,
,
,
,
, A Catalog of Rich Clusters of Galaxies, The Astrophysical Journal Supplement Series, vol.70, p.1, 1989.
, Mapping the kinetic Sunyaev-Zel'dovich effect toward MACS J0717.5+3745 with NIKA, A&A, vol.598, p.115, 2017.
URL : https://hal.archives-ouvertes.fr/in2p3-01337533
, Substructure and merger detection in resolved NIKA Sunyaev-Zel'dovich images of distant clusters, A&A, vol.614, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01714786
, The Sixth Data Release of the Sloan Digital Sky Survey, ApJS, vol.175, pp.297-313, 2008.
, Kinematics of Cluster Galaxies and Their Relation to Galaxy Evolution, ApJ 878, vol.1, p.9, 2019.
, PACT I: Combining ACT and Planck data for optimal detection of tSZ signal, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02322557
, The Good, the Bad, and the Ugly: Statistical quality assessment of SZ detections, A&A, vol.580, p.138, 2015.
URL : https://hal.archives-ouvertes.fr/cea-01383656
, Non-Equilibrium Ionization State and Two-Temperature Structure in the Linked Region of Abell 399 and Abell 401, pp.19-22, 2008.
, Properties of the cosmological filament between two clusters: A possible detection of a large-scale accretion shock by Suzaku, A&A, vol.606, 2017.
, Catching Quenching Galaxies: The Nature of the WISE Infrared Transition Zone, ApJ, vol.794, p.13, 2014.
, Improved constraints on dark energy from Chandra X-ray observations of the largest relaxed galaxy clusters, MNRAS, vol.383, pp.879-896, 2008.
, Galaxy And Mass Assembly (GAMA): stellar mass growth of spiral galaxies in the cosmic web, pp.2287-2300, 2016.
, Galaxy And Mass Assembly (GAMA): trends in galaxy colours, morphology, and stellar populations with large-scale structure, group, and pair environments, MNRAS, vol.451, pp.3249-3268, 2015.
, A joint analysis of AMI and CARMA observations of the recently discovered SZ galaxy cluster system AMI-CL J0300+2613, MNRAS 433.3 (2013), pp.2036-2046
, Detailed Sunyaev-Zel'dovich study with AMI of 19 LoCuSS galaxy clusters: masses and temperatures out to the virial radius, MNRAS 425, vol.1, pp.162-203, 2012.
, Sunyaev-Zel'dovich observations with AMI of the hottest galaxy clusters detected in the XMM-Newton Cluster Survey, MNRAS 433, vol.4, pp.2920-2937, 2013.
, Classifying the large-scale structure of the universe with deep neural networks", MNRAS 484, pp.5771-5784, 2019.
, The hierarchical nature of the spin alignment of dark matter haloes in filaments, MNRAS, vol.440, pp.46-50, 2014.
, Galaxy Quenching from Cosmic Web Detachment". In: arXiv e-prints, 2016.
URL : https://hal.archives-ouvertes.fr/hal-02880340
, Multiscale phenomenology of the cosmic web, pp.2163-2187, 2010.
, The universal galaxy cluster pressure profile from a representative sample of nearby systems (REXCESS) and the Y S Z -M 500 relation, A&A, vol.517, p.92, 2010.
, LePHARE: Photometric Analysis for Redshift Estimate, 2011.
, Measuring and modelling the redshift evolution of clustering: the Hubble Deep Field North, pp.540-556, 1999.
, Quantifying the Bimodal Color-Magnitude Distribution of Galaxies, ApJ, vol.600, pp.681-694, 2004.
, Classification parameters for the emission-line spectra of extragalactic objects, pp.5-19, 1981.
, Differential Galaxy Evolution in Cluster and Field Galaxies at z?0.3, vol.527, pp.54-79, 1999.
, An optical catalog of galaxy clusters obtained from an adaptive matched filter finder applied to SDSS DR9 data, NA, vol.58, pp.61-71, 2018.
, Machine Learning in Astronomy: a practical overview". In: arXiv e-prints, 2019.
, Optical validation and characterization of Planck PSZ1 sources at the Canary Islands observatories. I. First year of ITP13 observations, A&A, vol.616, p.42, 2018.
URL : https://hal.archives-ouvertes.fr/hal-02354751
, Recovering galaxy cluster gas density profiles with XMM-Newton and Chandra, A&A, vol.608, p.88, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01669518
, The Halo Boundary of Galaxy Clusters in the SDSS, ApJ 841, vol.1, p.18, 2017.
, A new method for accurate estimation of velocity field statistics, MNRAS, vol.279, p.693, 1996.
, Planck pre-launch status: Design and description of the Low Frequency Instrument, A&A, vol.520, 2010.
URL : https://hal.archives-ouvertes.fr/in2p3-00714426
, SExtractor: Software for source extraction, A&AS, vol.117, pp.393-404, 1996.
, Two Micron All Sky Survey Photometric Redshift Catalog: A Comprehensive Three-dimensional Census of the Whole Sky, ApJS, vol.210, p.9, 2014.
, WISE x SuperCOSMOS Photometric Redshift Catalog: 20 Million Galaxies over 3/pi Steradians, ApJS, vol.225, p.5, 2016.
, Galaxy Clusters Discovered via the Sunyaev-Zel'dovich Effect in the 2500-Square-Degree SPT-SZ Survey, ApJS 216, vol.2, p.27, 2015.
, The COBE Mission: Its Design and Performance Two Years after Launch, ApJ, vol.397, p.420, 1992.
, The Northern ROSAT All-Sky (NORAS) Galaxy Cluster Survey. I. X-Ray Properties of Clusters Detected as Extended X-Ray Sources, ApJS, vol.129, pp.435-474, 2000.
, The ROSAT-ESO Flux Limited X-ray (REFLEX) Galaxy cluster survey. V. The cluster catalogue, A&A, vol.425, pp.367-383, 2004.
, Second ROSAT all-sky survey (2RXS) source catalogue, A&A, vol.588, p.103, 2016.
, How filaments of galaxies are woven into the cosmic web, Nature, vol.380, pp.603-606, 1996.
, Gas and galaxies in filaments between clusters of galaxies. The study of A399-A401, A&A, vol.609, p.49, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01704911
, Star formation rates and stellar masses from machine learning, A&A, vol.622, p.137, 2019.
URL : https://hal.archives-ouvertes.fr/hal-01990704
, Photometry of high-redshift blended galaxies using deep learning, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02447731
, Evidence for X-Radiation from the Radio Galaxy M87, ApJL, vol.150, p.199, 1967.
, The physical properties of star-forming galaxies in the low-redshift Universe, MNRAS 351, pp.1151-1179, 2004.
, The Massive and Distant Clusters of WISE Survey. III. Sunyaev-Zel'dovich Masses of Galaxy Clusters at z=1, ApJ 806, vol.1, p.26, 2015.
, Stellar population synthesis at the resolution of, pp.1000-1028, 2003.
URL : https://hal.archives-ouvertes.fr/hal-00012948
, Spectral evolution of galaxies. I -Early-type systems, ApJ, vol.273, pp.105-127, 1983.
, The 400 Square Degree ROSAT PSPC Galaxy Cluster Survey: Catalog and Statistical Calibration, ApJS 172, vol.2, pp.561-582, 2007.
, Star formation and dust attenuation properties in galaxies from a statistical ultraviolet-to-far-infrared analysis, MNRAS 360, pp.1413-1425, 2005.
URL : https://hal.archives-ouvertes.fr/hal-00019596
, The Southern SHARC catalogue: a ROSAT survey for distant galaxy clusters, MNRAS, vol.341, pp.1093-1108, 2003.
, Structures and Components in Galaxy Clusters: Observations and Models, pp.141-185, 2015.
, Cosmic X-ray Sources, Galactic and Extragalactic, Science, vol.152, pp.66-71, 1966.
, The Calibration of Mid-Infrared Star Formation Rate Indicators, ApJ, vol.666, pp.870-895, 2007.
, Dust Extinction of the Stellar Continua in Starburst Galaxies: The Ultraviolet and Optical Extinction Law, ApJ, vol.429, p.582, 1994.
, eROSITA on SRG. A X-ray all-sky survey mission, Memorie della Societa Astronomica Italiana Supplementi, vol.17, p.159, 2011.
, Cosmology with the Sunyaev-Zel'dovich Effect, ARA&A, vol.40, pp.643-680, 2002.
, A fast Bayesian approach to discrete object detection in astronomical data sets -PowellSnakes I, MNRAS, vol.393, pp.681-702, 2009.
, PowellSnakes II: a fast Bayesian approach to discrete object detection in multi-frequency astronomical data sets, MNRAS 427, vol.2, pp.1384-1400, 2012.
, Nexus of the Cosmic Web, Thirteenth Marcel Grossmann Meeting: On Recent Developments in Theoretical and Experimental General Relativity, Astrophysics and Relativistic Field Theories, pp.2115-2117, 2015.
, NEXUS: tracing the cosmic web connection, MNRAS 429, vol.2, pp.1286-1308, 2013.
, Evolution of the cosmic web, MNRAS 441, vol.4, pp.2923-2973, 2014.
, The Distribution of Hot Gas in Clusters of Galaxies, p.677, 1978.
, Where Are the Baryons?, In: ApJ, vol.514, pp.1-6, 1999.
, Galactic Stellar and Substellar Initial Mass Function, PASP 115, vol.809, pp.763-795, 2003.
URL : https://hal.archives-ouvertes.fr/hal-00014537
, The Splashback Feature around DES Galaxy Clusters: Galaxy Density and Weak Lensing Profiles, vol.1, p.83, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01872309
, Evolution of the most massive galaxies to z=0.6 -I. A new method for physical parameter estimation, MNRAS 421, pp.314-332, 2012.
, Detecting effects of filaments on galaxy properties in the Sloan Digital Sky Survey III, MNRAS 466, vol.2, pp.1880-1893, 2017.
, Maps of the Southern Millimeter-wave Sky from Combined 2500 deg 2 SPT-SZ and Planck Temperature Data, ApJS 239, vol.1, p.10, 2018.
, Calibrating Star Formation in WISE Using Total Infrared Luminosity, ApJ, vol.850, p.68, 2017.
, Galaxy and Mass Assembly (GAMA): Mid-infrared Properties and Empirical Relations from WISE, ApJ, vol.782, p.90, 2014.
, Spin alignments within the cosmic web: a theory of constrained tidal torques near filaments, MNRAS, vol.452, pp.3369-3393, 2015.
, Intercluster filaments in a ?CDM Universe, MNRAS 359, vol.1, pp.272-282, 2005.
, VizieR Online Data Catalog: The 2dF Galaxy Redshift Survey 100k Data Release, VizieR Online Data Catalog, VII/226, 2001.
, ANNz: Estimating Photometric Redshifts Using Artificial Neural Networks, pp.345-351, 2004.
, The ROSAT All-Sky Survey: a Catalog of Clusters of Galaxies in a Region of 1 steradian around the South Galactic Pole, ApJS, vol.140, pp.239-264, 2002.
, Explanatory Supplement to the AllWISE Data Release Products, 2013.
, Discovery of an exceptionally bright giant arc at z = 2.369, gravitationally lensed by the Planck cluster PSZ1 G311.65-18.48, A&A, vol.590, p.4, 2016.
, Baryons in the Warm-Hot Intergalactic Medium, ApJ, vol.552, pp.473-483, 2001.
, The Baryon Oscillation Spectroscopic Survey of SDSS-III, AJ 145, vol.1, p.10, 2013.
URL : https://hal.archives-ouvertes.fr/in2p3-00731047
, Probing the missing baryons with the Sunyaev-Zel'dovich effect from filaments, A&A, vol.624, p.48, 2019.
, A slice of the universe, ApJL, vol.302, pp.1-5, 1986.
, General Physical Properties of External Galaxies, In: Handbuch der Physik, vol.53, p.311, 1959.
, Stellar Formation Rates for photometric samples of galaxies using machine learning methods, MNRAS, 2019.
, Simulating the physical properties of dark matter and gas inside the cosmic web, pp.656-672, 2006.
, Transfer learning for galaxy morphology from one survey to another, pp.93-100, 2019.
, A statistical approach to the theory of galaxy formation, In: Sovast, vol.22, pp.653-660, 1978.
, Galaxy and Mass Assembly (GAMA): survey diagnostics and core data release, MNRAS 413, vol.2, pp.971-995, 2011.
, GAMA: towards a physical understanding of galaxy formation, Astronomy and Geophysics, vol.50, 2009.
, Dancing in the dark: galactic properties trace spin swings along the cosmic web, MNRAS 444, vol.2, pp.1453-1468, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01428102
, AGN-driven quenching of star formation: morphological and dynamical implications for early-type galaxies, MNRAS 433, vol.4, pp.3297-3313, 2013.
, Searching for filaments and large-scale structure around DAFT/FADA clusters, A&A, vol.588, p.69, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01440710
, MACS: A Quest for the Most Massive Galaxy Clusters in the Universe, ApJ, vol.553, pp.668-676, 2001.
, The ROSAT Brightest Cluster Sample -I. The compilation of the sample and the cluster log N-log S distribution, MNRAS, vol.301, pp.881-914, 1998.
, A Systematic X-Ray Search for Clusters of Galaxies behind the Milky Way, ApJ, vol.580, pp.774-788, 2002.
, Warm-hot baryons comprise 5-10 per cent of filaments in the cosmic web, Nature, vol.528, pp.105-107, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01262613
, Spitzer Observations of Abell 1763. I. Infrared and Optical Photometry, AJ 139, pp.434-446, 2010.
, GOODS-Herschel: an infrared main sequence for star-forming galaxies, A&A, vol.533, p.119, 2011.
URL : https://hal.archives-ouvertes.fr/hal-01428167
, The reversal of the star formation-density relation in the distant universe, A&A, vol.468, pp.33-48, 2007.
, The Weak Lensing Masses of Filaments between Luminous Red Galaxies, MNRAS 468, pp.2605-2613, 2017.
, Evidence for a pressure discontinuity at the position of the Coma relic from Planck Sunyaev-Zel'dovich effect data, MNRAS, vol.447, pp.2497-2502, 2015.
, The Chandra Source Catalog, ApJS, vol.189, pp.37-82, 2010.
, A linear X-ray structure in the cluster A399: evidence of a past interaction with A401, MNRAS 285, pp.35-39, 1997.
, Starburst Galaxies in Cluster-feeding Filaments Unveiled by Spitzer, ApJL 672, p.9, 2008.
, The fraction of quiescent massive galaxies in the early Universe, A&A, vol.501, pp.15-20, 2009.
, emcee: The MCMC Hammer, p.306, 2013.
, High Metallicity of the X-Ray Gas Up to the Virial Radius of a Binary Cluster of Galaxies: Evidence of Galactic Superwinds at High-Redshift, pp.343-349, 2008.
, The X-Ray Structure of A399 and A401: A Pre-Merging Cluster Pair, pp.191-200, 1996.
, The Cosmic Baryon Budget, ApJ, vol.503, pp.518-530, 1998.
, Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position, Biological Cybernetics, vol.36, pp.193-202, 1980.
, Hot gas in massive haloes drives both mass quenching and environment quenching, MNRAS 447, vol.1, pp.374-391, 2015.
, Obscured Star Formation in Intermediate-Density Environments: A Spitzer Study of the Abell 901/902 Supercluster, ApJ, vol.690, pp.1883-1900, 2009.
, Non-standard grain properties, dark gas reservoir, and extended submillimeter excess, probed by Herschel in the Large Magellanic Cloud, A&A, vol.536, p.88, 2011.
URL : https://hal.archives-ouvertes.fr/hal-02105611
, Stellar Evolutionary Effects on the Abundances of Polycyclic Aromatic Hydrocarbons and Supernova-Condensed Dust in Galaxies, ApJ, vol.672, pp.214-243, 2008.
, Cluster richness-mass calibration with cosmic microwave background lensing, Nature Astronomy, vol.1, pp.795-799, 2017.
, A survey of the thermal and non-thermal properties of cosmic filaments, MNRAS 486.1 (2019), pp.981-1002
, Evolution of cosmic filaments and of their galaxy population from MHD cosmological simulations, MNRAS 462, vol.1, pp.448-463, 2016.
, Properties of cosmological filaments extracted from Eulerian simulations, MNRAS 453, vol.2, pp.1164-1185, 2015.
, Universal thermodynamic properties of the intracluster medium over two decades in radius in the X-COP sample, A&A, vol.621, p.41, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02354730
, The Extended Medium-Sensitivity Survey Distant Cluster Sample: X-Ray Cosmological Evolution, ApJL, vol.356, p.35, 1990.
, A New Method For Galaxy Cluster Detection. I. The Algorithm, AJ 120, pp.2148-2162, 2000.
, Ensemble samplers with affine invariance, Communications in Applied Mathematics and Computational Science, vol.5, issue.1, pp.65-80, 2010.
, HEALPix: A Framework for High-Resolution Discretization and Fast Analysis of Data Distributed on the Sphere, ApJ, vol.622, pp.759-771, 2005.
, A radio ridge connecting two galaxy clusters in a filament of the cosmic web, Science, vol.364, pp.981-984, 2019.
, The DAFT/FADA survey. I. Photometric redshifts along lines of sight to clusters in the z =, A&A, vol.523, p.21, 2010.
URL : https://hal.archives-ouvertes.fr/hal-01439504
, On the Infall of Matter Into Clusters of Galaxies and Some Effects on Their Evolution, ApJ, vol.176, p.1, 1972.
, An unprecedented view of galaxies and large-scale structure at 0.5 <, vol.566, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01113670
, Variations in integrated galactic initial mass functions due to sampling method and cluster mass function, A&A, vol.512, p.79, 2010.
, PRIMUS: Effects of Galaxy Environment on the Quiescent Fraction Evolution at z < 0.8, ApJ, vol.806, p.162, 2015.
, Large-scale mass distribution in the Illustris simulation, MNRAS, vol.457, pp.3024-3035, 2016.
, The SuperCOSMOS Sky Survey -II. Image detection, parametrization, classification and photometry, MNRAS 326, pp.1295-1314, 2001.
, The SuperCOSMOS Sky Survey -I. Introduction and description, MNRAS 326, pp.1279-1294, 2001.
, The SuperCOSMOS Sky Survey -III. Astrometry". In: MNRAS, vol.326, pp.1315-1327, 2001.
, The Atacama Cosmology Telescope: Sunyaev-Zel'dovich selected galaxy clusters at 148 GHz from three seasons of data, Journal of Cosmology and Astro-Particle Physics, issue.7, p.8, 2013.
, Monte Carlo sampling methods using Markov chains and their applications, Biometrika, vol.57, pp.97-109, 1970.
, Learning to Predict the Cosmological Structure Formation, 2018.
, Support Vector Machines, IEEE Intelligent Systems, vol.13, pp.1541-1672, 1998.
, The ROSAT North Ecliptic Pole Survey: The X-Ray Catalog, ApJS, vol.162, issue.2, pp.304-328, 2006.
, Filtering techniques for the detection of Sunyaev-Zel'dovich clusters <br/> in multifrequency maps, MNRAS, vol.336, pp.1057-1068, 2002.
, Random Decision Forests, Proceedings of the Third International Conference on Document Analysis and Recognition, vol.1, 1995.
, The WARPS Survey. VII. The WARPS-II Cluster Catalog, ApJS, vol.176, pp.374-413, 2008.
, Extragalactic nebulae, ApJ, vol.64, pp.321-369, 1926.
, A Catalog of Visual-like Morphologies in the 5 CANDELS Fields Using Deep Learning, ApJS, vol.221, p.8, 2015.
URL : https://hal.archives-ouvertes.fr/hal-02453326
, MILCANN : A neural network assessed tSZ map for galaxy cluster detection, 2017.
, Measuring the hydrostatic mass bias in galaxy clusters by combining Sunyaev-Zel'dovich and CMB lensing data, A&A, vol.610, p.4, 2018.
, Combined analysis of galaxy cluster number count, thermal Sunyaev-Zel'dovich power spectrum, and bispectrum, A&A, vol.604, p.71, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01582375
, MILCA, a modified internal linear combination algorithm to extract astrophysical emissions from multifrequency sky maps, A&A, vol.558, p.118, 2013.
URL : https://hal.archives-ouvertes.fr/in2p3-00498624
, Accurate photometric redshifts for the CFHT legacy survey calibrated using the VIMOS VLT deep survey, pp.841-856, 2006.
URL : https://hal.archives-ouvertes.fr/hal-00082529
, A critique of Rees's theory of primordial gravitational radiation, MNRAS, vol.156, p.1, 1972.
, xGASS: gas-rich central galaxies in small groups and their connections to cosmic web gas feeding, MNRAS 466, vol.4, pp.4795-4812, 2017.
, Extending the Nearby Galaxy Heritage with WISE: First Results from the WISE Enhanced Resolution Galaxy Atlas, AJ 145, vol.1, p.6, 2013.
, Fossil evidence for spin alignment of Sloan Digital Sky Survey galaxies in filaments, MNRAS 408, vol.2, pp.897-918, 2010.
, The Hi Distribution of the Milky Way, pp.27-61, 2009.
, On the mass of the X-ray binary cluster A399/A401, MNRAS 192, pp.109-112, 1980.
, Stellar masses and star formation histories for 10 5 galaxies from the Sloan Digital Sky Survey, MNRAS 341, pp.33-53, 2003.
, Development and performance of the MUSE calibration unit, strumentation Engineers (SPIE) Conference Series, vol.8446, p.84465, 2012.
, Star Formation in Galaxies Along the Hubble Sequence, ARA&A, vol.36, pp.189-232, 1998.
, Dust-corrected Star Formation Rates of Galaxies. I. Combinations of H? and Infrared Tracers, ApJ, vol.703, pp.1672-1695, 2009.
, SINGS: The SIRTF Nearby Galaxies Survey, pp.928-952, 2003.
, KINGFISH-Key Insights on Nearby Galaxies: A Far-Infrared Survey with Herschel: Survey Description and Image Atlas, p.1347, 2011.
, Star Formation in the Milky Way and Nearby Galaxies, ARA&A, vol.50, pp.531-608, 2012.
, Adam: A Method for Stochastic Optimization, 2014.
, The Sunyaev-Zel'dovich effect at 5": RX J1347.5-1145 imaged by ALMA, PASJ 68, vol.5, p.88, 2016.
, Machine-learning identification of galaxies in the WISExSuperCOSMOS all-sky catalogue, A&A, vol.596, p.39, 2016.
, Galaxies flowing in the oriented saddle frame of the cosmic web, pp.3227-3254, 2019.
URL : https://hal.archives-ouvertes.fr/hal-01909616
, Galaxy evolution in the metric of the cosmic web, MNRAS 474, vol.1, pp.547-571, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01669677
, And yet it flips: connecting galactic spin and the cosmic web, In: arXiv e-prints, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02166554
, Formation of Galaxy Clusters, ARA&A, vol.50, pp.353-409, 2012.
, On the variation of the initial mass function, pp.231-246, 2001.
, Environment from cross-correlations: connecting hot gas and the quenching of galaxies, 2019.
, From voids to filaments: environmental transformations of galaxies in the SDSS, A&A, vol.600, p.6, 2017.
, Dusty Infrared Galaxies: Sources of the Cosmic Infrared Background, ARA&A 43, vol.1, pp.727-768, 2005.
URL : https://hal.archives-ouvertes.fr/hal-01840625
, COSMOS2015 photometric redshifts probe the impact of filaments on galaxy properties, MNRAS, vol.474, pp.5437-5458, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01703924
, Intracluster Medium of the Merging Cluster A3395, ApJ, vol.743, p.78, 2011.
, Planck pre-launch status: The HFI instrument, from specification to actual performance, A&A, vol.520, p.9, 2010.
URL : https://hal.archives-ouvertes.fr/in2p3-00535714
, Searching for Comets on the World Wide Web: The Orbit of 17P/Holmes from the Behavior of Photographers, AJ 144, vol.2, p.46, 2012.
, Identification of the "unidentified" IR emission features of interstellar dust ?, In: A&A, vol.500, pp.279-282, 1984.
, The Quenching Timescale and Quenching Rate of Galaxies, ApJ 832, vol.1, p.29, 2016.
, Tracing the cosmic web, MNRAS 473, vol.1, pp.1195-1217, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01645378
, SDSS DR7 superclusters. The catalogues, A&A, vol.539, p.80, 2012.
, Machine learning cosmological structure formation, MNRAS 479 (Sept. 2018), pp.3405-3414
, The VIMOS Public Extragalactic Redshift Survey (VIPERS): galaxy segregation inside filaments at z = 0.7, MNRAS 465, vol.4, pp.3817-3822, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01555108
, Weak Lensing for Precision Cosmology, ARA&A, vol.56, pp.393-433, 2018.
, The observed growth of massive galaxy clusters -I. Statistical methods and cosmological constraints, MNRAS, vol.406, pp.1759-1772, 2010.
, Stellar masses of SDSS-III BOSS galaxies at z=0.5 and constraints to galaxy formation models, MNRAS 435, pp.2764-2792, 2013.
, The Atacama Cosmology Telescope: Sunyaev-Zel'dovich-Selected Galaxy Clusters at 148 GHz in the 2008 Survey, ApJ 737, vol.2, p.61, 2011.
, Galaxies infalling into groups: filaments versus isotropic infall, MNRAS 455, pp.127-135, 2016.
, The Evolution of the Fractions of Quiescent and Star-forming Galaxies as a Function of Stellar Mass Since z = 3: Increasing Importance of Massive, Dusty Star-forming Galaxies in the Early Universe, ApJ, vol.827, p.25, 2016.
, Baryons in the Cosmic Web of IllustrisTNG -I: gas in knots, filaments, sheets, and voids, MNRAS, vol.486, pp.3766-3787, 2019.
, A Preliminary Measurement of the Cosmic Microwave Background Spectrum by the Cosmic Background Explorer (COBE) Satellite, ApJL, vol.354, p.37, 1990.
, The BAHAMAS project: calibrated hydrodynamical simulations for large-scale structure cosmology, pp.2936-2965, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01554260
, Catalog extraction in SZ cluster surveys: a matched filter approach, A&A 459, pp.341-352, 2006.
URL : https://hal.archives-ouvertes.fr/in2p3-00025680
, Planck early results. III. First assessment of the Low Frequency Instrument inflight performance, A&A, vol.536, 2011.
URL : https://hal.archives-ouvertes.fr/in2p3-00555352
, Equation of State Calculations by Fast Computing Machines, The Journal of Chemical Physics, vol.21, pp.1087-1092, 1953.
, GALEX: a UV telescope to map the star formation history of the universe, Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series. Vol. 10569. Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, p.1056912, 2017.
, IRIS: A New Generation of IRAS Maps, ApJS, vol.157, pp.302-323, 2005.
, The high-mass end of the red sequence at z=0.55 from SDSS-III BOSS: completeness, bimodality and luminosity function, MNRAS 461, pp.1131-1153, 2016.
, Galaxy harassment and the evolution of clusters of galaxies, Nature, vol.379, pp.613-616, 1996.
, On the fast quenching of young low-mass galaxies up to z=0.6: new spotlight on the lead role of environment, /mnras/sty1543, vol.2, pp.2147-2160, 2018.
URL : https://hal.archives-ouvertes.fr/hal-02110579
, Astrophysics with the Spatially and Spectrally Resolved Sunyaev-Zeldovich Effects. A Millimetre/Submillimetre Probe of the Warm and Hot Universe, vol.17, p.17, 2019.
URL : https://hal.archives-ouvertes.fr/hal-01937678
, The 160 Square Degree ROSAT Survey: The Revised Catalog of 201 Clusters with Spectroscopic Redshifts, ApJ, vol.594, pp.154-171, 2003.
, A double radio halo in the close pair of galaxy clusters Abell 399 and Abell 401, A&A, vol.509, p.86, 2010.
, Testing X-Ray Measurements of Galaxy Clusters with Cosmological Simulations, ApJ, vol.655, pp.98-108, 2007.
, F-VIPGI: a new adapted version of VIPGI for FORS2 spectroscopy. Application to a sample of 16 X-ray selected galaxy clusters at 0.6 ? z ? 1.2, A&A, vol.550, p.9, 2013.
, The assembly of galaxies in a hierarchically clustering universe, MNRAS 275, pp.56-66, 1995.
, A Universal Density Profile from Hierarchical Clustering, ApJ, vol.490, pp.493-508, 1997.
, The Structure of Cold Dark Matter Halos, ApJ, vol.462, p.563, 1996.
, The Infrared Astronomical Satellite (IRAS) mission, In: ApJL, vol.278, pp.1-6, 1984.
, Observations of the missing baryons in the warm-hot intergalactic medium, Nature, vol.558, pp.406-409, 2018.
, Dynamics of cD Clusters of Galaxies. IV. Conclusion of a Survey of 25 Abell Clusters, AJ, vol.122, pp.2858-2873, 2001.
, Transient dust in warm debris disks. Detection of Fe-rich olivine grains, A&A, vol.542, p.90, 2012.
, The Equilibrium of Polytropic and Isothermal Cylinders, ApJ, vol.140, p.1056, 1964.
, Machine learning search for variable stars, MNRAS 475, pp.2326-2343, 2018.
, Photometric redshifts from SDSS images using a convolutional neural network, A&A, vol.621, p.26, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02017053
, Strangulation as the primary mechanism for shutting down star formation in galaxies, Nature, vol.521, pp.192-195, 2015.
, Mass and Environment as Drivers of Galaxy Evolution in SDSS and zCOS-MOS and the Origin of the Schechter Function, ApJ, vol.721, pp.193-221, 2010.
, The WARPS Survey. VI. Galaxy Cluster and Source Identifications from Phase I, ApJS, vol.140, pp.265-301, 2002.
, The MCXC: a meta-catalogue of x-ray detected clusters of galaxies, A&A, vol.534, p.109, 2011.
, The Evolution of the Quenching of Star Formation in Cluster Galaxies since z ? 1, ApJ, vol.876, p.40, 2019.
, MPDAF -A Python package for the analysis of VLT/MUSE data, 2017.
, Planck 2013 results. XXIX. The Planck catalogue of Sunyaev-Zeldovich sources, A&A, vol.571, p.29, 2014.
, Planck 2013 results. XXXII. The updated Planck catalogue of Sunyaev-Zeldovich sources, A&A, vol.581, p.14, 2015.
, Planck 2015 results. I. Overview of products and scientific results, A&A, vol.594, 2016.
, Planck 2015 results. XIII. Cosmological parameters, A&A, vol.594, p.13, 2016.
, Planck 2015 results. XXII. A map of the thermal Sunyaev-Zeldovich effect, A&A, vol.594, p.22, 2016.
, Planck 2015 results. XXVI. The Second Planck Catalogue of Compact Sources, A&A, vol.594, p.26, 2016.
, Planck 2015 results. XXVII. The second Planck catalogue of Sunyaev-Zeldovich sources, A&A, vol.594, p.27, 2016.
, Planck 2018 results. VIII. Gravitational lensing". In: arXiv e-prints, 2018.
, Planck early results. VIII. The all-sky early Sunyaev-Zeldovich cluster sample, A&A, vol.536, p.8, 2011.
, Planck intermediate results. LIII. Detection of velocity dispersion from the kinetic Sunyaev-Zeldovich effect, A&A, vol.617, p.48, 2018.
, Planck intermediate results. V. Pressure profiles of galaxy clusters from the Sunyaev-Zeldovich effect, A&A, vol.550, p.131, 2013.
, Planck intermediate results. VIII. Filaments between interacting clusters, A&A, vol.550, p.134, 2013.
, Planck intermediate results. X. Physics of the hot gas in the Coma cluster, A&A, vol.554, p.140, 2013.
, Planck intermediate results. XXVI. Optical identification and redshifts of Planck clusters with the RTT150 telescope, A&A, vol.582, p.29, 2015.
, Planck intermediate results. XXXVII. Evidence of unbound gas from the kinetic Sunyaev-Zeldovich effect, A&A, vol.586, p.140, 2016.
, Planck early results. IV. First assessment of the High Frequency Instrument in-flight performance, A&A, vol.536, p.4, 2011.
, CMB and SZ effect separation with constrained Internal Linear Combinations, pp.2481-2487, 2011.
URL : https://hal.archives-ouvertes.fr/in2p3-00709894
, MUSE observations of the lensing cluster SMACSJ2031.8-4036: new constraints on the mass distribution in the cluster core, MNRAS, vol.446, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01109664
, Hubble captures multiply-imaged ionizing radiation from strongly lensed galaxy at z=2.4, 2019.
, Fast cosmic web simulations with generative adversarial networks, Computational Astrophysics and Cosmology, vol.5, p.4, 2018.
, The Bright SHARC Survey: The Cluster Catalog, ApJS, vol.126, pp.209-269, 2000.
, U-Net: Convolutional Networks for Biomedical Image Segmentation". In: arXiv e-prints, 2015.
, The XMM-Newton serendipitous survey. VII. The third XMM-Newton serendipitous source catalogue, A&A, vol.590, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01554183
, redMaPPer. I. Algorithm and SDSS DR8 Catalog, ApJ, vol.785, p.104, 2014.
URL : https://hal.archives-ouvertes.fr/hal-02481733
, xCOLD GASS: The Complete IRAM 30 m Legacy Survey of Molecular Gas for Galaxy Evolution Studies, ApJS 233, vol.2, p.22, 2017.
, XMM-Newton observations of the binary cluster system Abell 399/401, pp.1439-1456, 2004.
, UV Star Formation Rates in the Local Universe, ApJS, vol.173, pp.267-292, 2007.
, The Luminosity Function and Stellar Evolution, ApJ, vol.121, p.161, 1955.
, Constraints from thermal Sunyaev-Zel'dovich cluster counts and power spectrum combined with CMB, A&A, vol.614, p.13, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01815259
, Constraints on the richness-mass relation and the optical-SZE positional offset distribution for SZE-selected clusters, MNRAS, vol.454, pp.2305-2319, 2015.
, Pre-processing of galaxies in cosmic filaments around AMASCFI clusters in the CFHTLS, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02073474
, Next generation cosmology: constraints from the Euclid galaxy cluster survey, MNRAS 459, vol.2, pp.1764-1780, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01440093
, Continuous fields and discrete samples: reconstruction through Delaunay tessellations, A&A, vol.363, pp.29-32, 2000.
, AMI SZ observations and Bayesian analysis of a sample of six redshift-one clusters of galaxies, MNRAS 431.1 (2013), pp.900-911
, The Cosmic Evolution Survey (COSMOS): Overview, ApJS, vol.172, pp.1-8, 2007.
, Nuclear Emission in Spiral Nebulae, ApJ, vol.97, p.28, 1943.
, The Baryon Census in a Multiphase Intergalactic Medium: 30% of the Baryons May Still be Missing, ApJ 759, vol.1, p.23, 2012.
, The complexity of galaxy populations at 0.4 < z < 1.3 revealed with unsupervised machine-learning algorithms, A&A, vol.617, p.70, 2018.
URL : https://hal.archives-ouvertes.fr/hal-02110535
, The Two Micron All Sky Survey (2MASS), Aj 131, pp.1163-1183, 2006.
, A Very Bright, Highly Magnified Lyman Break Galaxy at z = 3.07, ApJ, vol.654, pp.33-36, 2007.
URL : https://hal.archives-ouvertes.fr/hal-00193524
, ZAP -enhanced PCA sky subtraction for integral field spectroscopy, MNRAS, vol.458, pp.3210-3220, 2016.
, The persistent cosmic web and its filamentary structure -I. Theory and implementation, MNRAS 414, pp.350-383, 2011.
, The cosmological simulation code GADGET-2", In: MNRAS, vol.364, pp.1105-1134, 2005.
, First results from the IllustrisTNG simulations: matter and galaxy clustering, MNRAS 475, vol.1, pp.676-698, 2018.
, Simulations of the formation, evolution and clustering of galaxies and quasars, Nature, vol.435, pp.629-636, 2005.
, Dropout: A Simple Way to Prevent Neural Networks from Overfitting, Journal of Machine Learning Research, vol.15, pp.1929-1958, 2014.
, Characterization of a subsample of the Planck SZ source cluster catalogues using optical SDSS DR12 data, A&A, vol.617, p.71, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01774036
, Optical validation and characterization of Planck PSZ2 sources at the Canary Islands observatories. I. First year of LP15 observations, 2019.
, The interaction of matter and radiation in the hot model of the Universe, II, vol.7, pp.20-30, 1970.
, The Observations of Relic Radiation as a Test of the Nature of X-Ray Radiation from the Clusters of Galaxies, Comments on Astrophysics and Space Physics, vol.4, p.173, 1972.
, Detection of intercluster gas in superclusters using the thermal Sunyaev-Zel'dovich effect, A&A, vol.625, p.67, 2019.
URL : https://hal.archives-ouvertes.fr/hal-01806941
, A search for warm/hot gas filaments between pairs of SDSS Luminous Red Galaxies, MNRAS 483.1 (2019), pp.223-234
URL : https://hal.archives-ouvertes.fr/hal-01982821
, ComPRASS: a Combined Planck-RASS catalogue of Xray-SZ clusters, A&A, vol.626, p.7, 2019.
, Planck pre-launch status: The Planck mission, A&A, vol.520, 2010.
URL : https://hal.archives-ouvertes.fr/in2p3-00533343
, Bisous model-Detecting filamentary patterns in point processes, Astronomy and Computing, vol.16, pp.17-25, 2016.
, The Dark Energy Survey Collaboration, The Dark Energy Survey". In: arXiv e-prints, 2005.
, The Origin of the Mass-Metallicity Relation: Insights from 53,000 Starforming Galaxies in the Sloan Digital Sky Survey, ApJ, vol.613, pp.898-913, 2004.
, The ROSAT mission, Advances in Space Research, vol.2, pp.241-249, 1982.
, Absorption of Light in the Galactic System, p.214, 1930.
, Starvation as the primary quenching mechanism in galaxies, 2018.
, Deep learning for galaxy surface brightness profile fitting, MNRAS 475, pp.894-909, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01685290
, GAME: GAlaxy Machine learning for Emission lines, MNRAS 477, pp.1484-1494, 2018.
, Einstein observations of the region between the Abell clusters of galaxies A 401 and A 399, ApJ, vol.246, pp.99-103, 1981.
, Prospects for high-z cluster detections with Planck, based on a follow-up of 28 candidates using MegaCam at CFHT, A&A, vol.587, p.23, 2016.
, Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms, The American Journal of Psychology, vol.76, p.705, 1963.
, The Physics of Galaxy Cluster Outskirts, SSR 215, vol.1, p.7, 2019.
, The MUSE Data Reduction Pipeline: Status after Preliminary Acceptance Europe, Astronomical Data Analysis Software and Systems XXIII, vol.485, p.451, 2014.
, Design and capabilities of the MUSE data reduction software and pipeline, Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, vol.8451, p.84510, 2012.
, The stellar masses of galaxies from the 3.4 µm band of the WISE All-Sky Survey, MNRAS 433, vol.4, pp.2946-2957, 2013.
, Calibration of the Optical Mass Proxy for Clusters of Galaxies and an Update of the WHL12 Cluster Catalog, ApJ, vol.807, p.178, 2015.
, A Catalog of 132,684 Clusters of Galaxies Identified from Sloan Digital Sky Survey III, ApJS 199, vol.2, p.34, 2012.
, The Spitzer Space Telescope Mission, ApJS, vol.154, pp.1-9, 2004.
, The Wide-field Infrared Survey Explorer (WISE): Mission Description and Initial On-orbit Performance, Aj 140, pp.1868-1881, 2010.
, A Bright, Spatially Extended Lensed Galaxy at z = 1.7 Behind the Cluster RCS2 032727-132623, vol.724, pp.1182-1192, 2010.
, The two-point correlation of galaxy groups: probing the clustering of dark matter haloes, MNRAS, vol.357, pp.608-618, 2005.
, The Sloan Digital Sky Survey: Technical Summary, pp.1579-1587, 2000.
, ADADELTA: An Adaptive Learning Rate Method, 2012.
, Reprint, 1970.
, Gravitational instability: an approximate theory for large density perturbations, A&A, vol.500, pp.13-18, 1970.
, Studying Intercluster Galaxy Filaments through Stacking gmBCG Galaxy Cluster Pairs, ApJ 773, vol.2, p.115, 2013.
, Physical Properties of Large-scale Galactic Filaments, ApJ, vol.864, p.153, 2018.
, RadFil: A Python Package for Building and Fitting Radial Profiles for Interstellar Filaments, ApJ, vol.864, p.152, 2018.
, List of Figures 1.1 Left: content of our Universe according to Planck measurements, Right: baryon budget at low redshfit. Figure from de Graaff et, vol.86, p.217, 1937.
, Half of the baryons are today lying in the WHIM. Figure from Haider et, 2016.
, Distribution of galaxies in the SDSS survey
, Left: one of the first n-body simulations that reproduced the distribution of the matter in the Universe modelling gravity only. Figure from Doroshkevich and Shandarin, 1978.
, media/), Snapshot at z = 0 of the TNG300 simulation from Illustris-TNG. It shows the baryonic density field, in a region with sides of about 300 Mpc. Image from the Illustris-TNG website
, Comparison of nine methods to extract structures and classify them into nodes, filaments, walls, and voids. Figure from Libeskind et al, 2018.
, Histogram of the density 1 + ? for the four different environments: nodes, filaments, walls, and voids, obtained from numerical simulations. Figure from Cautun et al, 2014.
, Mass and volume fractions occupied by the Cosmic Web structures, which are the nodes, the filaments, the walls, and the voids, from numerical simulations. Figure from Cautun et al, 2014.
, Right: the active galaxy M74 observed with the same telescope, Difference between passive and active galaxies. Left: the passive galaxy M87 observed with the Hubble Space Telescope
, Starbursts in purple and passive galaxies in red lie in the upper and in the lower region of the diagram, respectively. Transitioning galaxies in green are populating the green valley, Schematic view of the SFR-M ? diagram
, The contribution of the different components have been reconstructed with models of Galliano, Dwek, and Chanial, Spectrum of the galaxy M82, with observations from Spitzer, 2008.
, Distribution of distance of galaxies to filaments D skel normalised by the mean intergalaxy separation < D z > for three selections of galaxies. Left: selection in mass. Middle: selection in galaxy type. Right: selection in mass for active galaxies. Arrows indicate the median values of the distributions, p.15, 2017.
, networks-the-eli5-way-3bd2b1164a53. The convolutional layers encode the information of the spatial features at different scales. The encoded information is injected in an ANN, that learns to classify the images. The last activation function, a sigmoid, gives a probability of belonging to a class (in that case, a car)
, 2D Gaussians have the same standard deviations ? = 3 and amplitudes A = 1 (in arbitrary units), Illustration of the stacking method. Left: 1000 Gaussians injected in a 1000×1000 pixels map
, Stacked measured profile in blue and input model (where the mean value of the map was added) in orange of the 2D Gaussians shown in Fig. 3.3. Errors on the stacked measured profiles were computed with bootstrap
, Illustration of the bootstrap technique used to compute the mean of a sample A of 1000 points from a normal distribution with a theoretical mean? = 0. Left: the histogram of the sample A. Right: the distribution ofB obtained by bootstrapping the sample A 1000 times is shown
, Top right: the Delaunay Tessellation of the points. Bottom: densities estimated with Delaunay triangles have been interpolated on a grid. Yellow pixels show over-dense regions while blue pixels show under-dense regions. The field has been smoothed for visualisation. The initial points are overlaid in orange
, Bottom right: the average radial profiles measured by RadFil. A Gaussian has been fitted to the profile, with a measure width ? m = 30.07 ± 0.03, in agreement with the theoretical one, i.e., ? = 30 pixels, Top left: model of filament spine in a 1000 × 1000 pixels map
, Right: histogram of the projected distance to the main sequence, d2ms. Three Gaussians are fitted: passive (in red), active (in blue), and transiting (in green) galaxies. The black dotted lines show the intersections of the Gaussians defining active and passive galaxies, in both the colour and the distance to the main sequence, 1 2D histogram of the projected distance to the main sequence in the SFR-M ? diagram vs. the W2-W3 colour, vol.148
, Left: luminosities of the sources of the training catalogue, Histograms showing the range of the input data
, The lines show the 1? to 5? contours. The dots show 100 randomly selected galaxies. The purple solid line traces the main sequence of star forming galaxies given by, SFR vs. M ? diagram for the sources of the training catalogue, 2007.
, Percentage score of the RF results on the validation sample as a function of the RF parameters M and d max (M being the number of trees and d max the maximum depth)
, Setting M = 40 and d max = 12 is enough in this case to optimise the RF, p.53
, Middle: RF trained to estimate SFR only. Right: RF trained to estimate both SFR and, Relevance of the input parameters during the training of the RF. Left: RF trained to estimate M ? only
, 1? and 3? contours of galaxy types as a function of the two most relevant parameters during the training of the RF: W2-W3 and LW1. Blue contours represent active galaxies, red contours passive galaxies, green contours transiting galaxies, and purple contours AGN, according to the BPT classification of the SDSS MPA/JHU DR8 catalogue, p.55
, with RF parameters set to M = 40 and d max = 12. Left: M ? estimations with the RF compared to M ? from the SDSS MPA-JHU DR8 catalogue. Right: SFR estimations with the RF compared to SFR from the
, 56 4.9 SFR and M ? obtained with the RF algorithm on the test set compared to the SDSS classification based on the BPT diagram. Colour code of the contours is the same as in Fig. 2.3 (in Chap. 2), Errors of the RF results obtained for sources in the test set (same errors as those presented in Fig. 4.7) as a function of redshift
, The lines represent the 1, 3, and 5? contours of the RF estimations. Right: SFR computed (for star-forming galaxies only) with the method of Cluver et al., 2014, using LW3, compared to SFR from the SDSS catalogue. The blue contours show the results of the RF for active galaxies only (i.e., BPT = 1). The red contours show the SFR estimated with the method of Cluver et al., 2014 for passive galaxies (i.e., BPT = -1). In both panels, the dashed line shows the one-to-one correlation, Comparisons on the test set. Left: M ? estimated with the method of, 2013.
, for the sources that match only once with the AllWISE catalogue in a 6" radius. Left: M ? estimated with the RF compared with the M ? provided by the SDSS MPA-JHU values catalogue. Middle: SFR estimated with the RF compared with the SFR provided by the xCOLDGAS catalogue, computed with combined UV and IR data. Right: SFR given by the SDSS MPA-JHU DR8 catalogue compared with the SFR from xCOLDGAS, p.59, 2017.
, Right: SFR estimated with the RF compared with SFR from the SDSS MPA-JHU DR8 catalogue, RF on the test sample, with only W1, W3, W1-W2 and W2-W3 in input, i.e., without any information about the redshift z. Left: M ? estimated with the RF compared with M ? from the SDSS MPA-JHU DR8 catalogue, p.60
, Right: scatter for SFR. The blue lines correspond to the scatters of the whole sample, regardless of the induced bias. The orange lines correspond to the scatters of bias-corrected properties from the biases modelled in Fig, Evolution of the scatters of the properties estimated with the RF as a function of the redshift error ? z (1 + z). Left: scatter for M ?, p.61
, Left: for ? z (1 + z) = 0.015 the errors on the SFR estimates are shown as a function of redshift. The blue line corresponds to the modelled bias. Right: same errors, corrected for the bias, Example of bias induced by the additional redshift error, p.61
, Evolution of the bias as a function of redshift, for different redshift errors, Left: bias for M ? estimates, p.62
, 535 sources in the WISExSCOS value-added catalogue in the range 0.1 < z < 0.3. Right: distributions of the active, transitioning, and passive galaxies of the WISExSCOS value-added catalogue as a function of redshift, Left: range of SFR and of M ? of the 15, vol.765, p.62
, Mollweide projection of the slice at z = 0.15 of the 3D passive galaxy density map constructed with the pyDTFE code. The map is smoothed at 30' for visualisation. The large-scale distribution of the galaxies is seen, together with artefacts from the WISE scanning strategy, and the masks of our galaxy and of the Magellanic cloud, p.64
, Sources are shown in a 30' radius around the arbitrary position (R.A. = 180 ? , Dec.= 0 ? ). Passive galaxies are selected, showing an over-density (light blue circle)
, The areas and colours of the circles depend on S /N fil . The pairs for which S /N fil > 2 are shown with green open circles. The red open circles indicate the final selection after exclusion of pairs lying in complex systems, Distribution of the 71 cluster pairs in the parameter space ?z-? sep
, Right: the pair A21-PSZ2 G114.90-34.35. The red circles show the radii r 500 of the clusters and the white pixels are masked from the Planck Catalog of Compact Sources and from other clusters in the fields. The black crosses show the centres of the SZ clusters, 2 Projected patches of the Planck MILCA SZ map for the two selected cluster pairs. Left: the pair A399-A401
, Front and profile schematic views of the model: the two clusters in green and in blue with two free parameters each, and the inter-cluster bridge in red with three free parameters. The length of the filament l is fixed to the distance between the two r 500 /2 of each cluster. A planar background with three free parameters is considered, p.72
, Residual Planck MILCA SZ map of the pair A399-A401 after subtracting the model (clusters + inter-cluster bridge) with the best-fit parameters from Tab. 5.2. The red circles represent the r 500 radii of each cluster, and the black crosses their central positions. The small-scale features seen at the position of the two clusters, p.73
, The red line shows the model (clusters + inter-cluster bridge) with the best-fit parameters from Tab. 5.2. The dotted green, blue, and red lines show the contributions of A399, A401, and of the inter-cluster bridge, respectively. Bottom panel: residuals after subtracting the model (clusters + inter-cluster bridge). Right: longitudinal cut across the A21-PSZ2 G114.90-34.35 pair, Top panel: the fuchsia line shows the Planck MILCA SZ data
, The green dots show the galaxies in the galaxy cluster A399, the blue ones the galaxies in A401, and the red dots those in the inter-cluster bridge. The purple circles indicate active galaxies. Right: Distributions of the S/Ns of over-densities for the three components of the pair A399-A401. The blue line corresponds to the cluster A401, the green line to A399, and the red one to the inter-cluster bridge, The labels are the same as those in Fig. 5.7. Right: Colour-colour diagram from the photometric bands W1, W2, and
, on the SDSS galaxies. The contours show the galaxies from the SDSS MPA-JHU DR8 catalogue, SFR-M ? diagram for the galaxies in the field of A399-A401. The labels are the same as in Fig. 5.7, 2004.
, 85 6.2 Selection of the SDSS-DR12 LOWZ/CMASS DisPerSE filaments used here. Left: 3D length distribution. The orange dotted line shows the limit between the short and the regular filaments, and the green dotted line shows the limit between the regular and the long filaments. Right: redshift distribution, Image showing the filaments extracted with DisPerSE (in blue) on SDSS galaxies
, WHL15 catalogues are masked with areas of radii from 0 to 5 × R 500 . The left and the right columns show the histogram of filament's lengths and the over-density profiles < 1 + ? gal > respectively, for the short, regular, and long filaments (from top to bottom). I have chosen the mask at 3 × R 500, p.88
, For the galaxy clusters without estimated radius in the Planck PSZ2 catalogue with z < 0.4, regions defined by areas of 0, 5, and 10 arcmin radii are masked. No difference is noticed and all plots are overlapping. I have therefore chosen to mask at 5 arcmin
, Two type of critical points given by Dis-PerSE are masked: the maxima density points and the bifurcation points. The regions around the critical points with z < 0.4 are masked from 0 to 20 arcmin. Right panel, the signal is decreasing with the size of the masks for short and regular filaments. Left panel, the masks remove the short filaments
, Orthographic projection of the union mask of the northern hemisphere used in this analysis, p.91
, In orange, the best fitted exponential model presented in Eq. 6.3. In red, the 100 measurements on random positions from the null tests, blue, the stacked radial profile of over-density < 1 + ? gal > of the galaxies from WISExSCOS catalogue around the 5559 filaments
, Right: residuals after substracting the exponential model fitted to the average over-density profile, shown in orange in Fig. 6.7, and the tiny offsets, Left: radial profiles of galaxy over-densities < 1 + ? gal > around cosmic filaments, for short, regular, and long filaments
, Radial stacked profiles of excess of M * and SFR: < 1 + ? SFR > and < 1 + ? M ? >, for the short, the regular, and the long filaments
, Stacked radial over-density profiles of active (in blue), transitioning (in green), and passive (in red) galaxies, as defined with the distance to the main sequence
, Right: < 1 + ? SFR > stacked radial profiles for the same galaxy types, Left: < 1 + ? M ? > stacked radial profiles for each galaxy types: active galaxies in blue, transitioning galaxies in green, and passive galaxies in red
, In blue, the quiescent fraction profile f Q , averaged around the 5559 selected filaments. In orange, the ?-model (Eq. 6.5) from different realisations of the MCMC, p.95
, Posterior distributions of the four parameters of the ?-model (Eq. 6.5), fitted to the quiescent fraction profile shown in Fig
, Left: the pixel distribution of the map at 353 GHz. A Gaussian is fitted in orange up to the statistical mode of the distribution. The mean and standard deviation of the fitted Gaussian are used to normalise the data. Right: pixel distribution after normalisation of the six Planck HFI frequency maps, p.104
, The architecture consists of a contracting part and an expansive part, vol.104, 2015.
, Planck no-z, and 50 MCXC galaxy clusters. Left: galaxy clusters recovered with different threshold of detection p max . Right: the number of new detected sources as a function of the threshold p max, Results on the test area (seventh HEALPIX pixel with n side = 2), containing 40 Planck z, vol.18
, The title in front of the patches indicate the map (the numbers indicate the frequencies of Planck HFI maps in GHz). The signature of the SZ effect is seen in the centres of the HFI frequency maps, together with a complex combination of infra-red sources and CMB structures, Stack of the SZ map and of the six HFI frequency maps at the positions of the 187 new detected sources in the test area
, 220 new detected sources in 16 different maps probing counterparts of galaxy cluster components in different wavelengths: the SZ MILCA y map, the six Planck HFI frequency maps, the IRIS map at 100 µm, the CMB lensing map, the four galaxy over-density maps based on all, and on red, green, and blue populations of galaxies from the WISExSCOS photometric redshift catalogue (in 0.1 < z < 0.3), and the ROSAT X-ray map, vol.13, p.107
, This show evidence of diffuse gas emission in X-ray for the 13,220 detections, that is decreasing as a function of their probability output by the U-Net. Error bars computed with the bootstrap method ensures the significance by re-sampling the profiles, Stacked radial profiles of the 13,220 new detected sources in the X-ray ROSAT map as a function of their associated probabilities p
, In blue, the fluxes of the 14,976 sources detected on the full sky with the U-Net, stacked on the SZ MILCA y map as a function of their associated probability p. For comparison, the average flux in the MILCA y map of the PSZ2 clusters is shown in orange, the average flux of the MCXC clusters is shown in green, and the average fluxes of the RM 50 and the RM 30 clusters are shown in red and purple, respectively. Error-bars are computed with bootstrap, Scaling relation between the probability output by the U-Net p and the flux in the MILCA y map
, From top to bottom: the Shapley super-cluster, the galaxy cluster pair A399-A401, the Coma supercluster, and the Leo cluster. From left to right: the model PSZ2z, PSZ2Z+MCXC, PSZ2z+MCXC+RM50, PSZ2Z+MCXC+RM30, and the SZ MILCA y map for comparison, SZ MILCA y map, for four emblematic large scale structures
, As indicated in Planck Collaboration et al., 2015a, the black dotted lines show the Planck mass limit for the medium-deep survey zone at 20% completeness (as defined in Planck Collaboration et al., 2014) for a redshift limit of z = 0, Distribution in the M-z plane of SZ clusters from different catalogues from Planck Collaboration, 2011.
, Image of the NTT at La Silla, in the southern part of the Atacama desert of Chile
, 4 Left: combined observations of NTT/EFOSC2 of the cluster PSZ1 G231.05-17.32 in the R band. Red diamonds represent the sources extracted from SExtractor with S/N > 1.5 ?, where I have removed the stars from the GAIA DR2 catalogue that matched a radius of 5, VLT in Paranal in Chile
, The five reduced spectra well fitted from MARZ, all at the same redshifts, z = 0.644 ± 0.006. The three first spectra. The two other are in Fig
, 1 Left: NTT/EFOSC2 reduced image of the field of the PSZ1 G311.65-18.48 cluster, where the giant arc has first been discovered. The giant arc is seen on the upper left part. Right: reconstructed RGB image of the giant arc from MUSE. Coloured circles indicate the multiple lensed sources. The light blue circles indicate the four images of the lensed galaxy at z = 2.37 producing the giant arc, The five reduced spectra well fitted from MARZ, all at the same redshifts
, ), redshifts, estimated radii r 500 . The last four columns indicate the angular separation in arcmin and in Mpc, and the S/Ns of the bridges in the SZ map (Sect. 5.2) and in galaxy over-density
, The best values are the median of the parameters distributions, and the error-bars are computed with the 16 th and the 84 th percentiles
, The four first rows are each positions of the four arcs of the giant arc. The second part is the multiple image detected with the continuum and SExtractor. The third part is the multiple image detected by muselet. The fourth part is the multiple image detected by visual inspections, Table of the four multiple images detected, used to generate the lensing model