Andrew Pellegrino | Searcharxiv

arXiv2020

Modelling of the Effects of Stellar Feedback during Star Cluster Formation Using a Hybrid Gas and N-Body Method

Understanding the formation of stellar clusters requires following the interplay between gas and newly formed stars accurately. We therefore couple the magnetohydrodynamics code FLASH to the N-body code ph4 and the stellar evolution code SeBa using the Astrophysical Multipurpose Software Environment (AMUSE) to model stellar dynamics, evolution, and collisional N-body dynamics and the formation of binary and higher-order multiple systems, while implementing stellar feedback in the form of radiation, stellar winds and supernovae in FLASH. We here describe the algorithms used for each of these processes. We denote this integrated package Torch. We then use this novel numerical method to simulate the formation and early evolution of several examples of open clusters of ~1000 stars formed from clouds with a mass range of 10^3-10^5 M_sun. Analyzing the effects of stellar feedback on the gas and stars of the natal clusters, we find that in these examples, the stellar clusters are resilient to disruption, even in the presence of intense feedback. This can even slightly increase the amount of dense, Jeans unstable gas by sweeping up shells; thus, a stellar wind strong enough to trap its own H II region shows modest triggering of star formation. Our clusters are born moderately mass segregated, an effect enhanced by feedback, and retained after the ejection of their natal gas, in agreement with observations.

astro-ph.SR↗

arXiv2018

The Clustering of High-Redshift (2.9 $\leq$ z $\leq$ 5.1) Quasars in SDSS Stripe 82

We present a measurement of the two-point autocorrelation function of photometrically-selected, high-$z$ quasars over $\sim$ 100 deg$^2$ on the Sloan Digitial Sky Survey Stripe 82 field. Selection is performed using three machine-learning algorithms, trained on known high-$z$ quasar colors, in a six-dimensional, optical/mid-infrared color space. Optical data from the Sloan Digitial Sky Survey is combined with overlapping deep mid-infrared data from the \emph{Spitzer} IRAC Equatorial Survey and the \emph{Spitzer}-HETDEX Exploratory Large-area survey. The selected quasar sample consists of 1378 objects and contains both spectroscopically-confirmed quasars and photometrically-selected quasar candidates. These objects span a redshift range of $2.9 \leq z \leq 5.1$ and are generally fainter than $i=20.2$; a regime which has lacked sufficient number density to perform autocorrelation function measurements of photometrically-classified quasars. We compute the angular correlation function of these data, marginally detecting quasar clustering. We fit a single power-law with an index of $δ= 1.39 \pm 0.618$ and amplitude of $θ_0 = 0.71 \pm 0.546$ arcmin. A dark-matter model is fit to the angular correlation function to estimate the linear bias. At the average redshift of our survey ($\langle z \rangle = 3.38$) the bias is $b = 6.78 \pm 1.79$. Using this bias, we calculate a characteristic dark-matter halo mass of 1.70--9.83$\times 10^{12}h^{-1} M_{\odot}$. Our bias estimate suggests that quasar feedback intermittently shuts down the accretion of gas onto the central super-massive black hole at early times. If confirmed, these results hint at a level of luminosity dependence in the clustering of quasars at high-$z$.

astro-ph.GA↗