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Expanding the mass range and techniques by which we search for dark matter is an important part of the worldwide particle physics program. Accelerator-based searches for dark matter and dark sector particles are a uniquely compelling part…
Designing nanophotonic structures traditionally grapples with the complexities of discrete parameters, such as real materials, often resorting to costly global optimization methods. This paper introduces an approach that leverages…
Dark matter direct detection searches for signals coming from dark matter scattering against nuclei at a very low recoil energy scale ~ 10 keV. In this paper, a simple non-relativistic effective theory is constructed to describe…
Cosmology observations indicate that our universe is composed of 25% dark matter (DM), yet we know little about its microscopic properties. Whereas the gravitational interaction of DM is well understood, its interaction with the Standard…
There is a vast menagerie of plausible candidates for the constituents of dark matter, both within and beyond extensions of the Standard Model of particle physics. Each of these candidates may have scattering (and other) cross section…
We demonstrate a machine learning approach designed to extract hidden chemistry/physics to facilitate new materials discovery. In particular, we propose a novel method for learning latent knowledge from material structure data in which…
The electron ionization predicted by the Migdal effect in dark matter-nucleus scattering enhances experimental sensitivity to sub-GeV dark matter. In this work, we demonstrate that lower-energy electron excitations provide a novel and…
The lack of an unambiguous signal for thermally produced dark matter particles in direct detection, indirect detection, and collider searches calls for broadening the search strategies by probing a wider range of dark matter masses with…
The direct detection of Dark Matter particles with mass below the GeV-scale is hampered by soft nuclear recoil energies and finite detector thresholds. For a given maximum relative velocity, the kinematics of elastic Dark Matter nucleus…
Drug discovery using deep learning has attracted a lot of attention of late as it has obvious advantages like higher efficiency, less manual guessing and faster process time. In this paper, we present a novel neural network for generating…
The combination of modern scientific computing with electronic structure theory can lead to an unprecedented amount of data amenable to intelligent data analysis for the identification of meaningful, novel, and predictive structure-property…
Sub-GeV dark matter candidates are of increasing interest, because long-favored candidates such as GeV-scale WIMPs have not been detected. For low-mass dark matter, model-independent constraints are weak or nonexistent. We show that for…
If dark matter has mass lower than around 1 GeV, it will not impart enough energy to cause detectable nuclear recoils in many direct-detection experiments. However, if dark matter is upscattered to high energy by collisions with cosmic…
Conventional dark matter direct detection experiments set stringent constraints on dark matter by looking for elastic scattering events between dark matter particles and nuclei in underground detectors. However these constraints weaken…
Dark matter particles with sufficiently large interactions with ordinary matter can scatter in the Earth's atmosphere and crust before reaching an underground detector. This Earth-shielding effect can induce a directional dependence in the…
A promising technology concept for sub-GeV dark matter detection is described, in which low-temperature microcalorimeters serve as the sensors and superfluid $^4$He serves as the target material. A superfluid helium target has several…
Collisions at high-energy particle colliders are a traditionally fruitful source of exotic particle discoveries. Finding these rare particles requires solving difficult signal-versus-background classification problems, hence machine…
Direct detection experiments have established the most stringent constraints on potential interactions between particle candidates for relic, thermal dark matter and Standard Model particles. To surpass current exclusion limits a new…
Probabilistic generative deep learning for molecular design involves the discovery and design of new molecules and analysis of their structure, properties and activities by probabilistic generative models using the deep learning approach.…
The first direct detection limits on dark matter in the MeV to GeV mass range are presented, using XENON10 data. Such light dark matter can scatter with electrons, causing ionization of atoms in a detector target material and leading to…