RESUMEN
This paper presents an approach to Solar Radiation Management (SRM) using a tethered solar shield at the modified gravitational L1 Lagrange point. Unlike previous proposals, which were constrained by the McInnes bound on shield surface density, our proposed configuration with a counterweight toward the Sun circumvents this limitation and potentially reduces the total mass by orders of magnitude. Furthermore, only 1% of the total weight must come from Earth, with ballast from lunar dust or asteroids serving as the remainder. This approach could lead to a significant cost reduction and potentially be more effective than previous space-based SRM strategies.
RESUMEN
COVID-19 dealt a formidable blow to the US economy. We present a joint analysis of the epidemiological and labor market outcomes across US states. We focus on the relationship across relevant indicators in the pre-vaccination era. As expected, we find strong correlation between changes in economic conditions and mobility. However, mobility fluctuations tend to be uncorrelated with local epidemics and occur simultaneously across most states. The magnitude of the mobility response is highly correlated with the rural vs. urban character of the area. Employment losses are most strongly associated with high population density and concentration of the leisure and hospitality industry. The relationship between job losses and the case fatality ratio is affected by the timing of the most severe COVID-19 waves.
RESUMEN
Deterministic epidemic models, such as the Susceptible-Infected-Recovered (SIR) model, are immensely useful even if they lack the nuance and complexity of social contacts at the heart of network science modeling. Here we present a simple modification of the SIR equations to include the heterogeneity of social connection networks. A typical power-law model of social interactions from network science reproduces the observation that individuals with a high number of contacts, "hubs" or "superspreaders", can become the primary conduits for transmission. Conversely, once the tail of the distribution is saturated, herd immunity sets in at a smaller overall recovered fraction than in the analogous SIR model. The new dynamical equations suggest that cutting off the tail of the social connection distribution, i.e., stopping superspreaders, is an efficient non-pharmaceutical intervention to slow the spread of a pandemic, such as the Coronavirus Disease 2019 (COVID-19).
RESUMEN
A reliable model of galaxy bias is necessary for interpreting data from future dense galaxy surveys. Conventional linear and quadratic bias models are unphysical, often predicting negative galaxy densities (δ g < -1) in voids, which potentially contain half of a survey's available cosmological information. Here we present a physically motivated alternative by assuming two energetically distinct subhalo states. Our approximations - namely, local galaxy formation, rough equivalence of galaxy-hosting subhaloes, and universal energetic favourability for the galaxy-hosting state - result in a bias model with only two free parameters; mathematically, the model (in the correct variables) yields a Fermi-Dirac distribution or (equivalently) an interactionless Ising model with an external field. The model yields sensible (and physical) predictions for both high- and low-density regions. We test the model using a catalogue of Millennium Simulation galaxies in cubical survey pixels with side lengths from 2 h -1-31 h -1 Mpc, at redshifts from 0 to 2. We find the two-state model markedly superior to linear and quadratic bias models on scales smaller than 10 h -1 Mpc, while those conventional models fare better on scales larger than 30 h -1 Mpc. Though the largest scale of applicability is likely to depend on the galaxy catalogue employed, the two-state model should be superior on any scale with a non-negligible fraction of cells devoid of galaxies.
RESUMEN
Future galaxy surveys hope to realize significantly tighter constraints on various cosmological parameters. The higher number densities achieved by these surveys will allow them to probe the smaller scales affected by non-linear clustering. However, in these regimes, the standard power spectrum can extract only a portion of such surveys' cosmological information. In contrast, the alternate statistic A* has the potential to double these surveys' information return, provided one can predict the A* power spectrum for a given cosmology. Thus, in this work we provide a prescription for this power spectrum P A* (k), finding that the prescription is typically accurate to about 5 per cent for near-concordance cosmologies. This prescription will thus allow us to multiply the information gained from surveys such as Euclid and WFIRST.