Orion at West Mountain

The individual polarization components of nonlinear Thomson scattering arise from the separate dimensions of electron figure-8 motion caused by a linearly polarized laser field. We present the first measurements of nonlinear Thomson scattering in both emission hemispheres. In the electron average rest frame, the shape of the electron figure-8 path is symmetric about the laser polarization dimension. However, the periodic electron velocity is intrinsically asymmetric. The full scattering emission pattern reveals this asymmetry and the direction that electrons move around the figure-8 path.

We observed the Seyfert 1 galaxy Mrk 817 during an intensive multiwavelength reverberation mapping campaign for 16 months. Here, we examine the behavior of narrow UV absorption lines seen in the Hubble Space Telescope/Cosmic Origins Spectrograph spectra, both during the campaign and in other epochs extending over 14 yr. We conclude that, while the narrow absorption outflow system (at -3750 km s(-1) with FWHM = 177 km s(-1)) responds to the variations of the UV continuum as modified by the X-ray obscurer, its total column density (log N-H = 19.5 (+0.61)(-0.13) cm(-2)) did not change across all epochs. The adjusted ionization parameter (scaled with respect to the variations in the hydrogen-ionizing continuum flux) is log U-H = -1.0(-0.3)(+0.1) . The outflow is located at a distance smaller than 38 pc from the central source, which implies a hydrogen density of n(H) > 3000 cm(-3). The absorption outflow system only covers the continuum emission source and not the broad emission line region, which suggests that its transverse size is small (< 10(16) cm), with potential cloud geometries ranging from spherical to elongated along the line of sight.

The National Transportation Noise Map predicts time-averaged road traffic noise across the continental United States (CONUS) based on annual average daily traffic counts. However, traffic noise can vary greatly with time. This paper outlines a method for predicting nationwide hourly varying source traffic sound emissions called the Vehicular Reduced-Order Observation-based Model (VROOM). The method incorporates three models that predict temporal variability of traffic volume, predict temporal variability of different traffic classes, and use Traffic Noise Model (TNM) 3.0 equations to give traffic noise emission levels based on vehicle numbers and class mix. Location-specific features are used to predict average class mix across CONUS. VROOM then incorporates dynamic traffic class mix data to obtain dynamic traffic class mix. TNM 3.0 equations then give estimated equivalent sound level emission spectra near roads with up to hourly resolution. Important temporal traffic noise characteristics are modeled, including diurnal traffic patterns, rush hours in urban locations, and weekly and yearly variation. Examples of the temporal variability are depicted and possible types of uncertainties are identified. Altogether, VROOM can be used to map national transportation noise with temporal and spectral variability.

Dusty circumnuclear disks (CNDs) in luminous early-type galaxies (ETGs) show regular, dynamically cold molecular gas kinematics. For a growing number of ETGs, Atacama Large Millimeter/sub-millimeter Array (ALMA) CO imaging and detailed gas-dynamical modeling facilitate moderate-to-high precision black hole (BH) mass (M BH) determinations. From the ALMA archive, we identified a subset of 26 ETGs with estimated M BH/M circle dot greater than or similar to 108 to a few x 109 and clean CO kinematics but that previously did not have sufficiently high-angular-resolution near-IR observations to mitigate dust obscuration when constructing stellar luminosity models. We present new optical and near-IR Hubble Space Telescope (HST) images of this sample to supplement the archival HST data, detailing the sample properties and data-analysis techniques. After masking the most apparent dust features, we measure stellar surface-brightness profiles and model the luminosities using the multi-Gaussian expansion (MGE) formalism. Some of these MGEs have already been used in CO dynamical modeling efforts to secure quality M BH determinations, and the remaining ETG targets here are expected to significantly improve the high-mass end of the current BH census, facilitating new scrutiny of local BH mass-host galaxy scaling relationships. We also explore stellar isophotal behavior and general dust properties, finding these CNDs generally become optically thick in the near-IR (A H greater than or similar to 1 mag). These CNDs are typically well aligned with the larger-scale stellar photometric axes, with a few notable exceptions. Uncertain dust impact on the MGE often dominates the BH mass error budget, so extensions of this work will focus on constraining CND dust attenuation.

This paper describes a compact, highly scalable, low-power, and multi-channel charge digitizer (MCCD) designed for synchrotron beam profile monitoring. The MCCD utilizes charge amplifiers, voltage amplifiers, programmable-gain ampli-fiers (PGAs), and ADCs to amplify and digitize input signals. The MCCD also demonstrates a novel stackable PCB design to easily reconfigure the channel count. Laboratory measurement results show a sample rate of 200 Hz per channel, gain of 6.64×1011 V/C, noise of 1.36×105e−rms , and power of 97 mW/channel.

Advances in artificial intelligence (AI) in the medical sector necessitate the development of AI literacy among future physicians. This article explores the pioneering efforts of the AI in Medicine Association (AIM) at Brigham Young University, which offers a framework for undergraduate pre-medical students to gain hands-on experience, receive principled education, explore ethical considerations, and learn appraisal of AI models. By supplementing formal, university-organized pre-medical education with a student-led, faculty-supported introduction to AI through an extracurricular academic association, AIM alleviates apprehensions regarding AI in medicine early and empowers students preparing for medical school to navigate the evolving landscape of AI in healthcare responsibly.