Ravi K. Sheth

I develop physical models and statistical methods which allow the data from large scale galaxy and cluster surveys to constrain models of galaxy formation and cosmology.

I have played a leading role in the development of what is now the standard model of nonlinear clustering and biasing: the Halo Model. It is the currently the best language for interpreting measurements of weak lensing, the thermal and kinematic Sunyaev-Zeldovich effects, and how galaxy clustering depends on galaxy type, both in real and in redshift space. In 2004, I showed that dark matter halo formation is correlated with environment; I also discussed why, and pointed out that understanding this correlation is necessary if the Halo Model is to be used as a precision tool for cosmology. This correlation was sufficiently unexpected and the consequences sufficiently important that a number of groups have since confirmed that the effect does indeed exist: the effect is now called Assembly Bias. I have also used physically motivated models to illustrate the ubiquity of what are now called scale-dependent bias, tidal bias and velocity bias.

My work on halo abundances and clustering forms the basis of methods which use clusters (e.g., X-ray luminosities, temperatures, the Sunyaev-Zeldovich effects, galaxy velocity dispersions) to study cosmology.  From 2004 to 2006 I extended the approach to predict how the morphology (sheets, filaments, voids) rather than simply the density, of large scale structure evolves.  I have since studied how these predictions are modified if the initial conditions were non-Gaussian, or if the force of gravity does not decrease as the inverse-square of separation.  

In 1996 I solved an old combinatorial problem on the partitions of integers which turns out to have interesting connections to coagulation and branching processes.  In 1998 I showed how to extend the approach to model the counts in cells distribution in the nonlinear density field.  In 2002, I developed a new method for estimating the evolution of the optical depth in the Lyman-alpha forest. In 2005, I showed how to use Mark Correlations to quantify and model environmental trends in the galaxy distribution.  In 2007 my collaborators and I showed that local black hole samples are a biased subset of all galaxies, a study that has seen renewed interest since 2016.  Between 2007-2009 I developed methods for making unbiased estimates of the galaxy luminosity function and galaxy scaling relations from photometric redshift surveys. The methods can have broader impact, since they can be applied to studies where peculiar velocities are an important component of the observed redshift: these include using star counts to model the structure of our galaxy, and estimating the luminosity function of dwarf galaxies in our local (·50Mpc) neighborhood. In 2013, I showed how to unify the Excursion Set and Peaks Theory descriptions of the Cosmic Web, and provided the first quantitative predictions for the effects of tides on the large scale structure of biased tracers, sometimes called nonlocal or tidal bias.  In 2020, I extended these methods to estimate the abundance of Primordial Black Holes.  In 2016 my collaborators and I described a new standard ruler for cosmological measurements.  In 2018 I showed that self-consistently accounting for stellar population gradients yields good agreement between dynamical (Jeans equation) and stellar population based estimates of the stellar masses in galaxies.