Related papers: Parametrizing the local dark matter speed distribu…
Dynamic Mode Decomposition (DMD) is a data-driven decomposition technique extracting spatio-temporal patterns of time-dependent phenomena. In this paper, we perform a comprehensive theoretical analysis of various variants of DMD. We provide…
Dark matter (DM) direct detection experiments aim to place constraints on the DM--nucleon scattering cross-section and the DM particle mass. These constraints depend sensitively on the assumed local DM density and velocity distribution…
Denoising Probabilistic Models (DPMs) represent an emerging domain of generative models that excel in generating diverse and high-quality images. However, most current training methods for DPMs often neglect the correlation between…
We test the consistency of dark matter velocity distributions obtained from dark matter-only numerical simulations with analytic predictions, using the publicly available Via Lactea 2 dataset as an example. We find that, well inside the…
Fuzzy dark matter (FDM) is a promising candidate for dark matter, characterized by its ultra-light mass, which gives rise to wave effects at astrophysical scales. These effects offer potential solutions to the small-scale issues encountered…
It is usually assumed that dark matter direct detection is sensitive to a large fraction of the dark matter (DM) velocity distribution. We propose an alternative form of dark matter-nucleus scattering which only probes a narrow range of DM…
Diffusion probabilistic models (DPMs) have achieved impressive success in visual generation. While, they suffer from slow inference speed due to iterative sampling. Employing fewer sampling steps is an intuitive solution, but this will also…
In this paper, we formulate a new model of density distribution for halos made of warm dark matter (WDM) particles. The model is described by a single microphysics parameter - the mass (or, equivalently, the maximal value of the initial…
In this paper, we investigate the modification of our expressions developed for the model-independent data analysis procedure of the reconstruction of the (time-averaged) one-dimensional velocity distribution of Galactic Weakly Interacting…
We compute the distribution of likelihoods from the non-parametric iterative smoothing method over a set of mock Pantheon-like type Ia supernova datasets. We use this likelihood distribution to test whether typical dark energy models are…
This work is focussed on the inversion task of inferring the distribution over parameters of interest leading to multiple sets of observations. The potential to solve such distributional inversion problems is driven by increasing…
Exotic dark matter together with the vacuum energy (associated with the cosmological constant) seem to dominate the Universe. Thus its direct detection is central to particle physics and cosmology. Supersymmetry provides a natural dark…
In many models of dark matter (DM), several production mechanisms contribute to its final abundance, often leading to a non-thermal momentum distribution. This makes it more difficult to assess whether such a model is consistent with…
Deconvolution is a statistical inverse problem to estimate the distribution of a random variable based on its noisy observations. Despite the extensive studies on the topic, deconvolution with unknown noise distribution remains as a…
Models of weak-scale supersymmetry offer viable dark matter (DM) candidates. Their parameter spaces are however rather large and complex, such that pinning down the actual parameter values from experimental data can depend strongly on the…
Computational image reconstruction algorithms generally produce a single image without any measure of uncertainty or confidence. Regularized Maximum Likelihood (RML) and feed-forward deep learning approaches for inverse problems typically…
Despite strong evidence for the existence of large amounts of dark matter (DM) in our Universe, there is no direct indication of its presence in our own solar system. All estimates of the local DM density rely on extrapolating results on…
We describe a new, non-parametric, method for reconstructing lensing mass distributions in multiple-image systems, and apply it to PG1115, for which time delays have recently been measured. It turns out that the image positions and the…
This paper proposes a probabilistic motion prediction method for long motions. The motion is predicted so that it accomplishes a task from the initial state observed in the given image. While our method evaluates the task achievability by…
Denoising diffusion models (DDMs) offer a flexible framework for sampling from high dimensional data distributions. DDMs generate a path of probability distributions interpolating between a reference Gaussian distribution and a data…