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The BICEP/Keck (BK) series of cosmic microwave background (CMB) polarization experiments has, over the past decade and a half, produced a series of field-leading constraints on cosmic inflation via measurements of the “B-mode” polarization of the CMB. Primordial B modes are directly tied to the amplitude of primordial gravitational waves (PGW), their strength parameterized by the tensor-to-scalar ratio, r, and thus the energy scale of inflation. Having set the most sensitive constraints to-date on r, σ(r) = 0.009 (r0.05 < 0.036, 95% C.L.) using data through the 2018 observing season (“BK18”), the BICEP/Keck program has continued to improve its dataset in the years since. We give a brief overview of the BK program and the “BK18” result before discussing the program’s ongoing efforts, including the deployment and performance of the Keck Array’s successor instrument, BICEP Array, improvements to data processing and internal consistency testing, new techniques such as delensing, and how those will ultimately serve to allow BK reach σ(r) ≲ 0.003 using data through the 2027 observing season. 

The most direct approach for characterizing the quantum dynamics of a strongly interacting system is to measure the time evolution of its full many-body state. Despite the conceptual simplicity of this approach, it quickly becomes intractable as the system size grows. An alternate approach is to think of the many-body dynamics as generating noise, which can be measured by the decoherence of a probe qubit. Here we investigate what the decoherence dynamics of such a probe tells us about the many-body system. In particular, we utilize optically addressable probe spins to experimentally characterize both static and dynamical properties of strongly interacting magnetic dipoles. Our experimental platform consists of two types of spin defects in nitrogen delta-doped diamond: nitrogen-vacancy colour centres, which we use as probe spins, and a many-body ensemble of substitutional nitrogen impurities. We demonstrate that the many-body system’s dimensionality, dynamics and disorder are naturally encoded in the probe spins’ decoherence profile. Furthermore, we obtain direct control over the spectral properties of the many-body system, with potential applications in quantum sensing and simulation.

 

Providing students with hands-on construction experiences enables them to apply conceptual knowledge to practical applications, but the high costs associated with this form of learning limit access to it. Therefore, this paper explores the use of augmented reality (AR) to enable students in a conventional classroom or lab setting to interact with virtual objects similar to how they would if they were physically constructing building components. More specifically, the authors tasked student participants with virtually constructing a wood-framed wall through AR with a Microsoft HoloLens. Participants were video-recorded and their behaviors were analyzed. Subsequently, observed behaviors in AR were analyzed and compared to expected behaviors in the physical environment. It was observed that students performing the tasks tended to mimic behaviors found in the physical environment in how they managed the virtual materials, leveraged physical tools in conjunction with virtual materials, and in their ability to recognize and fix mistakes. Some of the finer interactions observed with the virtual materials were found to be unique to the virtual environment, such as moving objects from a distance. Overall, these findings contribute to the understanding of how AR may be leveraged in classrooms to provide learning experiences that yield similar outcomes to those provided in more resource-intensive physical construction site environments. 

We present a deep learning-based method for estimating the neutrino energy of charged-current neutrino-argon interactions. We employ a recurrent neural network (RNN) architecture for neutrino energy estimation in the MicroBooNE experiment, utilizing liquid argon time projection chamber (LArTPC) detector technology. Traditional energy estimation approaches in LArTPCs, which largely rely on reconstructing and summing visible energies, often experience sizable biases and resolution smearing because of the complex nature of neutrino interactions and the detector response. The estimation of neutrino energy can be improved after considering the kinematics information of reconstructed final-state particles. Utilizing kinematic information of reconstructed particles, the deep learning-based approach shows improved resolution and reduced bias for the muon neutrino Monte Carlo simulation sample compared to the traditional approach. In order to address the common concern about the effectiveness of this method on experimental data, the RNN-based energy estimator is further examined and validated with dedicated data-simulation consistency tests using MicroBooNE data. We also assess its potential impact on a neutrino oscillation study after accounting for all statistical and systematic uncertainties and show that it enhances physics sensitivity. This method has good potential to improve the performance of other physics analyses.

Published by the American Physical Society2024