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Machine learning has rapidly become a tool of choice for the astronomical community. It is being applied across a wide range of wavelengths and problems, from the classification of transients to neural network emulators of cosmological simulations, and is shifting paradigms about how we generate and report scientific results. At the same time, this class of method comes with its own set of best practices, challenges, and drawbacks, which, at present, are often reported on incompletely in the astrophysical literature. With this paper, we aim to provide a primer to the astronomical community, including authors, reviewers, and editors, on how to implement machine learning models and report their results in a way that ensures the accuracy of the results, reproducibility of the findings, and usefulness of the method., Comment: 14 pages, 3 figures; submitted to the Bulletin of the American Astronomical Society

In order to accurately determine stellar properties, knowledge of the effective temperature of stars is vital. We implement Gaia and 2MASS photometry in the InfraRed Flux Method and apply it to over 360,000 stars across different evolutionary stages in the GALAH DR3 survey. We derive colour-effective temperature relations that take into account the effect of metallicity and surface gravity over the range 4000 to 8000 kelvin, from very metal-poor stars to super solar metallicities. The internal uncertainty of these calibrations is of order 40-80 kelvin depending on the colour combination used. Comparison against solar-twins, Gaia benchmark stars and the latest interferometric measurements validates the precision and accuracy of these calibrations from F to early M spectral types. We assess the impact of various sources of uncertainties, including the assumed extinction law, and provide guidelines to use our relations. Robust solar colours are also derived., Comment: MNRAS accepted. Include Gaia solar colours. Colour-effective temperature routines for Gaia DR2 and DR3 system available at https://github.com/casaluca/colte

High-multiplex and deep spectroscopic follow-up of upcoming panoramic deep-imaging surveys like LSST, Euclid, and WFIRST is a widely recognized and increasingly urgent necessity. No current or planned facility at a U.S. observatory meets the sensitivity, multiplex, and rapid-response time needed to exploit these future datasets. FOBOS, the Fiber-Optic Broadband Optical Spectrograph, is a near-term fiber-based facility that addresses these spectroscopic needs by optimizing depth over area and exploiting the aperture advantage of the existing 10m Keck II Telescope. The result is an instrument with a uniquely blue-sensitive wavelength range (0.31-1.0 um) at R~3500, high-multiplex (1800 fibers), and a factor 1.7 greater survey speed and order-of-magnitude greater sampling density than Subaru's Prime Focus Spectrograph (PFS). In the era of panoramic deep imaging, FOBOS will excel at building the deep, spectroscopic reference data sets needed to interpret vast imaging data. At the same time, its flexible focal plane, including a mode with 25 deployable integral-field units (IFUs) across a 20 arcmin diameter field, enables an expansive range of scientific investigations. Its key programmatic areas include (1) nested stellar-parameter training sets that enable studies of the Milky Way and M31 halo sub-structure, as well as local group dwarf galaxies, (2) a comprehensive picture of galaxy formation thanks to detailed mapping of the baryonic environment at z~2 and statistical linking of evolving populations to the present day, and (3) dramatic enhancements in cosmological constraints via precise photometric redshifts and determined redshift distributions. In combination with Keck I instrumentation, FOBOS also provides instant access to medium-resolution spectroscopy for transient sources with full coverage from the UV to the K-band., Comment: Submitted to the Astro2020 Decadal Survey as a facilities white paper

Galah is an ongoing high-resolution spectroscopic survey with the goal of disentangling the formation history of the Milky Way, using the fossil remnants of disrupted star formation sites which are now dispersed around the Galaxy. It is targeting a randomly selected, magnitude limited ($V \leq 14$) sample of stars, with the goal of observing one million objects. To date, 300,000 spectra have been obtained. Not all of them are correctly processed by parameter estimation pipelines and we need to know about them. We present a semi-automated classification scheme which identifies different types of peculiar spectral morphologies, in an effort to discover and flag potentially problematic spectra and thus help to preserve the integrity of the survey's results. To this end we employ a recently developed dimensionality reduction technique t-SNE (t-distributed Stochastic Neighbour Embedding), which enables us to represent the complex spectral morphology in a two-dimensional projection map while still preserving the properties of the local neighbourhoods of spectra. We find that the majority (178,483) of the 209,533 Galah spectra considered in this study represents normal single stars, whereas 31,050 peculiar and problematic spectra with very diverse spectral features pertaining to 28,579 stars are distributed into 10 classification categories: Hot stars, Cool metal-poor giants, Molecular absorption bands, Binary stars, H$\alpha$/H$\beta$ emission, H$\alpha$/H$\beta$ emission superimposed on absorption, H$\alpha$/H$\beta$ P-Cygni, H$\alpha$/H$\beta$ inverted P-Cygni, Lithium absorption, and Problematic. Classified spectra with supplementary information are presented in the catalogue, indicating candidates for follow-up observations and population studies of the short-lived phases of stellar evolution.

The GALAH survey is a large high-resolution spectroscopic survey using the newly commissioned HERMES spectrograph on the Anglo-Australian Telescope. The HERMES spectrograph provides high-resolution (R ~28,000) spectra in four passbands for 392 stars simultaneously over a 2 degree field of view. The goal of the survey is to unravel the formation and evolutionary history of the Milky Way, using fossil remnants of ancient star formation events which have been disrupted and are now dispersed throughout the Galaxy. Chemical tagging seeks to identify such dispersed remnants solely from their common and unique chemical signatures; these groups are unidentifiable from their spatial, photometric or kinematic properties. To carry out chemical tagging, the GALAH survey will acquire spectra for a million stars down to V~14. The HERMES spectra of FGK stars contain absorption lines from 29 elements including light proton-capture elements, alpha-elements, odd-Z elements, iron-peak elements and n-capture elements from the light and heavy s-process and the r-process. This paper describes the motivation and planned execution of the GALAH survey, and presents some results on the first-light performance of HERMES., Comment: 16 pages, 7 figures