Dependence of Dust Formation on the Supernova Explosion

Abstract

We investigate the properties, composition, and dynamics of dust formation and growth for a diverse set of core-collapse supernovae (CCSNe), with 15, 20, and 25 M ⊙ progenitor masses, explosion energies ranging from 0.5 to 120 foe, and varied engine type. These explosions are evolved with a 1D Lagrangian hydrodynamics code out to a minimum of 1157 days to model the ejecta as it expands and cools. A multigrain dust nucleation and growth model is applied to these results. We find that higher explosion energies lead to an earlier onset of dust formation, smaller grain sizes, and larger silicate abundances. Further, we see that nuclear burning during the explosion leads to enhanced formation of silicate dust. Finally, we build composite models from our suite to predict the efficiency of CCSN dust production as a function of metallicity.

Dust Formation and Destruction in Core Collapse Supernovae Explosions [Dissertation]

“…This thesis is arranged as follows: Dust formation is discussed in Chapter 2. I setup and ran the 1-D hydrodynamical codes on half the models in Brooker et al (2022). I refined a nucleating dust code, nudust (Brooker et al, 2021;Mauney & Stangl, 2022), and post processed dust formation on the models.…”

Carbonaceous dust grains seen in the first billion years of cosmic time

“…Indeed, dust production in SN ejecta has been regarded as a potential rapid channel for significant dust production in the early Universe 33 , its net efficiency depending on the grain destruction rate in the subsequent reverse shock 34 . However, substantial carbonaceous production in SN ejecta is expected only by some classes of models and for a certain subclass of scenarios (for example, non-rotating progenitors), whereas other models favour the formation of silicates or other types of dust 35 – 38 . In summary, our detection of carbonaceous dust at z ≃ 4–7 provides crucial constraints on the dust production models and scenarios in the early Universe.…”

mpi4py.futures: MPI-Based Asynchronous Task Execution for Python

“…Other projects using mpi4py.futures in their backends include DeepHyper [44], AidData's Global Chinese Development Finance Dataset [45], sndust [46], ParaCopasi [47], quoFEM [48], and Xopt [49].…”