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We present the Cardinal mock galaxy catalogs, a new version of the Buzzard simulation that has been updated to support ongoing and future cosmological surveys, including the Dark Energy Survey (DES), DESI, and LSST. These catalogs are based on a one-quarter sky simulation populated with galaxies out to a redshift ofz= 2.35 to a depth ofm_r= 27. Compared to the Buzzard mocks, the Cardinal mocks include an updated subhalo abundance matching model that considers orphan galaxies and includes mass-dependent scatter between galaxy luminosity and halo properties. This model can simultaneously fit galaxy clustering and group–galaxy cross-correlations measured in three different luminosity threshold samples. The Cardinal mocks also feature a new color assignment model that can simultaneously fit color-dependent galaxy clustering in three different luminosity bins. We have developed an algorithm that uses photometric data to further improve the color assignment model and have also developed a novel method to improve small-scale lensing below the ray-tracing resolution. These improvements enable the Cardinal mocks to accurately reproduce the abundance of galaxy clusters and the properties of lens galaxies in the DES data. As such, these simulations will be a valuable tool for future cosmological analyses based on large sky surveys.

 

Abstract Although silicon-based nanomaterials (Si-based NMs) can promote crop yield and alleviate biotic and abiotic stress, the underlying performance mechanisms are unknown. In the present study, the effect of the root application of Si-based NMs on the physiological responses of cherry radish (Raphanus sativus L.) was evaluated in a life cycle experiment. Root exposure to 0.1% (w/w) Si-based NMs significantly increased total fresh weight, total chlorophyll and carotenoids by 36.0%, 14.2% and 18.7%, respectively, relative to untreated controls. The nutritional content of the edible tissue was significantly enhanced, with an increase of 23.7% in reducing sugar, 24.8% in total sugar, and 232.7% in proteins; in addition, a number of nutritional elements (Cu, Mn, Fe, Zn, K, Ca, and P) were increased. Si-based NMs exposure positively altered the phytohormone network and decreased abscisic acid content, both of which promoted radish fresh weight. LC-MS-based metabolomic analysis shows that Si-based NMs increased the contents of most carbohydrates (e.g., α-D-glucose, acetylgalactosamine, lactose, fructose, etc.) and amino acids (e.g., asparagine, glutamic acid, glutamine, valine, arginine, etc.), subsequently improving overall nutritional values. Overall, nanoscale Si-based agrochemicals have significant potential as a novel strategy for the biofortification of vegetable crops in sustainable nano-enabled agriculture. 

We present Symphony, a compilation of 262 cosmological, cold-dark-matter-only zoom-in simulations spanning four decades of host halo mass, from 10¹¹–10¹⁵M_⊙. This compilation includes three existing simulation suites at the cluster and Milky Way–mass scales, and two new suites: 39 Large Magellanic Cloud-mass (10¹¹M_⊙) and 49 strong-lens-analog (10¹³M_⊙) group-mass hosts. Across the entire host halo mass range, the highest-resolution regions in these simulations are resolved with a dark matter particle mass of ≈3 × 10⁻⁷times the host virial mass and a Plummer-equivalent gravitational softening length of ≈9 × 10⁻⁴times the host virial radius, on average. We measure correlations between subhalo abundance and host concentration, formation time, and maximum subhalo mass, all of which peak at the Milky Way host halo mass scale. Subhalo abundances are ≈50% higher in clusters than in lower-mass hosts at fixed sub-to-host halo mass ratios. Subhalo radial distributions are approximately self-similar as a function of host mass and are less concentrated than hosts’ underlying dark matter distributions. We compare our results to the semianalytic modelGalacticus, which predicts subhalo mass functions with a higher normalization at the low-mass end and radial distributions that are slightly more concentrated than Symphony. We useUniverseMachineto model halo and subhalo star formation histories in Symphony, and we demonstrate that these predictions resolve the formation histories of the halos that host nearly all currently observable satellite galaxies in the universe. To promote open use of Symphony, data products are publicly available athttp://web.stanford.edu/group/gfc/symphony.

 

This chapter presents research and evaluation results on the SAGE 2YC project's intentional focus on a cycle of change rooted in modeling evidence‐based pedagogies and facilitating change in teaching and leadership among faculty peers on multiple levels. Based on five years of qualitative and quantitative data involving 40 community colleges and 80 full‐time and adjunct STEM faculty, results showed that faculty change agents increased their use of evidence‐based teaching and faculty leadership roles. Changes in pedagogy contributed to improved course completion rates and reduced equity gaps between demographically diverse student groups. Carefully honed professional development strategies offered valuable lessons on supporting faculty learning, scaffolding and sharing lessons learned among faculty peers, and faculty leadership of campus reforms.

 

Objective/Research Question: This paper reports on data collected in a multi-year National Science Foundation grant project involving a professional development (PD) model built to support community college faculty as change agents (CAs). The research question was: How do disciplinary communities of practice (CoP) emerge among community college faculty working in teams? Methods: This research employed a mixed-method design. Data collection included interviews, focus-group sessions, reflective journals, observations, end-of-workshop evaluations, survey data with the 23 geoscience faculty CA, and data from a national survey. Data analysis used the principles of CoP. Results: When compared to other community college geoscience faculty nationally, the participants in our study reported greater levels of behaviors characteristic of CoP. The CoP emerged due to network building and resource sharing within the PD. The findings highlight the significance of structured PD on the development of robust disciplinary CoP. The initial orientation of CAs, existing institutional structures, and cultures of community colleges influenced the CoP. Putting lessons learned into practice, sharing outcomes, and leading regional PD for others contributed to strengthening of the CoP. Conclusions/Contributions: Intentional PD catalyzed the emergence of strong CoP among the community college geoscience faculty participants. The opportunities to connect with disciplinary colleagues teaching in community colleges who shared an interest in supporting student success and improving teaching practices and the opportunity to share lessons learned contributed to the CoP. Structured interactions, critical reflection, and leading colleagues in PD supported developing, maintaining, and growing the CoP.

 

The combination of galaxy–galaxy lensing (GGL) and galaxy clustering is a powerful probe of low-redshift matter clustering, especially if it is extended to the non-linear regime. To this end, we use an N-body and halo occupation distribution (HOD) emulator method to model the redMaGiC sample of colour-selected passive galaxies in the Dark Energy Survey (DES), adding parameters that describe central galaxy incompleteness, galaxy assembly bias, and a scale-independent multiplicative lensing bias Alens. We use this emulator to forecast cosmological constraints attainable from the GGL surface density profile ΔΣ(rp) and the projected galaxy correlation function wp, gg(rp) in the final (Year 6) DES data set over scales $r_p=0.3\!-\!30.0\, h^{-1} \, \mathrm{Mpc}$. For a $3{{\ \rm per\ cent}}$ prior on Alens we forecast precisions of $1.9{{\ \rm per\ cent}}$, $2.0{{\ \rm per\ cent}}$, and $1.9{{\ \rm per\ cent}}$ on Ωm, σ8, and $S_8 \equiv \sigma _8\Omega _m^{0.5}$, marginalized over all halo occupation distribution (HOD) parameters as well as Alens. Adding scales $r_p=0.3\!-\!3.0\, h^{-1} \, \mathrm{Mpc}$ improves the S8 precision by a factor of ∼1.6 relative to a large scale ($3.0\!-\!30.0\, h^{-1} \, \mathrm{Mpc}$) analysis, equivalent to increasing the survey area by a factor of ∼2.6. Sharpening the Alens prior to $1{{\ \rm per\ cent}}$ further improves the S8 precision to $1.1{{\ \rm per\ cent}}$, and it amplifies the gain from including non-linear scales. Our emulator achieves per cent-level accuracy similar to the projected DES statistical uncertainties, demonstrating the feasibility of a fully non-linear analysis. Obtaining precise parameter constraints from multiple galaxy types and from measurements that span linear and non-linear clustering offers many opportunities for internal cross-checks, which can diagnose systematics and demonstrate the robustness of cosmological results.

 

ABSTRACT Cosmological constraints from current and upcoming galaxy cluster surveys are limited by the accuracy of cluster mass calibration. In particular, optically identified galaxy clusters are prone to selection effects that can bias the weak lensing mass calibration. We investigate the selection bias of the stacked cluster lensing signal associated with optically selected clusters, using clusters identified by the redMaPPer algorithm in the Buzzard simulations as a case study. We find that at a given cluster halo mass, the residuals of redMaPPer richness and weak lensing signal are positively correlated. As a result, for a given richness selection, the stacked lensing signal is biased high compared with what we would expect from the underlying halo mass probability distribution. The cluster lensing selection bias can thus lead to overestimated mean cluster mass and biased cosmology results. We show that the lensing selection bias exhibits a strong scale dependence and is approximately 20–60 per cent for ΔΣ at large scales. This selection bias largely originates from spurious member galaxies within ±20–60 $h^{-1}\, \rm Mpc$ along the line of sight, highlighting the importance of quantifying projection effects associated with the broad redshift distribution of member galaxies in photometric cluster surveys. While our results qualitatively agree with those in the literature, accurate quantitative modelling of the selection bias is needed to achieve the goals of cluster lensing cosmology and will require synthetic catalogues covering a wide range of galaxy–halo connection models. 

ABSTRACT We simulate the scientific performance of the Nancy Grace Roman Space Telescope High Latitude Survey (HLS) on dark energy and modified gravity. The 1.6-yr HLS Reference survey is currently envisioned to image 2000 deg2 in multiple bands to a depth of ∼26.5 in Y, J, H and to cover the same area with slit-less spectroscopy beyond z = 3. The combination of deep, multiband photometry and deep spectroscopy will allow scientists to measure the growth and geometry of the Universe through a variety of cosmological probes (e.g. weak lensing, galaxy clusters, galaxy clustering, BAO, Type Ia supernova) and, equally, it will allow an exquisite control of observational and astrophysical systematic effects. In this paper, we explore multiprobe strategies that can be implemented, given the telescope’s instrument capabilities. We model cosmological probes individually and jointly and account for correlated systematics and statistical uncertainties due to the higher order moments of the density field. We explore different levels of observational systematics for the HLS survey (photo-z and shear calibration) and ultimately run a joint likelihood analysis in N-dim parameter space. We find that the HLS reference survey alone can achieve a standard dark energy FoM of >300 when including all probes. This assumes no information from external data sets, we assume a flat universe however, and includes realistic assumptions for systematics. Our study of the HLS reference survey should be seen as part of a future community-driven effort to simulate and optimize the science return of the Roman Space Telescope. 

ABSTRACT We explore synergies between the Nancy Grace Roman Space Telescope and the Vera Rubin Observatory’s Legacy Survey of Space and Time (LSST). Specifically, we consider scenarios where the currently envisioned survey strategy for the Roman Space Telescope’s High Latitude Survey (HLS reference), i.e. 2000 deg2 in four narrow photometric bands is altered in favour of a strategy of rapid coverage of the LSST area (to full LSST depth) in one band. We find that in only five months, a survey in the W-band can cover the full LSST survey area providing high-resolution imaging for >95 per cent of the LSST Year 10 gold galaxy sample. We explore a second, more ambitious scenario where the Roman Space Telescope spends 1.5 yr covering the LSST area. For this second scenario, we quantify the constraining power on dark energy equation-of-state parameters from a joint weak lensing and galaxy clustering analysis. Our survey simulations are based on the Roman Space Telescope exposure-time calculator and redshift distributions from the CANDELS catalogue. Our statistical uncertainties account for higher order correlations of the density field, and we include a wide range of systematic effects, such as uncertainties in shape and redshift measurements, and modelling uncertainties of astrophysical systematics, such as galaxy bias, intrinsic galaxy alignment, and baryonic physics. We find a significant increase in constraining power for the joint LSST + HLS wide survey compared to LSST Y10 (FoMHLSwide = 2.4 FoMLSST) and compared to LSST + HLS (FoMHLSwide = 5.5 FoMHLSref).