ESSENCE: Strategies and Initial Observations

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Scientific paper

The ESSENCE project seeks to constrain the properties of the dark energy by discovering and following more than 200 Type Ia supernovae (SNe) over the redshift range 0.2 < z < 0.8 (see Garnavich et al. 2002 poster). We present the strategy designed to achieve this result over the five year lifetime of the project, and the results of our first year of observations. In order to obtain the balance of wide field of view and depth, we chose to base our supernova search on the CTIO Blanco 4m telescope combined with the 8Kx8K Mosaic imager, which provides a field of view of 0.36 deg2. Through the NOAO Survey program, we have been allocated half nights every other night in dark and grey time for three months (Oct, Nov, Dec) each year over a five year period (2002 -- 2006). Observations are performed in three filters, V, R, and I, so that the search itself provides the data for multi-color light curves on all supernovae discovered. Exposure times from 60s to 600s in R are used to balance the discovery of supernovae across the desired range in redshift. We have selected fields covering ~ 8 square degrees to cover the area necessary to discover the relatively nearby SNe. Each field will be observed every fourth night, providing adequate sampling of the resulting SN light curves. Based on published SN rates, these observations should provide 30 to 50 Type Ia SNe per three-month observing season, well distributed in redshift from z of 0.2 to 0.8. SuperMacho+SuperNova (SM+SN) pipeline system (see Miceli et al. 2002 poster). This pipeline performs both standard data reduction through flat-fielding and transient detection and analysis. The results are immediately ingested into the SM+SN database (see Hiriart et al. 2002 poster), which provides efficient access for classification tasks and light curve generation. As of this writing we have just begun observations for the project. Results of our first three-month observing season will be presented. The ESSENCE project is partially supported by NSF grant AST-0206329.

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