arXiv subjects

Luz L. Jimenez Vela

Publications and source records attributed to Luz L. Jimenez Vela.

3 recordsLinked to original sources

arXiv2026

From 2D to 3D: Recovering Turbulent Density Dispersions from Noisy Data

Turbulence plays a central role in shaping the structure and dynamics of the interstellar medium (ISM), governing the star formation rate (SFR) and the initial mass function (IMF). A key consequence of turbulence is the generation of density fluctuations, which regulate the amount of dense gas available for star formation. Accurate measurements of the three-dimensional (3D) turbulent density dispersion are therefore essential for understanding molecular-cloud structure and star formation. However, observations typically provide only two-dimensional (2D) column densities and are often affected by measurement/detector noise. The Brunt method estimates the 3D density dispersion from 2D column-density maps, but it does not account for finite signal-to-noise ratio (SNR). Here, we extend the method to recover the 3D turbulent density dispersion from noise-contaminated observations. Using numerical simulations spanning a range of density perturbation amplitudes and noise types, we identify a characteristic noise wavenumber, k_noise, corresponding to the intersection of the signal and noise spectra. Restricting the Brunt reconstruction to wavenumbers below k_noise yields a denoised density-dispersion estimate that closely reproduces the noise-free result. We provide a practical prescription to determine k_noise directly from the measurement SNR and image resolution. Alternatively, if the noise spectrum is known, it can be subtracted directly from the observed spectrum, eliminating the need to estimate k_noise. The proposed correction recovers the noise-free density dispersion with errors of <~5% for SNR>=3 and <~15% for SNR>=1, enabling substantially more reliable estimates of turbulent density fluctuations from noisy column-density data.

astro-ph.GA↗

arXiv2024

Collapsing molecular clouds with tracer particles: Part II, Collapse Histories

In order to develop a complete theory of star formation, one essentially needs to know two things: what collapses, and how long it takes. This is the second paper in a series, where we query how long a parcel of gas takes to collapse and the process it undergoes. We embed pseudo-Lagrangian tracer particles in simulations of collapsing molecular clouds, identify the particles that end in dense knots, and then examine the collapse history of the gas. We find a nearly universal behavior of cruise-then-collapse, wherein a core stays at intermediate densities for a significant fraction of its life before finally collapsing. We identify time immediately before each core collapses, $t_{\rm{sing}}$, and examine how it transitions to high density. We find that the time to collapse is uniformly distributed between $0.25 t_{\rm{ff}}$ and the end of the simulation at $\sim 1 t_{\rm{ff}}$, and that the duration of collapse is universally short, $Δt \sim 0.1 t_{\rm{ff}}$, where $t_{\rm{ff}}$ is the free-fall time at the mean density. We describe the collapse in three stages; collection, hardening, and singularity. Collection sweeps low density gas into moderate density. Hardening brings kinetic and gravitational energies into quasi-equipartition. Singularity is the free-fall collapse, forming an envelope in rough energy balance and central over density in $\sim 0.1 t_{\rm{ff}}$.

astro-ph.GA↗

arXiv2022

Collapsing Molecular Clouds with Tracer Particles: Part I, What Collapses?

To understand the formation of stars from clouds of molecular gas, one essentially needs to know two things: What gas collapses, and how long it takes to do so. We address these questions by embedding pseudo-Lagrangian tracer particles in three simulations of self-gravitating turbulence. We identify prestellar cores at the end of the collapse, and use the tracer particles to rewind the simulations to identify the preimage gas for each core at the beginning of each simulation. This is the first of a series of papers, wherein we present the technique and examine the first question: What gas collapses? For the preimage gas at the t=0, we examine a number of quantities; the probability distribution function (PDF) for several quantities, the structure function for velocity, several length scales, the volume filling fraction, the overlap between different preimages, and fractal dimension of the preimage gas. Analytic descriptions are found for the PDFs of density and velocity for the preimage gas. We find that the preimage of a core is large and sparse, and we show that gas for one core comes from many turbulent density fluctuations and a few velocity fluctuations. We find that binary systems have preimages that overlap in a fractal manner. Finally, we use the density distribution to derive a novel prediction of the star formation rate.

astro-ph.GA↗