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Stochastic Gravitational Lensing and the Nature of Dark Matter

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  1. Stochastic Gravitational Lensingand the Nature of Dark Matter Chuck Keeton Rutgers University with: Arthur Congdon (Rutgers), Greg Dobler (Penn), Scott Gaudi (Harvard), Arlie Petters (Duke), Paul Schechter (MIT) Gravitational lens database --http://cfa-www.harvard.edu/castles

  2. Outline • Cold Dark Matter 101 • Gravitational Lensing 101/201 • Evidence for dark matter substructure • catastrophe theory • Stochastic gravitational lensing • random critical point theory • marked spatial point processes • Some statistical issues • Bayesian inference • small datasets • testing relations, not just parameters

  3. The Preposterous Universe Can we go beyond merely quantifying dark matter and dark energy, to learn about fundamental physics?

  4. The Cold Dark Matter (CDM) Paradigm • Dark matter is assumed to be • “cold”: non-relativistic • “collisionless”: only feels gravity • axions, neutralinos, lightest supersymmetric particle, … • Successful in explaining large-scale properties of the universe. • global geometry, distribution of galaxies, cosmic microwave background, … • Successful in describing many features of galaxies and clusters. • the “missing mass” • But several challenges (crises?) related to the distribution of dark matter on small scales.

  5. cluster of galaxies,~1015 Msun CDM halos are lumpy Predictions: • Hierarchical structure formation:small objects form first, then aggregate into larger objects. • Small objects are dense, so they can maintain their integrity during mergers. • Large halos contain the remnants of their many progenitors -substructure. • Clump-hunting: How to find them? single galaxy,~1012 Msun (Moore et al. 1999; also Klypin et al. 1999)

  6. cluster of galaxies,~1015 Msun CDM halos are lumpy Clusters look like this - good! vs. single galaxy,~1012 Msun (Moore et al. 1999; also Klypin et al. 1999)

  7. cluster of galaxies,~1015 Msun CDM halos are lumpy Galaxies don’t -bad? vs. single galaxy,~1012 Msun (Moore et al. 1999; also Klypin et al. 1999)

  8. A Substructure Crisis? CDM seems to overpredict substructure. What does it mean? Particle physics • Maybe dark matter isn’t cold and collisionless. (CDM is wrong!) • Maybe it is warm, self-interacting, fuzzy, sticky, … Astrophysics • We only see clumps if they contain stars and/or gas. • Maybe astrophysical processes suppress star formation in small objects, so most clumps are invisible.

  9. A Substructure Crisis? CDM seems to overpredict substructure. What does it mean? Particle physics • Maybe dark matter isn’t cold and collisionless. (CDM is wrong!) • Maybe it is warm, self-interacting, fuzzy, sticky, … Astrophysics • We only see clumps if they contain stars and/or gas. • Maybe astrophysical processes suppress star formation in small objects, so most clumps are invisible. Need to search for a large population of “invisible” objects!

  10. Strong Gravitational Lensing S   L O Lens equation: The bending is sensitive to all mass, be itluminous or dark, smooth or lumpy.

  11. Point Mass Lens sources • Bending angle: • Lens equation: • Two images for every source position. • Source directly behind lens Einstein ring with radius qE. lens 2 images of each source Einstein ring radius “Of course, there is not much hope of observing this phenomenon directly.” (Einstein, 1936 Science 84:506)

  12. Microlensing! Data mining: Need to distinguish microlensing from variable stars. (MACHO project)

  13. “Double” Lensing by Galaxies:Hubble Space Telescope Images “Quad” “Ring” (Zwicky, 1937 Phys Rev 51:290)

  14. Quad Radio Lenses 10 = 4+4+2 Double

  15. What is lensing good for? Strong lensing • Multiple imaging of some distant source. • Used to study the dark matter halos of galaxies and clusters of galaxies. Microlensing • Temporary brightening of a star in our galaxy. • Used to probe for dark stellar-mass objects in our own galaxy. Weak lensing • Small, correlated distortions in the shapes of distant galaxies. • Used to study the large-scale distribution of matter in the universe.

  16. Extended Mass Distributions: 2-d Gravity • Work with 2-d angle vectors on the sky. • Interpret bending angle as 2-d gravity force  gradient of 2-d gravitational potential. • Extended mass distribution: • General lens equation:

  17. Fermat’s Principle • Time delay surface: • Lens equation: • Lensed images are critical points of . • minimum • saddle • maximum

  18. Lensing and Catastrophe Theory • Reinterpet lens equation as a mapping: • Jacobian: • The critical points of the mapping  are important… • Observability: image brightness given by

  19. Catastrophes in Lensing 1 3/2 5/4 Critical curves: det J = 0 (Two curves.) Caustics: Image number changes by 2 Fold and cusp catastrophes.

  20. Substructure  complicated catastrophes! (Bradac et al. 2002)

  21. (Schechter & Wambsganss 2002)

  22. Parametric Mass Modeling Data • Positions and brightnesses of the images. 3Nimg • (Maybe a few other observables.) … Parameters • Mass and shape of lens galaxy. 3 • Tidal shear field. 2 • Position and brightness of source. 3 • Substructure. ? Public software --http://www.physics.rutgers.edu/~keeton/gravlens

  23. Lensing and Substructure Fact • In 4-image lenses, the image positions can be fit by smooth lens models. • The flux ratios cannot. Interpretation • Flux ratios are perturbed by substructure in the lens potential. (Mao & Schneider 1998; Metcalf & Madau 2001; Dalal & Kochanek 2002) • Recall: • positions determined by i: fitruefismooth • brightnesses determined by fij: fijtrue = fijsmooth + fijsub

  24. Substructure Statistics • Can always(?) add one or two clumps and get a good model. • More interesting are clump population statistics. Are they: • Consistent with known populations of substructure? (globular clusters, dwarf galaxies, …) • Consistent with CDM predictions? • None of the above?

  25. From Lensing to Dark Matter Physics • Find lenses with flux ratio anomalies. • catastrophe theory • How do the statistics of anomalies depend on properties of the substructure population? • random critical point theory • marked spatial point processes • Measure properties of substructure population. • Bayesian inference • small datasets • Compare with CDM predictions. • testing relations, not just parameters • How do substructure population statistics depend on physical properties of dark matter?

  26. Link #1: Finding flux ratio anomalies(CRK, Gaudi & Petters 2003 ApJ 598:138; 2005 ApJ 635:35) • Do the “anomalies” really indicate substructure?Or just a failure of imagination in our (parametric) lens models? • Complaints about “model dependence”…Real problem is use of global failures to probe local features. • Fortunately, catastrophe theory enables a local lensing analysis that leads to some generic statements… Use mathematical theory to develop a statistical analysis to apply to astronomical data.

  27. folds: A1-A2 0 PG 1115+080

  28. cusps: A-B+C  0 B2045+265 (Fassnacht et al. 1999)

  29. Theory of fold catastrophes in lensing • Jacobian: • Fold critical point: (in appropriate coordinates) • General perturbation theory analysis near fold point: At lowest order, the two images mirror one another.

  30. Connect to observables: • Rfold vanishes with the distance between the images. • But with an unknown coefficient!

  31. Derive p(Rfold | d1,d2) Afold depends on: • f derivatives • Physical parameters: galaxy shapes -- from observed galaxy samples tidal shear -- from theoretical models Monte Carlos: • Generate ~106 mock quads. • Extract conditional probability density. What is the range of Rfold in realistic smooth lenses? If real lenses lie outside this range, they must not be smooth.  substructure. Analysis relies on generic properties of fold catastrophes.

  32. Archetypal lenses

  33. Real lenses

  34. Real lenses

  35. The Fold and Cusp Relations Violations of the generic relations: • 5 anomalies among 12 fold lenses • 3 anomalies among 4 cusp lenses • (No firm conclusions about 6 cross lenses) Catastrophe theory reveals generic features … which guide data analysis … and provide a rigorous foundation for substructure studies. Substructure exists, and is relatively common.

  36. Link #2: Theory of Stochastic Lensing • Now must understand what happens when we add substructure. • Formally, system is described by where qi and {pi} are random variables. • Images are critical points of random critical point theory. • Positions qi are independent and identically distributed; and {pi} are independent of qi (we hope) marked spatial point process.

  37. What I want • Given distributions for qi and {pi}, I want to compute distributions for the image properties -- especially P(m). • Analytically, if possible. • Explore large parameter spaces. • Gain general insights, not just specific results. • Clumps are independent and identically distributed  could use characteristic function method. • But I can’t do the (inverse) Fourier transforms.

  38. Physical Insight Newton: gravity outside a spherical object is insensitive to the object’s internal structure. 

  39. Some analytic results… Implication: To lowest order, all that matters is the average density in substructure.

  40. minimum saddle Open questions • For certain kinds of substructure, minima and saddles respond in opposite directions. • But which direction? • Why? • How generic is that result? • Signal seems to be present in data; what does it tell us about substructure? (Schechter & Wambsganss 2002)

  41. Some statistical issues • Given p(m|{sub}), use Bayesian inference to constrain substructure parameters. • Current data: 22 quad lenses • 8 anomalies in 16 fold/cusp lenses • ? anomalies in 6 cross lenses • Future samples: 100s or 1000s, each with its own probability density. • To test dark matter physics, will want to examine relations.

  42. Conclusions • Gravitational lensing is a unique probe of dark matter. • Flux ratio anomalies substructure  dark matter physics. • Can do brute force analysis. But interdisciplinary approach yields much deeper results. • We can reliably identify anomalies. • We can understand what aspects of substructure we can measure. • We will eventually understand how substructure probes dark matter physics. • We pose interesting math/stats questions … then use the answers to do exciting physics/astronomy!

  43. OLD SLIDES

  44. Optics converging lens diverging lens

  45. Gravitational Optics

  46. Gravitational Deflection of Light r M Predicted by Einstein, observed by SirArthur Eddington in the solar eclipse of 1919.