This is the first of a series of papers presenting the Modules for Experiments in Stellar Astrophysics (MESA) Isochrones and Stellar Tracks (MIST) project, a new comprehensive set of stellar evolutionary tracks and isochrones computed using MESA, a state-of-the-art open-source 1D stellar evolution package. In this work, we present models with solar-scaled abundance ratios covering a wide range of ages ($5 \leq \rm \log(Age)\;[yr] \leq 10.3$), masses ($0.1 \leq M/M_{\odot} \leq 300$), and metallicities ($-2.0 \leq \rm [Z/H] \leq 0.5$). The models are self-consistently and continuously evolved from the pre-main sequence to the end of hydrogen burning, the white dwarf cooling sequence, or the end of carbon burning, depending on the initial mass. We also provide a grid of models evolved from the pre-main sequence to the end of core helium burning for $-4.0 \leq \rm [Z/H] < -2.0$. We showcase extensive comparisons with observational constraints as well as with some of the most widely used existing models in the literature. The evolutionary tracks and isochrones can be downloaded from the project website at this http URL

https://iopscience.iop.org/article/10.3847/0004-637X/823/2/102/pdf

Mesa isochrones and stellar tracks (mist). i. solar-scaled models

Astronomical data analysis software and systems

We describe the Sloan Digital Sky Survey IV (SDSS-IV), a project encompassing three major spectroscopic programs. The Apache Point Observatory Galactic Evolution Experiment 2 (APOGEE-2) is observing hundreds of thousands of Milky Way stars at high resolution and high signal-to-noise ratios in the near-infrared. The Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey is obtaining spatially resolved spectroscopy for thousands of nearby galaxies (median $z\sim 0.03$). The extended Baryon Oscillation Spectroscopic Survey (eBOSS) is mapping the galaxy, quasar, and neutral gas distributions between $z\sim 0.6$ and 3.5 to constrain cosmology using baryon acoustic oscillations, redshift space distortions, and the shape of the power spectrum. Within eBOSS, we are conducting two major subprograms: the SPectroscopic IDentification of eROSITA Sources (SPIDERS), investigating X-ray AGNs and galaxies in X-ray clusters, and the Time Domain Spectroscopic Survey (TDSS), obtaining spectra of variable sources. All programs use the 2.5 m Sloan Foundation Telescope at the Apache Point Observatory; observations there began in Summer 2014. APOGEE-2 also operates a second near-infrared spectrograph at the 2.5 m du Pont Telescope at Las Campanas Observatory, with observations beginning in early 2017. Observations at both facilities are scheduled to continue through 2020. In keeping with previous SDSS policy, SDSS-IV provides regularly scheduled public data releases; the first one, Data Release 13, was made available in 2016 July.

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Sloan Digital Sky Survey IV: Mapping the Milky Way, Nearby Galaxies and the Distant Universe

MESA Isochrones and Stellar Tracks (MIST). I: Solar-Scaled Models

Our Galaxy, the Milky Way, is a benchmark for understanding disk galaxies. It is the only galaxy whose formation history can be studied using the full distribution of stars from faint dwarfs to supergiants. The oldest components provide us with unique insight into how galaxies form and evolve over billions of years. The Galaxy is a luminous (L⋆) barred spiral with a central box/peanut bulge, a dominant disk, and a diffuse stellar halo. Based on global properties, it falls in the sparsely populated “green valley” region of the galaxy color-magnitude diagram. Here we review the key integrated, structural and kinematic parameters of the Galaxy, and point to uncertainties as well as directions for future progress. Galactic studies will continue to play a fundamental role far into the future because there are measurements that can only be made in the near field and much of contemporary astrophysics depends on such observations.

The Galaxy in Context: Structural, Kinematic, and Integrated Properties

Gaia DR3 has provided the community with about one million RVS spectra covering the CaII triplet region. In the next Gaia data releases, we anticipate the number of RVS spectra to successively increase from several 10 million spectra to eventually more than 200M spectra. Thus, stellar spectra are produced on an"industrial scale"with numbers well above those for current and anticipated ground based surveys. However, many of these spectra have low S/N (from 15 to 25 per pixel), such that they pose problems for classical spectral analysis pipelines and therefore alternative ways to tap into these large datasets need to be devised. We aim to leverage the versatility/capabilities of machine learning techniques for supercharged stellar parametrization, by combining Gaia RVS spectra with the full set of Gaia products and high-resolution, high-quality spectroscopic reference data sets. We develop a hybrid Convolutional Neural-Network (CNN) which combines the Gaia DR3 RVS spectra, photometry (G, Bp, Rp), parallaxes, and XP coefficients to derive atmospheric parameters (Teff, log(g), and overall [M/H]) and chemical abundances ([Fe/H] and [$\alpha$/M]). We trained the CNN with a high-quality training sample based on APOGEE DR17 labels. With this CNN, we derived homogeneous atmospheric parameters and abundances for 841300 stars, that remarkably compared to external data-sets. The CNN is robust against noise in the RVS data, and very precise labels are derived down to S/N=15. We managed to characterize the [$\alpha$/M]-[M/H] bimodality from the inner regions to the outer parts of the Milky Way, which has never been done using RVS spectra or similar datasets. This work is the first to combine machine-learning with such diverse datasets (spectroscopy, astrometry, and photometry), and paves the way for the large scale machine-learning analysis of Gaia-RVS spectra from future data releases.

Beyond Gaia DR3: tracing the [{\alpha}/M]-[M/H] bimodality from the inner to the outer Milky Way disc with Gaia RVS and Convolutional Neural-Networks

We briefly review the various machine learning methods and discuss how they can be used in efficient identification and analysis of spectroscopic binary stars. They can be treated as complementary to conventional methods, and we argue that some amount of human oversight is always needed and in fact highly beneficial when employing machine learning. We propose that a general dimensionality reduction technique can serve to diagnose and classify a given data set, and in case of GALAH spectra, our method quite effectively reveals a population of SB2 and SB3 systems. Once identified, the binary spectra can be analysed with the help of generative models, which can be constructed using machine learning techniques such as The Cannon and The Payne. Furthermore, in the case of spectroscopically unresolved multiple stars, we can recover the multiple contributions to an observed spectrum by reversing the process and proceeding from analysis to identification. (Less)

Machine learning techniques meet binaries

We use more than 872,000 mid-to-high resolution (R $\sim$ 20,000) spectra of stars from the GALAH survey to discern the spectra of diffuse interstellar bands (DIBs). We use four windows with the wavelength range from 4718 to 4903, 5649 to 5873, 6481 to 6739, and 7590 to 7890 \AA, giving a total coverage of 967 \AA. We produce $\sim$400,000 spectra of interstellar medium (ISM) absorption features and correct them for radial velocities of the DIB clouds. Ultimately, we combine the 33,115 best ISM spectra into six reddening bins with a range of $0.1 \,\mathrm{mag}<E\mathrm{(B-V)}<0.7\, \mathrm{mag}$. A total of 183 absorption features in these spectra qualify as DIBs, their fitted model parameters are summarized in a detailed catalogue. From these, 64 are not reported in the literature, among these 17 are certain, 14 are probable and 33 are possible. We find that the broad DIBs can be fitted with a multitude of narrower DIBs. Finally, we create a synthetic DIB spectrum at unit reddening which should allow us to narrow down the possible carriers of DIBs and explore the composition of the ISM and ultimately better model dust and star formation as well as to correct Galactic and extragalactic observations. The majority of certain DIBs show a significant excess of equivalent width when compared to reddening. We explain this with observed lines of sight penetrating more uniform DIB clouds compared to clumpy dust clouds.

Gregor Traven

Papers

Beyond Gaia DR3: tracing the [{\alpha}/M]-[M/H] bimodality from the inner to the outer Milky Way disc with Gaia RVS and Convolutional Neural-Networks

Machine learning techniques meet binaries

The GALAH survey: New diffuse interstellar bands found in residuals of 872,000 stellar spectra