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Found 8 papers, 4 papers with code
A deep learning framework for jointly extracting spectra and source-count distributions in astronomy
Astronomical observations typically provide three-dimensional maps, encoding the distribution of the observed flux in (1) the two angles of the celestial sphere and (2) energy/frequency.
Bayesian Simulation-based Inference for Cosmological Initial Conditions
The proposed technique is applicable to generic (non-differentiable) forward simulators and allows sampling from the posterior for the underlying field.
Stochastic Super-resolution of Cosmological Simulations with Denoising Diffusion Models
In recent years, deep learning models have been successfully employed for augmenting low-resolution cosmological simulations with small-scale information, a task known as "super-resolution".
Dim but not entirely dark: Extracting the Galactic Center Excess' source-count distribution with neural nets
The two leading hypotheses for the Galactic Center Excess (GCE) in the $\textit{Fermi}$ data are an unresolved population of faint millisecond pulsars (MSPs) and dark-matter (DM) annihilation.
The Earth Mover's Pinball Loss: Quantiles for Histogram-Valued Regression
Although ubiquitous in the sciences, histogram data have not received much attention by the Deep Learning community.
The GCE in a New Light: Disentangling the $γ$-ray Sky with Bayesian Graph Convolutional Neural Networks
In simulated data, our neural network (NN) is able to reconstruct the flux of inner Galaxy emission components to on average $\sim$0. 5%, comparable to the non-Poissonian template fit (NPTF).
A unified framework for 21cm tomography sample generation and parameter inference with Progressively Growing GANs
Creating a database of 21cm brightness temperature signals from the Epoch of Reionisation (EoR) for an array of reionisation histories is a complex and computationally expensive task, given the range of astrophysical processes involved and the possibly high-dimensional parameter space that is to be probed.
A black box for dark sector physics: Predicting dark matter annihilation feedback with conditional GANs
Traditionally, incorporating additional physics into existing cosmological simulations requires re-running the cosmological simulation code, which can be computationally expensive.
Cosmology and Nongalactic Astrophysics
