Related papers: Photon detection probability prediction using one-…
The Deep Underground Neutrino Experiment (DUNE) is a next generation long baseline (1300 km) neutrino oscillation experiment. The neutrino beam measurements will be performed by a near detector (ND) and far detector (FD). The far detector…
The Argon Dark Matter (ArDM-1t) experiment is a ton-scale liquid argon (LAr) double-phase time projection chamber designed for direct Dark Matter searches. Such a device allows to explore the low energy frontier in LAr with a charge imaging…
Traditionally, inference in liquid xenon direct detection dark matter experiments has used estimators of event energy or density estimation of simulated data. Such methods have drawbacks compared to the computation of explicit likelihoods,…
We introduce a novel generative autoencoder network model that learns to encode and reconstruct images with high quality and resolution, and supports smooth random sampling from the latent space of the encoder. Generative adversarial…
The Deep Underground Neutrino Experiment (DUNE) is a leading-edge experiment for long-baseline neutrino oscillation studies, neutrino astrophysics and nucleon decay searches. ProtoDUNE-Dual Phase (DP) is a 6x6x6 m$^3$ liquid argon…
We demonstrate efficient generation of collinearly propagating, highly nondegenerate photon pairs in a periodically-poled lithium niobate cw parametric downconverter with an inferred pair generation rate of 1.4*10^7/s/mW of pump power.…
The 35-ton prototype for the Deep Underground Neutrino Experiment far detector was a single-phase liquid argon time projection chamber with an integrated photon detector system, all situated inside a membrane cryostat. The detector took…
Liquid Argon Time Projection Chambers are planned to comprise a central role in the future of the U.S. High Energy Physics neutrino program. In particular, this detector technology will form the basis for the 40 kton Deep Underground…
ArDM is a new-generation WIMP detector which will measure simultaneously light and charge from scintillation and ionization of liquid argon. Our goal is to construct, characterize and operate a 1 ton liquid argon underground detector. The…
Detailed understanding of the ionization process in noble liquid detectors is important for their use in applications such as the search for dark matter and coherent elastic neutrino-nucleus scattering. The response of noble liquid…
Liquid argon is an excellent medium for detecting particles, given its yields and transport properties of light and charge. The technology of liquid argon time projection chambers has reached its full maturity after four decades of…
We study single-image super-resolution algorithms for photons at collider experiments based on generative adversarial networks. We treat the energy depositions of simulated electromagnetic showers of photons and neutral-pion decays in a toy…
The rapid advancement in generative AI models has enabled the creation of photorealistic images. At the same time, there are growing concerns about the potential misuse and dangers of generated content, as well as a pressing need for…
The capture of scintillation light emitted by liquid Argon and Xenon under molecular excitations by charged particles is still a challenging task. Here we present a first attempt to design a device able to grab sufficiently high luminosity…
Generative adversarial networks (GANs) are able to model the complex highdimensional distributions of real-world data, which suggests they could be effective for anomaly detection. However, few works have explored the use of GANs for the…
Generative adversarial networks (GANs) are widely used in image generation tasks, yet the generated images are usually lack of texture details. In this paper, we propose a general framework, called Progressively Unfreezing Perceptual GAN…
In this paper, we address the task of semantic-guided image generation. One challenge common to most existing image-level generation methods is the difficulty in generating small objects and detailed local textures. To address this, in this…
Automatic detection of anomalies such as weapons or threat objects in baggage security, or detecting impaired items in industrial production is an important computer vision task demanding high efficiency and accuracy. Most of the available…
Muon Tomography (MT) based on the measurement of multiple scattering of atmospheric cosmic ray muons traversing shipping containers is a promising candidate for identifying threatening high-Z materials. Since position-sensitive detectors…
With the recent advances in machine learning for quantum chemistry, it is now possible to predict the chemical properties of compounds and to generate novel molecules. Existing generative models mostly use a string- or graph-based…