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Smooth Particle Lensing

Smooth Particle Lensing. Dominique Aubert Observatoire Astronomique de Strasbourg Coll : Adam Amara (CEA/Saclay), R.Benton Metcalf (MPA/Garching), C. Pichon(IAP). Overview. SPL is a technique to generate a synthetic lensing signal from numerical simulations in 2D

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Smooth Particle Lensing

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  1. Smooth Particle Lensing Dominique Aubert Observatoire Astronomique de Strasbourg Coll: Adam Amara (CEA/Saclay), R.Benton Metcalf (MPA/Garching), C. Pichon(IAP)

  2. Overview • SPL is a technique to generate a synthetic lensing signal from numerical simulations • in 2D • Uses directly the density sampling performed by the simulations (with noise control) • Uses a combination of techniques used in N-Body Simulations (Tree + adapt. Smoothing+ PM)

  3. The overall signal • For a population of particles, quantities that derive from the potential are given by : • True for the convergence, the shear and the « force » (the deflection angle), all linear opération on the potential (e.g. the flexion) • Tree based summation « à la » Barnes & Hut can improve the efficiency. Scale as log Np. • Cells are opened according to a viewing angle criterion. May result in direct particle summation.

  4. A smooth description • DM Particles are described as two-dimensionnal gaussians in the lens plane • Knowing the position X and the « extent »of the DM particle, several elementary quantities can be deduced : the potential, the deflection, the shear, the convergence

  5. An Adaptative Technique • Tree-based techniques are self adaptative by nature. High precision on the force induced by the particles close to the sampling position & larger approximation on distant particles. • In the current implementation, the particle extent is a function of the local density (deduced by nearest neighbours search).

  6. SPL Pipeline Arbitrary Distribution of rays Particles from simulation 2D-Tree Local Density Summation Potential and its derivatives (or any linear operation)

  7. 1D Validation Constant smoothing Adapt. smoothing Model : Softened isothermal sphere (rc=rh/2000) 1e7 particles

  8. 2D magnification FFT 40962 (1802 shown here) SPL same resolution SPL 10242 constant smoothing SPL 10242 adapt.smoothing

  9. Flexible Sampling • Computing power can be focused on relevant regions

  10. Toward Large Scales • Large scales features contributes to signal within objects, groups etc… • Periodic boundary conditions may be required to study large areas • SPL can be extended to a Tree-Particle-Mesh • Under heavy developpement…

  11. A TreePM • Large scale effects: density is sampled on a grid and Poisson equation is solved in Fourier space. • Small scales remain the realm of the Tree Bagla & Ray (2003)

  12. TreePM on a SIS Model : Softened isothermal sphere (rc=rh/2000) 1e6 particles

  13. TreePM on Cosmological Volume Déflection angle PM Déflection angle Déflection angle TreePM Tree 1024x1024 maps

  14. TreePM on Cosmological Volume 1024x1024 maps Log. Polar grids alpha kappa

  15. Summary • The Tree part described in « Smooth Particle Lensing »: D.Aubert, A. Amara & R.B. Metcalf, MNRAS, 2007 • The TreePM should follow soon Adaptative smoothing + + +

  16. 1D Validation Model : Softened isothermal sphere (rc=rh/2000) 1e7 particles

  17. Critics and Caustics Simulation resolution affects the location and shape of critical lines. …it works also in non spherical geometry.

  18. Conclusions • Written in Fortran 90 • ~10-4 sec/ray for a 16 millions particles • 512 Mo RAM Peak for the previous cosmological simulation • The Tree part described in « Smooth Particle Lensing »: D.Aubert, A. Amara & R.B. Metcalf, MNRAS, 2007 • The TreePM should follow soon

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