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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. 

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. 

Abstract Galaxy clusters identified via the Sunyaev-Zel’dovich effect (SZ) are a key ingredient in multi-wavelength cluster cosmology. We present and compare three methods of cluster identification: the standard Matched Filter (MF) method in SZ cluster finding, a Convolutional Neural Networks (CNN), and a ‘combined’ identifier. We apply the methods to simulated millimeter maps for several observing frequencies for a survey similar to SPT-3G, the third-generation camera for the South Pole Telescope. The MF requires image pre-processing to remove point sources and a model for the noise, while the CNN requires very little pre-processing of images. Additionally, the CNN requires tuning of hyperparameters in the model and takes cutout images of the sky as input, identifying the cutout as cluster-containing or not. We compare differences in purity and completeness. The MF signal-to-noise ratio depends on both mass and redshift. Our CNN, trained for a given mass threshold, captures a different set of clusters than the MF, some with SNR below the MF detection threshold. However, the CNN tends to mis-classify cutouts whose clusters are located near the edge of the cutout, which can be mitigated with staggered cutouts. We leverage the complementarity of the two methods, combining the scores from each method for identification. The purity and completeness are both 0.61 for MF, and 0.59 and 0.61 for CNN. The combined method yields 0.60 and 0.77, a significant increase for completeness with a modest decrease in purity. We advocate for combined methods that increase the confidence of many low signal-to-noise clusters.