Yun Wang


Associate Professor of Physics & Astronomy
Univ. of Oklahoma
Regents' Award for Superior Research (2006)
B.S. 1985 Tsinghua Univ., P.R. China
M.S. 1987 Carnegie-Mellon
Ph.D. 1991 Carnegie-Mellon
Past Positions:
Postdoctoral Positions at
Univ. of Florida,
Fermilab Astrophysics Center,
Princeton University;
Visiting Assistant Professor at
Univ. of Notre Dame.

Hubble Deep Field South

RESEARCH DESCRIPTION

My research has focused on extracting fundamental physics from cosmological data, in particular, probing dark energy and early universe physics using supernovae (SNe), cosmic microwave background anisotropy (CMB), and cosmic large scale structure data.

I. Probing Dark Energy.
Our Universe has been observed to be undergoing accelerated expansion today. The unknown reason for this cosmic acceleration is referred to as "dark energy". At present, we do not know whether it is a new energy component of the Universe with negative pressure, or a modification of Einstein's theory of gravity (i.e., general relativity). Solving the mystery of the nature of dark energy is the most important problem in cosmology today. Dark energy can be probed using various techniques, most notably, using Type la supernovae (SNe la) as cosmological standard candles. I have done fundamental work in the use of supernovae to probe dark energy. My work has ranged from survey strategy, optimal data analysis, to the modeling of weak lensing effects. In the last several years, I have focused on using galaxy redshift surveys to probe dark energy and test gravity theories.

Here is some of the work that I have done:

Optimization of Dark Energy Projects: I did the feasibility study of a deep supernova survey on a dedicated telescope in 1998; the number of supernovae expected from such a survey surprised and encouraged many observers. In 2001, I studied the relative merits of a very deep survey of supernovae as compared to shallower wide-field surveys in placing constraints on dark energy. Based on this, I developed a mission concept for the NASA-DOE Joint Dark Energy Mission (JDEM), Joint Efficient Dark-energy Investigation (JEDI), in collaboration with Arlin Crotts (Columbia) and Peter Garnavich (Notre Dame). JEDI has significantly impacted the design of space missions to probe dark energy (including Euclid and WFIRST).

Extracting Dark Energy Constraints from Data: In a series of papers starting in 2001, I have shown the value in analyzing the dark energy density instead of (or in addition to) its equation of state. This is because the dark energy density is more closely related to observables (hence better constrained), and can also probe a greater range of dark energy models than the dark energy equation of state. I have also demonstrated the importance of making model-independent parametrizations of dark energy, and improving the robustness of the analysis by imposing priors from complementary observational data (such as cosmic microwave background and galaxy clustering) in a consistent manner (2001-2004). I have also worked on using weak lensing (2003), Lyman-alpha forest (2003), and galaxy clustering (2004) as complementary probes of dark energy. Wang & Tegmark (2004) establishes a consistent and robust theoretical framework for extracting dark energy constraints from data. Wang & Tegmark (2005) presents a new robust supernova data analysis technique that minimizes systematic errors and shows the potential of JEDI in measuring dark energy density. Wang (2009) derives an accurate measurement of dark energy density as a function of cosmic time using current data.

Weak Lensing Systematic of Supernova Cosmology: I have shown how the weak lensing effect of supernovae can be analytically modeled by a universal probability density function derived from the matter power spectrum (2002). I set up a framework for removing or minimizing the effect of weak lensing of supernovae on cosmological constraints by flux-averaging (I have made my flux-averaging code available to the public on my website) (2000,2004). Most recently, I have shown that weak lensing effects may have already begun to set in and must be dealt with in deriving robust constraints on dark energy (2005).

Probing Dark Energy and Testing Gravity with Galaxy Redshift Surveys: Modified gravity models provide an alternative to dark energy in explaining cosmic acceleration as caused by modification of Einstein's theory of gravity. A given measurement of cosmic expansion history H(z) can be fit by either dark energy or modified gravity models. But for given H(z), dark energy and modified gravity models predict different growth rates for cosmic large scale structure f_g(z). Wang (2008) shows that a feasible and relatively modest galaxy redshift survey covering >10,000 square degrees and the redshift range of 0.5 to 2 can rule out a broad class of modified gravity models. Wang et al. (2010) demonstrates that assuming that gravity is not modified, the dark energy constraints are significantly tightened when the growth rate information is used together with H(z).

II. Other Research.

The primordial power spectrum P_in(k) opens an window into early universe physics. I pioneered the model-independent measurement of P_in(k) from data in a paper published in ApJ in 1999. Wang & Mathews (2002) made the first measurement of the primordial power spectrum from CMB data. Pia Mukherjee and I have developed new and powerful techniques using wavelets to extract the primordial power spectrum (2003-2005). Our study of simulated data show that our technique can reliably recover features in the primordial power spectrum. The application of our technique to the CMB data from WMAP has revealed possible features that may be related to unusual inflationary physics. It will be interesting to see what results we will obtain when the four year WMAP data and Planck data become available.

Pia Mukherjee and I have studied primordial non-Gaussianity using the WMAP data (2004). I have also worked on prospects for constraining cosmological models with the extragalactic CMB temperature (2001), constraints on extra dimensions from cosmological and terrestrial measurements (2001), and constraints on neutrino degeneracy from the cosmic microwave background and primordial nucleosynthesis (2002).

III. Future Research Plan.
I plan to continue playing a leading role in dark energy research. In the next five to ten years, I will focus on studying various research topics critical to the ground and space-based galaxy redshift surveys including BOSS, Euclid, and WFIRST, and other dark energy projects ALPACA and LSST. In the next ten to fifteen years, I will continuously examine the observational constraints on dark energy from observational data, and compare them with viable new models, in an effort to solve the mystery of dark energy.


Selected Publications:

Yun Wang, et al., Designing a space-based galaxy redshift survey to probe dark energy, MNRAS, 409, 737 (2010)
Yun Wang, Distance Measurements from Supernovae and Dark Energy Constraints, PRD 80, 123525 (2009)
Yun Wang, Differentiating dark energy and modified gravity with galaxy redshift surveys, JCAP 05 (2008) 021
Yun Wang, and Pia Mukherjee, Observational Constraints on Dark Energy and Cosmic Curvature, PRD, 76, 103533 (2007)
Yun Wang, and Pia Mukherjee, Robust Dark Energy Constraints from Supernovae, Galaxy Clustering, and Three-Year Wilkinson Microwave Anisotropy Probe Observations, ApJ, 650, 1 (2006)
Yun Wang, and Max Tegmark, Uncorrelated Measurements of the Cosmic Expansion History and Dark Energy from Supernovae, Phys. Rev. D 71, 103513 (2005)
Yun Wang, and Max Tegmark, New Dark Energy Constraints from Supernovae, Microwave Background and Galaxy Clustering, Phys. Rev. Lett., 92, 241302 (2004)
Pia Mukherjee, and Yun Wang, Model-Independent Reconstruction of the Primordial Power Spectrum from WMAP Data, ApJ, 599, 1 (2003)
Yun Wang, Daniel E. Holz, and Dipak Munshi, A Universal Probability Distribution Function for Weak-lensing Amplification, ApJ Letter, 572, L15-L18 (2002)
Yun Wang and Peter Garnavich, Measuring Time-Dependence of Dark Energy Density from Type Ia Supernova Data, ApJ 552, 445 (2001).
Yun Wang, Flux-averaging Analysis of Type Ia Supernova Data'', ApJ, 536, 531 (2000).
Yun Wang, Supernova Pencil Beam Survey, ApJ, 531, 676 (2000) [astro-ph/9806185].
Yun Wang, David N. Spergel, and Michael Strauss, Cosmology in the Next Millennium: Combining MAP and SDSS Data to Constrain Inflationary Models, ApJ, 510, 20 (1999)