Cosmological distance indicators

We review three distance measurement techniques beyond the local universe: (1) gravitational lens time delays, (2) baryon acoustic oscillation (BAO), and (3) HI intensity mapping. We describe the principles and theory behind each method, the ingredients needed for measuring such distances, the current observational results, and future prospects. Time delays from strongly lensed quasars currently provide constraints on $H_0$ with < 4% uncertainty, and with 1% within reach from ongoing surveys and efforts. Recent exciting discoveries of strongly lensed supernovae hold great promise for time-delay cosmography. BAO features have been detected in redshift surveys up to z <~ 0.8 with galaxies and z ~ 2 with Ly-$\alpha$ forest, providing precise distance measurements and $H_0$ with < 2% uncertainty in flat $\Lambda$CDM. Future BAO surveys will probe the distance scale with percent-level precision. HI intensity mapping has great potential to map BAO distances at z ~ 0.8 and beyond with precisions of a few percent. The next years ahead will be exciting as various cosmological probes reach 1% uncertainty in determining $H_0$, to assess the current tension in $H_0$ measurements that could indicate new physics.Comment: Review article accepted for publication in Space Science Reviews (Springer), 45 pages, 10 figures. Chapter of a special collection resulting from the May 2016 ISSI-BJ workshop on Astronomical Distance Determination in the Space Ag

Strong Lens Time Delay Challenge: II. Results of TDC1

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. \nteamsA\ teams participated in TDC1, submitting results from \nmethods\ different method variants. After a describing each method, we compute and analyze basic statistics measuring accuracy (or bias) $A$, goodness of fit $\chi^2$, 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 $\chi^2<1.5$, 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.Comment: referee's comments incorporated; to appear in Ap