METALS IN THE ICM LESSON 3. OUTLINE OF THE LESSON INSTRUMENTATION FOR X-RAY SPECTROSCOPY MEASUREMENT...

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Transcript of METALS IN THE ICM LESSON 3. OUTLINE OF THE LESSON INSTRUMENTATION FOR X-RAY SPECTROSCOPY MEASUREMENT...

  • Slide 1
  • METALS IN THE ICM LESSON 3
  • Slide 2
  • OUTLINE OF THE LESSON INSTRUMENTATION FOR X-RAY SPECTROSCOPY MEASUREMENT TRICKS ENRICHMENT PROCESSES AND RELATIVE CONTRIBUTION OF SUPERNOVAE (SNIa AND SNII) WHAT CAN WE INFER AND WHAT CAN WE DO WITH METALS X-RAY LINES
  • Slide 3
  • AN X-RAY SPECTROMETER ENERGY RESOLUTION E (keV) ENERGY BANDPASS E = E max E min (keV) QUANTUM EFFICIENCY Q counts/photon EFFECTIVE AREA A (cm 2 ) RESOLVING POWER R = / = E/E E (keV) x () = 12.4 If you observe a source flux F (photons/cm 2 s keV) for a time t the number of detected photons is And the number of counts per energy bin is The precision of a narrow line measurement is determined by uncertainty in n
  • Slide 4
  • AN X-RAY SPECTROMETER So the key quantity is the product A x Q x E The overwhelmingly important development goal of improving energy resolution E can be matched in practice by improvement of equal magnitude in effective area or throughput ( A x Q) Better energy resolution requires also bigger X-ray mirrors. The energy resolution is taken as the FWHM of the response to a monocromatic photon which is approximately Gaussian. For example XMM 150 eV at 6 keV (EPIC) and 4650 cm 2 at 1 keV CHANDRA 210 eV at 6 keV (ACIS-S) and 800 cm 2 at 1 keV (We hope !) CON-X 2 eV at 6 keV and 15000 cm 2 XEUS 2 eV at 6 keV and 23000 cm 2 Remember that if AQ is increased in proportion to the decrease in E, the detected count rate N/t increases for a given flux so to obtain spectra of higher energy resolution requires detectors of ever increasing count rate capacity (already a problem ! Pile up in CCD)
  • Slide 5
  • X-RAY SPECTROMETERS ENERGY DISPERSIVE: photon energies are derived from the signal pulse heights at some detector output WAWELENGHT DISPERSIVE: X-ray wavelength is derived from the photon arrival position in the spectrum dispersed by a grating or Bragg crystal
  • Slide 6
  • In this class of spectrometers we absorb single photons, we create a population of signal carriers and count the signal carriers to obtain information on position, arrival time and E of the photon. If the mean energy required to create a signal carrier is W, then the number of signal carriers is N = E/W And the uncertainty in enegy is given by variance on N so 2 = FN F is the Fano Factor So for the energy resolution and resolving power we have E = 2.36 (F x E x W) 1/2 and R = A x E 1/2 The resolving power increases as the square root of the energy For Xe proportional counters W = 25 eV and F = 0.2 For Si CCD W = 3.6 eV ad F = 0.1 ENERGY DISPERSIVE SPECTROSCOPY
  • Slide 7
  • PERSEUS WITH MECS PERSEUS WITH EPIC A comparison of spectral resolution between a gas proportional counter (MECS on board SAX) and a Si CCD (EPIC-PN) on board XMM. We will return later on residuals in the fits
  • Slide 8
  • DISPERSIVE SPECTROMETERS DISPERSIVE SPECTROMETERS All convert into dispersion angle and hence into focal plane position in an X- ray imaging detector BRAGG CRYSTAL SECTROMETERS (EINSTEIN, SPECTRUM X-GAMMA): Resolving power up to 2700 but disadvantages of multiplicity of cristals, low throughput, no spatially resolved spectroscopy n x = 2d x sin TRANSMISSION GRATINGS (EINSTEIN, EXOSAT, CHANDRA) m x = p x sin where m is the order of diffraction and p the grating period REFLECTION GRATINGS (XMM) m x = p (cos - cos ) The resolving power for gratings is given by, assuming a focal lenght f and a position X relative to the optical axis in the focal plane X = f tan f sin X = f so is constant
  • Slide 9
  • DISPERSIVE SPECTROMETERS DISPERSIVE SPECTROMETERS The disadvantages are the very low effective area (140 cm 2 at the peak for RGS), the limited bandpass (0.35-2.5 keV for RGS) and very limited spatially resolved spectroscopy. But the resolving power is R = 500 corresponding to an energy resolution of 2 eV at 1 keV ( 70 eV at 1 keV for CCD) M87 RGS Spectra Sakeliou et al. (2002)
  • Slide 10
  • ABUNDANCE MEASUREMENTS ABUNDANCE MEASUREMENTS The plasma in the ICM is modeled according to the CORONAL model. The basic assumptions are: Elastic Coloumb collision time scale shorter than the age or cooling time, so particles (electrons in particular) have a Maxwell-Boltzmann distribution at temperature T Collisional (de)excitation processes slower than radiative decays (low densities) so all ions start from the ground state Radiation field on the gas is insignificant (no stimulate emission) Gas is optically thin, no radiation transport Time scales for ionization and recombination considerably less than any relevant hydrodinamic time scale, so the plasma is in collisional equilibrium So you use a plasma code like MEKAL or APEC and you have the abundance of your favourite element
  • Slide 11
  • ABUNDANCE MEASUREMENTS ABUNDANCE MEASUREMENTS The analysis is very straightforward compared to the analysis of stellar or H II region spectra, where the effects of radiative transfer, dust and ionization balance are very complex and difficult to remove accurately. In many respects, it is easier to derive the abundances from X- ray spectra of the ICM in clusters than in any other field of astrophysics Mushotzky et al. (1996) But there are some subtle tricks, when you consider: Resonance scattering Temperature structure
  • Slide 12
  • RESONANCE SCATTERING RESONANCE SCATTERING Absorption of a line photon followed by immediate reemission in an other direction. Although for densities and temperatures typical of clusters the gas is optically thin to Thomson scattering for the continuum, it can be optically thin in the resonance X-ray lines (in particular in the denser core) and in particular in the Fe He emission line at 6.7 keV (the most important emission line !) No longer optically thin, the surface brightness is distort and you underestimate abundances in the core of the cluster. You can measure it by making the ratio of a supposed optically thick (Fe He ) and optically thin (Fe He at 7.90 keV) lines and see if there are deviation for the prediction of the plasma code.
  • Slide 13
  • RESONANT SCATTERING IN PERSEUS ? RESONANT SCATTERING IN PERSEUS ? If there is resonant scattering, you underestimate abundance in the prominent line at 6.7 keV you cannot reproduce the line at 7.8 keV and you see residuals in the fit
  • Slide 14
  • RESONANT SCATTERING IN PERSEUS ? RESONANT SCATTERING IN PERSEUS ? Assuming optically thin emission Using only K Correcting the apparent abundances Molendi et al. 1998 But the excess could be due also to overabundance of Ni respect to solar ratios (Dupke & Arnaud 2001). The Ni He is at 7.80 keV and you cannot distinguish with the MECS resolution
  • Slide 15
  • If you fit the XMM spectra with a MEKAL model you find again an excess RESONANT SCATTERING IN PERSEUS ? RESONANT SCATTERING IN PERSEUS ? Fe He 7.90 keVNi He 7.80 keV As we can see leaving the abundance of Ni free, the excess is due to this element and not to an anomalosly high Fe He line. There is no resonant scattering
  • Slide 16
  • XMM OBSERVATION OF M87 DRAMATIC DECREASE OF ABUDANCES WITHIN 1 ARCMIN OF THE CORE (WITH 1T MODELS ) RESONANT LINE SCATTERING (BOHRINGER ET AL. 2001) CONTINUUM OPACITY (MATHEWS ET AL. 2001) BOHRINGER ET AL. 2001
  • Slide 17
  • XMM OBSERVATION OF M87 Fitting multi T spectrum with single temperature models give underestimated abundances (Fe bias Buote 2000) Molendi & Gastaldello 2001
  • Slide 18
  • WHAT WE KNOW Intracluster gas is metal rich abundances 0.3 solar with little dispersion up to redshift z = 0.4 (Allen & Fabian 98) Metals are made in stars in the galaxies and then expelled particularly via supernovae Iron mass highly correlated with optical light from ellipticals and S0 (Arnaud et al. 92)
  • Slide 19
  • WHAT WE KNOW Abundance gradients in cool core clusters, while in not relaxed clusters flat abundance profiles (De Grandi & Molendi 2001)
  • Slide 20
  • AND WHAT WE DONT KNOW THE ENRICHMENT PROCESSES How metals leave the galaxies to pollute the ICM THE DIFFERENT CONTRIBUTION OF THE TWO DIFFERENT TYPES OF SNe (SNII AND SNIa) AS A FUNCTION OF THE POSITION IN THE CLUSTER
  • Slide 21
  • SNIa vs. SNII Progenitors have short lifetimes 10 6 - 10 7 yr yields in solar masses (Nomoto 1997) O 1.80 Mg 0.11 Si 0.21 Fe 0.1 Progenitors evolve over 10 9 yr yields in solar masses O 0.14 Mg 8e-3 Si 0.15 Fe 0.7
  • Slide 22
  • ENRICHMENT MECHANISM protogalactic winds (Larson & Dinerstein 1975, Kauffmann & Charlot 1998) ram pressure stripping (Gunn & Gott 1972, Toniazzo & Schindler 2001) galaxy-galaxy interactions later galactic winds from cD driven by SNIa or AGN and confined by ICM pressure and gravitational potential (Renzini 1993, Dupke & White 2000)
  • Slide 23
  • GALACTIC WINDS Supernova driven superwinds develop when the supernova rate per unit volume is high enough that supernova remnants overlap before they cool. These superbubbles return large amount of kinetic energy (thermalized via shock) along with metal enrich ejecta creating a hot high pressure gas. Breaking out the disk expands at high velocity ( 1000 km/s). But you do not observe the wind but its interaction H CONTINUUM WHITE CROSSES ARE 50 YOUNG SNR DETECTED IN RADIO Strickland 2002
  • Slide 24
  • RAM PRESSURE STRIPPING Ram-pressure stripping occurs when a galaxy is moving through th