Day 1196

Thursday.

1612.02173
A cooperative approach among methods for photometric redshifts estimation: an application to KiDS data
Cavuoti, et al

Using a KiDS subset of ~25,000 galaxies with measured spec-z's, derive photo-z's using i) 3 different empirical methods based on supervised machine learning, ii) the BPZ model and iii) classical SED template fitting procedure (Le Phare).  Confirm that, in the regions of photometric parameter space properly sampled by the spectroscopic templates, machine learning methods provide better redshift estimates, with a lower scatter and a smaller fraction of outliers.  SED fitting techniques, however, provide useful information on the galaxy spectral type which can be effectively used to constrain systematic errors and to better characterize potential catastrophic outliers.  Such classification is then used to specialize the training of regression machine learning models, by demonstrating that a hybrid approach, involving SED fitting and machine learning in a single collaborative framework, can be effectively used to improve the accuracy of photo-z estimates. 


1612.02264
Cosmological constraints with weak lensing peak counts and second-order statistics in a large-field survey
Peel, et al

Peak statistics in weak lensing maps access the non-Gaussian information contained in the LS distribution of matter in the Universe.  They are therefore a promising complement to 2pt and higher-order statistics to constrain the cosmological models.  To prepare for the high-precision data of next-generation surveys, assess the constraining power of peak counts in a simulated Euclid-like survey on the cosmo parameters Omega_m, sigma_8, and w_0.  In particular, study how the Camelus model -- a fast stochastic algorithm for predicting peaks -- can be applied to such large surveys.  Measure the peak count abundance in a mock shear catalogue of ~5k sq. deg. using a multi scale mass map filtering technique.  Then constrain the parameters of the mock survey using Camelus combined with approximate Bayesian computation (ABC).  Find that peak statistic yield a tight but significantly biased constraint in the sigma_8-Omega_m plane, indicating the need to better understand and control the model's systematics.  Calibrate the model to remove the bias and compare results to those from the 2PCF measured on the same field.  In this case, find the derived parameter Sigma_8=sigma_8(Omega_m/0.27)^alpha = 0.76±0.03 with alpha=0.65 for peaks, while for 2PCF the values is SIgma_8=0.76±0.02 with alpha=0.70.  Therefore see comparable constraining power between the 2 probes, and the offset of their sigma_8-Omega_m degeneracy directions suggests that a combined analysis would yield tighter constraints than either measure alone.  As expected, w_0 cannot be well constrained without a tomographic analysis, but its degeneracy directions with the other two varied parameters are still clear for both peaks and 2PCF.