Measuring Dark Matter Profiles Non-Parametrically In Dwarf Spheroidals: An Application To Draco

We introduce a novel implementation of orbit-based (or Schwarzschild) modeling that allows dark matter density profiles to be calculated non-parametrically in nearby galaxies. Our models require no assumptions to be made about velocity anisotropy or the dark matter profile. The technique can be applied to any dispersion-supported stellar system, and we demonstrate its use by studying the Local Group dwarf spheroidal galaxy (dSph) Draco. We use existing kinematic data at larger radii and also present 12 new radial velocities within the central 13 pc obtained with the VIRUS-W integral field spectrograph on the 2.7 m telescope at McDonald Observatory. Our non-parametric Schwarzschild models find strong evidence that the dark matter profile in Draco is cuspy for 20 = 20 pc is well fit by a power law with slope alpha = -1.0 +/- 0.2, consistent with predictions from cold dark matter simulations. Our models confirm that, despite its low baryon content relative to other dSphs, Draco lives in a massive halo.NSF-0908639Astronom

CAR APPLICATIONS

A digital key framework (e.g., that is executed by a computing device, such as a smartphone, a tablet computer, a smartwatch, etc.) may provide applications with data about a vehicle (e.g., a motorcycle, a bus, an RV, a semi-trailer truck, a tractor or other type of farm equipment, a train, a plane, a drone, a helicopter, a personal transport vehicle, etc.) to enable the applications to provide more integrated, contextual, and helpful services. The digital key framework may serve as a dedicated interface for applications (e.g., navigation applications), enabling seamless integration with various services and functionalities. Such integration may allow mapping and navigation apps, car-sharing platforms, service providers, and other applications to access contextual information provided by digital key framework, enhancing their offerings and providing more personalized experiences to a user. Other benefits may include, but are not limited to, efficient maintenance management, convenient refueling/charging assistance, optimized route planning, increased vehicle safety, seamless family sharing, parental controls and restricted driving, and other innovative use cases

Representational structure or task structure? Bias in neural representational similarity analysis and a Bayesian method for reducing bias

<div><p>The activity of neural populations in the brains of humans and animals can exhibit vastly different spatial patterns when faced with different tasks or environmental stimuli. The degrees of similarity between these neural activity patterns in response to different events are used to characterize the representational structure of cognitive states in a neural population. The dominant methods of investigating this similarity structure first estimate neural activity patterns from noisy neural imaging data using linear regression, and then examine the similarity between the estimated patterns. Here, we show that this approach introduces spurious bias structure in the resulting similarity matrix, in particular when applied to fMRI data. This problem is especially severe when the signal-to-noise ratio is low and in cases where experimental conditions cannot be fully randomized in a task. We propose Bayesian Representational Similarity Analysis (BRSA), an alternative method for computing representational similarity, in which we treat the covariance structure of neural activity patterns as a hyper-parameter in a generative model of the neural data. By marginalizing over the unknown activity patterns, we can directly estimate this covariance structure from imaging data. This method offers significant reductions in bias and allows estimation of neural representational similarity with previously unattained levels of precision at low signal-to-noise ratio, without losing the possibility of deriving an interpretable distance measure from the estimated similarity. The method is closely related to Pattern Component Model (PCM), but instead of modeling the estimated neural patterns as in PCM, BRSA models the imaging data directly and is suited for analyzing data in which the order of task conditions is not fully counterbalanced. The probabilistic framework allows for jointly analyzing data from a group of participants. The method can also simultaneously estimate a signal-to-noise ratio map that shows where the learned representational structure is supported more strongly. Both this map and the learned covariance matrix can be used as a structured prior for maximum <i>a posteriori</i> estimation of neural activity patterns, which can be further used for fMRI decoding. Our method therefore paves the way towards a more unified and principled analysis of neural representations underlying fMRI signals. We make our tool freely available in Brain Imaging Analysis Kit (BrainIAK).</p></div

Re-growth of stellar disks in mature galaxies: The two component nature of NGC 7217 revisited with VIRUS-W

Previous studies have reported the existence of two counter-rotating stellar disks in the early-type spiral galaxy NGC7217. We have obtained high-resolution optical spectroscopic data (R ~ 9000) with the new fiber-based Integral Field Unit instrument VIRUS-W at the 2.7m telescope of the McDonald Observatory in Texas. Our analysis confirms the existence of two components. However, we find them to be co-rotating. The first component is the more luminous (~ 77% of the total light), has the higher velocity dispersion (~ 170 km/s) and rotates relatively slowly (projected $v_{max}$ = 50 km/s). The lower luminosity second component, (~ 23% of the total light), has a low velocity dispersion (~ 20 km/s) and rotates quickly (projected $v_{max}$ = 150 km/s). The difference in the kinematics of the two stellar components allows us to perform a kinematic decomposition and to measure the strengths of their Mg and Fe Lick indices separately. The rotational velocities and dispersions of the less luminous and faster component are very similar to those of the interstellar gas as measured from the [OIII] emission. Morphological evidence of active star formation in this component further suggests that NGC7217 may be in the process of (re)growing a disk inside a more massive and higher dispersion stellar halo. The kinematically cold and regular structure of the gas disk in combination with the central almost dust-free morphology allows us to compare the dynamical mass inside of the central 500pc with predictions from a stellar population analysis. We find agreement between the two if a Kroupa stellar initial mass function is assumed.Comment: accepted for publication by MNRA

Statistical Computations Underlying the Dynamics of Memory Updating

Psychophysical and neurophysiological studies have suggested that memory is not simply a carbon copy of our experience: Memories are modified or new memories are formed depending on the dynamic structure of our experience, and specifically, on how gradually or abruptly the world changes. We present a statistical theory of memory formation in a dynamic environment, based on a nonparametric generalization of the switching Kalman filter. We show that this theory can qualitatively account for several psychophysical and neural phenomena, and present results of a new visual memory experiment aimed at testing the theory directly. Our experimental findings suggest that humans can use temporal discontinuities in the structure of the environment to determine when to form new memory traces. The statistical perspective we offer provides a coherent account of the conditions under which new experience is integrated into an old memory versus forming a new memory, and shows that memory formation depends on inferences about the underlying structure of our experience.Templeton FoundationAlfred P. Sloan Foundation (Fellowship)National Science Foundation (U.S.) (NSF Graduate Research Fellowship)National Institute of Mental Health (U.S.) (NIH Award Number R01MH098861

A Reflection Principle for the Control of Molecular Photodissociation in Solids: Model Simulation for F2 in Ar

Laser pulse induced photodissociation of molecules in rare gas solids is investigated by representative quantum wavepackets or classical trajectories which are directed towards, or away from cage exits, yielding dominant photodissociation into different neighbouring cages. The directionality is determined by a sequence of reflections inside the relief provided by the slopes of the potential energy surface of the excited system, which in turn depend on the initial preparation of the matrix isolated system, e.g. by laser pulses with different frequencies or by vibrational pre-excitation of the cage atoms. This reflection principle is demonstrated for a simple, two-dimensional model of F2 in Ar

The VIRUS-P Exploration of Nearby Galaxies (VENGA): Survey Design and First Results

VENGA is a large-scale extragalactic IFU survey, which maps the bulges, bars and large parts of the outer disks of 32 nearby normal spiral galaxies. The targets are chosen to span a wide range in Hubble types, star formation activities, morphologies, and inclinations, at the same time of having vast available multi-wavelength coverage from the far-UV to the mid-IR, and available CO and 21cm mapping. The VENGA dataset will provide 2D maps of the SFR, stellar and gas kinematics, chemical abundances, ISM density and ionization states, dust extinction and stellar populations for these 32 galaxies. The uniqueness of the VIRUS-P large field of view permits these large-scale mappings to be performed. VENGA will allow us to correlate all these important quantities throughout the different environments present in galactic disks, allowing the conduction of a large number of studies in star formation, structure assembly, galactic feedback and ISM in galaxies.Comment: 7 pages, 3 figures, proceedings of the "Third Biennial Frank N. Bash Symposium, New Horizons in Astronomy" held in Austin, TX, Oct. 2009. To be published in the Astronomical Society of the Pacific Conference Series, eds. L. Stanford, L. Hao, Y. Mao, J. Gree

Photodissociation Dynamics of Molecular Fluorine in an Argon Matrix Induced by Ultrashort Laser Pulses

The electronic excitation induced by ultrashort laser pulses and the subsequent photodissociation dynamics of molecular fluorine in an argon matrix are studied. The interactions of photofragments and host atoms are modeled using a Diatomics-In-Molecule Hamiltonian. Two types of methods are compared: Quantum-classical simulations where the nuclei are treated classically, with surface-hopping algorithms to describe either radiative or non-radiative transitions between different electronic states. Fully quantum-mechanical simulations, but for a model system of reduced dimensionality, in which the two most essential degrees of freedom are considered. Some of the main results are: The sequential energy transfer events from the photoexcited F2 into the lattice modes are such that the ``reduced dimensionality'' model is valid for the first 200 fs. This, in turn, allows us to use the quantum results to investigate the details of the excitation process with short laser pulses. Thus, it also serves as a reference for the quantum-classical ``surface hopping'' model of the excitation process. Moreover, it supports the validity of a laser pulse control strategy developed on the basis of the ``reduced dimensionality'' model. Both in the quantum and quantum-classical simulations, the separation of the F atoms following photodissociation does not exceed 20 bohr. The cage exit mechanisms appear qualitatively similar in the two sets of simulations but quantum effects are quantitatively important. Nonlinear effects are important in determining the photoexcitation yield. In summary, this paper demonstrates that quantum-classical simulations combined with reduced dimensionality quantum calculations can be a powerful approach to the analysis and control of the dynamics of complex systems