. .. Motivations,

. .. Diffuse-sz-emission,

.. .. Summary,

. Bibliography,

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, 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.

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, 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

. Iras, . Iso, H. ;. Wise, and . Galliano, 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

. Elbaz, 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

M. ?. , 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

. .. Sfr, 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

. Wen, 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.

. Saintonge, 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

. Screen-shot-of-the-webservice, 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

. Brinchmann, 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

. The-psz2, . Mcxc, and A. Redmapper, 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

F. Ronneberger and ;. .. Brox, 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