The peculiar globular cluster Palomar 1 and persistence in the SDSS-APOGEE data base

Jahandar, Farbod; Venn, Kim A.; Shetrone, Matthew; Irwin, M. J.; Bovy, Jo; Sakari, Charli M.; Kielty, Collin; Digby, Ruth A. R.; Frinchaboy, Peter M.

doi:10.1093/mnras/stx1592

Cited by 8 publications

(3 citation statements)

References 44 publications

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“…They allow the results obtained from a detailed analysis of a small calibration set to be transfered to an entire large data set of millions of spectra in a matter of minutes and can thus be a great aid in the development of the next generation of spectroscopy tools. They could also be of use in earlier stages of the data processing, for example, for homogenizing spectra taken with different instruments (e.g., the northern and southern APOGEE spectrographs) or for removing instrumental systematics such as persistence (e.g., Jahandar et al 2017). Such applications would be easy to…”

Section: Discussionmentioning

confidence: 99%

Deep learning of multi-element abundances from high-resolution spectroscopic data

Leung

Bovy

2018

Monthly Notices of the Royal Astronomical Society

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147

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Deep learning with artificial neural networks is increasingly gaining attention, because of its potential for data-driven astronomy. However, this methodology usually does not provide uncertainties and does not deal with incompleteness and noise in the training data. In this work, we design a neural network for high-resolution spectroscopic analysis using APOGEE data that mimics the methodology of standard spectroscopic analyses: stellar parameters are determined using the full wavelength range, but individual element abundances use censored portions of the spectrum. We train this network with a customized objective function that deals with incomplete and noisy training data and apply dropout variational inference to derive uncertainties on our predictions. We determine parameters and abundances for 18 individual elements at the ≈ 0.03 dex level, even at low signal-to-noise ratio. We demonstrate that the uncertainties returned by our method are a realistic estimate of the precision and they automatically blow up when inputs or outputs outside of the training set are encountered, thus shielding users from unwanted extrapolation. By using standard deep-learning tools for GPU acceleration, our method is extremely fast, allowing analysis of the entire APOGEE data set of ≈ 250, 000 spectra in ten minutes on a single, low-cost GPU. We release the stellar parameters and 18 individual-element abundances with associated uncertainty for the entire APOGEE DR14 dataset. Simultaneously, we release astroNN, a well-tested, open-source python package developed for this work, but that is also designed to be a general package for deep learning in astronomy. astroNN is available at https://github.com/henrysky/astroNN with extensive documentation at http://astroNN.readthedocs.io.

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Section: Discussionmentioning

confidence: 99%

Deep learning of multi-element abundances from high-resolution spectroscopic data

Leung

Bovy

2018

Monthly Notices of the Royal Astronomical Society

Self Cite

147

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“…The APOGEE team have also flagged specific stars and visits for a variety of other reasons. Stars from the DR13 dataset with either a STARFLAG or ASPCAPFLAG were removed from the dataset, which includes stars contaminated with persistence (see Jahandar et al 2017;Nidever et al 2015), stars that were flagged for having a bright neighbour, or having estimated parameters near the parameter grid edge of the synthetic models. Furthermore, stars with [Fe/H] < −3 were removed, or those with a radial velocity scatter, v scatter , greater than 1 km s −1 .…”

Section: Pre-processing Of the Input Datamentioning

confidence: 99%

An application of deep learning in the analysis of stellar spectra

Fabbro

Venn

O'Briain

et al. 2017

Monthly Notices of the Royal Astronomical Society

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103

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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.

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“…For instance, efforts have been made towards incorporating non-LTE and 3D hydrodynamic effects in the model atmospheres (e.g., Amarsi 2015;Amarsi et al 2016;Kovalev et al 2019). Other methods have been proposed to isolate spectral regions -only using spectral regions where the astrophysics and instrumental effects are better understood (e.g., Jahandar et al 2017;Ting et al 2019). Furthermore, others have attempted to reduce this gap by augmenting the synthetic data through sampling and adding noise to make the spectra look more realistic (e.g., Bialek et al 2020).…”

Section: Introductionmentioning

confidence: 99%

Cycle-StarNet: Bridging the gap between theory and data by leveraging large datasets

O'Briain,

Ting,

Fabbro

et al. 2020

Preprint

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Spectroscopy provides an immense amount of information on stellar objects, and this field continues to grow with recent developments in multi-object data acquisition and rapid data analysis techniques. Current automated methods for analyzing spectra are either (a) data-driven models, which require large amounts of data with prior knowledge of stellar parameters and elemental abundances, or (b) based on theoretical synthetic models that are susceptible to the gap between theory and practice. In this study, we present a hybrid generative domain adaptation method to turn simulated stellar spectra into realistic spectra, learning from the large spectroscopic surveys. We use a neural network to emulate computationally expensive stellar spectra simulations, and then train a separate unsupervised domain-adaptation network that learns to relate the generated synthetic spectra to observational spectra. Consequently, the network essentially produces data-driven models without the need for a labelled training set. As a proof of concept, two case studies are presented. The first of which is the auto-calibration of synthetic models without using any standard stars. To accomplish this, synthetic models are morphed into spectra that resemble observations, thereby reducing the gap between theory and observations. The second case study is the identification of the elemental source of missing spectral lines in the synthetic modelling. These sources are predicted by interpreting the differences between the domain-adapted and original spectral models. To test our ability to identify missing lines, we use a mock dataset and show that, even with noisy observations, absorption lines can be recovered when they are absent in one of the domains. While we focus on spectral analyses in this study, this method can be applied to other fields, which use large data sets and are currently limited by modelling accuracy. Lastly, the code used in this study is made publicly available on github a) .

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The peculiar globular cluster Palomar 1 and persistence in the SDSS-APOGEE data base

Cited by 8 publications

References 44 publications

Deep learning of multi-element abundances from high-resolution spectroscopic data

Deep learning of multi-element abundances from high-resolution spectroscopic data

An application of deep learning in the analysis of stellar spectra

Cycle-StarNet: Bridging the gap between theory and data by leveraging large datasets

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