Optimization of Multi-Object Spectroscopy in Astronomy

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Optimization of Multi-Object Spectroscopy in Astronomy

  1. 1. Optimisation of Multi-Object Spectroscopy in Astronomy Brent Miszalski SALT Research Fellow brent@saao.ac.zaSunday 18 March 12
  2. 2. Overview • Galaxy redshift surveys • Multi-object spectroscopy (MOS) • MOS field configuration by simulated annealing • MOS at the Southern African Large Telescope (SALT) Miszalski et al. 2006, MNRAS, 371,1537Sunday 18 March 12
  3. 3. NGC 1376Sunday 18 March 12
  4. 4. M 101Sunday 18 March 12
  5. 5. Hubble Ultra Deep FieldSunday 18 March 12
  6. 6. Hubble’s law • Expansion of the universe produces a Doppler-shift in light of galaxies towards red end of spectrum • The ‘redshift’ z=(λ- λ0)/λ0 is related to recessional velocity of each galaxy V~cz • V=H 0dSunday 18 March 12
  7. 7. Galaxies cluster togetherSunday 18 March 12
  8. 8. Comoving distance DC - distance between two galaxiesDensity parameters matter dark energy curvatureSunday 18 March 12
  9. 9. Millenium Simulation (Springel et al. 2005)Sunday 18 March 12
  10. 10. We need more redshifts • Measuring fundamental cosmological parameters depends on statistical analysis of large scale structure • A few thousand galaxies is not enough • Need hundreds of thousands or millions • Cannot do this one object at a time...Sunday 18 March 12
  11. 11. Multi-Object Spectroscopy • Developed in late 80s/early 90s • Highly successful but very complex (more focus on getting instrument working, rather than optimising it)Sunday 18 March 12
  12. 12. 2dF: Two-degree Field facility 4-m Anglo-Australian Telescope Lewis et al. (2002)Sunday 18 March 12
  13. 13. Sunday 18 March 12
  14. 14. Sunday 18 March 12
  15. 15. Sunday 18 March 12
  16. 16. wavelengthSunday 18 March 12
  17. 17. wavelengthSunday 18 March 12
  18. 18. 2dFGRS (Colless et al. 2001)Sunday 18 March 12
  19. 19. N(z)~250,000!Sunday 18 March 12
  20. 20. wigglez.swin.edu.au Wigglez Drinkwater et al. 2010Sunday 18 March 12
  21. 21. wigglez.swin.edu.au Wigglez Drinkwater et al. 2010 Blake et al. 2010Sunday 18 March 12
  22. 22. wigglez.swin.edu.au Wigglez Drinkwater et al. 2010 Blake et al. 2010Sunday 18 March 12
  23. 23. A challenging optimisation problem • 400 fibres to match up to N targets (up to ~1000) • Targets have priorities 1(lowest) to 9(highest) • Limited fibre reach • Fibres and buttons cannot collide, but fibre crossover ok • Uniformly sample targets [no structure imprint] • Prefer straighter fibres [quicker config times]Sunday 18 March 12
  24. 24. Fibre and target reachSunday 18 March 12
  25. 25. Fibre and target reachSunday 18 March 12
  26. 26. Sunday 18 March 12
  27. 27. Simulated Annealing • Donnelly et al. (1992) first proposed and implemented SA for field configuration, but not fast enough back then • SA simulates slow cooling of physical systems (e.g. glass), making small random changes at each temperature level • Metropolis (1953) algorithm determines whether a change is accepted • Fewer and fewer “bad” changes are accepted at lower temperaturesSunday 18 March 12
  28. 28. Travelling Salesman Problem Numerical Recipes (Ch. 10) (b) large river penalty (c) negative river penalty!Sunday 18 March 12
  29. 29. Annealing schedule • Start with unallocated fibres, a few hundred targets and an initial temperature Ti • Slowly cool Ti by multiplication with (1-ΔT) • Randomly choose new targets for each fibre, multiple times (up to 105 swaps per ΔT) • The randomisation of each fibre occurs in four ways • Metropolis (1953) algorithm accepts or denies each change, depending on global ‘quality’ of field • Reach quasi-static equilibrium at each temperatureSunday 18 March 12
  30. 30. Four randomisation casesbefore afterSunday 18 March 12
  31. 31. Metropolis algorithmSunday 18 March 12
  32. 32. Metropolis algorithm Boltzmann distribution in statistical mechanicsSunday 18 March 12
  33. 33. Objective function target close straighten priority pairs fibres maximise me!Sunday 18 March 12
  34. 34. Objective function TemperatureSunday 18 March 12
  35. 35. A sample run E TemperatureSunday 18 March 12
  36. 36. Simulations • Both uniform and clustered fields • Also use actual cosmological simulations (mock catalogues) • Different priority distributions • Fields with close pairs • LOTS of trial and error in selecting best algorithm parameters • Usually configure 1000 fields eachSunday 18 March 12
  37. 37. Total target yieldSunday 18 March 12
  38. 38. Total target yieldSunday 18 March 12
  39. 39. Target priorities lowest highestSunday 18 March 12
  40. 40. Target priorities lowest highestSunday 18 March 12
  41. 41. Target prioritiesSunday 18 March 12
  42. 42. Target prioritiesSunday 18 March 12
  43. 43. Target prioritiesSunday 18 March 12
  44. 44. UniformityOxford SASunday 18 March 12
  45. 45. OLD (Oxford)Sunday 18 March 12
  46. 46. NEW (Annealing)Sunday 18 March 12
  47. 47. Fibre straightness γ=0.0 γ=0.125 γ=2.0Sunday 18 March 12
  48. 48. Fibre straightness γ=0.0 γ=0.125 γ=2.0Sunday 18 March 12
  49. 49. Algorithm summary • Power is in contained in the objective function • Performance far exceeds previous algorithms • Both in raw target yield and flexibility • Routinely used by astronomers at AAT since 2006 • Routinely used by several large galaxy redshift surveys • Generic algorithm suitable to many other MOS instruments • Opportune time to apply it to MOS masks at SALT!Sunday 18 March 12
  50. 50. SALT • Biggest single telescope in Southern Hemisphere! • 11.1m x 9.8m optical mirror • Refurbished instrumentation: April 2011 • Second science semester starts in May 2012 • Multi-object capability: instead of fibres, use slit-masks • MOS is currently being tested/commissioned • Perfect time to explore optimisation of mask designphoto: Lisa CrauseSunday 18 March 12
  51. 51. Sunday 18 March 12
  52. 52. Sunday 18 March 12
  53. 53. MOS masks • Cheaper than developing a robot + fibre system • Use laser to cut slits in carbon fibre mask • Mask is placed in focal plane of telescope • Each slit produces a spectrum • Challenge is to ‘pack in’ the best arrangement of slits in one mask • A unique set of constraints c.f. fibre optimisationSunday 18 March 12
  54. 54. Laser mask cutter MOS @ SALT Slit mask cutter software GUISunday 18 March 12
  55. 55. courtesy David Gilbank ~1/2 degree IMACS on Magellan 6.5-m telescope ChileSunday 18 March 12
  56. 56. courtesy David Gilbank ~1/2 degree IMACS on Magellan 6.5-m telescope ChileSunday 18 March 12
  57. 57. courtesy David Gilbank slitsSunday 18 March 12
  58. 58. courtesy David GilbankSunday 18 March 12
  59. 59. AIMS project • An exploratory study for a new mask design algorithm • Dr Brent Miszalski (SAAO/SALT) • Dr David Gilbank (SAAO) • Prof Bruce Bassett (AIMS/SAAO/UCT) • Design clear guidelines necessary for algorithm development to start • Identify most efficient and clever ways to conduct basic operations needed in a mask algorithmSunday 18 March 12
  60. 60. MOS mask design issues • What data structures to use in algorithm? • Hashes, vectors, lists, etc. Best choices == faster • How to tilt slits to capture > 1 target in field? • What randomisation steps to choose? • Shifting slit centres, extending slit size?? • Shuffling groups of slits? Adding new slits? • How do we best define a “good” mask design? • Quantify completeness? Ensemble designs?Sunday 18 March 12
  61. 61. MOS mask design issues • What is the best way to explore the parameter space of the problem? • Monte carlo simulations, statistics on real input data • Review previous MOS algorithms (especially mask design algorithms) • Most algorithms in the literature could be considerably improved • Your work could be used routinely at SALT!Sunday 18 March 12
  62. 62. Applications • An improved MOS algorithm has multiple applications • Not just cosmological surveys (most of which are done on smaller telescopes with larger fields) • Globular clusters - spectroscopy of individual stars • Galaxy clusters - studying cluster properties as a function of redshift to bring new insights into galaxy formation and evolution, cosmology.Sunday 18 March 12
  63. 63. Omega Centauri (ESO)Sunday 18 March 12
  64. 64. Über cluster (D. Gilbank) z~0.7Sunday 18 March 12
  65. 65. Thank you! brent@saao.ac.zaSunday 18 March 12
  66. 66. Sunday 18 March 12

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