The coming few years are likely to witness a dramatic increase in high quality Sn data as current surveys add more high redshift supernovae to their inventory and as newer and deeper supernova experiments become operational .
Given the current variety in dark energy models and the expected improvement in observational data , an accurate and versatile diagnostic of dark energy is the need of the hour .
This paper examines the Statefinder diagnostic in the light of the proposed SNAP satellite which is expected to observe about 2000 supernovae per year .
We show that the Statefinder is versatile enough to differentiate between dark energy models as varied as the cosmological constant on the one hand , and quintessence , the Chaplygin gas and braneworld models , on the other .
Using SNAP data , the Statefinder can distinguish a cosmological constant ( w = -1 ) from quintessence models with w \geq - 0.9 and Chaplygin gas models with \kappa \leq 15 at the 3 \sigma level if the value of \Omega _ { m } is known exactly .
The Statefinder gives reasonable results even when the value of \Omega _ { m } is known to only \sim 20 \% accuracy .
In this case , marginalizing over \Omega _ { m } and assuming a fiducial LCDM model allows us to rule out quintessence with w \geq - 0.85 and the Chaplygin gas with \kappa \leq 7 ( both at 3 \sigma ) .
These constraints can be made even tighter if we use the Statefinders in conjunction with the deceleration parameter .
The Statefinder is very sensitive to the total pressure exerted by all forms of matter and radiation in the universe .
It can therefore differentiate between dark energy models at moderately high redshifts of z \lower 3.87 pt \hbox { $ \buildrel < \over { \sim } $ } ~ { } 10 .