Related papers: Modeling early-universe energy injection with Dens…

We present a new public Python package, DarkHistory, for computing the effects of dark matter annihilation and decay on the temperature and ionization history of the early universe. DarkHistory simultaneously solves for the evolution of the…

Decaying or annihilating dark matter and other exotic energy injections can modify the spectrum of the universe's photon bath, resulting in e.g. new contributions to spectral distortions of the cosmic microwave background blackbody spectrum…

Dark matter annihilation or de-excitation, decay of metastable species, or other new physics may inject energetic electrons and photons into the photon-baryon fluid during and after recombination. As such particles cool, they partition…

We present a forward-modelling simulation framework designed to model the data products from the Dark Energy Survey (DES). This forward-model process can be thought of as a transfer function -- a mapping from cosmological and astronomical…

Deep learning is having a tremendous impact in many areas of computer science and engineering. Motivated by this success, deep neural networks are attracting an increasing attention in many other disciplines, including physical sciences. In…

With an aim towards modeling cosmologies beyond the $\Lambda$CDM paradigm, we demonstrate the automatic construction of recombination history emulators while enforcing a prior of causal dynamics. These methods are particularly useful in the…

Any injection of electromagnetically interacting particles during the cosmic dark ages will lead to increased ionization, heating, production of Lyman-alpha photons and distortions to the energy spectrum of the cosmic microwave background,…

We calculate the evolution of the early universe through the epochs of weak decoupling, weak freeze-out and big bang nucleosynthesis (BBN) by simultaneously coupling a full strong, electromagnetic, and weak nuclear reaction network with a…

In warm dark matter scenarios structure formation is suppressed on small scales with respect to the cold dark matter case, reducing the number of low-mass halos and the fraction of ionized gas at high redshifts and thus, delaying…

Owing to the remarkable development of deep learning technology, there have been a series of efforts to build deep learning-based climate models. Whereas most of them utilize recurrent neural networks and/or graph neural networks, we design…

We use explainable neural networks to connect the evolutionary history of dark matter halos with their density profiles. The network captures independent factors of variation in the density profiles within a low-dimensional representation,…

Neural network deployment on low-cost embedded systems, hence on microcontrollers (MCUs), has recently been attracting more attention than ever. Since MCUs have limited memory capacity as well as limited compute-speed, it is critical that…

One of the main challenges in modeling massive stars to the onset of core collapse is the computational bottleneck of nucleosynthesis during advanced burning stages. The number of isotopes formed requires solving a large set of…

Accurate models of radiative cooling are a fundamental ingredient of modern cosmological simulations. Without cooling, accreted baryons will not efficiently dissipate their energy and collapse to the centres of haloes to form stars. It is…

In this paper we discuss the possible distortions of the ionization history of the universe caused by an injection of non-thermal energy due to decays of hypothetical Super Heavy Dark Matter (SHDM) particles. These particles are usually…

The discovery of nongravitational interactions between dark matter and the Standard Model would be an important step in unraveling the nature of dark matter. If such an interaction exists, it would have profound implications on how dark…

Deep learning models have been widely used during the last decade due to their outstanding learning and abstraction capacities. However, one of the main challenges any scientist has to face using deep learning models is to establish the…

We revisit the theoretical priors used for inferring Dark Energy (DE) parameters. Any DE model must have some form of a tracker mechanism such that it behaved as matter or radiation in the past. Otherwise, the model is fine-tuned. We…

We discuss a model of non perturbative decay of dark energy into hot and cold dark matter. This model provides a mechanism from the field theory to realize the energy transfer from dark energy into dark matter, which is the requirement to…

We propose a new mechanism by which dark matter (DM) can affect the early universe. The hot interior of a macroscopic DM, or macro, can behave as a heat reservoir so that energetic photons are emitted from its surface. This results in…