相关论文: Ten-dimensional neural network emulator for the no…
The Dark Matter Particle Explorer (DAMPE) is a space-based Cosmic-Ray (CR) observatory with the aim, among others, to study Cosmic-Ray Electrons (CREs) up to 10 TeV. Due to the low CRE rate at multi-TeV energies, we aim to increasing the…
Big-Bang Nucleosynthesis (BBN) predictions of primordial light-element abundances offer a powerful probe of early-Universe physics. However, high-accuracy numerical BBN calculations have become a major computational bottleneck for…
We propose a lightweight deep convolutional neural network (lCNN) to estimate cosmological parameters from simulated three-dimensional dark matter (DM) halo distributions and associated statistics. The training dataset comprises 2000…
We study the growth of density perturbations in a universe with unstable dark matter particles. The mass (m_\nu) range 30 eV < m_\nu < 10 keV with lifetimes ($t_d$) in the range 10^7 sec < t_d < 10^16 sec are considered. We calculate the…
We present 72 cosmological dark matter-only $N$-body zoom-in simulations with initial conditions beyond cold, collisionless dark matter (CDM), as the first installment of the COZMIC suite. We simulate Milky Way (MW) analogs with linear…
21cm tomography opens a window to directly study astrophysics and fundamental physics of early epochs in our Universe's history, the Epoch of Reionisation (EoR) and Cosmic Dawn (CD). Summary statistics such as the power spectrum omit…
We present a machine-learning model for generating super-resolution $N$-body simulations with non-vanishing spatial curvature, conditioned on a given low-resolution field, $\Omega_k$, $\Omega_\mathrm{m}$, $\sigma_8$, $h$, and redshift. By…
Understanding the structure of our universe and the distribution of matter is an area of active research. As cosmological surveys grow in complexity, the development of emulators to efficiently and effectively predict matter power spectra…
We propose an alternative approach to the construction of fitting functions to the nonlinear matter power spectrum extracted from $N$-body simulations based on the relative matter power spectrum $\delta(k,a)$, defined as the fractional…
The concordance model in cosmology, $\Lambda$CDM, is able to fit the main cosmological observations with a high level of accuracy. However, around 95% of the energy content of the Universe within this framework remains still unknown. In…
We propose a new, likelihood-free approach to inferring the primordial matter power spectrum and cosmological parameters from arbitrarily complex forward models of galaxy surveys where all relevant statistics can be determined from…
In a recent paper, four of the present authors proposed a class of dark matter models where generalized parity symmetry leads to equality of dark matter abundance with baryon asymmetry of the Universe and predicts dark matter mass to be…
A large fraction of cosmological information on dark energy and gravity is encoded in the nonlinear regime. Precision cosmology thus requires precision modeling of nonlinearities in general dark energy and modified gravity models. We modify…
Testing a subset of viable cosmological models beyond General Relativity (GR), with implications for cosmic acceleration and the Dark Energy associated with it, is within the reach of Rubin Observatory Legacy Survey of Space and Time (LSST)…
Computing the inverse covariance matrix (or precision matrix) of large data vectors is crucial in weak lensing (and multi-probe) analyses of the large scale structure of the universe. Analytically computed covariances are noise-free and…
The direct detection of particle dark matter through its scattering with nucleons is of fundamental importance to understand the nature of DM. In this work, we propose that the high-energy neutrino detectors like IceCube can be used to…
Emulator embedded neural networks, which are a type of physics informed neural network, leverage multi-fidelity data sources for efficient design exploration of aerospace engineering systems. Multiple realizations of the neural network…
We implement a sample-efficient method for rapid and accurate emulation of semi-analytical galaxy formation models over a wide range of model outputs. We use ensembled deep learning algorithms to produce a fast emulator of an updated…
We build a field level emulator for cosmic structure formation that is accurate in the nonlinear regime. Our emulator consists of two convolutional neural networks trained to output the nonlinear displacements and velocities of N-body…
We propose a novel approach using neural networks (NNs) to differentiate between cosmological models, and implemented LIME as an interpretability approach to identify the key features influencing our model's decisions. We show the potential…