arXiv subjects

Dhruv Zimmerman

Publications and source records attributed to Dhruv Zimmerman.

4 recordsLinked to original sources

arXiv2026

Dust in the Average Galaxy: Attenuation, Emission, and Opacity from $0<z<7$

We present constraints on the dust emission and attenuation properties of galaxies across 0 10^{10.5}M_\odot$. We derive empirical relationships for the effective attenuation, dust temperature, fraction of star formation that is unobscured, and dust-to-stellar mass ratio as functions of redshift and stellar mass. We separate the first order effect of star/dust geometry from dust grain properties by combining constraints on the IR SED, UV SED, and dust mass surface density. Importantly, we measure over an order of magnitude decrease in $κ_{UV}/κ_{FIR}$--the ratio of dust mass absorption coefficients in the UV at 1600Å and FIR at 500$μ$m--from z~0 to z~7. A depressed $κ_{UV}/κ_{FIR}$ is consistent with a deficit of small dust grains, possibly attributable to the intense radiation fields of high-$z$ star formation; indeed, we find a redshift-invariant inverse relationship between $κ_{UV}/κ_{FIR}$ and $Σ_{SFR}$. Most evolution in the dust-to-stellar ratio is at $z<1$, the product of mild downward evolution in the dust-to-gas ratio combined with steep evolution in the gas-to-stellar ratio. The significant evolution and dynamic range of $κ_{UV}/κ_{FIR}$ and prevailing disconnect between the UV/optical and FIR regimes emphasize that direct dust constraints are irreplaceable for the majority of star-forming galaxies at z<7, not just the most extreme star-formers.

astro-ph.GA↗

arXiv2026

The Lifecycle and Emission Properties of PAHs in Cosmological Hydrodynamic Galaxy Formation Simulations

We present the first cosmological model for the lifecycle and luminous properties of PAHs in galaxies as they evolve from z=6-->0. We model 40 zoom-in galaxies, coupled with an on-the-fly model for the evolution of dust grains in the ISM. We assume that PAHs are ultrasmall (a < 13 Angstrom) carbonaceous dust grains, and couple this model with single-photon excitation calculations to compute the emergent mid-infrared spectra. (1) If we assume that dust is large upon formation, then PAHs are naturally able to form in situ in the ISM via grain-grain shattering. Interstellar collision velocities increase in low density, diffuse gas in our model; as galaxies evolve, the increase in fractional mass of diffuse gas drives an increase in grain-grain collision velocities and a corresponding rise in the PAH mass fraction (qPAH) from ~5 x 10^{-4} at z~4 to ~10^{-2} at z~0. (2) Increased PAH production in the diffuse ISM results in an inverse relationship between qPAH and the molecular gas fraction. (3) The PAH light-to-mass ratio scales linearly with the radiation field intensity (LPAH/MPAH ~ G_0) but anti-correlates with qPAH, because high-Sigma_SFR galaxies have a denser ISM that suppresses shattering. This means the physical qPAH and observed LPAH/LFIR do not evolve in lockstep. (4) The PAH-metallicity relationship (PZR) arises naturally in this framework: galaxies enrich and grow their diffuse ISM fraction simultaneously, linking rising metallicity to rising qPAH. Our models represent the first to reproduce the PZR observed across z=0-2. (5) The LPAH-SFR and LPAH-M_mol relations emerge from two effects: more massive galaxies have larger PAH reservoirs, and higher-SFR galaxies excite their PAHs more efficiently per unit mass. Taken together, these results suggest that grain-grain shattering in the diffuse ISM is the main driver behind the evolution of cosmic PAH abundances.

astro-ph.GA↗

arXiv2026

The Growth of Dust in Galaxies in the First Billion Years with Applications to Blue Monsters

A combination of JWST observations at z~12-14 and ALMA observations of extremely dust-rich systems at z~6 has demonstrated that dust grows extremely fast in the early Universe, with galaxies amassing up to 10^7 Msun of dust in just 500 Myr between z=12->6. In this paper we demonstrate, via a series of numerical experiments conducted in cosmological zoom-in simulations, that a likely pathway for this dust accumulation in the first formed galaxies is through production at early times via supernovae, followed by the rapid growth on ultrasmall dust grains. Our main results follow. The stellar production of dust dominates until z ~ 10-11 at which point galaxies transition to a growth-dominated regime. We employ a Shapley analysis to demonstrate that the local density is the dominant factor driving dust growth, followed by the grain size distribution. A rapid rise in the small-to-large grain ratio with decreasing redshift (owing to grain-grain shattering) drives growth through increased dust surface area per unit mass. Growth models are necessary to match the dust content of ALMA detected sources at z ~ 6. Finally, we demonstrate that ``blue monsters'', massive, UV-bright galaxies at $z>10$ with extremely blue continuum slopes likely have dust-to stellar mass ratios 10^-4-10^-3, but their top-heavy grain size distributions render them optically thin in the UV, providing a natural explanation for their observed properties without requiring exotic dust geometries.

astro-ph.GA↗

arXiv2025

Linking Warm Dark Matter to Merger Tree Histories via Deep Learning Networks

Dark matter (DM) halos form hierarchically in the Universe through a series of merger events. Cosmological simulations can represent this series of mergers as a graph-like ``tree'' structure. Previous work has shown these merger trees are sensitive to cosmology simulation parameters, but as DM structures, the outstanding question of their sensitivity to DM models remains unanswered. In this work, we investigate the feasibility of deep learning methods trained on merger trees to infer Warm Dark Matter (WDM) particles masses from the DREAMS simulation suite. We organize the merger trees from 1,024 zoom-in simulations into graphs with nodes representing the merger history of galaxies and edges denoting hereditary links. We vary the complexity of the node features included in the graphs ranging from a single node feature up through an array of several galactic properties (e.g., halo mass, star formation rate, etc.). We train a Graph Neural Network (GNN) to predict the WDM mass using the graph representation of the merger tree as input. We find that the GNN can predict the mass of the WDM particle ($R^2$ from 0.07 to 0.95), with success depending on the graph complexity and node features. We extend the same methods to supernovae and active galactic nuclei feedback parameters $A_\text{SN1}$, $A_\text{SN2}$, and $A_\text{AGN}$, successfully inferring the supernovae parameters. The GNN can even infer the WDM mass from merger tree histories without any node features, indicating that the structure of merger trees alone inherits information about the cosmological parameters of the simulations from which they form.

astro-ph.GA↗