Blind chemical tagging with DBSCAN: prospects for spectroscopic surveys

Price-Jones, Natalie; Bovy, Jo

doi:10.1093/mnras/stz1260

Cited by 27 publications

(16 citation statements)

References 70 publications

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“…Furthermore does the publication of the spectra allow scientists to apply machine learning or clustering algorithms onto the data (see e.g. Price- Jones & Bovy 2019).…”

Section: Scientific Avenues For the Use Of Galah Dr3mentioning

confidence: 99%

The GALAH+ survey: Third data release

Buder

Sharma

Kos

et al. 2021

Monthly Notices of the Royal Astronomical Society

408

295

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The ensemble of chemical element abundance measurements for stars, along with precision distances and orbit properties, provides high-dimensional data to study the evolution of the Milky Way. With this third data release of the Galactic Archaeology with HERMES (GALAH) survey, we publish 678 423 spectra for 588 571 mostly nearby stars (81.2% of stars are within < 2 kpc), observed with the HERMES spectrograph at the Anglo-Australian Telescope. This release (hereafter GALAH+ DR3) includes all observations from GALAH Phase 1 (bright, main, and faint survey, 70%), K2-HERMES (17%), TESS-HERMES (5%), and a subset of ancillary observations (8%) including the bulge and > 75 stellar clusters. We derive stellar parameters Teff, log g, [Fe/H], vmic, vbroad, and vradusing our modified version of the spectrum synthesis code Spectroscopy Made Easy (sme) and 1D marcs model atmospheres. We break spectroscopic degeneracies in our spectrum analysis with astrometry from Gaia DR2 and photometry from 2MASS. We report abundance ratios [X/Fe] for 30 different elements (11 of which are based on non-LTE computations) covering five nucleosynthetic pathways. We describe validations for accuracy and precision, flagging of peculiar stars/measurements and recommendations for using our results. Our catalogue comprises 65% dwarfs, 34% giants, and 1% other/unclassified stars. Based on unflagged chemical composition and age, we find 62% young low-α, 9% young high-α, 27% old high-α, and 2% stars with [Fe/H] ≤ −1. Based on kinematics, 4% are halo stars. Several Value-Added-Catalogues, including stellar ages and dynamics, updated after GaiaeDR3, accompany this release and allow chrono-chemodynamic analyses, as we showcase.

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“…Furthermore does the publication of the spectra allow scientists to apply machine learning or clustering algorithms onto the data (see e.g. Price- Jones & Bovy 2019).…”

Section: Scientific Avenues For the Use Of Galah Dr3mentioning

confidence: 99%

The GALAH+ survey: Third data release

Buder

Sharma

Kos

et al. 2021

Monthly Notices of the Royal Astronomical Society

408

295

View full text Add to dashboard Cite

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“…For example, supervised spectral classification A&A 629, A34 (2019) has been adopted in works such as those by Bailer-Jones et al (1998), Singh et al (1998), Bailer-Jones (2002), Rodríguez et al (2004), Giridhar et al (2006), Manteiga et al (2009), and Navarro et al (2012). Unsupervised spectral classification was also explored in works such as Sánchez Almeida et al (2009,2010,2016), Vanderplas & Connolly (2009), Daniel et al (2011), Morales-Luis et al (2011), Sánchez Almeida & Allende Prieto (2013), Fernández-Trincado et al (2017), Matijevič et al (2017), Price-Jones & Bovy (2017, 2019, Traven et al (2017), Valentini et al (2017), Garcia-Dias et al (2018), and Reis et al (2018).…”

Section: Introductionmentioning

confidence: 99%

“…Kos et al (2018) used GALactic Archaeology with HERMES (GALAH) data to spot new members of the Pleiades cluster. The application of machine-learning algorithms to stellar abundances was also employed by da Silva et al 2012, Ting et al (2012), Jofré et al (2017), Anders et al (2018), Boesso & Rocha-Pinto (2018), and Price-Jones & Bovy (2019).…”

Section: Introductionmentioning

confidence: 99%

Machine learning in APOGEE

Garcia‐Dias

Prieto

Alméida

et al. 2019

A&A

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Context. The vast volume of data generated by modern astronomical surveys offers test beds for the application of machine-learning. In these exploratory applications, it is important to evaluate potential existing tools and determine those that are optimal for extracting scientific knowledge from the available observations. Aims. We explore the possibility of using unsupervised clustering algorithms to separate stellar populations with distinct chemical patterns. Methods. Star clusters are likely the most chemically homogeneous populations in the Galaxy, and therefore any practical approach to identifying distinct stellar populations should at least be able to separate clusters from each other. We have applied eight clustering algorithms combined with four dimensionality reduction strategies to automatically distinguish stellar clusters using chemical abundances of 13 elements. Our test-bed sample includes 18 stellar clusters with a total of 453 stars. Results. We have applied statistical tests showing that some pairs of clusters (e.g., NGC 2458–NGC 2420) are indistinguishable from each other when chemical abundances from the Apache Point Galactic Evolution Experiment (APOGEE) are used. However, for most clusters we are able to automatically assign membership with metric scores similar to previous works. The confusion level of the automatically selected clusters is consistent with statistical tests that demonstrate the impossibility of perfectly distinguishing all the clusters from each other. These statistical tests and confusion levels establish a limit for the prospect of blindly identifying stars born in the same cluster based solely on chemical abundances. Conclusion. We find that some of the algorithms we explored are capable of blindly identify stellar populations with similar ages and chemical distributions in the APOGEE data. Even though we are not able to fully separate the clusters from each other, the main confusion arises from clusters with similar ages. Because some stellar clusters are chemically indistinguishable, our study supports the notion of extending weak chemical tagging that involves families of clusters instead of individual clusters.

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“…When working with a large dataset, it is usually useful to clump similar data together by dividing the data into smaller categories [20], [22]. In robotic control filed, the environment of robotic is complex, dynamic, and uncertain.…”

Section: A Density-based Spatial Clustering Of Applications With Noise (Dbscan)mentioning

confidence: 99%

Toward a Clustering-Based Approach for Self-Adjusting Impact Factors in Robotic Control Model

et al. 2019

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In mobile robotic control models, control parameters are always generated by sensors' information and a set of Impact Factors (IFs, such as the P-value in the PID model). The IFs take forms of fixed coefficients in control models and need to be pre-defined at design-time. However, when operating in an open environment, IFs of the control model are expected to be adjusted automatically at run-time in order to adapt to the environment changes and improve the operation of robotics. This paper presents a clustering-based approach to continuously updating the IFs in robot control model. The proposed approach utilizes the density-based clustering method to classify environmental changes based on the effects of these changes on robots. In each cluster, the regression method is designed to learn the relationship between IFs and environment changes, and therefore generate corresponding IF adjustment model. Such approach can decrease the mutual interference of environmental changes and enhance the rationality of robotic actions. The paper presents the self-adjusting framework and designs corresponding IFs update algorithms. This paper develops robotics path-following scenario and object-following scenario in open environment and conducts experiments to evaluate the effectiveness of the proposed approach. The results show that the proposed approach has faster response to environmental changes than DQN and MPC approaches, along with a lower deviation of robot's actions. INDEX TERMSMobile robotic, impact factors, open environment, clustering-based, self-adjusting.

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Blind chemical tagging with DBSCAN: prospects for spectroscopic surveys

Cited by 27 publications

References 70 publications

The GALAH+ survey: Third data release

The GALAH+ survey: Third data release

Machine learning in APOGEE

Toward a Clustering-Based Approach for Self-Adjusting Impact Factors in Robotic Control Model

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