MILCANN : A neural network assessed tSZ map for galaxy cluster detection

@article{Hurier2017MILCANNA,
  title={MILCANN : A neural network assessed tSZ map for galaxy cluster detection},
  author={Guillaume Hurier and Nabila Aghanim and Marian Douspis},
  journal={arXiv: Cosmology and Nongalactic Astrophysics},
  year={2017}
}
We present the first combination of thermal Sunyaev-Zel'dovich (tSZ) map with a multi-frequency quality assessment of the sky pixels based on Artificial Neural Networks (ANN) aiming at detecting tSZ sources from sub-millimeter observations of the sky by Planck. We construct an adapted full-sky ANN assessment on the fullsky and we present the construction of the resulting filtered and cleaned tSZ map, MILCANN. We show that this combination allows to significantly reduce the noise fluctuations… 
Deep learning for Sunyaev–Zel’dovich detection in Planck
The Planck collaboration has extensively used the six Planck HFI frequency maps to detect the Sunyaev–Zel’dovich (SZ) effect with dedicated methods, for example by applying (i) component separation
Spectral imaging of the thermal Sunyaev–Zel’dovich effect in X-COP galaxy clusters: method and validation
The imaging of galaxy clusters through the Sunyaev–Zel’dovich effect is a valuable tool to probe the thermal pressure of the intra-cluster gas, especially in the outermost regions where X-ray
Optical follow-up study of 32 high-redshift galaxy cluster candidates from Planck with the William Herschel Telescope
The Planck satellite has detected cluster candidates via the Sunyaev Zel’dovich (SZ) effect, but the optical follow-up required to confirm these candidates is still incomplete, especially at high
Probing cosmology and cluster astrophysics with multiwavelength surveys – I. Correlation statistics
Upcoming multiwavelength astronomical surveys will soon discover all massive galaxy clusters and provide unprecedented constraints on cosmology and cluster astrophysics. In this paper, we
An extension of the Planck galaxy cluster catalogue
We present a catalogue of galaxy clusters detected in the Planck all-sky Compton parameter maps and identified using data from the WISE and SDSS surveys. The catalogue comprises about 3000 clusters
DeepSZ: Identification of Sunyaev-Zel'dovich Galaxy Clusters using Deep Learning
TLDR
Three methods of cluster identification are presented and compared: the standard Matched Filter method in SZ cluster finding, a method using Convolutional Neural Networks, and a ‘combined’ identifier, which advocates for combined methods that increase the confidence of many lower signal-to-noise clusters.

References

SHOWING 1-4 OF 4 REFERENCES
MILCA, a modified internal linear combination algorithm to extract astrophysical emissions from multifrequency sky maps
The analysis of current Cosmic Microwave Background (CMB) experiments is based on the interpretation of multi-frequency sky maps in terms of different astrophysical components and it requires
Explanatory Supplement
  • ARA&A, 40,
  • 2002
Explanatory Supplement to the AllWISE Data Release Products