Day 902

Tuesday.


1506.02036
An analytical model for galaxy metallicity: what do metallicity relations tell us about star formation and outflow?
Lu, Blanc, Benson

Develop a simple analytical model that tracks galactic metallicities governed by star formation and feedback to gain insight from the observed galaxy stellar mass-metallicity relations over a large range of stellar masses and redshifts.  The model reveals the following implications of star formation and feedback processes in galaxy formation.  First, the observed metallicity relations provide a stringent upper limit for the averaged outflow mass-loading factors of local galaxies, which is ~20 for M*~1e9 Msun galaxies and monotonically decreases to ~1 for M*~1e11 Msun galaxies.  Second, the inferred upper-limit for the outflow mass-loading factor sensitively depends on whether the outflow is metal-enriched with respect to the ISM metallicity.  In half of the metals ejected from SNe leave the galaxy in metal-enrihed winds, the outflow mass-loading factor for galaxies at any mass can barely be higher than ~10, which puts strong constraints on galaxy formation models.  Third, the relatively lower stellar-phase to gas-phase metallicity ratio for lower-mass galaxies indicate that low-mass galaxies are still rapidly enriching their metallicities in recent times, while high-mass galaxies are more settled, which seems to show a downsizing effect in the metallicity evolution of galaxies.  The analysis presented in the paper demonstrates the importance of accurate measurements of galaxy metallicities and the cold gas fraction of galaxies at different redshifts for constraining star formation and feedback processes, and demonstrates the power of these relations in constraining the physics of galaxy formation.


1506.02040
Computing the three-point correlation function of galaxies in $\mathcal{O}(N^2)$ time
Slepian, Eisenstein

Present an algorithm that computes the multipole coefficients of the galaxy 3PCF without explicitly considering triplets of galaxies.  Rather, centering on each galaxy in the survey, it expands the radially-binned density field in spherical harmonics and combines these to form the multiples without ever requiring the relative angle between a pair about the central.  This approach scales with number and number density in the same way as the 2PCF, allowing runtimes that are comparable, and 500x faster than a naive triple count.  It is exact in angle and easily handles edge correction.  Demonstrate the algorithm on the LasDamas SDSS-DR7 mock catalogs, computing an edge corrected 3PCF out to 90 Mpc/h in under an hour on modest computing resources.  Expect this algorithm will render it possible to obtain the large-scale 3PCF for upcoming surveys such as Euclid, LSST, and DESI.


1506.02192
Constraining cosmology with shear peak statistics: tomographic analysis
Martinet, Bartlett, Kiessling, Sartoris

The abundance of peaks in WL maps is a potentially powerful cosmo tool, complementary to measurements of the shear power spectrum.  Study peaks detected directly in shear maps, rather than convergence maps, an approach that has the advantage of working directly with the observable quantity, the galaxy ellipticity catalog.  Using large numbers of numerical simulations to accurately predict the abundance of peaks and their covariance, quantify the cosmo constraints attainable by a large-area survey similar to that expected from the Euclid mission, focusing on the density parameter Omega_m, and on the PS normalization sigma8, for illustration.  Present a tomographic peak counting method that improves the conditional (marginal) constraints by a factor 1.2(2) over those from a 2d (i.e., non-tomographic) peak-count analysis.  Find that peak statistics provide constraints an order of magnitude less accurate than those from the cluster sample in the ideal situation of a perfectly known observable-mass relation, however, when the scaling relation is not known a priori, the constraints are comparable and orthogonal in the parameter plane, highlighting the value of using both clusters and shear-peak statistics.