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Clusters as calibration tools

Clusters as calibration tools. S. Molendi (IASF-MI). Motivation. To what level can we trust the calibration of our instruments? How far can we push spectral modelling before we end up fitting instrument systematics rather than astrophysically relevant features?. The final frontier?.

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Clusters as calibration tools

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  1. Clusters as calibration tools S. Molendi(IASF-MI)

  2. Motivation To what level can we trust the calibration of our instruments? How far can we push spectral modelling before we end up fitting instrument systematics rather than astrophysically relevant features?

  3. The final frontier? This is a frontier that is worth exploring, major future X-ray missions are not around the corner, in many respects XMM-Newton and Chandra will be the best experiments for many years to come.

  4. Cross Cal with Clusters Has enjoyed some success Lots of photons and no pile-up! No need for simultaneous observations Spectrum is not a simple power-law however at the level of precision we are dealing with are there any pure pl spectra in the X-ray sky? Background can be a limitation

  5. The Sample WORK IN PROGRESS only EPIC to be extended to Chandra 16 observations of 13 objects spectra selected to be: Observed in thin or medium filters high SB 1T 2 < kT < 8 0.015 < z < 0.09 long exposures

  6. The Sample source ring T SB Z arcmin keV erg cm2s-1amin2 Solar units ------------------------------------------------------ 2A0335 2 5 3.2 5.0e-13 0.46 A85 3 5 6.0 4.5e-13 0.39 A262 2 4 2.2 3.0e-13 0.31 A478 2 5 6.5 5.7e-13 0.31 A496 4 7 4.5 2.7e-13 0.31 A1060 2 5 3.0 4.2e-13 0.46 A1650 1 2 5.8 4.0e-13 0.35 A1795 3 5 6.0 3.6e-13 0.3 A2029 2 4 7.6 7.5e-13 0.37 A2199 2 5 4.2 7.5e-13 0.44 A2597 2 4 3.7 8.9e-14 0.28 AWM7 2 4 3.7 8.7e-13 0.28 MKW3s 2 4 3.6 3.2e-13 0.30

  7. Background

  8. Background

  9. Analysis Fit spectra with 1T and 2T models In most cases no substantial improvement with 2T, even when improvement is substantial, typically for objects with better statystics, 2T modeling is un-physical, possibly associated to systematics

  10. Calibration Consider data fromeach camera individualy

  11. X-ray vs radio NH ● MOS1 ● MOS2 ●pn

  12. X-ray vs radio NH

  13. Investigating residuals Explore residuals in the form of ratio data/model Heavily regroup data (beyond resolution limit) to achieve few % errors

  14. 2A 0335 Ratio data/model

  15. Investigating residuals Computemean residuals averagedoverall 16 observations Statisticalerrors are reduced, systematicsshould show up

  16. MOS 1 Deviations from a simple thermal model

  17. MOS 2 Deviations from a simple thermal model

  18. pn Deviations from a simple thermal model

  19. Stability of residuals Do differentobjects show similarresiduals?

  20. MOS2 Coldvs Hot

  21. pn Coldvs Hot

  22. MOS1 SamplevsPerseus

  23. MOS2 SamplevsPerseus

  24. pn SamplevsPerseus

  25. Investigating residuals Residuals at least in some instancesappeartobesimilar in differentobjects Spectralmodel 1T, hot cold, 4T forPerseus--unlikely Instrumentsystematics • Redistribution -- rmf • Effective Area -- arf

  26. Centaurus pn soft band residua

  27. Perseus pn soft band residua

  28. Mkn 421 pn soft band residua

  29. Centaurus mos soft band residua

  30. Perseus mos soft band residua

  31. De Grandi+SM 09 Implications Systematicslikelyrelatedtormf, particularlytrueforpn MOS lesscertain

  32. Cross-Calibration Compare differentinstruments

  33. MOS1 & pn NH vs MOS2 NH ● MOS1 ●pn

  34. MOS1 & pnkTvs MOS2 kT ● MOS1 ●pn

  35. MOS1, MOS2 &pnvspn model

  36. MOS1, MOS2 &pnvspn model

  37. MOS1, MOS2 &pnvspn model

  38. MOS1 & MOS2 vspn model

  39. Summary Calibration Redistributionproblem on pn MOS, ifthereisoneit’s smaller • Cross-calibration • Good to 3% in 0.7-3.5 keV band • problem with Effective areas at energy

  40. ACIS S3 vs EPIC pn We compare Chandra ACIS S3 with EPIC pn About 3×106 events for each spectrum extracted from annulus with bounding radii of 1' and 2 ‘ Used old and new Chandra calibrations (CALDB 4.1.1 with hrmaD1996-12-20axeffaN0008.fits) Multi T spectral model (Molendi & Gastaldello 09)

  41. ACIS S3 vs EPIC pn We start from the EPIC pn spectrum

  42. ACIS S3 vs EPIC pn We start from the EPIC pn spectrum Perform fit with multi T model

  43. ACIS S3 vs EPIC pn We start from the EPIC pn spectrum Perform fit with multi T model Fold best fitting model with ACIS response and compare it with ACIS spectrum

  44. ACIS S3 vs EPIC pn ACIS S3 Plot residuals in the form of ratio data/model Renorm applied to match spectra at 1.5 keV pn

  45. ACIS S3 vs EPIC pn ACIS S3 Plot residuals in the form of ratio data/model Renorm applied to match spectra at 1.5 keV pn

  46. ACIS S3 vs EPIC pn ACIS S3 Similar result when using a different region Annulus with bounding radii of 2' and 3 ‘ Showing only plot with new ACIS calibrations pn

  47. Cross Cal Differences are almosteverywherelessthan ~5% Major discrepancy in 0.7-1.0 keVrange pn and ACIS S3 spectralshapes are now in muchbetter agreement!

  48. ACIS S3 vs EPIC pn ACIS S3 Residuals in the form of ratio data/model Renorm applied to match spectra at 1.5 keV pn

  49. ACIS S3 vs EPIC MOS ACIS S3 MOS2 MOS2 appears to be more similar to ACIS in the 0.7-1 keV band. MOS1 appears to be somewhere btwn. MOS2 and pn.

  50. Flux cross-calpn vs ACIS Bothfigureshaverenormfactors: 5% for the first; 15% for the second: let’s take them out.

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