The stability of Azores Aug 2017 John Webb, UNSW/CMS fundamental - - PowerPoint PPT Presentation

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The stability of Azores Aug 2017 John Webb, UNSW/CMS fundamental - - PowerPoint PPT Presentation

Jan 30, 2023 161 likes •616 views

The stability of Azores Aug 2017 John Webb, UNSW/CMS fundamental constants Cambridge Illustra?on: Scien?fic American : Inconstant Constants , Barrow & Webb. Ar?st: J-F. Podevin, www.podevin.com Main project collaborators: MaMhew

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The stability of fundamental constants

Azores Aug 2017 John Webb, UNSW/CMS Cambridge

Illustra?on: Scien?fic American : Inconstant Constants, Barrow & Webb. Ar?st: J-F. Podevin, www.podevin.com

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Main project collaborators:

  • MaMhew Bainbridge (Leicester)
  • John Barrow (CMS, Cambridge)
  • Bob Carswell (IoA, Cambridge)
  • Chung-Chi Lee (DAMPT, Cambridge)
  • Darren Dougan (PhD student UNSW)
  • Vincent Dumont (Berkeley)
  • Victor Flambaum (UNSW)
  • Dinko Milakovic (PhD student, ESO)
  • Gillian Nave (NIST, Colorado)
  • Lydia Tchang-Brillet (Paris)
  • Michael Wilczynska (PhD student UNSW)
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Dimensionless ra4os – things we can actually check

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Keck Observatory Mauna Kea, Hawaii Telescopes and instruments ~$230M.

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European Southern Observatory VLT Paranal, Chile Telescopes and instruments ~$470M.

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From raw data like this… (the colours are ar4ficial) A 1-d spectrum is produced:

Transi?ons frequently seen include: HI, OI, SiII, CII, FeII, MgII, ZnII, CrII, NiII, CIII, CIV, SiIV, NV, OVI, H2

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  • Ground state is most sensi?ve to varia?ons in alpha so important to compare

transi?ons from different mul?plets, rather than alkali doublets

  • Lots of mul?plets are detected so sta?s?cs are good
  • Different sensi?vi?es to alpha (including opposite signs of shi_s) create a

unique paMern – difficult to emulate with a simple calibra?on distor?on. Ec Ei

Represents different observed FeII multiplets Low mass nucleus. Electron feels small poten4al and moves slowly: small rela4vis4c correc4on High mass nucleus. Electron feels large poten4al and moves quickly: large rela4vis4c correc4on

The Many-Mul4plet method – Rela4vis4c Hartree-Fock

  • calcula4ons. Enables use of different mul4plets simultaneously - order of magnitude

improvement over previous Alkali Doublet method

Dzuba, Flambaum, Webb, Phys.Rev.LeB., 82, 888, 1999 Webb, Flambaum, Churchill, Drinkwater, Barrow, Phys.Rev.LeB., 82, 884, 1999

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Transi4ons shiQ in different direc4ons and by different amounts – a unique paRern

Dzuba, Flambaum, Webb, Phys.Rev.LeB., 82, 888, 1999 Webb, Flambaum, Churchill, Drinkwater, Barrow, Phys.Rev.LeB., 82, 884, 1999

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Spectral modelling

  • VPFIT*. Fits mul?ple Voigt profiles using non-linear least-

squares with ?ed parameters

  • Can be up to several hundred free parameters
  • Descent direc?on depends on deriva?ves of χ2 with respect to

each of the free parameters

  • Finite difference deriva?ves or semi-analy?c?
  • No unique model - ever!
  • Despite that, parameter solu?ons in fact are stable and

reproducible (shown later)

* Carswell & Webb, hMps://www.ast.cam.ac.uk/~rfc/vpfit.html

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Frac4onal uncertainty in the deriva4ve of the intensity as a func4on of finite deriva4ve step-size

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Frac?onal uncertainty on H(u) (at fixed a) as a func?on of look-up table resolu?on

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Frac?onal uncertainty on H(u) (at fixed a) as a func?on of look-up table interpola?on

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Frac?onal uncertainty on deriva?ve of H(u) (at fixed a) as a func?on of look-up table resolu?on

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Frac?onal uncertainty on deriva?ve of H(u) (at fixed a) as a func?on of look-up table interpola?on

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Lessons from the above:

  • 1. Just because a method seems “well established”, it is some?mes

important to go back and check the basics

  • 2. Make sure you use enough data when using polynomials to

interpolate (method used in this case was Lagrange interpola?on

  • 3. Look-up tables are used commonly for calcula?ng complex

expressions that would otherwise require computa?onally ?me- demanding numerical integra?ons

  • 4. VPFIT is being improved to significantly improve the precision of

Voigt func?on H(a,u) calcula?ons and the deriva?ves of the Voigt func?on. Both are used internally. Both are important for

  • p?mal analysis of high signal-to-noise, high resolu?on quasar

spectra.

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α in two hemispheres

VLT + Keck Keck VLT Keck VLT

King, Webb, Murphy, Flambaum, Carswell, Bainbridge, Wilczynska, Koch,. MNRAS, 422, 3370, 2012 Webb; King, Murphy, Flambaum, Carswell, Bainbridge, PRL, 107, 191101, 2011

300 measurements. No

  • ther comparable sample

YET…

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Different paBerns in different direc?ons

VLT Keck

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4.2σ evidence for a Δα/α dipole from VLT + Keck

Δα/α = c + A cos(θ)

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Keck & VLT dipoles independently agree, p=6%

VLT Keck Combined

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Low and high redshi_ cuts are consistent in direc?on. Effect is larger at high redshi_.

z > 1.6 z < 1.6 Combined

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Distance dependence

∆α/α vs BrcosΘ for the model ∆α/α=BrcosΘ+m showing the gradient in α along the best-fit dipole. The best- fit direc?on is at right ascension 17.4 ± 0.6 hours, declina?on −62 ± 6 degrees, for which B = (1.1 ± 0.2) × 10−6 GLyr−1 and m = (−1.9 ± 0.8) × 10−6. This dipole+monopole model is sta?s?cally preferred over a monopole-only model also at the 4.2σ level. A cosmology with parameters (H0 , ΩM , ΩΛ ) = (70.5, 0.2736, 0.726).

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Wavelength calibra?on

Thorium-Argon lamps at telescope.

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Evidence for large-scale wavelength distor?ons

Note the zero point is at the central wavelength

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BLACK: Original results – no distor?on correc?on RED: Distor?on corrected results

Distor4on does not explain the VLT results

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Removing the human element – applying AI to varying constants

New method, combining three procedures into one AI process:

  • Gene?c algorithm
  • Local non-linear least-squares
  • Bayesian model averaging
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The challenge: complicated data – need models with many free (and ?ed) parameters

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A gene4c algorithm doesn’t necessarily emulate what a human does – no unique model!

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Every genera?on has a distribu?on of candidate Δα/α solu?ons

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Δα/α solu?on is stable to first guesses and probably stable to small changes in the model

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The first 1000 new measurements

  • f the fine structure constant at

high redshi_ using AI

Approximate many-mul?plet sample sizes: Already published: 300 Currently: Up to about 600 many mul?plets + lots

  • f doublets

Possible using exis?ng archival data: ~1500

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“Raijin” (named a_er the Shinto God of thunder, lightning and storms) Na?onal Computa?onal Infrastructure, ANU, Canberra, Australia

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January-June 2017: 230,000 hours on “Raijin”, the world’s 24th most powerful computer

  • 57,864 cores (Intel Xeon Sandy Bridge technology, 2.6 GHz) in 3602 compute nodes
  • 56 NVIDIA Tesla K80 GPUs
  • 162 TBytes of main memory
  • Mellanox FDR 56 Gb/sec Infiniband full fat tree interconnect
  • 12.5 PBytes of high-performance opera?onal storage capacity
  • This provides a peak performance of approximately 1.37 Pflops
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420 newly available spectra from Keck and VLT archive

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★ Bulk flow α WMAP max. ΔT Great AMractor SN1a

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Conclusions

  • 1. We have hints of spa?al varia?on from the largest available sample of high

redshi_ measurements of alpha. More measurements are on the way. Of course, independent methods are highly desirable.

  • 2. Long-range wavelength distor?ons do not explain the puta?ve dipole.
  • 3. A fully-automated AI method has been developed which does beMer than a

human, permiˆng a far larger sample of measurements and removing any possible bias.

  • 4. Supercomputer calcula?ons are currently being done to produce the first 1000

cosmological measurements of alpha.

  • 5. VPFIT is being improved in terms of precision of Voigt func?on and Voigt

deriva?ve precisions. This should improve robustness and accuracy of error es?mates.