift error of 2.8% for galaxies and 4.8% for quasars, despite the small sample size available. GaSNet-II can process ~40k spectra in less than one minute, on a normal Desktop GPU. This makes the pipeline particularly suitable for real-time analyses of Stage-IV survey observations and an ideal tool for feedback loops aimed at night-by-night survey strategy optimization