相关论文: DarkBit: A GAMBIT module for computing dark matter…
The difficult task of observing Dark Matter subhaloes is of paramount importance since it would constrain Dark Matter particle properties (cold or warm relic) and confirm once again the longstanding $\Lambda$CDM model. In the near future…
We present a novel approach for the search of dark matter in the DarkSide-50 experiment, relying on Bayesian Networks. This method incorporates the detector response model into the likelihood function, explicitly maintaining the connection…
We propose a novel method to significantly enhance the signal rate in qubit-based dark matter detection experiments with the help of quantum interference. Various quantum sensors possess ideal properties for detecting wave-like dark matter,…
Mirror dark matter offers a framework to explain the existing dark matter direct detection experiments. Here we confront this theory with the most recent experimental data, paying attention to the various known systematic uncertainties, in…
We present jorbit, a python/JAX library designed to enable modern data-driven numerical studies of the solar system. Written entirely in JAX, an auto-differentiable and optionally GPU accelerated language behind many current large-scale…
The question of the nature of the dark matter in the Universe remains one of the most outstanding unsolved problems in basic science. One of the best motivated particle physics candidates is the lightest supersymmetric particle, assumed to…
The field of dark matter detection is a highly visible and highly competitive one. In this paper, we propose recommendations for presenting dark matter direct detection results particularly suited for weak-scale dark matter searches,…
We describe the Blacklight code, intended for post-processing general-relativistic magnetohydrodynamic simulation data. Beyond polarized ray tracing of synchrotron radiation, it can produce a number of outputs that aid in analyzing data…
We present a novel method for both forecasting and recasting upper limits (ULs) on dark matter (DM) annihilation cross sections, $\left< \sigma v \right>^{UL}$, or decay lifetime $\tau^{LL}$ . The forecasting method relies solely on the…
The next decade will bring massive new data sets from experiments of the direct detection of weakly interacting massive particle (WIMP) dark matter. The primary goal of these experiments is to identify and characterize the dark-matter…
The search for new comparably light (well below the electroweak scale) feebly interacting particles is an exciting possibility to explain some mysterious phenomena in physics, among them the origin of Dark Matter. The sensitivity study…
Automated tools for the computation of particle physics' processes have become the backbone of phenomenological studies beyond the standard model. Here, we present MadDM v3.2. This release enables the fully automated computation of…
We present a new module of the micrOMEGAs package for the calculation of WIMP-nuclei elastic scattering cross sections relevant for the direct detection of dark matter through its interaction with nuclei in a large detector. With this new…
SuperIso Relic is a public computing program for the calculation of flavour observables and relic density in supersymmetry (MSSM and NMSSM). We present new extensions of the code dedicated to the calculation of dark matter direct and…
Whilst the need for dark matter was established almost a century ago, only its gravitational interaction has been confirmed so far, allowing for plethora of models for dark matter. The Weakly Interacting Massive Particles (WIMPs) category…
Future gravitational wave observatories can probe dark matter by detecting the dephasing in the waveform of binary black hole mergers induced by dark matter overdensities. Such a detection hinges on the accurate modelling of the dynamical…
We describe SuperIso Relic, a public program for the calculation of dark matter relic density and direct and indirect detection rates, which includes in addition the SuperIso routines for the calculation of flavour physics observables.…
The proliferation of edge devices has unlocked unprecedented opportunities for deep learning model deployment in computer vision applications. However, these complex models require considerable power, memory and compute resources that are…
DerivKit is a Python package for derivative-based statistical inference. It implements stable numerical differentiation and derivative assembly utilities for Fisher-matrix forecasting and higher-order likelihood approximations in scientific…
Studying the impact of new-physics models on low-energy observables necessitates matching to effective field theories at the relevant mass thresholds. We introduce the first public version of Matchete, a computer tool for matching…