Dark Energy with Clusters with
LSST
Steve Allen
Ian Dell’Antonio
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)
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
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:
1) Critical to the success of the program
2) Required now (with a long lead time)
3) We should be equipped to make progress on it
4) Simulations should be available (or part of the program) to
address key issues
5) It should address the systematic uncertainties in cluster dark
energy measurements
6) Connections to existing Stage III surveys (and how we acquire the
data and expertise)
7) Be “interesting” – someone should want to work on it!
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.
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.
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.
Where do we go from here?
• Over the next 2-3 weeks
1. Identification of teams that are interested in
working on these issues
2. Initial prioritization of the tasks to be
investigated
3. Establish a regular meeting/work schedule
Where do we go from here?
• Over the summer
1. Selection of highest priority tasks to be
pursued
2. Writing of relevant sections of DESC white
paper
3. Preliminary assignment of work areas for
DESC matrix
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)
Lensing Shear Peaks: between WL and Clusters?
Morgan May, Zoltan Haiman, Jan Kratochvil, Xiuyuan Yang
Motivation: Suspected that background of projections in shear selected galaxy clusters
had cosmological information.
Currently: simulationslensing mapspeaks (simulations of different cosmologies
with Blue Gene at BNL—ray tracing (currently at 1’ smoothing) to shear
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
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 selfcalibration
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