ComPRASS: a Combined <i>Planck</i>-RASS catalogue of X-ray-SZ clusters

Tarrío, Paula; Melin, Jean-Baptiste; Arnaud, M.

doi:10.1051/0004-6361/201834979

Cited by 23 publications

(28 citation statements)

References 66 publications

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“…In the most conservative case when training with the Planck_z catalogue of 1,094 sources, 200 MCXC clusters that are not in the Planck catalogue are recovered, together with more than 18,000 new potential sources detected with the U-Net above a detection threshold p max = 0.1. This confirm results obtained in Hurier et al (2017) and Tarrío et al (2019), where they detected about two times more galaxy clusters than in the Planck catalogue using Planck data. Although the detection threshold has been set to a low value, the different stacks of the 18,415 sources detected with the U-Net in bins of their associated SZ prediction index shows hints of galaxy cluster signatures.…”

Section: Discussion and Summarysupporting

confidence: 92%

Deep learning for Sunyaev–Zel’dovich detection in Planck

Bonjean

2020

A&A

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The Planck collaboration has extensively used the six Planck HFI frequency maps to detect the Sunyaev-Zel'dovich (SZ) effect with dedicated methods, e.g., by applying (i) component separation to construct a full sky map of the y parameter or (ii) matched multifilters to detect galaxy clusters via their hot gas. Although powerful, these methods may still introduce biases in the detection of the sources or in the reconstruction of the SZ signal due to prior knowledge (e.g., the use of the GNFW profile model as a proxy for the shape of galaxy clusters, which is accurate on average but not on individual clusters). In this study, we use deep learning algorithms, more specifically a U-Net architecture network, to detect the SZ signal from the Planck HFI frequency maps. The U-Net shows very good performance, recovering the Planck clusters in a test area. In the full sky, Planck clusters are also recovered, together with more than 18,000 other potential SZ sources, for which we have statistical hints of galaxy cluster signatures by stacking at their positions several full sky maps at different wavelengths (i.e., the CMB lensing map from Planck, maps of galaxy over-densities, and the ROSAT X-ray map). The diffuse SZ emission is also recovered around known large-scale structures such as Shapley, A399-A401, Coma, and Leo. Results shown in this proof-of-concept study are promising for potential future detection of galaxy clusters with low SZ pressure with this kind of approach, and more generally for potential identification and characterisation of large-scale structures of the Universe via their hot gas.

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Section: Discussion and Summarysupporting

confidence: 92%

Deep learning for Sunyaev–Zel’dovich detection in Planck

Bonjean

2020

A&A

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“…We identified all clusters within 2.5 degrees of the pointing centre in the ELAIS-N1 and Lockman Hole fields that were present in the second Planck catalogue of Sunyaev-Zel'dovich detected sources (PSZ2; Planck Collaboration et al 2016a), the Meta-Catalogue of X-ray detected Clusters of galaxies (MCXC; Piffaretti et al 2011) or the Combined Planck-RASS catalogue of X-ray-SZ clusters (ComPRASS; Tarrío et al 2019). Seven clusters in the aforementioned catalogues are present in the deep fields, of which the details are given in Table 1.…”

Section: Methodsmentioning

confidence: 99%

Diffuse radio emission from galaxy clusters in the LOFAR Two-metre Sky Survey Deep Fields

Osinga

Weeren

Boxelaar

et al. 2021

A&A

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Low-frequency radio observations are revealing an increasing number of diffuse synchrotron sources from galaxy clusters, primarily in the form of radio halos or radio relics. The existence of this diffuse synchrotron emission indicates the presence of relativistic particles and magnetic fields. It is still an open question as to exactly what mechanisms are responsible for the population of relativistic electrons driving this synchrotron emission. The LOFAR Two-metre Sky Survey Deep Fields offer a unique view of this problem. Reaching noise levels below 30 μJy beam−1, these are the deepest images made at the low frequency of 144 MHz. This paper presents a search for diffuse emission in galaxy clusters in the first data release of the LOFAR Deep Fields. We detect a new high-redshift radio halo with a flux density of 8.9 ± 1.0 mJy and corresponding luminosity of P144MHz = (3.6 ± 0.6) × 1025 W Hz−1 in an X-ray detected cluster at z = 0.77 with a mass estimate of M500 = 3.3−1.7+1.1 × 1014 M⊙. Deep upper limits are placed on clusters with non-detections. We compare the results to the correlation between halo luminosity and cluster mass derived for radio halos found in the literature. This study is one of a few to find diffuse emission in low mass (M500 < 5 × 1014 M⊙) systems and shows that deep low-frequency observations of galaxy clusters are fundamental for opening up a new part of parameter space in the study of non-thermal phenomena in galaxy clusters.

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“…The approach proposed in this paper was initially designed with the purpose of confirming cluster candidates of the Combined Planck-RASS (ComPRASS) catalogue (Tarrío et al 2019). This all-sky catalogue of galaxy clusters and cluster candidates was validated by careful cross-identification with previously known clusters, especially in the SDSS and the South Pole Telescope (SPT) footprints.…”

Section: Introductionmentioning

confidence: 99%

Photometric redshifts for the Pan-STARRS1 survey

Tarrío

Zarattini

2020

A&A

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We present a robust approach to estimating the redshift of galaxies using Pan-STARRS1 photometric data. Our approach is an application of the algorithm proposed for the SDSS Data Release 12. It uses a training set of 2 313 724 galaxies for which the spectroscopic redshift is obtained from SDSS, and magnitudes and colours are obtained from the Pan-STARRS1 Data Release 2 survey. The photometric redshift of a galaxy is then estimated by means of a local linear regression in a 5D magnitude and colour space. Our approach achieves an average bias of Δ̅z̅n̅o̅r̅m̅ = −1.92 × 10−4, a standard deviation of σ(Δznorm) = 0.0299, and an outlier rate of Po = 4.30% when cross-validating the training set. Even though the relation between each of the Pan-STARRS1 colours and the spectroscopic redshifts is noisier than for SDSS colours, the results obtained by our approach are very close to those yielded by SDSS data. The proposed approach has the additional advantage of allowing the estimation of photometric redshifts on a larger portion of the sky (∼3/4 vs ∼1/3). The training set and the code implementing this approach are publicly available at the project website.

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ComPRASS: a Combined Planck-RASS catalogue of X-ray-SZ clusters

Cited by 23 publications

References 66 publications

Deep learning for Sunyaev–Zel’dovich detection in Planck

Deep learning for Sunyaev–Zel’dovich detection in Planck

Diffuse radio emission from galaxy clusters in the LOFAR Two-metre Sky Survey Deep Fields

Photometric redshifts for the Pan-STARRS1 survey

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