相关论文: Inferring Warm Dark Matter Masses with Deep Learni…

We propose a novel method to train deep convolutional neural networks which learn from multiple data sets of varying input sizes through weight sharing. This is an advantage in chemometrics where individual measurements represent exact…

We present two matched sets of five simulations each, covering five presently favored simple modifications to the standard cold dark matter (CDM) scenario. One simulation suite, with a linear box size of 75 Mpc/h, is designed for high…

We discuss the possibility that the cold dark matter mass profiles contain information on the cosmological constant, and that such information constrains the nature of cold dark matter (CDM). We call this approach Modified Dark Matter…

We present a physically-motivated topology of a deep neural network that can efficiently infer extensive parameters (such as energy, entropy, or number of particles) of arbitrarily large systems, doing so with O(N) scaling. We use a form of…

We present a comprehensive analysis of the halo model of cosmological large to small-scale structure statistics in the case of warm dark matter (WDM) structure formation scenarios. We include the effects of WDM on the linear matter power…

Cold Dark Matter (CDM) models of galaxy formation had been remarkably successful to explain a number of observations in the past decade. However, with both the theoretical modeling and the observations being improved, CDM models have been…

We train deep learning models on thousands of galaxy catalogues from the state-of-the-art hydrodynamic simulations of the CAMELS project to perform regression and inference. We employ Graph Neural Networks (GNNs), architectures designed to…

Recent high-resolution simulations that include Cold Dark Matter (CDM) and baryons have shown that baryonic physics can dramatically alter the dark matter structure of galaxies. These results modify our predictions for observed galaxy…

The predicted mass function of dark matter halos is essential in connecting observed galaxy cluster counts and models of galaxy clustering to the properties of the primordial density field. We determine the mass function in the concordance…

Deep learning is a powerful analysis technique that has recently been proposed as a method to constrain cosmological parameters from weak lensing mass maps. Due to its ability to learn relevant features from the data, it is able to extract…

The simulated matter distribution on large scales is studied using core-sampling, cluster analysis, inertia tensor analysis, and minimal spanning tree techniques. Seven simulations in large boxes for five cosmological models with COBE…

Astrophysical processes such as feedback from supernovae and active galactic nuclei modify the properties and spatial distribution of dark matter, gas, and galaxies in a poorly understood way. This uncertainty is one of the main theoretical…

Studies of flux anomalies statistics and perturbations in stellar streams have the potential to constrain models of warm dark matter (WDM), including sterile neutrinos. Producing these constraints requires a parametrization of the WDM mass…

Here we discuss what are perhaps the two most popular variants of CDM that might agree with the data: \lcdm\ and CHDM. While the predictions of COBE-normalized \lcdm\ and CHDM both agree well with the available data on scales of $\sim 10$…

Observations of bright protoplanetary disks often show annular gaps in their dust emission. One interpretation of these gaps is disk-planet interaction. If so, fitting models of planetary gaps to observed protoplanetary disk gaps can reveal…

Accurate photometric redshift estimation is critical for observational cosmology, especially in large-scale surveys where spectroscopic measurements are impractical. Traditional approaches include template fitting and machine learning, each…

We describe a novel end-to-end approach using Machine Learning to reconstruct the power spectrum of cosmological density perturbations at high redshift from observed quasar spectra. State-of-the-art cosmological simulations of structure…

While deep neural networks (NN) significantly advance image compressed sensing (CS) by improving reconstruction quality, the necessity of training current CS NNs from scratch constrains their effectiveness and hampers rapid deployment.…

$\Lambda$-Warm Dark Matter (WDM) has been proposed as alternative scenario to $\Lambda$ cold dark matter (CDM), motivated by discrepancies at the scale of dwarf galaxies, with less small-scale power and realized by collisionless particles…

We discuss variants of Cold Dark Matter (CDM) dominated cosmological models that give good agreement with a range of observations. We consider models with hot dark matter, tilt, $\Omega < 1$, or a cosmological constant. We also discuss the…