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start [2024/10/15 15:14] Déborah Paradis |
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| ====== Home ====== | ====== Home ====== | ||
| - | Welcome to the "**Centre d'Analyse de Données Etendues**" (CADE) or "//Analysis Center for Extended Data//". CADE provides maps of ancillary astronomical data to users in the astronomical community. The emphasis of CADE is on extended sky emission. The database is provided in the [[http://healpix.jpl.nasa.gov|HEALPix]] sky pixelization scheme. | + | Welcome to the "**Centre d'Analyse de Données Etendues**" (CADE) or "//Analysis Center for Extended Data//". CADE provides maps of ancillary astronomical data to users in the astronomical community. The emphasis of CADE is on extended sky emission. The database is provided in the [[https://irsa.ipac.caltech.edu/healpix/index.html|HEALPix]] sky pixelization scheme. |
| - | An increasing number of large survey projects use a unique hierarchical pixelization of the celestial sphere called [[http://healpix.jpl.nasa.gov|HEALPix]]. | + | An increasing number of large survey projects use a unique hierarchical pixelization of the celestial sphere called [[https://irsa.ipac.caltech.edu/healpix/index.html|HEALPix]]. |
| CADE pursues a strategy of ancillary data ingestion in the HEALPix format, generating partial or all-sky HEALPix files from astronomical data that is represented using the more traditional local World Coordinate System (WCS) FITS format. The ingestion tool is based on the drizzling library, and uses a strategy where the surface of pixel intersection is computed as presented in Appendix A of Paradis et al., 2012, A&A, 543, 103. This method allows fast ingestion and guarantees the photometric accuracy of the transformation with minimal data loss during the transformation from a local WCS FITS map to HEALPix. | CADE pursues a strategy of ancillary data ingestion in the HEALPix format, generating partial or all-sky HEALPix files from astronomical data that is represented using the more traditional local World Coordinate System (WCS) FITS format. The ingestion tool is based on the drizzling library, and uses a strategy where the surface of pixel intersection is computed as presented in Appendix A of Paradis et al., 2012, A&A, 543, 103. This method allows fast ingestion and guarantees the photometric accuracy of the transformation with minimal data loss during the transformation from a local WCS FITS map to HEALPix. | ||
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| * ** NEW: The first SED Extractor tool (in Python), which extracts a Spectral Energy Distribution from HEALPix, WCS or mixture of HEALPix-WCS FITS files | * ** NEW: The first SED Extractor tool (in Python), which extracts a Spectral Energy Distribution from HEALPix, WCS or mixture of HEALPix-WCS FITS files | ||
| ** | ** | ||
| - | * ** NEW: The first prediction maps derived from Neural Networks at 353 and 217 GHz at the 37'' angular resolution ** | + | * ** NEW: The first prediction maps derived from Neural Networks at 353 and 217 GHz at the 37'' angular resolution ** |
| /*==== Future developments ==== | /*==== Future developments ==== | ||