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

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

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

Stellar Population Survey Using 4MOST (4MOST-StePS)

Galaxy spectra encode in their continuum and absorption/emission features a wealth of information on galaxy physics, mass assembly and chemical enrichment history. The 4MOST-StePS survey will collect high-quality spectra (with a median signal-to-noise ratio of about 30 Å–1, and resolution R ~ 5000) for a sample of about 3300 galaxies brighter than IAB = 20.5 within the RA-Dec-z footprint of the WAVES-Deep survey. These spectra will provide a precise empirical description of the evolutionary path of massive galaxies in the intermediate redshift range (0.3 < z < 0.7) between the LEGA-C and SDSS surveys. The locations of the galaxies within the cosmic web, unveiled by WAVES-Deep, will disclose the connection between galaxy properties and environment, down to the scales of galaxy pairs

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

International audienceWEAVE, 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