Very Long Baseline Neutrino Oscillations, The BNL VLBNLO Concept

A wide energy-band neutrino beam sent over a very long baseline to a massive detector can break the degeneracies in the neutrino oscillation parameters. It can measure the disappearance parameters with precision and determine the mass hierarchy. If $\theta_{13}$ is large enough the CP violating phase can be measured with neutrino running alone and anti-neutrino running can confirm CPV and improve the parameter measurements. Brookhaven National Laboratory is pursuing such an experiment.Comment: 3 pages, 3 figures, espcrc2.sty. To be published in the proceedings of NuFact0

Background Study on nu_e Appearance from a nu_mu Beam in Very Long Baseline Neutrino Oscillation Experiments with a Large Water Cherenkov Detector

There is a growing interest in very long baseline neutrino oscillation experimentation using accelerator produced neutrino beam as a machinery to probe the last three unmeasured neutrino oscillation parameters: the mixing angle theta_13, the possible CP violating phase delta_CP and the mass hierarchy, namely, the sign of delta-m^2_32. Water Cherenkov detectors such as IMB, Kamiokande and Super-Kamiokande have shown to be very successful at detecting neutrino interactions. Scaling up this technology may continue to provide the required performance for the next generation of experiments. This report presents the latest effort to demonstrate that a next generation (> 100 kton) water Cherenkov detector can be used effectively for the rather difficult task of detecting nu_e events from the neutrino oscillation nu_mu -> nu_e despite the large expected potential background resulting from pi^0 events produced via neutral current interactions.Comment: 13 pages. typo in uncertainty in conclusion fixe

Beam Tests of Ionization Chambers for the NuMI Neutrino Beam

We have conducted tests at the Fermilab Booster of ionization chambers to be used as monitors of the NuMI neutrino beamline. The chambers were exposed to proton fluxes of up to 10$^{12}$ particles/cm$^2$/1.56$\mu$s. We studied space charge effects which can reduce signal collection from the chambers at large charged particle beam intensities.Comment: submitted to IEEE Trans Nucl. Sc

Effectiveness of denoising diffusion probabilistic models for fast and high-fidelity whole-event simulation in high-energy heavy-ion experiments

Artificial intelligence (AI) generative models, such as generative adversarial networks (GANs), variational auto-encoders, and normalizing flows, have been widely used and studied as efficient alternatives for traditional scientific simulations. However, they have several drawbacks, including training instability and inability to cover the entire data distribution, especially for regions where data are rare. This is particularly challenging for whole-event, full-detector simulations in high-energy heavy-ion experiments, such as sPHENIX at the Relativistic Heavy Ion Collider and Large Hadron Collider experiments, where thousands of particles are produced per event and interact with the detector. This work investigates the effectiveness of Denoising Diffusion Probabilistic Models (DDPMs) as an AI-based generative surrogate model for the sPHENIX experiment that includes the heavy-ion event generation and response of the entire calorimeter stack. DDPM performance in sPHENIX simulation data is compared with a popular rival, GANs. Results show that both DDPMs and GANs can reproduce the data distribution where the examples are abundant (low-to-medium calorimeter energies). Nonetheless, DDPMs significantly outperform GANs, especially in high-energy regions where data are rare. Additionally, DDPMs exhibit superior stability compared to GANs. The results are consistent between both central and peripheral centrality heavy-ion collision events. Moreover, DDPMs offer a substantial speedup of approximately a factor of 100 compared to the traditional Geant4 simulation method.Comment: 11 pages, 7 figure

Rethinking CycleGAN: Improving Quality of GANs for Unpaired Image-to-Image Translation

An unpaired image-to-image (I2I) translation technique seeks to find a mapping between two domains of data in a fully unsupervised manner. While the initial solutions to the I2I problem were provided by the generative adversarial neural networks (GANs), currently, diffusion models (DM) hold the state-of-the-art status on the I2I translation benchmarks in terms of FID. Yet, they suffer from some limitations, such as not using data from the source domain during the training, or maintaining consistency of the source and translated images only via simple pixel-wise errors. This work revisits the classic CycleGAN model and equips it with recent advancements in model architectures and model training procedures. The revised model is shown to significantly outperform other advanced GAN- and DM-based competitors on a variety of benchmarks. In the case of Male2Female translation of CelebA, the model achieves over 40% improvement in FID score compared to the state-of-the-art results. This work also demonstrates the ineffectiveness of the pixel-wise I2I translation faithfulness metrics and suggests their revision. The code and trained models are available at https://github.com/LS4GAN/uvcgan