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Galaxy cluster mass halos ("clusters") in a dark matter simulation are matched to nodes in several different cosmic webs found using the Disperse cosmic web finder. The webs have different simulation smoothings and Disperse parameter choices; for each, 4 methods are considered for matching Disperse nodes to clusters. For most of the webs, Disperse nodes outnumber clusters, but not every cluster has a Disperse node match (and sometimes $>1$ cluster matches to the same Disperse node). The clusters frequently lacking a matching Disperse node have a different distribution of local shear trends and perhaps merger histories. It might be interesting to see in what other ways, e.g., observational properties, these clusters differ. For the webs with smoothing $\leq$ 2.5 $Mpc/h$, and all but the most restrictive matching criterion, $\sim$3/4 of the clusters always have a Disperse node counterpart. The nearest cluster to a given Disperse node and vice versa, within twice the smoothing length, obey a cluster mass-Disperse node density relation. Cluster pairs where both clusters match Disperse nodes can also be assigned the filaments between those nodes, but as the web and matching methods are varied, most such filaments do not remain. There is an enhancement of subhalo counts and halo mass between cluster pairs, averaging over cluster pairs assigned Disperse filaments increases the enhancement. The approach here also lends itself to comparing nodes across many cosmic web constructions, using the fixed underlying cluster distribution to make a correspondence., Comment: 15 pages, 15 Figures. Revised version incorporating many helpful suggestions from the referee; more information about nodes matched or not to clusters, cluster pair profile quantities changed

One emerging application of machine learning methods is the inference of galaxy cluster masses. In this note, machine learning is used to directly combine five simulated multiwavelength measurements in order to find cluster masses. This is in contrast to finding mass estimates for each observable, normally by using a scaling relation, and then combining these scaling law based mass estimates using a likelihood. We also illustrate how the contributions of each observable to the accuracy of the resulting mass measurement can be compared via model-agnostic Importance Permutation values. Thirdly, as machine learning relies upon the accuracy of the training set in capturing observables, their correlations, and the observational selection function, and as the machine learning training set originates from simulations, two tests of whether a simulation's correlations are consistent with observations are suggested and explored as well., Comment: 12 pages, final version to appear in MNRAS. Expanded explanations, and added examples

The expansion of the Universe is understood to have accelerated during two epochs: in its very first moments during a period of Inflation and much more recently, at $z < 1$, when Dark Energy is hypothesized to drive cosmic acceleration. The undiscovered mechanisms behind these two epochs represent some of the most important open problems in fundamental physics. The large cosmological volume at $2 < z < 5$, together with the ability to efficiently target high-$z$ galaxies with known techniques, enables large gains in the study of Inflation and Dark Energy. A future spectroscopic survey can test the Gaussianity of the initial conditions up to a factor of ~50 better than our current bounds, crossing the crucial theoretical threshold of $\sigma(f_{NL}^{\rm local})$ of order unity that separates single field and multi-field models. Simultaneously, it can measure the fraction of Dark Energy at the percent level up to $z = 5$, thus serving as an unprecedented test of the standard model and opening up a tremendous discovery space., Comment: Science white paper submitted to the Astro2020 Decadal Survey

Upcoming imaging surveys, such as LSST, will provide an unprecedented view of the Universe, but with limited resolution along the line-of-sight. Common ways to increase resolution in the third dimension, and reduce misclassifications, include observing a wider wavelength range and/or combining the broad-band imaging with higher spectral resolution data. The challenge with these approaches is matching the depth of these ancillary data with the original imaging survey. However, while a full 3D map is required for some science, there are many situations where only the statistical distribution of objects (dN/dz) in the line-of-sight direction is needed. In such situations, there is no need to measure the fluxes of individual objects in all of the surveys. Rather a stacking procedure can be used to perform an `ensemble photo-z'. We show how a shallow, higher spectral resolution survey can be used to measure dN/dz for stacks of galaxies which coincide in a deeper, lower resolution survey. The galaxies in the deeper survey do not even need to appear individually in the shallow survey. We give a toy model example to illustrate tradeoffs and considerations for applying this method. This approach will allow deep imaging surveys to leverage the high resolution of spectroscopic and narrow/medium band surveys underway, even when the latter do not have the same reach to high redshift., Comment: 5 pages, 3 figures

Galaxies evolve via a complex interaction of numerous different physical processes, scales and components. In spite of this, overall trends often appear. Simplified models for galaxy histories can be used to search for and capture such emergent trends, and thus to interpret and compare results of galaxy formation models to each other and to nature. Here, two approximations are applied to galaxy integrated star formation rate histories, drawn from a semi-analytic model grafted onto a dark matter simulation. Both a lognormal functional form and principal component analysis (PCA) approximate the integrated star formation rate histories fairly well. Machine learning, based upon simplified galaxy halo histories, is somewhat successful at recovering both fits. The fits to the histories give fixed time star formation rates which have notable scatter from their true fixed time rates at final time, especially for quiescent and "green valley" galaxies, and more so for the PCA fit. For classifying galaxies into subfamilies sharing similar integrated histories, both approximations are better than using final stellar mass or specific star formation rate. Several subsamples from the simulation illustrate how these simple parameterizations can provide points of contact for comparisons between different galaxy formation samples, or more generally, models. As a side result, the halo masses of simulated galaxies with early peak star formation rate (according to the lognormal fit) lie on one of two tracks. The small fraction of galaxies with a lower halo mass at peak star formation rate appear to stall in their halo growth, even though they are central in their host halos., Comment: Final version, to appear in the MNRAS. Helpful suggestions and tests from referee now included. Reference to PCA work on star formation rate histories now correctly attributed to Sparre et al (2015)

Dark matter simulations can serve as a basis for creating galaxy histories via the galaxy-dark matter connection. Here, one such model by Becker (2015) is implemented with several variations on three different dark matter simulations. Stellar mass and star formation rates are assigned to all simulation subhalos at all times, using subhalo mass gain to determine stellar mass gain. The observational properties of the resulting galaxy distributions are compared to each other and observations for a range of redshifts from 0-2. Although many of the galaxy distributions seem reasonable, there are noticeable differences as simulations, subhalo mass gain definitions, or subhalo mass definitions are altered, suggesting that the model should change as these properties are varied. Agreement with observations may improve by including redshift dependence in the added-by-hand random contribution to star formation rate. There appears to be an excess of faint quiescent galaxies as well (perhaps due in part to differing definitions of quiescence). The ensemble of galaxy formation histories for these models tend to have more scatter around their average histories (for a fixed final stellar mass) than the two more predictive and elaborate semi-analytic models of Guo et al (2013) and Henriques et al (2015), and require more basis fluctuations (using PCA) to capture 90 percent of the scatter around their average histories. The codes to plot model predictions (in some cases alongside observational data) are publicly available to test other mock catalogues at https://github.com/jdcphysics/validation . Information on how to use these codes is in the appendix., Comment: Paper also serves as documentation for public code validating mock catalogues which have M*, star formation rates, stellar mass to halo mass information (redshifts 0 to ~ 4, most data sets for 0 to 2)

Acoustic waves traveling through the early Universe imprint a characteristic scale in the clustering of galaxies, QSOs and inter-galactic gas. This scale can be used as a standard ruler to map the expansion history of the Universe, a technique known as Baryon Acoustic Oscillations (BAO). BAO offer a high-precision, low-systematics means of constraining our cosmological model. The statistical power of BAO measurements can be improved if the `smearing' of the acoustic feature by non-linear structure formation is undone in a process known as reconstruction. In this paper we use low-order Lagrangian perturbation theory to study the ability of $21\,$cm experiments to perform reconstruction and how augmenting these surveys with galaxy redshift surveys at relatively low number densities can improve performance. We find that the critical number density which must be achieved in order to benefit $21\,$cm surveys is set by the linear theory power spectrum near its peak, and corresponds to densities achievable by upcoming surveys of emission line galaxies such as eBOSS and DESI. As part of this work we analyze reconstruction within the framework of Lagrangian perturbation theory with local Lagrangian bias, redshift-space distortions, ${\bf k}$-dependent noise and anisotropic filtering schemes., Comment: 10 pages, final version to appear in MNRAS, helpful suggestions from referee and others included

Halos, filaments, sheets and voids in the cosmic web can be defined in terms of the eigenvalues of the smoothed shear tensor and a threshold $\lambda_{\rm th}$. Using analytic methods, we construct mean maps centered on these types of structures for Gaussian random fields corresponding to cosmological initial conditions. Each map also requires a choice of shear at the origin; we consider three possibilities. We find characteristic sizes, shapes and other properties of the central objects in these mean maps and explore how these properties change with varying the threshold and smoothing scale, i.e. varying the separation of the cosmic web into different kinds of components. The mean maps become increasingly complex as the threshold $\lambda_{\rm th}$ decreases to zero. We also describe scatter around these mean maps, subtleties which can arise in their construction, and some comparisons between halos in the maps and collapsed halos at final times., Comment: 16 pages, 8 figures. Added explanations and examples in response to comments from referee and others. Final version to appear in MNRAS