English

Dark Energy Survey Year 1 Results: Multi-Probe Methodology and Simulated Likelihood Analyses

Cosmology and Nongalactic Astrophysics 2017-06-29 v1

Abstract

We present the methodology for and detail the implementation of the Dark Energy Survey (DES) 3x2pt DES Year 1 (Y1) analysis, which combines configuration-space two-point statistics from three different cosmological probes: cosmic shear, galaxy-galaxy lensing, and galaxy clustering, using data from the first year of DES observations. We have developed two independent modeling pipelines and describe the code validation process. We derive expressions for analytical real-space multi-probe covariances, and describe their validation with numerical simulations. We stress-test the inference pipelines in simulated likelihood analyses that vary 6-7 cosmology parameters plus 20 nuisance parameters and precisely resemble the analysis to be presented in the DES 3x2pt analysis paper, using a variety of simulated input data vectors with varying assumptions. We find that any disagreement between pipelines leads to changes in assigned likelihood Δχ20.045\Delta \chi^2 \le 0.045 with respect to the statistical error of the DES Y1 data vector. We also find that angular binning and survey mask do not impact our analytic covariance at a significant level. We determine lower bounds on scales used for analysis of galaxy clustering (8 Mpc h1~h^{-1}) and galaxy-galaxy lensing (12 Mpc h1~h^{-1}) such that the impact of modeling uncertainties in the non-linear regime is well below statistical errors, and show that our analysis choices are robust against a variety of systematics. These tests demonstrate that we have a robust analysis pipeline that yields unbiased cosmological parameter inferences for the flagship 3x2pt DES Y1 analysis. We emphasize that the level of independent code development and subsequent code comparison as demonstrated in this paper is necessary to produce credible constraints from increasingly complex multi-probe analyses of current data.

Keywords

Cite

@article{arxiv.1706.09359,
  title  = {Dark Energy Survey Year 1 Results: Multi-Probe Methodology and Simulated Likelihood Analyses},
  author = {E. Krause and T. F. Eifler and J. Zuntz and O. Friedrich and M. A. Troxel and S. Dodelson and J. Blazek and L. F. Secco and N. MacCrann and E. Baxter and C. Chang and N. Chen and M. Crocce and J. DeRose and A. Ferte and N. Kokron and F. Lacasa and V. Miranda and Y. Omori and A. Porredon and R. Rosenfeld and S. Samuroff and M. Wang and R. H. Wechsler and T. M. C. Abbott and F. B. Abdalla and S. Allam and J. Annis and K. Bechtol and A. Benoit-Levy and G. M. Bernstein and D. Brooks and D. L. Burke and D. Capozzi and M. Carrasco Kind and J. Carretero and C. B. D'Andrea and L. N. da Costa and C. Davis and D. L. DePoy and S. Desai and H. T. Diehl and J. P. Dietrich and A. E. Evrard and B. Flaugher and P. Fosalba and J. Frieman and J. Garcia-Bellido and E. Gaztanaga and T. Giannantonio and D. Gruen and R. A. Gruendl and J. Gschwend and G. Gutierrez and K. Honscheid and D. J. James and T. Jeltema and K. Kuehn and S. Kuhlmann and O. Lahav and M. Lima and M. A. G. Maia and M. March and J. L. Marshall and P. Martini and F. Menanteau and R. Miquel and R. C. Nichol and A. A. Plazas and A. K. Romer and E. S. Rykoff and E. Sanchez and V. Scarpine and R. Schindler and M. Schubnell and I. Sevilla-Noarbe and M. Smith and M. Soares-Santos and F. Sobreira and E. Suchyta and M. E. C. Swanson and G. Tarle and D. L. Tucker and V. Vikram and A. R. Walker and J. Weller},
  journal= {arXiv preprint arXiv:1706.09359},
  year   = {2017}
}