Dark Energy with Clusters with LSST

1. Dark Energy with Clusters with LSST Steve Allen Ian Dell’Antonio

2. Why clusters? Add significant and complementary information to other probes. Excellent probe of both growth and expansion histories (complementary to WL, RSS  gravity tests). Reduced sensitivity to some common systematics (e.g. some photo-z errors, shear calibration) than other probes Significant potential gain with LSST + corollary data vs. Stage III experiments Interesting redshift range (0<z<2)

3. Key topics • Theoretical/Simulation requirement • Statistical analysis framework • Cluster finding and sample selection • Cluster masses and mass-observable relations • Cluster distributions/Other cluster Observables/Shear peaks vs. clusters • Integration with joint cosmological probes

4. Our charge (Adapted from Bhuv) Develop a list of tasks that are critical in the next three years (and identify tasks that can be delayed until later). The tasks should be: • Critical to the success of the program • Required now (with a long lead time) • We should be equipped to make progress on it • Simulations should be available (or part of the program) to address key issues • It should address the systematic uncertainties in cluster dark energy measurements • Connections to existing Stage III surveys (and how we acquire the data and expertise) • Be “interesting” – someone should want to work on it!

5. Cluster science 1 • Strategy: • Define requirements for cluster cosmology including theoretical basis, inference framework, cluster finding and mass calibration. • Identify external data required for optimal results, e.g., X-ray , SZ, near-IR, spectroscopic follow-up. • Identify work to be done, partnerships/collaborations to be formed, priorities and resources required. • Theoretical basis: simulating the distribution of galaxy clusters: • Identify predictions needed for cluster science, the cosmological and astrophysical models to be studied, and the accuracy and precision required. • Carry out the required simulations. • Inference framework: • Develop framework required to model observed distribution of galaxy clusters and all relevant calibration and scaling relations self-consistently (accounting for all known biases, correlations and systematic uncertainties). • Identify optimal choices of nuisance parameters and parameterizations for e.g. mass functions, scaling relations, systematic biases etc. • End-to-end testing against simulations.

6. Cluster science 2 • Mass calibration: weak lensing • Determine required accuracy of cluster WL mass calibration across mass and redshift range of interest. • Extend STEP-like simulations into cluster shear regime (g~0.1). (Modest extension of requirement for WL group). • Cosmological /ray-tracing simulations to determine systematic bias and uncertainties in cluster WL mass calibration with different methods. • Understand impacts of photo-z uncertainties on WL mass calibration. How well can we do with LSST alone? What external data will we need? (Link to other groups). • Magnification vs. shear based methods. • Investigate key instrumental effects • Mass calibration: scaling relations: • Identify required multi-wavelength mass proxies and strategy for acquiring them. • Determine key mass-observable scaling relations from current data (informing overall strategy and priors/nuisance parameters for modeling). • Tomographic analyses: • Cosmological measurements from redshift dependent weak lensing shear • Cosmological measurements from strong lensing signals • Cluster finding • Determine optimal strategies for finding clusters with LSST alone and in combination with external data (e.g. X-ray, SZ). • Develop optimized algorithms for cluster finding, maximizing purity and completeness. Test with simulations.

7. Example: cluster WL mass calibration. Some specific tasks for next few years. • The impact of photo-z uncertainties on cluster WL mass calibration (coordinate with photo-z groups) • Use existing and (where possible) newly-acquired LSST-like data for representative fields with comprehensive spectroscopic follow-up (and/or a large number of broad and medium-band filters, e.g. COSMOS) to asses the impact of photo-z uncertainties on cluster WL mass calibration. • Identify magnitude/color ranges where LSST photo-z's will be sufficient to enable robust mass calibration, and those where external data will be required (e.g. high redshifts) • What is the impact of contamination by cluster member galaxies? How to mitigate? • Cosmological /ray-tracing simulations for cluster WL mass calibration (coordinate with simulation group) • Extend current work to cover full mass and redshift range of interest, and explore range of models (e.g. NFW, truncated NFW etc) and fitting ranges to find optimal solution. • How do we identify optimal cluster centers for shear measurements when working with LSST data alone? (Similar issue for cluster finding). Explore covariance with other, multiwavelength observables in real data. • Improved imaging simulations for Clusters (coordinate with WL group) • Extend STEP-like simulations into shear regime of clusters (g~0.1). • quantify influence of stellar reflection halos, galactic cirrus, variations in sky background, photometric measurements on the galaxy cluster measurements • Investigate the effect of deblending on cluster shear measurements • Investigate how telescope/detector effects influence cluster lensing measurements (mostly, but not all shared with WL—connect with project scientists. • Investigate what we can extract from the deep drilling fields through imsim simulations.

8. Where do we go from here? • Over the next 2-3 weeks 1. Identification of teams that are interested in working on these issues • Initial prioritization of the tasks to be investigated • Establish a regular meeting/work schedule

9. Where do we go from here? • Over the summer 1. Selection of highest priority tasks to be pursued • Writing of relevant sections of DESC white paper • Preliminary assignment of work areas for DESC matrix

10. Contacts! • If you are interested in joining us in this work over the summer and beyond, contact Ian Dell’Antonio (Ian_Dell’antonio@brown.edu) Steve Allen (swa@stanford.edu)

11. Motivation: Suspected that background of projections in shear selected galaxy clusters had cosmological information. Currently: simulationslensingmapspeaks (simulations of different cosmologies with Blue Gene at BNL—ray tracing (currently at 1’ smoothing) to shear Lensing Shear Peaks: between WL and Clusters? Morgan May, ZoltanHaiman, Jan Kratochvil, XiuyuanYang Adding shear peak statistics to power spectrum improves constraints by ~ factor of 2. Model the cooling and contraction of baryons in DM halos, by steepening halo profile. Find halos, remove particle, Replace with analytic NFW

12. peak counts: • strong increase in # of high peaks • very little change in # of low peaks power spectrum: increase on small scales. Change in power spectrum and peak counts, by 50% increase concentration parameter low peaks contain much of the cosmology information Use halo finder to figure out where the peaks come from Preliminary result: peak counts less biased than power spectrum, and in different direction. suggests possibility of self-calibration

13. Why now?, Planning –determine simulations and match with observations • Lensing peaks are a new probe of dark energy. Great potential, but need more work to firmly establish as a key probe. • Study effect of systematic errors - will be different than for other probes. Study with Imsim (Debbie Bard) • Combine multiple redshifts, smoothing scales, combine with other probes: extract maximum information from lensing maps • Baryon effects - only baryon cooling