Model-Independent Stellar and Planetary Masses from Multi-Transiting Exoplanetary Systems

Precise exoplanet characterization requires precise classification of exoplanet host stars. The masses of host stars are commonly estimated by comparing their spectra to those predicted by stellar evolution models. However, spectroscopically determined properties are difficult to measure accurately for stars that are substantially different from the Sun, such as M-dwarfs and evolved stars. Here, we propose a new method to dynamically measure the masses of transiting planets near mean-motion resonances and their host stars by combining observations of transit timing variations with radial velocity measurements. We derive expressions to analytically determine the mass of each member of the system and demonstrate the technique on the Kepler-18 system. We compare these analytic results to numerical simulations and find the two are consistent. We identify eight systems for which our technique could be applied if follow-up radial velocity measurements are collected. We conclude this analysis would be optimal for systems discovered by next generation missions similar to TESS or PLATO, which will target bright stars that are amenable to efficient RV follow-up.Comment: 9 pages, 1 figure, submitted to Ap

Speech Sensorimotor Learning through a Virtual Vocal Tract

Studies of speech sensorimotor learning often manipulate auditory feedback by modifying isolated acoustic parameters such as formant frequency or fundamental frequency using near real-time resynthesis of a participant\u27s speech. An alternative approach is to engage a participant in a total remapping of the sensorimotor working space using a virtual vocal tract. To support this approach for studying speech sensorimotor learning we have developed a system to control an articulatory synthesizer using electromagnetic articulography data. Articulator movement data from the NDI Wave System are streamed to a Maeda articulatory synthesizer. The resulting synthesized speech provides auditory feedback to the participant. This approach allows the experimenter to generate novel articulatory-acoustic mappings. Moreover, the acoustic output of the synthesizer can be perturbed using acoustic resynthesis methods. Since no robust speech-acoustic signal is required from the participant, this system will allow for the study of sensorimotor learning in any individuals, even those with severe speech disorders. In the current work we present preliminary results that demonstrate that typically-functioning participants can use a virtual vocal tract to produce diphthongs within a novel articulatory-acoustic workspace. Once sufficient baseline performance is established, perturbations to auditory feedback (formant shifting) can elicit compensatory and adaptive articulatory responses

Vocal Classification of Vocalizations of a Pair of Asian Small-Clawed Otters to Determine Stress

Asian Small-Clawed Otters (Aonyx cinerea) are a small, protected but threatened species living in freshwater. They are gregarious and live in monogamous pairs for their lifetimes, communicating via scent and acoustic vocalizations. This study utilized a hidden Markov model (HMM) to classify stress versus non-stress calls from a sibling pair under professional care. Vocalizations were expertly annotated by keepers into seven contextual categories. Four of these—aggression, separation anxiety, pain, and prefeeding—were identified as stressful contexts, and three of them—feeding, training, and play—were identified as non-stressful contexts. The vocalizations were segmented, manually categorized into broad vocal type call types, and analyzed to determine signal to noise ratios. From this information, vocalizations from the most common contextual categories were used to implement HMM-based automatic classification experiments, which included individual identification, stress vs non-stress, and individual context classification. Results indicate that both individual identity and stress vs non-stress were distinguishable, with accuracies above 90%, but that individual contexts within the stress category were not easily separable

Surface and Atmospheric Contributions to Passive Microwave Brightness Temperatures

Physically-based passive microwave precipitation retrieval algorithms require a set of relationships between satellite observed brightness temperatures (TB) and the physical state of the underlying atmosphere and surface. These relationships are typically non-linear, such that inversions are ill-posed especially over variable land surfaces. In order to better understand these relationships, this work presents a theoretical analysis using brightness temperature weighting functions to quantify the percentage of the TB resulting from absorption/emission/reflection from the surface, absorption/emission/scattering by liquid and frozen hydrometeors in the cloud, the emission from atmospheric water vapor, and other contributors. The results are presented for frequencies from 10 to 874 GHz and for several individual precipitation profiles as well as for three cloud resolving model simulations of falling snow. As expected, low frequency channels (<89 GHz) respond to liquid hydrometeors and the surface, while the higher frequency channels become increasingly sensitive to ice hydrometeors and the water vapor sounding channels react to water vapor in the atmosphere. Low emissivity surfaces (water and snow-covered land) permit energy downwelling from clouds to be reflected at the surface thereby increasing the percentage of the TB resulting from the hydrometeors. The slant path at a 53deg viewing angle increases the hydrometeor contributions relative to nadir viewing channels and show sensitivity to surface polarization effects. The TB percentage information presented in this paper answers questions about the relative contributions to the brightness temperatures and provides a key piece of information required to develop and improve precipitation retrievals over land surfaces

Detection Thresholds of Falling Snow from Satellite-Borne Active and Passive Sensors

Precipitation, including rain and snow, is a critical part of the Earth's energy and hydrology cycles. Precipitation impacts latent heating profiles locally while global circulation patterns distribute precipitation and energy from the equator to the poles. For the hydrological cycle, falling snow is a primary contributor in northern latitudes during the winter seasons. Falling snow is the source of snow pack accumulations that provide fresh water resources for many communities in the world. Furthermore, falling snow impacts society by causing transportation disruptions during severe snow events. In order to collect information on the complete global precipitation cycle, both liquid and frozen precipitation must be collected. The challenges of estimating falling snow from space still exist though progress is being made. These challenges include weak falling snow signatures with respect to background (surface, water vapor) signatures for passive sensors over land surfaces, unknowns about the spherical and non-spherical shapes of the snowflakes, their particle size distributions (PSDs) and how the assumptions about the unknowns impact observed brightness temperatures or radar reflectivities, differences in near surface snowfall and total column snow amounts, and limited ground truth to validate against. While these challenges remain, knowledge of their impact on expected retrieval results is an important key for understanding falling snow retrieval estimations. Since falling snow from space is the next precipitation measurement challenge from space, information must be determined in order to guide retrieval algorithm development for these current and future missions. This information includes thresholds of detection for various sensor channel configurations, snow event system characteristics, snowflake particle assumptions, and surface types. For example, can a lake effect snow system with low (approx 2.5 km) cloud tops having an ice water content (IWC) at the surface of 0.25 g / cubic m and dendrite snowflakes be detected? If this information is known, we can focus retrieval efforts on detectable storms and concentrate advances on achievable results. Here, the focus is to determine thresholds of detection for falling snow for various snow conditions over land and lake surfaces. The results rely on simulated Weather Research Forecasting (WRF) simulations of falling snow cases since simulations provide all the information to determine the measurements from space and the ground truth. Sensitivity analyses were performed to better ascertain the relationships between multifrequency microwave and millimeter-wave sensor observations and the falling snow/underlying field of view. In addition, thresholds of detection for various sensor channel configurations, snow event system characteristics, snowflake particle assumptions, and surface types were studied. Results will be presented for active radar at Ku, Ka, and W-band and for passive radiometer channels from 10 to 183 GHz

Psychiatric Medications and Stigmatizing Attitudes in College Students

Research suggests that biological explanations of mental illness include the promotion of the effectiveness of medication, and that such explanations lead to greater attributions of responsibility and potentially greater stigmatizing emotional and behavioral reactions. This study examined whether college students\u27 attitudes toward a fellow student with mental illness are affected by whether the latter is described as having benefitted previously from medication. Results suggest that the promotion of psychiatric medications as helpful may increase stigmatizing attitudes by peers against fellow students with mental illness

Microwave Properties of Ice-Phase Hydrometeors for Radar and Radiometers: Sensitivity to Model Assumptions

A simplied framework is presented for assessing the qualitative sensitivities of computed microwave properties, satellite brightness temperatures, and radar reflectivities to assumptions concerning the physical properties of ice-phase hydrometeors. Properties considered included the shape parameter of a gamma size distribution andthe melted-equivalent mass median diameter D0, the particle density, dielectric mixing formula, and the choice of complex index of refraction for ice. We examine these properties at selected radiometer frequencies of 18.7, 36.5, 89.0, and 150.0 GHz; and radar frequencies at 2.8, 13.4, 35.6, and 94.0 GHz consistent with existing and planned remote sensing instruments. Passive and active microwave observables of ice particles arefound to be extremely sensitive to the melted-equivalent mass median diameter D0 ofthe size distribution. Similar large sensitivities are found for variations in the ice vol-ume fraction whenever the geometric mass median diameter exceeds approximately 1/8th of the wavelength. At 94 GHz the two-way path integrated attenuation is potentially large for dense compact particles. The distribution parameter mu has a relatively weak effect on any observable: less than 1-2 K in brightness temperature and up to 2.7 dB difference in the effective radar reflectivity. Reversal of the roles of ice and air in the MaxwellGarnett dielectric mixing formula leads to a signicant change in both microwave brightness temperature (10 K) and radar reflectivity (2 dB). The choice of Warren (1984) or Warren and Brandt (2008) for the complex index of refraction of ice can produce a 3%-4% change in the brightness temperature depression