Multi-wavelength cross-correlation methods
Mara Salvato
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(With a thanks to F. Guglielmetti & M. Brusa)
Multi-wavelength cross-correlation methods Mara Salvato (With a - - PowerPoint PPT Presentation
Multi-wavelength cross-correlation methods Mara Salvato (With a thanks to F. Guglielmetti & M. Brusa) 1 Structure of the talk: Why multi wavelength cross-matches are important All what can go wrong Methods descriptions
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(With a thanks to F. Guglielmetti & M. Brusa)
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Salvato+09,11, Fotopoulou+12, Hsu+14, Ananna+16, Marchesi+16
Lehmer+05 vs Virani+06
Xue+11 vs Rangel+13
the offset is due to the reference image and to the source detection code. Make sure to correct at least for the former, by registering your multi wavelength catalog to the ref. image In crowded areas, coordinates make the difference (and we will never know the truth!)
Hsu+14
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CFHTL T0007 release
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R_Auto-Modelmag
LaMassa+15
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~Pineau et al. 11 (2XMM) Fioc et al 2014 (ASPECT)
Rutledge+2000 Brusa et al. 05, 07, 10, (XMM-COSMOS) Civano et al. 12 (C-COSMOS) Laird et al. 09 (AEGIS) Brusa et al. 09, Luo et al. 10, Xue et al. 11 (CDFS) Georgakakis & Nandra 11 (XMM-SDSS) Nandra+ 2015 (AEGIS-X) La Massa+15 (STRIPE 82) Fotopoulou+2015 (XMM-XXL) Menzel+2015 (XMM-XXL) and more
Roseboom+2009 (SCUBA) Naylor+2013 Dongwai+2015 (Radio) Rosen+2015 Hsu+2014 (CDFS) Salvato+16 and more
Brusa+ 07
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Sutherland&Saunders 92
Naylor+13
magnitude
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The posterior probability for a cross-match between an X-ray source, i, and a potential counterpart j, is given by
normalising factor which takes account the mean sky density of potential counterparts (ρs, units deg−2), and the expected fraction of X-ray sources that have a true counter- part, ηx .
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11643 ROSAT ctp in DR12
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Courtesy: Iris Traulsen
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Cross-matches, at least for X-ray sources remain non trivial, ( and I did not mention blending, variability and proper motion issues)
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ML can be unstable because data driven. Bayesian approach is more stable and can be applied also to a single X-ray source. Bayesian as in B&S08 allows to work simultaneously on N catalogs but the underling assumption is that the same sources are present in all catalogs Combining this with pure positional codes, makes the results stronger If using priors, make sure that they are unbiassed.