Publications

LSST will supply up to 10^6 supernovae (SNe) to constrain dark energy through the distance-redshift (DL-z) test. Obtaining spectroscopic SN redshifts (spec-zs) is unfeasible; alternatives are suboptimal and may be biased. We propose a powerful multi-tracer generalization of the Alcock-Paczynski test that pairs redshift-free distance tracers and an overlapping galaxy redshift survey. Cross-correlating 5×104 redshift-free SNe with DESI or Euclid outperforms the classical DL-z test with spec-zs for all SN. Our method also applies to gravitational wave sirens or any redshift-free distance tracer.

The direct detection of gravitational waves has provided new opportunities for studying the universe, but also new challenges, such as the detection and characterization of stochastic gravitational-wave backgrounds at different gravitational-wave frequencies. In this paper we examine two different methods for their description, one based on the amplitude of a gravitational-wave signal and one on its Stokes parameters. We find that the Stokes parameters are able to describe anisotropic and correlated backgrounds, whereas the usual power spectra of the amplitudes cannot -- i.e. the Stokes spectra are sensitive to properties such as the spatial distribution of the gravitational-wave sources in a realistic backgrounds.

We present an update to the EVEREST K2 pipeline that addresses various limitations in the previous version and improves the photometric precision of the de-trended light curves. We develop a fast regularization scheme for third order pixel level decorrelation (PLD) and adapt the algorithm to include the PLD vectors of neighboring stars to enhance the predictive power of the model and minimize overfitting, particularly for faint stars. We also modify PLD to work for saturated stars and improve its performance on extremely variable stars. On average, EVEREST 2.0 light curves have 10-20% higher photometric precision than those in the previous version, yielding the highest precision light curves at all Kp magnitudes of any publicly available K2 catalog. For most K2 campaigns, we recover the original Kepler precision to at least Kp = 14, and to at least Kp = 15 for campaigns 1, 5, and 6. We also de-trend all short cadence targets observed by K2, obtaining even higher photometric precision for these stars. All light curves for campaigns 0-8 are available online in the EVEREST catalog, which will be continuously updated with future campaigns. EVEREST 2.0 is open source and is coded in a general framework that can be applied to other photometric surveys, including Kepler and the upcoming TESS mission.

Deep learning is a promising tool to determine the physical model that describes our universe. To handle the considerable computational cost of this problem, we present CosmoFlow: a highly scalable deep learning application built on top of the TensorFlow framework. CosmoFlow uses efficient implementations of 3D convolution and pooling primitives, together with improvements in threading for many element-wise operations, to improve training performance on Intel(C) Xeon Phi(TM) processors. We also utilize the Cray PE Machine Learning Plugin for efficient scaling to multiple nodes. We demonstrate fully synchronous data-parallel training on 8192 nodes of Cori with 77% parallel efficiency, achieving 3.5 Pflop/s sustained performance. To our knowledge, this is the first large-scale science application of the TensorFlow framework at supercomputer scale with fully-synchronous training. These enhancements enable us to process large 3D dark matter distribution and predict the cosmological parameters ΩM, σ8 and ns with unprecedented accuracy.

The biology and behavior of adults differ substantially from those of developing animals, and cell-specific information is critical for deciphering the biology of multicellular animals. Thus, adult tissue-specific transcriptomic data are critical for understanding molecular mechanisms that control their phenotypes. We used adult cell-specific isolation to identify the transcriptomes of C. elegans' four major tissues (or "tissue-ome"), identifying ubiquitously expressed and tissue-specific "enriched" genes. These data newly reveal the hypodermis' metabolic character, suggest potential worm-human tissue orthologies, and identify tissue-specific changes in the Insulin/IGF-1 signaling pathway. Tissue-specific alternative splicing analysis identified a large set of collagen isoforms. Finally, we developed a machine learning-based prediction tool for 76 sub-tissue cell types, which we used to predict cellular expression differences in IIS/FOXO signaling, stage-specific TGF-β activity, and basal vs. memory-induced CREB transcription. Together, these data provide a rich resource for understanding the biology governing multicellular adult animals.

We present a joint analysis of the rest-frame ultraviolet (UV) luminosity functions of continuum-selected star-forming galaxies and galaxies dominated by active galactic nuclei (AGNs) at z∼ 4. These 3,740 z∼ 4 galaxies are selected from broad-band imaging in nine photometric bands over 18 deg2 in the \textit{Spitzer}/HETDEX Exploratory Large Area Survey (SHELA) field. The large area and moderate depth of our survey provide a unique view of the intersection between the bright end of the galaxy UV luminosity function (MAB<−22) and the faint end of the AGN UV luminosity function. We do not separate AGN-dominated galaxies from star-formation-dominated galaxies, but rather fit both luminosity functions simultaneously. These functions are best fit with a double power-law (DPL) for both the galaxy and AGN components, where the galaxy bright-end slope has a power-law index of −3.80±0.10, and the corresponding AGN faint-end slope is αAGN=−1.49+0.30−0.21. We cannot rule out a Schechter-like exponential decline for the galaxy UV luminosity function, and in this scenario the AGN luminosity function has a steeper faint-end slope of −2.08+0.18−0.11. Comparison of our galaxy luminosity function results with a representative cosmological model of galaxy formation suggests that the molecular gas depletion time must be shorter, implying that star formation is more efficient in bright galaxies at z=4 than at the present day. If the galaxy luminosity function does indeed have a power-law shape at the bright end, the implied ionizing emissivity from AGNs is not inconsistent with previous observations. However, if the underlying galaxy distribution is Schechter, it implies a significantly higher ionizing emissivity from AGNs at this epoch.

Large stellar surveys are sensitive to interstellar dust through the effects of reddening. Using extinctions measured from photometry and spectroscopy, together with three-dimensional (3D) positions of individual stars, it is possible to construct a three-dimensional dust map. We present the first continuous map of the dust distribution in the Galactic disk out to 7 kpc within 100 pc of the Galactic midplane, using red clump and giant stars from SDSS APOGEE DR14. We use a non-parametric method based on Gaussian Processes to map the dust density, which is the local property of the ISM rather than an integrated quantity. This method models the dust correlation between points in 3D space and can capture arbitrary variations, unconstrained by a pre-specified functional form. This produces a continuous map without line-of-sight artefacts. Our resulting map traces some features of the local Galactic spiral arms, even though the model contains no prior suggestion of spiral arms, nor any underlying model for the Galactic structure. This is the first time that such evident arm structures have been captured by a dust density map in the Milky Way. Our resulting map also traces some of the known giant molecular clouds in the Galaxy and puts some constraints on their distances, some of which were hitherto relatively uncertain.

Measuring the full distribution of individual particles is of fundamental importance to characterize many-body quantum systems through correlation functions at any order. Here, we demonstrate the possibility to reconstruct the momentum-space distribution of three-dimensional interacting lattice gases atom by atom. This is achieved by detecting individual metastable $$^4He*$$ atoms in the far-field regime of expansion, when released from an optical lattice. We benchmark our technique with quantum Monte Carlo calculations, demonstrating the ability to resolve momentum distributions of superfluids occupying $$10^5$$ lattice sites. It permits a direct measure of the condensed fraction across phase transitions, as we illustrate on the superfluid-to-normal transition. Our single-atom-resolved approach opens a route to investigate interacting lattice gases through momentum correlations.

We present a theoretical investigation of the voltage-driven metal insulator transition based on solving coupled Boltzmann and Hartree-Fock equations to determine the insulating gap and the electron distribution in a model system -- a one dimensional charge density wave. Electric fields that are parametrically small relative to energy gaps can shift the electron distribution away from the momentum-space region where interband relaxation is efficient, leading to a highly non-equilibrium quasiparticle distribution even in the absence of Zener tunnelling. The gap equation is found to have regions of multistability; a non-equilibrium analogue of the free energy is constructed and used to determine which phase is preferred.