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Efficient Selection and Classification in the 2XMMp Catalogue for XMM-Newton Observations

This document summarizes the processes and challenges faced by the 2XMMp team in selecting observations for the XMM-Newton Survey Science Centre. The presentation discusses the observational quality classification system (OBSFLAG), where observations are categorized based on the presence of spurious detections. It highlights the importance of quick field identifications, noting that 83% of public observations were included in the screening process. The ultimate goal is to effectively classify fields and identify targets, navigating the complexities associated with varying observation types.

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Efficient Selection and Classification in the 2XMMp Catalogue for XMM-Newton Observations

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Presentation Transcript

  1. 20th Survey Science Centre Consortium meeting Field content and target identification Ann-Marie Stobbart Anja Schroeder, Mike Watson and the XMM-Newton Survey Science Centre

  2. 2XMMp: selecting observations • 2XMMp contains a subset of the total number of observations to be included in the final 2XMM catalogue. • Subset selected via visual screening of each observation + sources + automatic flags •  Quick field identifications based on the number of ‘spurious’ detections i.e. those NOT automatically flagged as spurious Ann-Marie Stobbart

  3. 2XMMp: selecting observations • Observations classified into one of four quality flags (OBSFLAG=0-3) •  ‘Good’ observations (OBSFLAG=0-2) included • i.e. those with no or few obvious spurious detections (83% public observations with EPIC data) Ann-Marie Stobbart

  4. Observation classes: OBSFLAG=0 52% : no obvious spurious detections (may include one or two non-obvious ones) Ann-Marie Stobbart

  5. Observation classes: OBSFLAG=1 30% : one or more spurious detections (but usually < 8 ) Ann-Marie Stobbart

  6. Observation classes: OBSFLAG=2 0.9% : class 1 + missed detections in a group of very densely positioned sources Ann-Marie Stobbart

  7. Observation classes: OBSFLAG=3 17% : not accepted for inclusion in the catalogue Ann-Marie Stobbart

  8. Field identification • 2XMMp screening effectively independent of target itself • 2XMM Serendipitous catalogue • Aim for 2XMM is to classify XMM-Newton fields by the type of target within them • But, is the target: • a single object ? • a whole field ? • extended ? Ann-Marie Stobbart

  9. Field identification • 5 proposed field classifications: • Point source • Extended source • Small extended • Large extended • Field (e.g. The Lockman Hole) • Not detected (e.g. OM calibration field) • Unknown Ann-Marie Stobbart

  10. Field ID : Large extended SIMBAD PROPOSAL NED RA_PNT DEC_PNT XSA NGC 55 (WEST): 0028740101 : X-ray background and surveys Ann-Marie Stobbart

  11. Field ID : Large extended SIMBAD PROPOSAL NED RA_PNT DEC_PNT XSA NGC 4258: 0059140401 : AGN, QSOs, BL-Lacs and XRB Ann-Marie Stobbart

  12. Target identification • Aim to flag the target of the observation • Difficulties, e.g. for galaxies  no source to flag unambiguously • Simplest solution  flag the targets from the ‘point source’ field classification Ann-Marie Stobbart

  13. Summary • 2XMMp screening: quick field identifications  83% public observations included • Aim for 2XMM: field and target identifications  work in progress • Difficulties: • Finding a position that best identifies the target in order to classify the field • finding an unambiguous target position in order to flag the target Ann-Marie Stobbart

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