We report the discovery, tracking and detection circumstances for 85 trans-Neptunian objects (tnos) from the first 42 deg 2 of the Outer Solar System Origins Survey (ossos). This ongoing r-band Solar System survey uses the 0.9 deg 2 field-ofview MegaPrime camera on the 3.6 m Canada-France-Hawaii Telescope. Our orbital elements for these tnos are precise to a fractional semi-major axis uncertainty < 0.1%. We achieve this precision in just two oppositions, as compared to the normal 3-5 oppositions, via a dense observing cadence and innovative astrometric technique. These discoveries are free of ephemeris bias, a first for large trans-Neptunian surveys. We also provide the necessary information to enable models of tno orbital distributions to be tested against our tno sample. We confirm the existence of a cold "kernel" of objects within the main cold classical Kuiper belt, and infer the existence of an extension of the "stirred" cold classical Kuiper belt to at least several au beyond the 2:1 mean motion resonance with Neptune. We find that the population model of Petit et al. (2011) remains a plausible representation of the Kuiper belt. The full survey, to be completed in 2017, will provide an exquisitely characterized sample of important resonant tno populations, ideal for testing models of giant planet migration during the early history of the Solar System.

Spectroscopic surveys require fast and efficient analysis methods to maximize their scientific impact. Here we apply a deep neural network architecture to analyze both SDSS-III APOGEE DR13 and synthetic stellar spectra. When our convolutional neural network model (StarNet) is trained on APOGEE spectra, we show that the stellar parameters (temperature, gravity, and metallicity) are determined with similar precision and accuracy as the APOGEE pipeline. StarNet can also predict stellar parameters when trained on synthetic data, with excellent precision and accuracy for both APOGEE data and synthetic data, over a wide range of signal-to-noise ratios. In addition, the statistical uncertainties in the stellar parameter determinations are comparable to the differences between the APOGEE pipeline results and those determined independently from optical spectra. We compare StarNet to other data-driven methods; for example, StarNet and the Cannon 2 show similar behaviour when trained with the same datasets, however StarNet performs poorly on small training sets like those used by the original Cannon. The influence of the spectral features on the stellar parameters is examined via partial derivatives of the StarNet model results with respect to the input spectra. While StarNet was developed using the APOGEE observed spectra and corresponding ASSET synthetic data, we suggest that this technique is applicable to other wavelength ranges and other spectral surveys.

Since the advent of Gaia astrometry, it is possible to identify massive accreted systems within the Galaxy through their unique dynamical signatures. One such system, Gaia-Sausage-Enceladus (GSE), appears to be an early “building block” given its virial mass &gt;1010 M⊙ at infall (z ∼ 1 − 3). In order to separate the progenitor population from the background stars, we investigate its chemical properties with up to 30 element abundances from the GALAH+ Survey Data Release 3 (DR3). To inform our choice of elements for purely chemically selecting accreted stars, we analyse 4164 stars with low-α abundances and halo kinematics. These are most different to the Milky Way stars for abundances of Mg, Si, Na, Al, Mn, Fe, Ni, and Cu. Based on the significance of abundance differences and detection rates, we apply Gaussian mixture models to various element abundance combinations. We find the most populated and least contaminated component, which we confirm to represent GSE, contains 1049 stars selected via [Na/Fe] vs. [Mg/Mn] in GALAH+ DR3. We provide tables of our selections and report the chrono-chemodynamical properties (age, chemistry, and dynamics). Through a previously reported clean dynamical selection of GSE stars, including $30 &lt; \sqrt{J_R / \, \mathrm{kpc\, km\, s^{-1}}} &lt; 55$, we can characterise an unprecedented 24 abundances of this structure with GALAH+ DR3. With our chemical selection we characterise the dynamical properties of the GSE, for example mean $\sqrt{J_R / \, \mathrm{kpc\, km\, s^{-1}}} = 26^{+9}_ {-14}$. We find only $(29\pm 1)\%$ of the GSE stars within the clean dynamical selection region. Our methodology will improve future studies of accreted structures and their importance for the formation of the Milky Way.