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Ryan C. Ogliore

Measurements of Sulfur Isotopes in the Galactic Cosmic Rays: Possible Evidence for Galactic Chemical Evolution. Ryan C. Ogliore. Space Radiation Lab. California Institute of Technology. Instrumentation - LET development Data Analysis - Increase CRIS data set (large angles, solar max)

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Ryan C. Ogliore

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  1. Measurements of Sulfur Isotopes in the Galactic Cosmic Rays: Possible Evidence for Galactic Chemical Evolution Ryan C. Ogliore Space Radiation Lab California Institute of Technology

  2. Instrumentation - LET development Data Analysis - Increase CRIS data set (large angles, solar max) Propagation Modeling – Better cross-section values Source Abundance Interpretation – Galactic Chemical Evolution

  3. Charged particle passing through matter Energy loss: primarily through excitation/ionization – Attractive Coulomb forces between incident charged particle and orbital electrons gives impulse to electrons in target material

  4. Bethe-Bloch formula for relativistic ionisation loss: -dE = the total energy loss particle (Z) in length dx I = mean ionisation potential of the target atom, Ne = number density of electrons, v = incident particle's velocity (lorentz factor = γ) log –dE/dx log E

  5. Range of high energy particle is found by integrating energy loss rate from the particle's initial kinetic energy until it stops: E=(γ-1)Mc2 ; dE=d(γMc2)=Mvγ3dv Yielding: This can be approximated by a power law: Where k and α are empirical quantities (1) So R=R(Z,M,E0)

  6. Two stacked detectors: incoming particle (Z,M,E,θ) will deposit energy = ΔE in the first detector and E' in the second detector. (2) Solve for mass using (1) and (2):

  7. For the energy deposited in the ΔE and E' detectors: So: Therefore when dE/dx is plotted against E', particles separate into bands corresponding to their charge (Z) and sub-bands corresponding to their mass (M).

  8. How do the detectors measure energy? • Energetic charged particle can give energy to electron in valence band: • - Exciting the electron to conduction band • Leaving behind a free acceptor site “hole” in valence band • Requires ~3.6 eV to generate electron-hole pair in silicon Si bandgap = 1.1 eV P-Type N-Type Doping silicon: p-type (extra holes) or n-type (extra electrons) P-N junction P N P N P N Electrons Electrons Holes Holes

  9. In depletion region, electric field will cause: Created free electrons swept to n-side Created free holes swept to p-side - Electrical signal that can be amplified, shaped and digitized. - Output is a pulse-height that is proportional to energy P-side N-side Contact potential = ~1V yields poor detector performance: Slow movement of charge carriers Can be lost due to trapping or recombination Small active volume of detector Additional bias voltage is required for a useful detector: “reverse bias” negative on p-side, positive on n-side further restricts current flow across junction active volume extends across wafer

  10. Mass Resolution Taking partial derivatives with respect to each measured quantity will show how the mass resolution couples to measurement uncertainties. Each partial is proportional to M, so total mass res. is proportional to mass

  11. To distinguish isotopes (elements), there has to be an inflection point between the two adjacent gaussians. The presence of an inflection point is determined by the widths of the peaks and their relative heights Stone, 1975

  12. Energy Loss Fluctuations (σΔE) The energy deposited in the ΔE detector is due to a large number of ion-electron collisions: subject to statistics. For CRIS and most LET particles – thick absorber approximation is valid (many collisions) Gaussian shape of energy loss distribution. Leo – Techniques for Nuclear and Particle Experiments Probability of energy loss κ = (mean energy loss in detector)/Wmax Wmax = max energy transfer (head-on collision) κ ≈ 2.8 for LET ∆E=L1+L2 , E’=L3

  13. Multiple Coulomb Scattering (σθ) Incident particles experience angle deflections due to many elastic Coulomb scatterings from silicon nuclei in the detector (energy loss of incident particle is negligible). Two ways this effects mass resolution 1) Scattering in a previous detector changes incident angle, secθ,resulting in an miscalculated pathlength in subsequent detector. 2) Scattering in the detector itself – pathlength through the detector is altered. Williams, Ph.D. Thesis Charge State Fluctuations (σΔE) As the incident ion slows down, it picks up and loses electrons (stochastic process). Average charge of ion decreases Energy deposited in the detector decreases Important for low-energy, high-Z

  14. Detector Thickness Uncertainty (σL) Mass resolution is proportional to the accuracy of the detector maps: Variation of 1 μm in a 20 μm detector = 5% variation in signal

  15. STEREO - Solar Terrestrial Relations Observatory Two identical, sun-pointed observatories in heliocentric orbit drifting away from the earth, one leading and one lagging Scheduled for launch in February 2006

  16. LET – Low Energy Telescope Top Cover M.I.S.C. Electronics Board Upper Shields L3 Detectors Inner Detector Housing L2 Detectors Detector Collimators L1 Detectors L1 Detectors Main Housing Lower Shields Front-End Electronics Bottom Covers

  17. Particle detection on LET is done with silicon semiconductor detectors: L1: 20 μm x 2 cm2 L2: 50 μm x 10.2 cm2 L3: 1 mm x 15.6 cm2 Detectors are made of ion-implanted silicon LET uses the standard dE/dx vs E' technique for particle identification

  18. Investigation of sulfur isotopes in galactic cosmic rays using the Cosmic-Ray Isotope Spectrometer aboard ACE CRIS CRIS

  19. CRIS Schematic Four stacks of 15 silicon detectors. Each detector is 10 cm in diameter and 3 mm thick. Large geometry factor: ~250 cm2sr Scintillating Optical Fiber Trajectory (SOFT) hodoscope for trajectory information. zenith angle resolution: < 0.1º Position resolution: < 1mm Energy range for mass analysis O 60-280 MeV/nuc S 97-356 MeV/nuc Fe 115-570 MeV/nuc Achieves 0.1-0.25 amu mass resolution for elements 2≤Z≤30.

  20. CRIS is able to resolve isotopes well Large geometry factor (250 cm2sr) contributes to good statistics

  21. CRIS local isotope abundances have been acquired for: The first ~2.5 years of the ACE mission (11/1997 – 04/2000) Incident particle trajectories <25° of the normal to the detector surfaces Particles penetrating at least three detectors in the stack. Data analysis is needed for many particles that are not in data set Increased date range (05/2000 – current) adds 62% to data set Range 2 particles add 21% to data set Greater than 25° particles adds ~150% to data set Total contribution: more than three times the statistics than previously studied – decrease statistical error bars by a factor of 0.55

  22. 25º Range 2

  23. Calculate Source Abundances from CRIS isotope data: Model propagation of GCR in Galaxy Leaky Box Model: Particles are emitted by a number of point source objects distributed throughout a finite volume (the Galaxy). Particles freely diffuse inside the volume When they encounter the boundary they can either be reflected or with some probability, escape.

  24. Escape from the finite volume is modeled by a “catastrophic” loss term in the propagation equation. The steady-state GCR propagation model (injection, deceleration, catastrophic loss terms are balanced): σαji and σpji= spallation cross-sections for production of nucleus i by bombardment of target j by alphas and protons E , E’ = kinetic energy per nuc of product (i) and parent (j) vi = velocity of particle i τe = lifetime against escape Λesc = ρv τe = mean free path against escape Ni(E) = cosmic ray number density of species i at energy/nuc E τi = decay lifetime for unstable nuclei γ = lorentz factor qi(E) = source term (constant in space and time) bi(E) = ionization energy loss per nuc, –(∂E/ ∂t)i σαj and σpj = total destruction cross-sections of nuclei by alphas and protons nHe and nH = mean number density of He and H in ISM

  25. Local fluxes used by propagation model are local interstellar fluxes, not fluxes at 1 AU. To reconcile fluxes at 1 AU with interstellar fluxes, we must take into account Solar Modulation... Longair, High Energy Astrophysics 1 A high energy charged particle propagating through the heliosphere to 1 AU will scatter and diffuse inward in the magnetic irregularities of the outward-flowing solar wind. Outward flow of solar wind will convect particle outwards. Also, the particle will undergo adiabatic energy loss due to the outward expansion of magnetic field irregularities in the solar wind. The amount of modulation varies with the eleven-year solar cycle.

  26. Use a “tracer isotope” instead of solving for Λesc - Same parents as isotopes of interest but absent from source - For sulfur: 33S is a good tracer Stone and Wiedenbeck, 1979 Using the tracer isotope, we can write an expression for the source abundance in terms of only observable quantities (the observable flux of the tracer isotope instead of Λesc in the propagation equation). Primary abundance of j = qj Observed flux of tracer isotope = φi Secondary production of i during propagation = Si Secondary contribution to observed flux = φi(Sj/Si)

  27. η = (nn-np)/(nn+np) Stone and Wiedenbeck, 1979 34S/32S source as a function of local 34S/32S at 700 MeV/nuc for various values of the tracer abundance 33S/32S. Points indicate sources with various levels of neutron excess (η), solar is “Cameron” point, with local abundances deduced from propagating the source through an exponential distribution of pathlengths with mean Λ = 5.5 g/cm2

  28. Contributions to uncertainty in the source 34S/32S : Statistics Production cross-section errors (spallation from heavier nuclei: σαji and σpji). ~6e4 S counts in the CRIS data set → cross-section errors will dominate Percentage contribution of parent to secondary production of daughter Stone and Wiedenbeck, 1979

  29. Measurements are available for several of the major contributors to secondary production of sulfur over a range of energies: 56Fe, 33S, 36Ar, 40Ca, 38Ar, 37Cl, 55Mn, … Measurements claim errors of <5% All other contributions: Semi-empirical formulas (~25% accuracy) Estimating the contributions of all cross-section errors to 34S and 32S abundances… In total, we can know the total cross-section for sulfur isotope production to ~15%

  30. How do cross-section errors relate to uncertainty in secondary production? Stone and Wiedenbeck, 1979 - Many uncorrelated errors in cross-section contributions from various parent isotopes helps with uncertainty in secondary production to isotopes of interest - Cross-section ratios may have smaller uncertainties. This would improve secondary production uncertainty (which is proportional to Sj/Si)

  31. Galactic Chemical Evolution Using models for star formation and stellar nucleosynthesis as well as spectroscopic observations, theories of the elemental and isotopic evolution of the Galaxy have been developed. GCE theories can predict what changes have occurred in the composition of Galactic material between the time of the formation of the solar system 4.6 Gyr ago and the cosmic ray age (~15 Myr) What enhancements to the isotopic abundances of sulfur do these models predict for the galactic cosmic ray material compared to the solar system?

  32. Timmes, et al 1995 Wilson, 1998 Metallicity has increased with time since the formation of our Galaxy Processing of lighter elements to heavier elements by stars Almost all initial CNO nuclei are converted to 14N during H-burning. He-burning: α-capture upon 14N leads to 22Ne: 14N(α, γ )18F(e+ν)18O(α, γ)22Ne Abundance of neutron-rich 22Ne depends (linearly) on the initial CNO abundance. Initial CNO depends on Z. X(22Ne)≈0.02([Z]), 20Ne is independent of Z, so 22Ne/20Ne varies linearly with Z The abundance of 22Ne during He-burning determines the neutron-excess, η=(nn-np)/(nn+np) in all future stages of the star. This effects the isotopic ratios of other elements

  33. Explosive oxygen burning (Woosley et al.) Woosley & Weaver 1973

  34. From Wilson calculation of evolution of Z with time: At solar system formation: Z/Zsolar = 1 Present: Z/Zsolar = 1.5 η varies linearly with Z: X(22Ne)≈0.02([Z]), η≡∑(1-2Zi/Ai)Xiη≈(1/11)X(22Ne) ≈ 0.002([Z]) Solar composition neutron-excess ≈ 2x10-3 (Cameron, 1973) 50% increase in Z → 50% increase in η: Present η = 3x10-3 From Woosley et al., explosive burning of oxygen: 34S/32S should increase by 50% from solar system formation to present Consistent with increase in 18O/16O around [Z]=1 from Timmes et al 1995 Also consistent with Woosley & Weaver 1981: Predict a factor of about 2.3 increase in 34S/32S for a 2.5 increase in Z (or η)

  35. The closer η at the GCR source is to solar-like composition, the more precise the measurements must be to discern this difference. For 15% cross-section errors: 2.6x10-3 is distinguishable from solar The η =3x10-3 predicted by GCE should be identifiable with these errors Max cross-section errors to distinguish η from solar at 2σ level 15 solar 2.6 neutron-excess: 103η Stone and Wiedenbeck, 1979

  36. Isotopic ratios of other elements could be good probes for evidence of GCE: Woosley & Weaver 1973 38Ar/36Ar 38Ar=11% primary, 36Ar=49% primary, tracer = 37Ar 42Ca/40Ca 42Ca=2% primary, 40Ca=98% primary, tracer = 41Ca (44Ca/40Ca 44Ca=7% primary)

  37. - Other possible contributions to a larger than solar 34S/32S : Contributions from Wolf-Rayet stars Increased cosmic ray production closer to center of Galaxy Both would increase [34S/32S] Previous Measurements: Thayer (1997) Ulysses High Energy Telescope 34S/32S = 6.2% ± 2.6% ± ≤2.9% (Solar System = 4.4%) Can also investigate: Meyer et al, 1997 FIP vs. Volatility - Elemental fractionation of cosmic rays - Sulfur is volatile, intermediate-high FIP - Isotopes will have small error bars

  38. Conclusions / Timeline With a large data set and new, accurate measurements of key cross-sections, evidence for galactic chemical evolution can be investigated in the isotopic abundances of sulfur, argon and perhaps calcium. Instrumentation work on LET continues with tests of two flight instruments at Michigan State accelerator in early July Fall 2004 – begin work on CRIS data analysis. Continue work on propagation modeling, source abundance analysis through later 2004 and early 2005. Thesis writing: mid-2005 Thesis defense: late 2005 / early 2006

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