The Circular Velocity Curve of the Milky Way from 55 to 2525 kpc

We measure the circular velocity curve $v_{\rm c}(R)$ of the Milky Way with the highest precision to date across Galactocentric distances of $5\leq R \leq 25$ kpc. Our analysis draws on the $6$-dimensional phase-space coordinates of $\gtrsim 23,000$ luminous red-giant stars, for which we previously determined precise parallaxes using a data-driven model that combines spectral data from APOGEE with photometric information from WISE, 2MASS, and Gaia. We derive the circular velocity curve with the Jeans equation assuming an axisymmetric gravitational potential. At the location of the Sun we determine the circular velocity with its formal uncertainty to be $v_{\rm c}(R_{\odot}) = (229.0\pm0.2)\rm\,km\,s^{-1}$ with systematic uncertainties at the $\sim 2-5\%$ level. We find that the velocity curve is gently but significantly declining at $(-1.7\pm0.1)\rm\,km\,s^{-1}\,kpc^{-1}$, with a systematic uncertainty of $0.46\rm\,km\,s^{-1}\,kpc^{-1}$, beyond the inner $5$ kpc. We exclude the inner $5$ kpc from our analysis due to the presence of the Galactic bar, which strongly influences the kinematic structure and requires modeling in a non-axisymmetric potential. Combining our results with external measurements of the mass distribution for the baryonic components of the Milky Way from other studies, we estimate the Galaxy's dark halo mass within the virial radius to be $M_{\rm vir} = (7.25\pm0.26)\cdot 10^{11}M_{\odot}$ and a local dark matter density of $\rho_{\rm dm}(R_{\odot}) = 0.30\pm0.03\,\rm GeV\,cm^{-3}$.Comment: Accepted for publication in ApJ. All data can be downloaded here: https://doi.org/10.5281/zenodo.146805

Mapping Antarctic crevasses and their evolution with deep learning applied to satellite radar imagery

The fracturing of glaciers and ice shelves in Antarctica influences their dynamics and stability. Hence, data on the evolving distribution of crevasses are required to better understand the evolution of the ice sheet, though such data have traditionally been difficult and time-consuming to generate. Here, we present an automated method of mapping crevasses on grounded and floating ice with the application of convolutional neural networks to Sentinel-1 synthetic aperture radar backscatter data. We apply this method across Antarctica to images acquired between 2015 and 2022, producing a 7.5-year record of composite fracture maps at monthly intervals and 50 m spatial resolution and showing the distribution of crevasses around the majority of the ice sheet margin. We develop a method of quantifying changes to the density of ice shelf fractures using a time series of crevasse maps and show increases in crevassing on Thwaites and Pine Island ice shelves over the observational period, with observed changes elsewhere in the Amundsen Sea dominated by the advection of existing crevasses. Using stress fields computed using the BISICLES ice sheet model, we show that much of this structural change has occurred in buttressing regions of these ice shelves, indicating a recent and ongoing link between fracturing and the developing dynamics of the Amundsen Sea sector.</p

Locating Ice Sheet Grounding Lines Using Satellite Radar Interferometry and Altimetry

In this thesis, I use synthetic aperture radar (SAR) and radar altimeter data to make new observations of Antarctic and Greenland ice sheet grounding lines. I use ERS SAR data acquired between 1992 and 2011 to map the Petermann Glacier grounding line on 17 occasions using quadruple difference interferometric SAR (QDInSAR). Over the 19-year period, the grounding line position varied by 0.5 km, on average, with no significant trend over time. Although tidal forcing explains a fraction (34 %) of the movement, localised variations in the glacier thickness could explain it all were they to alter the glaciers hydrostatic balance as they advect downstream – a hitherto unconsidered possibility that would reduce the accuracy with which changes in grounding line position can be detected. Next, I developed a new technique for detecting grounding lines using differential range direction offset tracking (DRDOT) in incoherent SAR data. I then applied this technique to a sequence of 11 TerraSAR-X images acquired in 2009 over Petermann Glacier. The DRDOT technique is able to reproduce the shape and location of the grounding line with an estimated lateral precision of 0.8 km and, although this is 30 times poorer than QDInSAR, provides a complementary method given the paucity of coherent SAR data. Finally, I developed another new method for detecting the grounding line as the break in ice sheet surface slope computed from CryoSat-2 elevation measurements. I then applied this technique to map grounding lines in the sectors of Antarctica buttressed by the Filchner-Ronne, Ekström, Larsen-C, and Amundsen Sea ice shelves. The technique is able to map the grounding line to within 4.5 km, on average, and, although this is far poorer than either QDInSAR or DRDOT, it is computationally efficient and can succeed where SAR-based methods fail, offering an additional complementary approach

Spectrophotometric parallaxes with linear models: Accurate distances for luminous red-giant stars

With contemporary infrared spectroscopic surveys like APOGEE, red-giant stars can be observed to distances and extinctions at which Gaia parallaxes are not highly informative. Yet the combination of effective temperature, surface gravity, composition, and age - all accessible through spectroscopy - determines a giant's luminosity. Therefore spectroscopy plus photometry should enable precise spectrophotometric distance estimates. Here we use the APOGEE-Gaia-2MASS-WISE overlap to train a data-driven model to predict parallaxes for red-giant branch stars with $0<\log g\leq2.2$ (more luminous than the red clump). We employ (the exponentiation of) a linear function of APOGEE spectral pixel intensities and multi-band photometry to predict parallax spectrophotometrically. The model training involves no logarithms or inverses of the Gaia parallaxes, and needs no cut on the Gaia parallax signal-to-noise ratio. It includes an L1 regularization to zero out the contributions of uninformative pixels. The training is performed with leave-out subsamples such that no star's astrometry is used even indirectly in its spectrophotometric parallax estimate. The model implicitly performs a reddening and extinction correction in its parallax prediction, without any explicit dust model. We assign to each star in the sample a new spectrophotometric parallax estimate; these parallaxes have uncertainties of a few to 15 percent, depending on data quality, which is more precise than the Gaia parallax for the vast majority of targets, and certainly any stars more than a few kpc distance. We obtain 10-percent distance estimates out to heliocentric distances of $20\,$kpc, and make global maps of the Milky Way's disk.Comment: Submitted to ApJ, comments are welcome. All data can be downloaded here: https://doi.org/10.5281/zenodo.146805

Label Transfer from APOGEE to LAMOST: Precise Stellar Parameters for 450,000 LAMOST Giants

In this era of large-scale stellar spectroscopic surveys, measurements of stellar attributes ("labels," i.e. parameters and abundances) must be made precise and consistent across surveys. Here, we demonstrate that this can be achieved by a data-driven approach to spectral modeling. With The Cannon, we transfer information from the APOGEE survey to determine precise Teff, log g, [Fe/H], and [$\alpha$/M] from the spectra of 450,000 LAMOST giants. The Cannon fits a predictive model for LAMOST spectra using 9952 stars observed in common between the two surveys, taking five labels from APOGEE DR12 as ground truth: Teff, log g, [Fe/H], [\alpha/M], and K-band extinction $A_k$. The model is then used to infer Teff, log g, [Fe/H], and [$\alpha$/M] for 454,180 giants, 20% of the LAMOST DR2 stellar sample. These are the first [$\alpha$/M] values for the full set of LAMOST giants, and the largest catalog of [$\alpha$/M] for giant stars to date. Furthermore, these labels are by construction on the APOGEE label scale; for spectra with S/N > 50, cross-validation of the model yields typical uncertainties of 70K in Teff, 0.1 in log g, 0.1 in [Fe/H], and 0.04 in [$\alpha$/M], values comparable to the broadly stated, conservative APOGEE DR12 uncertainties. Thus, by using "label transfer" to tie low-resolution (LAMOST R $\sim$ 1800) spectra to the label scale of a much higher-resolution (APOGEE R $\sim$ 22,500) survey, we substantially reduce the inconsistencies between labels measured by the individual survey pipelines. This demonstrates that label transfer with The Cannon can successfully bring different surveys onto the same physical scale.Comment: 27 pages, 14 figures. Accepted by ApJ on 16 Dec 2016, implementing suggestions from the referee reports. Associated code available at https://github.com/annayqho/TheCanno

Grounding line migration from 1992 to 2011 on Petermann Glacier, North-West Greenland

We use satellite radar interferometry to investigate changes in the location of the Petermann Glacier grounding line between 1992 and 2011. The grounding line location was identified in 17 quadruple-difference interferograms produced from European Remote Sensing (ERS)-1/2 data – the most extensive time series assembled at any ice stream to date. There is close agreement (20.6 cm) between vertical displacement of the floating ice shelf and relative tide amplitudes simulated by the Arctic Ocean Dynamics-based Tide Model 5 (AODTM-5) Arctic tide model. Over the 19 a period, the groundling line position varied by 470 m, on average, with a maximum range of 7.0 km observed on the north-east margin of the ice stream. Although the mean range (2.8 km) and variability (320 m) of the grounding line position is considerably lower if the unusually variable north-east sector is not considered, our observations demonstrate that large, isolated movements cannot be precluded, thus sparse temporal records should be analysed with care. The grounding line migration observed on Petermann Glacier is not significantly correlated with time (R2 = 0.22) despite reported ice shelf thinning and episodes of large iceberg calving, which suggests that unlike other ice streams, on the south-west margin of the Greenland ice sheet, Petermann Glacier is dynamically stable

Public Health Research Implementation and Translation: Evidence from Practice-Based Research Networks

BACKGROUND: Research on how best to deliver efficacious public health strategies in heterogeneous community and organizational contexts remains limited. Such studies require the active engagement of public health practice settings in the design, implementation, and translation of research. Practice-based research networks (PBRNs) provide mechanisms for research engagement, but until now they have not been tested in public health settings. PURPOSE: This study uses data from participants in 14 public health PBRNs and a national comparison group of public health agencies to study processes influencing the engagement of public health settings in research implementation and translation activities. METHODS: A cross-sectional network analysis survey was fielded with participants in public health PBRNs approximately 1 year after network formation (n=357) and with a nationally representative comparison group of U.S. local health departments not participating in PBRNs (n=625). Hierarchic regression models were used to estimate how organizational attributes and PBRN network structures influence engagement in research implementation and translation activities. Data were collected in 2010-2012 and analyzed in 2012. RESULTS: Among PBRN participants, both researchers and practice agencies reported high levels of engagement in research activities. Local public health agencies participating in PBRNs were two to three times more likely than nonparticipating agencies to engage in research implementation and translation activities (p \u3c 0.05). Participants in less densely connected PBRN networks and in more peripheral locations within these networks reported higher levels of research engagement, greater perceived benefits from engagement, and greater likelihood of continued participation. CONCLUSIONS: PBRN networks can serve as effective mechanisms for facilitating research implementation and translation among public health practice settings