We introduce a patch to the commonly used public codes CAMB and CosmoMC that allows the user to implement a general modification of the equations describing the growth of cosmological perturbations, while preserving the covariant conservation of the energy-momentum. This patch replaces the previously publicly released code MGCAMB, while also extending it in several ways. The new version removes the limitation of late-time-only modifications to the perturbed Einstein equations, and includes several parametrization introduced in the literature. To demonstrate the use of the patch, we obtain joint constraints on the neutrino mass and parameters of a scalar-tensor gravity model from CMB, SNe and ISW data as measured from the correlation of CMB with large scale structure.

We present the results of the first strong lens time delay challenge. The motivation, experimental design, and entry level challenge are described in a companion paper. This paper presents the main challenge, TDC1, which consisted of analyzing thousands of simulated light curves blindly. The observational properties of the light curves cover the range in quality obtained for current targeted efforts (e.g., COSMOGRAIL) and expected from future synoptic surveys (e.g., LSST), and include simulated systematic errors. Seven teams participated in TDC1, submitting results from 78 different method variants. After a describing each method, we compute and analyze basic statistics measuring accuracy (or bias) A, goodness of fit χ 2 , precision P , and success rate f . For some methods we identify outliers as an important issue. Other methods show that outliers can be controlled via visual inspection or conservative quality control. Several methods are competitive, i.e., give |A| < 0.03, P < 0.03, and χ 2 < 1.5, with some of the methods already reaching sub-percent accuracy. The fraction of light curves yielding a time delay measurement is typically in the range f =20-40%. It depends strongly on the quality of the data: COSMOGRAIL-quality cadence and light curve lengths yield significantly higher f than does sparser sampling. Taking the results of TDC1 at face value, we estimate that LSST should provide around 400 robust time-delay measurements, each with P < 0.03 and |A| < 0.01, comparable to current lens modeling uncertainties. In terms of observing strategies, we find that A and f depend mostly on season length, while P depends mostly on cadence and campaign duration.

We present a science forecast for the eBOSS survey. Focusing on discrete tracers, we forecast the expected accuracy of the baryonic acoustic oscillation (BAO), the redshift-space distortion (RSD) measurements, the f NL parameter quantifying the primordial non-Gaussianity, the dark energy and modified gravity parameters. We also use the line-of-sight clustering in the Ly-α forest to constrain the total neutrino mass. We find that eBOSS LRGs, ELGs and Clustering Quasars (CQs) can achieve a precision of 1%, 2.2% and 1.6%, respectively, for spherically averaged BAO distance measurements. Using the same samples, the constraint on f σ 8 is expected to be 2.5%, 3.3% and 2.8% respectively. For primordial non-Gaussianity, eBOSS alone can reach an accuracy of σ(f NL ) ∼ 10−15. eBOSS can at most improve the dark energy Figure of Merit (FoM) by a factor of 3 for the Chevallier-Polarski-Linder (CPL) parametrisation, and can well constrain three eigenmodes for the general equation-of-state parameter. eBOSS can also significantly improve constraints on modified gravity parameters by providing the RSD information, which is highly complementary to constraints obtained from weak lensing measurements. A principle component analysis (PCA) shows that eBOSS can measure the eigenmodes of the effective Newton's constant to 2% precision; this is a factor of 10 improvement over that achievable without eBOSS. Finally, we derive the eBOSS constraint (combined with Planck, DES and BOSS) on the total neutrino mass, σ(Σm ν ) = 0.03eV (68% CL), which in principle makes it possible to distinguish between the two scenarios of neutrino mass hierarchies.