Publications

The primary sample of the {\it Gaia} Data Release 1 is the Tycho-Gaia Astrometric Solution (TGAS): ≈ 2 million Tycho-2 sources with improved parallaxes and proper motions relative to the initial catalog. This increased astrometric precision presents an opportunity to find new binary stars and moving groups. We search for high-confidence comoving pairs of stars in TGAS by identifying pairs of stars consistent with having the same 3D velocity using a marginalized likelihood ratio test to discriminate candidate comoving pairs from the field population. Although we perform some visualizations using (bias- corrected) inverse parallax as a point estimate of distance, the likelihood ratio is computed with a probabilistic model that includes the covariances of parallax and proper motions and marginalizes the (unknown) true distances and 3D velocities of the stars. We find 13,085 comoving star pairs among 10,606 unique stars with separations as large as 10 pc (our search limit). Some of these pairs form larger groups through mutual comoving neighbors: many of these pair networks correspond to known open clusters and OB associations, but we also report the discovery of several new comoving groups. Most surprisingly, we find a large number of very wide (>1 pc) separation comoving star pairs, the number of which increases with increasing separation and cannot be explained purely by false-positive contamination. Our key result is a catalog of high-confidence comoving pairs of stars in TGAS. We discuss the utility of this catalog for making dynamical inferences about the Galaxy, testing stellar atmosphere models, and validating chemical abundance measurements.

Resource-efficiency is important for biological function of neurons. Using the perceptron as a model of a neuron, we show that resource-efficient learning implies sparse neural connectivity. The perceptron associates inputs to outputs by adjusting its synaptic weights. The learned synaptic weights are proposed to be the most resource-efficient by minimizing a biological resource cost given by the total absolute synaptic weight (l1-norm). Analytical methods from statistical physics and numerical simulations demonstrate that a resource-efficient perceptron has sparse connectivity. Sparseness decreases and resource usage increases with the number of associations to be learned. Our results have implications for synaptic connectivity in the cerebellum, where supervised learning is believed to happen.

The mitotic spindle ensures the faithful segregation of chromosomes. Here we combine the first large-scale serial electron tomography of whole mitotic spindles in early C. elegans embryos with live-cell imaging to reconstruct all microtubules in 3D and identify their plus- and minus-ends. We classify them as kinetochore (KMTs), spindle (SMTs) or astral microtubules (AMTs) according to their positions, and quantify distinct properties of each class. While our light microscopy and mutant studies show that microtubules are nucleated from the centrosomes, we find only a few KMTs directly connected to the centrosomes. Indeed, by quantitatively analysing several models of microtubule growth, we conclude that minus-ends of KMTs have selectively detached and depolymerized from the centrosome. In toto, our results show that the connection between centrosomes and chromosomes is mediated by an anchoring into the entire spindle network and that any direct connections through KMTs are few and likely very transient.

We describe a fast, stable algorithm for the solution of the inverse acoustic scattering problem in two dimensions. Given full aperture far field measurements of the scattered field for multiple angles of incidence, we use Chen's method of recursive linearization to reconstruct an unknown sound speed at resolutions of thousands of square wavelengths in a fully nonlinear regime. Despite the fact that the underlying optimization problem is formally ill-posed and non-convex, recursive linearization requires only the solution of a sequence of linear least squares problems at successively higher frequencies. By seeking a suitably band-limited approximation of the sound speed profile, each least squares calculation is well-conditioned and involves the solution of a large number of forward scattering problems, for which we employ a recently developed, spectrally accurate, fast direct solver. For the largest problems considered, involving 19,600 unknowns, approximately one million partial differential equations were solved, requiring approximately two days to compute using a parallel MATLAB implementation on a multi-core workstation.

We study the controlled manipulation of the Jahn-Teller metal state of fulleride compounds using nonequilibrium dynamical mean-field theory. This anomalous metallic state is a spontaneous orbital-selective Mott phase, which is characterized by one metallic and two insulating orbitals. Using protocols based on transiently reduced hopping amplitudes or periodic electric fields, we demonstrate the possibility to switch orbitals between Mott insulating and metallic on a subpicosecond time scale, and to rotate the order parameter between three equivalent states that can be distinguished by their anisotropic conductance. The Jahn-Teller metal phase of alkali-doped fullerides thus provides a platform for ultrafast persistent memory devices.

We present a proper motion measurement for the halo globular cluster Pyxis, using HST/ACS data as the first epoch, and GeMS/GSAOI Adaptive Optics data as the second, separated by a baseline of about 5 years. This is both the first measurement of the proper motion of Pyxis and the first calibration and use of Multi-Conjugate Adaptive Optics data to measure an absolute proper motion for a faint, distant halo object. Consequently, we present our analysis of the Adaptive Optics data in detail. We obtain a proper motion of mu_alpha cos(delta)=1.09+/-0.31 mas/yr and mu_delta=0.68+/-0.29 mas/yr. From the proper motion and the line-of-sight velocity we find the orbit of Pyxis is rather eccentric with its apocenter at more than 100 kpc and its pericenter at about 30 kpc. We also investigate two literature-proposed associations for Pyxis with the recently discovered ATLAS stream and the Magellanic system. Combining our measurements with dynamical modeling and cosmological numerical simulations we find it unlikely Pyxis is associated with either system. We examine other Milky Way satellites for possible association using the orbit, eccentricity, metallicity, and age as constraints and find no likely matches in satellites down to the mass of Leo II. We propose that Pyxis probably originated in an unknown galaxy, which today is fully disrupted. Assuming that Pyxis is bound and not on a first approach, we derive a 68% lower limit on the mass of the Milky Way of 0.95*10^12 M_sun.

The design of systems or models that work robustly under uncertainty and environmental fluctuations is a key challenge in both engineering and science. This is formalized in the design-centering problem, which is defined as finding a design that fulfills given specifications and has a high probability of still doing so if the system parameters or the specifications fluctuate randomly. Design centering is often accompanied by the problem of quantifying the robustness of a system. Here we present a novel adaptive statistical method to simultaneously address both problems. Our method, L p-Adaptation, is inspired by the evolution of robustness in biological systems and by randomized schemes for convex volume computation. It is able to address both problems in the general, non-convex case and at low computational cost. We describe the concept and the algorithm, test it on known benchmarks, and demonstrate its real-world applicability in electronic and biological systems. In all cases, the present method outperforms the previous state of the art. This enables re-formulating optimization problems in engineering and biology as design centering problems, taking global system robustness into account.

The swimming direction of biological or artificial microscale swimmers tends to be randomised over long time-scales by thermal fluctuations. Bacteria use various strategies to bias swimming behaviour and achieve directed motion against a flow, maintain alignment with gravity or travel up a chemical gradient. Herein, we explore a purely geometric means of biasing the motion of artificial nanorod swimmers. These artificial swimmers are bimetallic rods, powered by a chemical fuel, which swim on a substrate printed with teardrop-shaped posts. The artificial swimmers are hydrodynamically attracted to the posts, swimming alongside the post perimeter for long times before leaving. The rods experience a higher rate of departure from the higher curvature end of the teardrop shape, thereby introducing a bias into their motion. This bias increases with swimming speed and can be translated into a macroscopic directional motion over long times by using arrays of teardrop-shaped posts aligned along a single direction. This method provides a protocol for concentrating swimmers, sorting swimmers according to different speeds, and could enable artificial swimmers to transport cargo to desired locations.

Analysing computations in neural circuits often uses simplified models because the actual neuronal implementation is not known. For example, a problem in vision, how the eye detects image motion, has long been analysed using Hassenstein-Reichardt (HR) detector or Barlow-Levick (BL) models. These both simulate motion detection well, but the exact neuronal circuits undertaking these tasks remain elusive. We reconstructed a comprehensive connectome of the circuits of Drosophila‘s motion-sensing T4 cells using a novel EM technique. We uncover complex T4 inputs and reveal that putative excitatory inputs cluster at T4’s dendrite shafts, while inhibitory inputs localize to the bases. Consistent with our previous study, we reveal that Mi1 and Tm3 cells provide most synaptic contacts onto T4. We are, however, unable to reproduce the spatial offset between these cells reported previously. Our comprehensive connectome reveals complex circuits that include candidate anatomical substrates for both HR and BL types of motion detectors.

Analysing computations in neural circuits often uses simplified models because the actual neuronal implementation is not known. For example, a problem in vision, how the eye detects image motion, has long been analysed using Hassenstein-Reichardt (HR) detector or Barlow-Levick (BL) models. These both simulate motion detection well, but the exact neuronal circuits undertaking these tasks remain elusive. We reconstructed a comprehensive connectome of the circuits of Drosophila‘s motion-sensing T4 cells using a novel EM technique. We uncover complex T4 inputs and reveal that putative excitatory inputs cluster at T4’s dendrite shafts, while inhibitory inputs localize to the bases. Consistent with our previous study, we reveal that Mi1 and Tm3 cells provide most synaptic contacts onto T4. We are, however, unable to reproduce the spatial offset between these cells reported previously. Our comprehensive connectome reveals complex circuits that include candidate anatomical substrates for both HR and BL types of motion detectors.