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X-ray data and analysis techniques

This presentation by Andreas Zezas from the Harvard-Smithsonian Center for Astrophysics explores the intricate journey of photons as they traverse various astronomical phenomena. It covers the process of transforming photon energy into electric pulses for analysis, details instrumental effects affecting data quality, and introduces statistical methodologies for interpreting X-ray data. Key topics include the characteristics of X-ray sources, including accreting black holes and neutron stars, as well as the implications of measuring spatial and spectral properties.

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X-ray data and analysis techniques

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  1. X-ray data and analysis techniques Andreas Zezas Harvard-Smithsonian Center for Astrophysics

  2. The complicated life of photons • Gas in the path • Source • Telescope + Instruments

  3. X-ray sources • Compact objects • accreting black-holes • neutron stars etc • jets • Stars • Supernova remnants • Hot gas • galactic outflows, clusters of galaxies

  4. The complicated life of photons Telescope + Instruments http://chandra.harvard.edu/edu/chandra1017.html

  5. The complicated life of photons Telescope + Instruments • Instrument converts photon energy (E) to electric pulse Discretization converts electric pulse intensity to channel number (e.g. PI channel), pixel etc. http://chandra.harvard.edu/edu/chandra1017.html

  6. CDF-N Brandt etal, 2003 The complicated life of photons Telescope + Instruments • Instrumental effects: • Detection inefficiency • Blurring • image (PSF) • spectrum (RMF)

  7. The complicated life of photons Telescope + Instruments … or in other words • So from observed data D(PI) we want to recover S(E) • RMF, ARF are calibration data • Similar form for imaging data :

  8. S(E | sp. param.) RMF ARF Update parameter S_obs(E | sp. param.) 2 Accept / reject fit How we do it ?

  9. How we do it ? 2 statistic : convenient we understand it (we think) gives goodness of fit BUT requires Gaussian errors. C-statistic (or Cash statistic) works with Poisson data BUT does not give goodness of fit is not fully understood (e.g. bins with 0’s)

  10. What is S(E) ? atten, bbody, bbodyfreq, beta1d, beta2d, box1d, box2d, bpl1d, const1d, const2d, cos, delta1d, delta2d, dered, devaucouleurs, edge, erf, erfc, farf, farf2d, fpsf, fpsf1d, frmf, gauss1d, gauss2d, gridmodel, hubble, jdpileup, linebroad, lorentz1d, lorentz2d, nbeta, ngauss1d, poisson, polynom1d, polynom2d, powlaw1d, ptsrc1d, ptsrc2d, rsp, rsp2d, schechter, shexp, shexp10, shlog10, shloge, sin, sqrt, stephi1d, steplo1d, tan, tpsf, tpsf1d, usermodel, xs, xsabsori, xsacisabs, xsapec, xsbapec, xsbbody, xsbbodyrad, xsbexrav, xsbexriv, xsbknpower, xsbmc, xsbremss, xsbvapec, xsc6mekl, xsc6pmekl, xsc6pvmkl, xsc6vmekl, xscabs, xscemekl, xscevmkl, xscflow, xscompbb, xscompls, xscompst, xscomptt, xsconstant, xscutoffpl, xscyclabs, xsdisk, xsdiskbb, xsdiskline, xsdiskm, xsdisko, xsdiskpn, xsdust, xsedge, xsequil, xsexpabs, xsexpdec, xsexpfac, xsgabs, xsgaussian, xsgnei, xsgrad, xsgrbm, xshighecut, xshrefl, xslaor, xslorentz, xsmeka, xsmekal, xsmkcflow, xsnei, xsnotch, xsnpshock, xsnsa, xsnteea, xspcfabs, xspegpwrlw, xspexrav, xspexriv, xsphabs, xsplabs, xsplcabs, xsposm, xspowerlaw, xspshock, xspwab, xsraymond, xsredden, xsredge, xsrefsch, xssedov, xssmedge, xsspline, xssrcut, xssresc, xssssice, xsstep, xstbabs, xstbgrain, xstbvarabs, xsuvred, xsvapec, xsvarabs, xsvbremss, xsvequil, xsvgnei, xsvmcflow, xsvmeka, xsvmekal, xsvnei, xsvnpshock, xsvphabs, xsvpshock, xsvraymond, xsvsedov, xswabs, xswndabs, xsxion, xszbbody, xszbremss, xszedge, xszgauss, xszhighect, xszpcfabs, xszphabs, xszpowerlw, xsztbabs, xszvarabs, xszvfeabs, xszvphabs, xszwabs, xszwndabs (Sherpa models)

  11. What is S(E) ? • Power-law accreting sources, jets synchrotron emission (relativistic electrons in magnetic fields) • Thermal plasma (hot gas) Line emission measure temperature, density, pressure, metal content

  12. What is S(E) ?

  13. Z = 0.25 Z Z = 1.0 Z kT = 0.8 keV kT = 2.5 keV kT = 6.0 keV

  14. So what is S(E) Baldi etal, 2005, in press

  15. So what is S(E) Zezas etal, 2005

  16. Prestwich et al, 2003 Spectra : few counts • Few counts: Use hardness ratio • Ratio (in various flavors) of intensity in two bands, e.g. : • , , • Problems : • HRs in the Poisson regime (T. Park) • Separate source populations in HR diagrams (mixing etc) • Determine confidence intervals for spectral parameters

  17. Why all the fuss ? Thermal plasma : Temperature can constrain properties of the source Abundance provides clues to its history Line emission : DEM can constrain models for emitting regions of stars Relativistic FeKa line provides clues on black-hole physics Black-body : Temperature can constrain the nature of compact objects (neutron stars, quark stars, black-holes) Absorption : Given information on the nature of the intervening gas

  18. Spatial analysis • Goals : • Separate point-like from extended sources • Measure the parameters of extended component

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