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Howard, Andrew

Publications and source records attributed to Howard, Andrew.

15 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Multichannel photodissociation dynamics in CS 2 studied by ultrafast electron diffraction

In this study, the structural dynamics of photoexcited gas-phase carbon disulfide (CS 2 ) molecules are investigated using ultrafast electron diffraction. The dynamics were triggered by excitation of the optically bright 1 B 2 ( 1 Σ u + ) state by an ultraviolet femtosecond laser pulse centred at 200 nm. In accordance with previous studies, rapid vibrational motion facilitates a combination of internal conversion and intersystem crossing to lower-lying electronic states. Photodissociation via these electronic manifolds results in the production of CS fragments in the electronic ground state and dissociated singlet and triplet sulphur atoms. The structural dynamics are extracted from the experiment using a trajectory-fitting filtering approach, revealing the main characteristics of the singlet and triplet dissociation pathways. Finally, the effect of the time-resolution on the experimental signal is considered and an outlook to future experiments provided.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Multi-Particle Three-Dimensional Covariance Imaging: “Coincidence” Insights into the Many-Body Fragmentation of Strong-Field Ionized D 2 O

We demonstrate the applicability of covariance analysis to three-dimensional velocity-map imaging experiments using a fast timestamping detector. Studying the photofragmentation of strong-field doubly ionized D$_2$O molecules, we show that combining high count rate measurements with covariance analysis yields the same level of information typically limited to the `gold standard' of true, low count rate coincidence experiments, when averaging over a large ensemble of photofragmentation events. This increases the effective data acquisition rate by approximately two orders of magnitude, enabling a new class of experimental studies. This is illustrated through an investigation into the dependence of three-body D$_2$O$^{2+}$ dissociation on the intensity of the ionizing laser, revealing mechanistic insights into the nuclear dynamics driven during the laser pulse. Here, the experimental methodology laid out, with its drastic reduction in acquisition time, is expected to be of great benefit to future photofragment imaging studies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Crystal Structure of Cocosin, A Potential Food Allergen from Coconut ( Cocos nucifera )

Coconut ( Cocos nucifera ) is an important palm tree. Coconut fruit is widely consumed. The most abundant storage protein in coconut fruit is cocosin (a likely food allergen), which belongs to the 11S globulin family. Cocosin was crystallized near a century ago, but its structure remains unknown. By optimizing crystallization conditions and cryoprotectant solutions, we were able to obtain cocosin crystals that diffracted to 1.85 Å. The cocosin gene was cloned from genomic DNA isolated from dry coconut tissue. The protein sequence deduced from the predicted cocosin coding sequence was used to guide model building and structure refinement. We report the structure of cocosin was determined for the first time, and it revealed a typical 11S globulin feature of a double layer doughnut-shaped hexamer.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Revised Stellar Properties of Kepler Targets for the Q1-17 (DR25) Transit Detection Run

The determination of exoplanet properties and occurrence rates using Kepler data critically depends on our knowledge of the fundamental properties (such as temperature, radius, and mass) of the observed stars. We present revised stellar properties for 197,096 Kepler targets observed between Quarters 1–17 (Q1-17), which were used for the final transiting planet search run by the Kepler Mission (Data Release 25, DR25). Similar to the Q1–16 catalog by Huber et al., the classifications are based on conditioning published atmospheric parameters on a grid of Dartmouth isochrones, with significant improvements in the adopted method and over 29,000 new sources for temperatures, surface gravities, or metallicities. In addition to fundamental stellar properties, the new catalog also includes distances and extinctions, and we provide posterior samples for each stellar parameter of each star. Typical uncertainties are ∼27% in radius, ∼17% in mass, and ∼51% in density, which is somewhat smaller than previous catalogs because of the larger number of improved logg constraints and the inclusion of isochrone weighting when deriving stellar posterior distributions. On average, the catalog includes a significantly larger number of evolved solar-type stars, with an increase of 43.5% in the number of subgiants. We discuss the overall changes of radii and masses of Kepler targets as a function of spectral type, with a particular focus on exoplanet host stars.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

TWO STARS TWO WAYS: CONFIRMING A MICROLENSING BINARY LENS SOLUTION WITH A SPECTROSCOPIC MEASUREMENT OF THE ORBIT

Light curves of microlensing events involving stellar binaries and planetary systems can provide information about the orbital elements of the system due to orbital modulations of the caustic structure. Accurately measuring the orbit in either the stellar or planetary case requires detailed modeling of subtle deviations in the light curve. At the same time, the natural, Cartesian parameterization of a microlensing binary is partially degenerate with the microlens parallax. Hence, it is desirable to perform independent tests of the predictions of microlens orbit models using radial velocity (RV) time series of the lens binary system. To this end, we present 3.5 years of RV monitoring of the binary lens system OGLE-2009-BLG-020 L, for which Skowron et al. constrained all internal parameters of the 200–700 day orbit. Our RV measurements reveal an orbit that is consistent with the predictions of the microlens light curve analysis, thereby providing the first confirmation of orbital elements inferred from microlensing events.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2010

Predicting Long-Range Traversability from Short-Range Stereo-Derived Geometry

Based only on its appearance in imagery, this program uses close-range 3D terrain analysis to produce training data sufficient to estimate the traversability of terrain beyond 3D sensing range. This approach is called learning from stereo (LFS). In effect, the software transfers knowledge from middle distances, where 3D geometry provides training cues, into the far field where only appearance is available. This is a viable approach because the same obstacle classes, and sometimes the same obstacles, are typically present in the mid-field and the farfield. Learning thus extends the effective look-ahead distance of the sensors.

Turmon, Michael↗
NASA NTRS2010

360-Degree Visual Detection and Target Tracking on an Autonomous Surface Vehicle

This paper describes perception and planning systems of an autonomous sea surface vehicle (ASV) whose goal is to detect and track other vessels at medium to long ranges and execute responses to determine whether the vessel is adversarial. The Jet Propulsion Laboratory (JPL) has developed a tightly integrated system called CARACaS (Control Architecture for Robotic Agent Command and Sensing) that blends the sensing, planning, and behavior autonomy necessary for such missions. Two patrol scenarios are addressed here: one in which the ASV patrols a large harbor region and checks for vessels near a fixed asset on each pass and one in which the ASV circles a fixed asset and intercepts approaching vessels. This paper focuses on the ASV's central perception and situation awareness system, dubbed Surface Autonomous Visual Analysis and Tracking (SAVAnT), which receives images from an omnidirectional camera head, identifies objects of interest in these images, and probabilistically tracks the objects' presence over time, even as they may exist outside of the vehicle's sensor range. The integrated CARACaS/SAVAnT system has been implemented on U.S. Navy experimental ASVs and tested in on-water field demonstrations.

ASV (AUTONOMOUS SEA SURFACE VEHICLE)↗
NASA NTRS2005

Learning for Autonomous Navigation

Robotic ground vehicles for outdoor applications have achieved some remarkable successes, notably in autonomous highway following (Dickmanns, 1987), planetary exploration (1), and off-road navigation on Earth (1). Nevertheless, major challenges remain to enable reliable, high-speed, autonomous navigation in a wide variety of complex, off-road terrain. 3-D perception of terrain geometry with imaging range sensors is the mainstay of off-road driving systems. However, the stopping distance at high speed exceeds the effective lookahead distance of existing range sensors. Prospects for extending the range of 3-D sensors is strongly limited by sensor physics, eye safety of lasers, and related issues. Range sensor limitations also allow vehicles to enter large cul-de-sacs even at low speed, leading to long detours. Moreover, sensing only terrain geometry fails to reveal mechanical properties of terrain that are critical to assessing its traversability, such as potential for slippage, sinkage, and the degree of compliance of potential obstacles. Rovers in the Mars Exploration Rover (MER) mission have got stuck in sand dunes and experienced significant downhill slippage in the vicinity of large rock hazards. Earth-based off-road robots today have very limited ability to discriminate traversable vegetation from non-traversable vegetation or rough ground. It is impossible today to preprogram a system with knowledge of these properties for all types of terrain and weather conditions that might be encountered.

mixture of Gaussians↗
NASA NTRS2005

Learning for autonomous navigation

Autonomous off-road navigation of robotic ground vehicles has important applications on Earth and in space exploration. Progress in this domain has been retarded by the limited lookahead range of 3-D sensors and by the difficulty of preprogramming systems to understand the traversability of the wide variety of terrain they can encounter.

robotics↗
NASA NTRS2005

Learning for autonomous navigation : extrapolating from underfoot to the far field

Autonomous off-road navigation of robotic ground vehicles has important applications on Earth and in space exploration. Progress in this domain has been retarded by the limited lookahead range of 3-D sensors and by the difficulty of preprogramming systems to understand the traversability of the wide variety of terrain they can encounter. Enabling robots to learn from experience may alleviate both of these problems. We define two paradigms for this, learning from 3-D geometry and learning from proprioception, and describe initial instantiations of them we have developed under DARPA and NASA programs. Field test results show promise for learning traversability of vegetated terrain, learning to extend the lookahead range of the vision system, and learning how slip varies with slope.

autonomous robots↗