Generation Of Spectroscopic Surveys Research Articles

The Gaia-ESO Survey: Chemical evolution of Mg and Al in the Milky Way with machine learning

Context. To take full advantage of upcoming large-scale spectroscopic surveys, it will be necessary to parameterize millions of stellar spectra in an efficient way. Machine learning methods, especially convolutional neural networks (CNNs), will be among the main tools geared at achieving this task. Aims. We aim to prepare the groundwork for machine learning techniques for the next generation of spectroscopic surveys, such as 4MOST and WEAVE. Our goal is to show that CNNs can predict accurate stellar labels from relevant spectral features in a physically meaningful way. The predicted labels can be used to investigate properties of the Milky Way galaxy. Methods. We built a neural network and trained it on GIRAFFE spectra with their associated stellar labels from the sixth internal Gaia-ESO data release. Our network architecture contains several convolutional layers that allow the network to identify absorption features in the input spectra. The internal uncertainty was estimated from multiple network models. We used the t-distributed stochastic neighbor embedding tool to remove bad spectra from our training sample. Results. Our neural network is able to predict the atmospheric parameters Teff and log(g) as well as the chemical abundances [Mg/Fe], [Al/Fe], and [Fe/H] for 36 904 stellar spectra. The training precision is 37 K for Teff, 0.06 dex for log(g), 0.05 dex for [Mg/Fe], 0.08 dex for [Al/Fe], and 0.04 dex for [Fe/H]. Network gradients reveal that the network is inferring the labels in a physically meaningful way from spectral features. We validated our methodology using benchmark stars and recovered the properties of different stellar populations in the Milky Way galaxy. Conclusions. Such a study provides very good insights into the application of machine learning for the analysis of large-scale spectroscopic surveys, such as WEAVE and 4MOST Milky Way disk and bulge low- and high-resolution (4MIDABLE-LR and -HR). The community will have to put substantial efforts into building proactive training sets for machine learning methods to minimize any possible systematics.

Data-driven selection and spectral classification of white dwarf stars

ABSTRACT The next generation of spectroscopic surveys is expected to provide spectra for hundreds of thousands of white dwarf (WD) candidates in the upcoming years. Currently, spectroscopic classification of white dwarfs is mostly done by visual inspection, requiring substantial amounts of expert attention. We propose a data-driven pipeline for fast, automatic selection, and spectroscopic classification of WD candidates, trained using spectroscopically confirmed objects with available Gaia astrometry, photometry, and Sloan Digital Sky Survey (SDSS) spectra with signal-to-noise ratios ≥9. The pipeline selects WD candidates with improved accuracy and completeness over existing algorithms, classifies their primary spectroscopic type with ${\gtrsim}90\ \hbox{per cent}$ accuracy, and spectroscopically detects main sequence companions with similar performance. We apply our pipeline to the Gaia Data Release 3 cross-matched with the SDSS Data Release 17 (DR17), identifying 424 096 high-confidence WD candidates and providing the first catalogue of automated and quantifiable classification for 36 523 WD spectra. Both the catalogue and pipeline are made available online. Such a tool will prove particularly useful for the undergoing SDSS-V survey, allowing for rapid classification of thousands of spectra at every data release.

The assembly bias of emission-line galaxies

ABSTRACT The next generation of spectroscopic surveys will target emission-line galaxies (ELGs) to produce constraints on cosmological parameters. We study the large-scale structure traced by ELGs using a combination of a semi-analytical model of galaxy formation, a code that computes the nebular emission from H ii regions using the properties of the interstellar medium, and a large-volume, high-resolution N-body simulation. We consider fixed number density samples where galaxies are selected by their H α, [O iii] λ5007, or [O ii] λλ3727–3729 emission-line luminosities. We investigate the assembly bias signatures of these samples, and compare them to those of stellar mass- and star formation rate-selected samples. Interestingly, we find that the [O iii]- and [O ii]-selected samples display scale-dependent bias on large scales and that their assembly bias signatures are also scale dependent. Both these effects are more pronounced for lower number density samples. The [O iii] and [O ii] emitters that contribute most to the scale dependence tend to have a low gas-phase metallicity and are preferentially found in low-density regions. We also measure the baryon acoustic oscillation (BAO) feature and the β parameter related to the growth rate of overdensities. We find that the scale of the BAO peak is roughly the same for all selections and that β is scale dependent at large scales. Our results suggest that ELG samples include environmental effects that should be modelled in order to remove potential systematic errors that could affect the estimation of cosmological parameters.

Data-Driven Convergence Prediction of Astrobots Swarms

Astrobots are robotic artifacts whose swarms are used in astrophysical studies to generate the map of the observable universe. These swarms have to be coordinated with respect to various desired observations. Such coordination is so complicated that distributed swarm controllers cannot always coordinate enough astrobots to fulfill the minimum data desired to be obtained in the course of observations. Thus, a convergence verification is necessary to check the suitability of coordination before its execution. However, a formal verification method does not exist for this purpose. In this article, we instead use machine learning to predict the convergence of astrobots swarm. As the first solution to this problem, we propose a weighted <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -NN-based algorithm that requires the initial status of a swarm and its observational targets to predict its convergence. Our algorithm learns to predict based on the coordination data obtained from previous coordination of the desired swarm. This method first generates a convergence probability for each astrobot based on a distance metric. Then, these probabilities are transformed to either a complete or an incomplete categorical result. The method is applied to two typical swarms, including 116 and 487 astrobots. It turns out that the correct prediction of successful coordination may be up to 80% of overall predictions. Thus, these results witness the efficient accuracy of our predictive convergence analysis strategy. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Observatories involved in the generation of spectroscopic surveys always encounter limited resources to check the throughputs of their planned observations before their executions. The information yielded by an observation directly depends on the convergence rate of the observatory’s astrobots in that particular observation. Namely, if the astrobots’ convergence rate is below a minimum, then the observation has to be revoked and replanned. Thus, one may define another observation that fulfills the minimum-information requirement. There has been yet no analytical tool developed to verify the convergence rate of the coordination computed by the state-of-the-art trajectory planners of astrobots swarms. Thus, we propose to use a machine learning scheme to predict the desired convergence rate instead of involving in the infeasible process of finding its exact value. This method is a supervised method that requires the target-to-astrobot assignments table of an observation. The algorithm also needs a data set including previous coordination results of various observations of a particular swarm. The simulated scenarios manifest magnificent accuracies in the convergence predictions of some astrobots swarms corresponding to modern spectroscopic surveys, such as SDSS-V (including ~500 astrobots). Our strategy is based on the smallest subset of the astrobots’ features that have a pivotal role in convergence rates, say, the projected positions of targets on a hosting telescope’s focal plane. We argue that more explorations have to be considered to find other important features, such as the motion direction of each astrobot, which may even further improve the obtained prediction accuracies.

Optimizing spectroscopic and photometric galaxy surveys: efficient target selection and survey strategy

The next generation of spectroscopic surveys will have a wealth of photometric data available for use in target selection. Selecting the best targets is likely to be one of the most important hurdles in making these spectroscopic campaigns as successful as possible. Our ability to measure dark energy depends strongly on the types of targets that we are able to select with a given photometric data set. We show in this paper that we will be able to successfully select the targets needed for the next generation of spectroscopic surveys. We also investigate the details of this selection, including optimisation of instrument design and survey strategy in order to measure dark energy. We use color-color selection as well as neural networks to select the best possible emission line galaxies and luminous red galaxies for a cosmological survey. Using the Fisher matrix formalism we forecast the efficiency of each target selection scenario. We show how the dark energy figures of merit change in each target selection regime as a function of target type, survey time, survey density and other survey parameters. We outline the optimal target selection scenarios and survey strategy choices which will be available to the next generation of spectroscopic surveys.

An Objective Approach to Spectral Classification

The next generation of spectroscopic surveys, both Galactic and extra-galactic (e.g., SDSS, 2dF), present the challenge of classifying spectra in an efficient and objective manner. The standard approach to this problem has been to visually classify spectra based on a number of spectral features (e.g., the equivalent widths of emission lines). The size of new spectral surveys (&gt; 106 galaxies) and the desire to compare the luminosity and environments of galaxies with their spectral properties make these techniques infeasible. We describe here an automated classification scheme that is being developed for the SDSS.