arXiv subjects

Josh Taylor

Publications and source records attributed to Josh Taylor.

4 recordsLinked to original sources

arXiv2025

The Life and Times of Star-Forming Cores: an Analysis of Dense Gas in the STARFORGE Simulations

Dense gas in molecular clouds is an important signature of ongoing and future star formation. We identify and track dense cores in the STARFORGE simulations, following the core evolution from birth through dispersal by stellar feedback for typical Milky Way cloud conditions. Only $\sim$8% of cores host protostars, and most disperse before forming stars. The median starless and protostellar core lifetimes are $\sim 0.5-0.6$ Myr and $\sim0.8-1.1$ Myr, respectively, where the protostellar phase lasts $\sim 0.1^{+0.1}_{-0.05}$ Myr. While core evolution is stochastic, we find that virial ratios and linewidths decline in prestellar cores, coincident with turbulent decay. Collapse occurs over $\sim 0.1$ Myr, once the central density exceeds $\gtrsim 10^6$cm$^{-3}$. Starless cores, only, follow linewidth-size and mass-size relations, $\sigma \propto R^{0.3}$ and $M \propto R^1$. The core median mass, radius, and velocity dispersion scale weakly with the cloud magnetic field strength. We cluster the core properties and find that protostellar cores have $>80$% likelihood of belonging to three particular groups that are characterized by high central densities, compact radii, and lower virial parameters. Overall, core evolution appears to be universally set by the interplay of gravity and magnetized turbulence, while stellar feedback dictates protostellar core properties and sets the protostellar phase lifetime.

astro-ph.GA↗

arXiv2021

Convex optimization of bioprocesses

We optimize a general model of bioprocesses, which is nonconvex due to the microbial growth in the biochemical reactors. We formulate a convex relaxation and give conditions guaranteeing its exactness in both the transient and steady state cases. When the growth kinetics are modeled by the Monod function under constant biomass or the Contois function, the relaxation is a second-order cone program, which can be solved efficiently at large scales. We implement the model on a numerical example based on a wastewater treatment system.

math.OC↗

arXiv2021

Second-order cone optimization of the gradostat

We maximize the production of biogas in a gradostat at steady state. The physical decision variables are the water, substrate, and biomass entering each tank and the flows through the interconnecting pipes. Our main technical focus is the nonconvex constraint describing microbial growth. We formulate a relaxation and prove that it is exact when the gradostat is outflow connected, its system matrix is irreducible, and the growth rate satisfies a simple condition. The relaxation has second-order cone representations for the Monod and Contois growth rates. We extend the steady state models to the case of multiple time periods by replacing the derivatives with numerical approximations instead of setting them to zero. The resulting optimizations are second-order cone programs, which can be solved at large scales using standard industrial software.

math.OC↗

arXiv2020

Turbulence, Coherence and Collapse: Three Phases for Core Evolution

We study the formation, evolution and collapse of dense cores by tracking structures in a magnetohydrodynamic simulation of a star-forming cloud. We identify cores using the dendrogram algorithm and utilize machine learning techniques, including Neural Gas prototype learning and Fuzzy $c$-means clustering, to analyze the density and velocity dispersion profiles of cores together with six bulk properties. We produce a 2-d visualization using a Uniform Manifold Approximation and Projection (UMAP), which facilitates the connection between physical properties and three partially-overlapping phases: i) unbound turbulent structures (Phase I), ii) coherent cores that have low turbulence (Phase II), and iii) bound cores, many of which become protostellar (Phase III). Within Phase II we identify a population of long-lived coherent cores that reach a quasi-equilibrium state. Most prestellar cores form in Phase II and become protostellar after evolving into Phase III. Due to the turbulent cloud environment, the initial core properties do not uniquely predict the eventual evolution, i.e., core evolution is stochastic, and cores follow no one evolutionary path. The phase lifetimes are 1.0$\pm$0.1$\times$10$^5$ yr, 1.3$\pm$0.2$\times$10$^5$ yr, and 1.8$\pm$0.3$\times$10$^5$ yr for Phase I, II, and III, respectively. We compare our results to NH$_3$ observations of dense cores. Known coherent cores predominantly map into Phase II, while most turbulent pressure-confined cores map to Phase I or III. We predict that a significant fraction of observed starless cores have unresolved coherent regions and that $\gtrsim 20$% of observed starless cores will not form stars. Measurements of core radial profiles, in addition to the usual bulk properties, will enable more accurate predictions of core evolution.

astro-ph.GA↗