相关论文: HIGAN: Cosmic Neutral Hydrogen with Generative Adv…
Inferring model parameters from experimental data is a grand challenge in many sciences, including cosmology. This often relies critically on high fidelity numerical simulations, which are prohibitively computationally expensive. The…
Our current understanding of galaxy evolution still has many uncertainties associated with the details of accretion, processing, and removal of gas across cosmic time. The next generation of radio telescopes will image the neutral hydrogen…
Generative models based on latent variables, such as generative adversarial networks (GANs) and variational auto-encoders (VAEs), have gained lots of interests due to their impressive performance in many fields. However, many data such as…
Generative adversarial networks (GANs) have emerged as a powerful paradigm for producing high-fidelity data samples, yet their performance is constrained by the quality of latent representations, typically sampled from classical noise…
This study presents the methodology for creating a cost-efficient radio astronomy telescope that can be used to detect 21 cm emissions (1420.405 MHz) and determine the distribution and kinematics of neutral hydrogen specifically in the…
21-cm emission from neutral hydrogen during and before the epoch of cosmic reionisation is gravitationally lensed by material at all lower redshifts. Low-frequency radio observations of this emission can be used to reconstruct the projected…
We use high-resolution cosmological zoom-in simulations from the FIRE project to make predictions for the covering fractions of neutral hydrogen around galaxies at z=2-4. These simulations resolve the interstellar medium of galaxies and…
Strong gravitational lensing provides some of the deepest views of the Universe, enabling studies of high-redshift galaxies only possible with next-generation facilities without the lensing phenomenon. To date, 21 cm radio emission from…
Generative Adversarial Networks (GANs) have been used to model the underlying probability distribution of sample based datasets. GANs are notoriuos for training difficulties and their dependence on arbitrary hyperparameters. One recent…
Recently, Hyperbolic Spaces in the context of Non-Euclidean Deep Learning have gained popularity because of their ability to represent hierarchical data. We propose that it is possible to take advantage of the hierarchical characteristic…
Data scarcity and sparsity in bio-manufacturing poses challenges for accurate model development, process monitoring, and optimization. We aim to replicate and capture the complex dynamics of industrial bioprocesses by proposing the use of a…
We analyze synthetic neutral hydrogen (HI) absorption and emission spectral lines from a high- resolution, three-dimensional hydrodynamical simulation to quantify how well observational methods recover the physical properties of…
Simulating time-domain observations of gravitational wave (GW) detector environments will allow for a better understanding of GW sources, augment datasets for GW signal detection and help in characterizing the noise of the detectors,…
It is shown that 21 cm intensity mapping could be used in the near term to make cosmologically useful measurements. Large scale structure could be detected using existing radio telescopes, or using prototypes for dedicated redshift survey…
Astronomy of the 21st century increasingly finds itself with extreme quantities of data. This growth in data is ripe for modern technologies such as deep image processing, which has the potential to allow astronomers to automatically…
The mathematical forces at work behind Generative Adversarial Networks raise challenging theoretical issues. Motivated by the important question of characterizing the geometrical properties of the generated distributions, we provide a…
Observations of neutral hydrogen can provide a wealth of information about the kinematics of galaxies. To learn more about the large scale structures and accretion processes, the extended environment of galaxies have to be observed.…
Resolving the phase structure of neutral hydrogen (HI) is crucial for understanding the life cycle of the interstellar medium (ISM). However, accurate measurements of HI temperature and density are limited by the availability of background…
The generative adversarial network (GAN) aims to approximate an unknown distribution via a parameterized neural network (NN). While GANs have been widely applied in reinforcement and semi-supervised learning as well as computer vision…
High-resolution video generation has emerged as a crucial task in computer vision, with wide-ranging applications in entertainment, simulation, and data augmentation. However, generating temporally coherent and visually realistic videos…