Eiichiro Komatsu University of Texas at Austin Non-Gaussianity From Inflation April 19, 2006 CMB High-z Clusters LSS Observational Constraints on Non-Gaussianity

Why Study NG? (Why Care?!) Who said that CMB should be Gaussian? Don ’ t let people take it for granted! It is remarkable that the observed CMB is (very close to being) Gaussian. The WMAP map, when smoothed to 1 degree, is entirely dominated by the CMB signal. If it were still noise dominated, no one would be surprised that the map is Gaussian. The WMAP data are telling us that primordial fluctuations are very close to being Gaussian. How common is it to have something so close to being Gaussian in astronomy? E.g., Maxwellian velocity distribution, what else? It may not be so easy to explain that CMB is Gaussian, unless we have a compelling early universe model that predicts Gaussian primordial fluctuations: Inflation. “ Gaussianity ” should be taken as seriously as “ Flatness ”.

Gaussianity vs Flatness People are generally happy that geometry of our Universe is flat. 1-  total = (+0.013, ) (68% CL) (WMAP03+HST) Geometry of our Universe is consistent with being flat to ~3% accuracy at 95% CL. What do we know about Gaussianity? For  G  f NL  G 2, -54<f NL <114 (95% CL) (WMAP03) Primordial fluctuations are consistent with being Gaussian to ~0.001% accuracy at 95% CL. In a way, inflation is supported more by Gaussianity of primordial fluctuations than by flatness. (Just kidding.)

Let ’ s Hunt Some NG! The existing CMB data already suggest that primordial fluctuations are very close to being Gaussian; however, this does not imply, by any means, that they are perfectly Gaussian. In fact, we would be in a big trouble if f NL turned out to be too close to zero. Second-order GR perturbations in the standard cosmological model must produce f NL ~5 or so. (See Sabino Matarrese ’ s and Nicola Bartolo ’ s Talks, and Michelle Liguori ’ s Poster) Some inflationary models produce much larger f NL. We may be able to distinguish candidate inflationary models by NG. (Not to mention that the simplest, single-field, slow-roll models produce tiny NG.) The data keep getting better. If we could find it, it would lead us to something huge.

How Do We Test Gaussianity of CMB?

Finding NG? Two approaches to I. Null (Blind) Tests / “ Discovery ” Mode This approach has been most widely used in the literature. One may apply one ’ s favorite statistical tools (higher-order correlations, topology, isotropy, etc) to the data, and show that the data are (in)consistent with Gaussianity at xx% CL. PROS: This approach is model-independent. CONS: We don ’ t know how to interpret the results. “ The data are consistent with Gaussianity ” --- what physics do we learn from that? It is not clear what could be ruled out on the basis of this kind of test. II. “ Model-testing ” Mode Somewhat more recent approaches. Try to constrain “ NG parameter(s) ” (e.g., f NL ) PROS: We know what we are testing, we can quantify our constraints, and we can compare different data sets. CONS: Highly model-dependent. We may well be missing other important NG signatures.

Recent Tendency I. Null (Blind) Tests / “ Discovery ” Mode This approach is being applied mostly to the “ large- scale anomaly ” of the WMAP data. North-south asymmetry Quadrupole-octopole alignment Some pixels are too cold (Marcos Cruz ’ s Poster) “ Axis of Evil ” (Joao Magueijo ’ s Talk) Large-scale modulation II. “ Model-testing ” Mode A few versions of f NL have been constrained using the bispectrum, Minkowski functionals and other statistical methods.

App. II: What Do We Need? We need to know the predicted form of statistical tools as a function of model parameters to fit the data. For  G  f NL  G 2, there are only three statistical tools for which the analytical predictions are known: The angular bispectrum Komatsu & Spergel (2001); Babich & Zaldarriaga (2004) The angular trispectrum Okamoto & Hu (2002); Kogo & Komatsu (2006) Minkowski functionals Hikage, Komatsu & Matsubara (2006)

Simplified Model: Working assumption: f NL is independent of scales Clearly an oversimplification! (Note, however, that this form is actually predicted from curvaton models and the non- linear Sachs-Wolfe effect in the large-scale limit.) Why use this ansatz? Current observations are not yet sensitive to scale-dependence of f NL, but are only sensitive to the overall amplitude. See Creminelli et al. (2005) for an alternative ansatz. Sensitivity Goal: f NL ~1 Why f NL ~1? NG “ floor ” : the ubiquitous signal from the second-order GR produces something like f NL ~5, which would set the lower limit to which one may hope to detect the primordial non-Gaussianity. It may be possible to achieve f NL ~3 using the angular bispectrum from the Planck and CMBPol data. How do we go from there to f NL ~1?

Bartolo, Komatsu, Matarrese & Riotto (2004)

How Do They Look? Simulated temperature maps from f NL =0f NL =100 f NL =1000 f NL =5000

Is One-point PDF Useful? Conclusion: 1-point PDF is not very useful. (As far as CMB is concerned.) A positive f NL yields negatively skewed temperature anisotropy.

One-point PDF from WMAP The one-point distribution of CMB temperature anisotropy looks pretty Gaussian. Galaxy has been masked. Left to right: Q (41GHz), V (61GHz), W (94GHz). Spergel et al. (2006)

Angular Bispectrum, B lmn A simple statistic that captures all of the information contained in the third-order moment of CMB anisotropy. Theoretical predictions exist. Statistical properties well understood.

How Does It Look? Primordial Inflation Second-order PT Secondary Gravitational lensing Sunyaev-Zel’dovich effect Nuisance Radio point sources Our Galaxy Komatsu & Spergel (2001)

?Angular Bispectrum, B lmn ? Physical non-Gaussian signals should be generated in real space (via e.g., non-linear coupling), and thus should be more apparent in real space. The Central Limit Theorem makes a lm coefficients more Gaussian! Non-Gaussianity localized in real space is obscured and spread over many l’s and m’s. Challenges in the analysis Sky cut complicates the analysis in harmonic space in many ways. Computationally expensive (but not impossible). 8 hours on 16 procs of an SGI Origin 300 for measuring all configurations up to l=512.

Optimal “Cubic” Statistics Motivation We know the shape of the angular bispectrum. Find the “best statistic” in real space that is most sensitive to the kind of non-Gaussianity we are looking for. Results The statistics already combine all configurations of the bispectrum optimally. Optimized just for f NL : Maximum adaptation of the approach II times faster for l max =512 (30 sec vs 8 hours) 4000 times faster for l max =1024 (4 minutes vs 11 days) One can also find an optimized statistics just for point sources. Komatsu, Spergel & Wandelt (2005)

Cubic Estimator = Skewness of Filtered Maps B(x) is a Wiener reconstructed primordial potential field. A(x) picks out relevant configurations of the bispectrum.

Wiener-reconstructed Primordial Curvature Reconstruction can be made even better by including the polarization data. (See Ben Wandelt’s Talk) On the largest scale,

It Works Very Well. The statistics tested against simulations UNBIASEDUNCORRELATE D UNBIASED Komatsu et al. (2006)

Bispectrum Constraints Still far, far away from f NL ~1, but it could put some interesting limits on parameters of curvaton models, ghost inflation, and DBI inflation models. Komatsu et al. (2003); Spergel et al. (2006) (1yr) (3yr)

Angular Trispectrum, T lmpq (L) Why care?! Two reasons. The trispectrum could be non-zero even when the bispectrum is exactly zero. We may increase our sensitivity to primordial NG by including the trispectrum in the analysis.

Not For WMAP, But Perhaps For Planck… Trispectrum (~ f NL 2 ) is more sensitive than the bispectrum (~ f NL ) when f NL is large. At Planck resolution, the trispectrum would be detected more significantly than the bispectrum, if f NL > 50. Kogo & Komatsu (2006)

Minkowski Functionals Morphology Area Contour length Euler characteristics (or “Genus”) The number of hot spots minus cold spots. All quantities are evaluated as a function of the peak height relative to r.m.s.

MFs from WMAP (1yr) Komatsu et al. (2003); Spergel et al. (2006) (3yr) AreaContour LengthGenus

Analytical Calculations The analytical formulae should be very useful: we do not need to run NG simulations for doing MFs any more. The formulae indicate that MFs are actually as sensitive to f NL as the bispectrum; however, MFs do not contain any more information than the bispectrum does. Hikage, Komatsu & Matsubara (2006)

Using Galaxies Not only CMB, but also the large-scale structure of the universe does contain information about primordial fluctuations on large scales. (See Peter Coles’s Talk.) One example: Galaxy Bispectrum Cosmic Inflation Probe (CIP), a galaxy survey measuring 10 million galaxies at 3<z<6, would offer an opportunity to use this formula to constrain f NL ~5 (note that the scale measured by CIP is smaller than that measured by CMB by a factor of ~10!) CIP

Using High-z Objects Massive objects forming at high-z are extremely rare: they form at high peaks of (nearly) Gaussian random field. Even a slight distortion of a Gaussian tail can enhance (or reduce) the number of high-z object dramatically. The higher the mass is, or the higher the redshift is, the bigger the effect becomes.

The current WMAP limits still permit large changes in the number of objects at high z. A golden object, like a few times solar masses at z=3, would be a smoking gun. Implications for high-z objects Komatsu et al. (2003)

Summary There have been a lot of development in this field, and there is still a lot more to do. It is timely to have a workshop that focuses on non-Gaussianity from Inflation! We need more accurate predictions for the form of observables, such as the CMB bispectrum, trispectrum, Minkowski functionals, and others, from Various models of inflation Second-order PT, including second-order effects in Boltzmann equation Not just CMB! Galaxies and high-z objects might give us some surprises. Toward the sensitivity goal, f NL ~1. What would be the best way to achieve this sensitivity? Currently, the CMB bispectrum seems to be ahead of everything else. The temperature plus polarization bispectrum would allow us to get down to f NL ~3. How do we break this barrier? Yet, the real surprise might come from the Approach I.