Related papers: Parametrizing the local dark matter speed distribu…
We present a new class of direct detection signals; absorption of fermionic dark matter. We enumerate the operators through dimension six which lead to fermionic absorption, study their direct detection prospects, and summarize additional…
The traditional methods of estimating the Dark Matter (DM) mass scale crucially depend on the assumptions about the interaction mechanism between the DM and the Standard Model (SM) sectors, making it challenging to achieve precise mass…
Low rank inference on matrices is widely conducted by optimizing a cost function augmented with a penalty proportional to the nuclear norm $\Vert \cdot \Vert_*$. However, despite the assortment of computational methods for such problems,…
We present an innovative approach to the methodology of dynamical modelling, allowing practical reconstruction of the underlying dark matter mass without assuming both the density and anisotropy functions. With this, the mass-anisotropy…
The traffic matrix estimation (TME) problem has been widely researched for decades of years. Recent progresses in deep generative models offer new opportunities to tackle TME problems in a more advanced way. In this paper, we leverage the…
In this work, we propose FastDPM, a unified framework for fast sampling in diffusion probabilistic models. FastDPM generalizes previous methods and gives rise to new algorithms with improved sample quality. We systematically investigate the…
The nature of dark matter (DM) is still debated. While cold DM (CDM) is the standard paradigm, warm DM (WDM) may ease some small-scale tensions in the $\Lambda$CDM framework. Line-intensity mapping (LIM) offers a novel probe of DM…
We present a novel method for computing reachability probabilities of parametric discrete-time Markov chains whose transition probabilities are fractions of polynomials over a set of parameters. Our algorithm is based on two key…
Recent quantitative parameter mapping methods including MR fingerprinting (MRF) collect a time series of images that capture the evolution of magnetization. The focus of this work is to introduce a novel approach termed as Deep Factor…
Directional detection is an important way to detect dark matter. An input to these experiments is the dark matter velocity distribution. Recent hydrodynamical simulations have shown that the dark matter velocity distribution differs…
The dynamic mode decomposition (DMD) is a data-driven approach that extracts the dominant features from spatiotemporal data. In this work, we introduce sparse-mode DMD, a new variant of the optimized DMD framework that specifically…
A leading candidate for astrophysical dark matter (DM) is a massive particle with a mass in the range from 50 GeV to greater than 10 TeV and an interaction cross section on the weak scale. The self-annihilation of such particles in…
In this paper we investigate a new phenomenological parameterization for unified dark matter and dark energy based on the polynomial expansion of the barotropic equation of state parameter $w$. Our parameterization provides well-behaving…
Recently, the distribution of velocity dispersion as far as 400kpc around red isolated galaxies was derived from statistical studies of satellites in the SDSS (Klypin & Prada 2009). This could help to constrain dark matter models at…
A distribution matcher (DM) encodes a binary input data sequence into a sequence of symbols with a desired target probability distribution. Several DMs, including shell mapping and constant-composition distribution matcher (CCDM), have been…
Dynamic mode decomposition (DMD) is a widely used data-driven algorithm for predicting the future states of dynamical systems. However, its standard formulation often struggles with poor long-term predictive accuracy. To address this…
This paper introduces a new two-parameter family of dwarf spheroidal (dSph) galaxy models. The density distribution has a Plummer profile and falls like the inverse fourth power of distance in projection, in agreement with the star-count…
We study the fixed design segmented regression problem: Given noisy samples from a piecewise linear function $f$, we want to recover $f$ up to a desired accuracy in mean-squared error. Previous rigorous approaches for this problem rely on…
In optical nano metrology numerical models are used widely for parameter reconstructions. Using the Bayesian target vector optimization method we fit a finite element numerical model to a Grazing Incidence X-Ray fluorescence data set in…
The dynamic mode decomposition (DMD) is a broadly applicable dimensionality reduction algorithm that approximates a matrix containing time-series data by the outer product of a matrix of exponentials, representing Fourier-like time…