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Register a new userVelocity and abundance precisions for future high-resolution spectroscopic surveys: a study for 4MOST
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In preparation for future, large-scale, multi-object, high-resolution spectroscopic surveys of the Galaxy, we present a series of tests of the precision in radial velocity and chemical abundances that any such project can achieve at a 4m class telescope. We briefly discuss a number of science cases that aim at studying the chemo-dynamical history of the major Galactic components (bulge, thin and thick disks, and halo) - either as a follow-up to the Gaia mission or on their own merits. Based on a large grid of synthetic spectra that cover the full range in stellar parameters of typical survey targets, we devise an optimal wavelength range and argue for a moderately high-resolution spectrograph. As a result, the kinematic precision is not limited by any of these factors, but will practically only suffer from systematic effects, easily reaching uncertainties <1 km/s. Under realistic survey conditions (namely, considering stars brighter than r=16 mag with reasonable exposure times) we prefer an ideal resolving power of R~20000 on average, for an overall wavelength range (with a common two-arm spectrograph design) of [395;456.5] nm and [587;673] nm. We show for the first time on a general basis that it is possible to measure chemical abundance ratios to better than 0.1 dex for many species (Fe, Mg, Si, Ca, Ti, Na, Al, V, Cr, Mn, Co, Ni, Y, Ba, Nd, Eu) and to an accuracy of about 0.2 dex for other species such as Zr, La, and Sr. While our feasibility study was explicitly carried out for the 4MOST facility, the results can be readily applied to and used for any other conceptual design study for high-resolution spectrographs.
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Large multi-object spectroscopic surveys require automated algorithms to optimise their observing strategy. One of the most ambitious upcoming spectroscopic surveys is the 4MOST survey. The 4MOST survey facility is a fibre-fed spectroscopic instrument on the VISTA telescope with a large enough field of view to survey a large fraction of the southern sky within a few years. Several Galactic and extragalactic surveys will be carried out simultaneously, so the combined target density will strongly vary. In this paper, we describe a new tiling algorithm that can naturally deal with the large target density variations on the sky and which automatically handles the different exposure times of targets. The tiling pattern is modelled as a marked point process, which is characterised by a probability density that integrates the requirements imposed by the 4MOST survey. The optimal tilling pattern with respect to the defined model is estimated by the tiles configuration that maximises the proposed probability density. In order to achieve this maximisation a simulated annealing algorithm is implemented. The algorithm automatically finds an optimal tiling pattern and assigns a tentative sky brightness condition and exposure time for each tile, while minimising the total execution time that is needed to observe the list of targets in the combined input catalogue of all surveys. Hence, the algorithm maximises the long-term observing efficiency and provides an optimal tiling solution for the survey. While designed for the 4MOST survey, the algorithm is flexible and can with simple modifications be applied to any other multi-object spectroscopic survey.
In this work we apply and expand on a recently introduced outlier detection algorithm that is based on an unsupervised random forest. We use the algorithm to calculate a similarity measure for stellar spectra from the Apache Point Observatory Galactic Evolution Experiment (APOGEE). We show that the similarity measure traces non-trivial physical properties and contains information about complex structures in the data. We use it for visualization and clustering of the dataset, and discuss its ability to find groups of highly similar objects, including spectroscopic twins. Using the similarity matrix to search the dataset for objects allows us to find objects that are impossible to find using their best fitting model parameters. This includes extreme objects for which the models fail, and rare objects that are outside the scope of the model. We use the similarity measure to detect outliers in the dataset, and find a number of previously unknown Be-type stars, spectroscopic binaries, carbon rich stars, young stars, and a few that we cannot interpret. Our work further demonstrates the potential for scientific discovery when combining machine learning methods with modern survey data.
Context. Several new multi-object spectrographs are currently planned or under construction that are capable of observing thousands of Galactic and extragalactic objects simultaneously. Aims. In this paper we present a probabilistic fibre-to-target assignment algorithm that takes spectrograph targeting constraints into account and is capable of dealing with multiple concurrent surveys. We present this algorithm using the 4-metre Multi-Object Spectroscopic Telescope (4MOST) as an example. Methods. The key idea of the proposed algorithm is to assign probabilities to fibre-target pairs. The assignment of probabilities takes the fibre positioners capabilities and constraints into account. Additionally, these probabilities include requirements from surveys and take the required exposure time, number density variation, and angular clustering of targets across each survey into account. The main advantage of a probabilistic approach is that it allows for accurate and easy computation of the target selection function for the different surveys, which involves determining the probability of observing a target, given an input catalogue. Results. The probabilistic fibre-to-target assignment allows us to achieve maximally uniform completeness within a single field of view. The proposed algorithm maximises the fraction of successfully observed targets whilst minimising the selection bias as a function of exposure time. In the case of several concurrent surveys, the algorithm maximally satisfies the scientific requirements of each survey and no specific survey is penalised or prioritised. Conclusions. The algorithm presented is a proposed solution for the 4MOST project that allows for an unbiased targeting of many simultaneous surveys. With some modifications, the algorithm may also be applied to other multi-object spectroscopic surveys.
The Perseus OB1 association hosts one of the most populous groupings of blue and red supergiants (Sgs) in the Galaxy. We discuss whether the massive O-type and blue/red Sg stars located in the Per OB1 region are members of the same population and examine their binary and runaway status. We gathered a total of 405 high-resolution spectra for 88 suitable candidates around 4.5 deg from the center of the association, and compiled Gaia DR2 astrometry for all of them. This was used to investigate membership and identify runaway stars. By obtaining high-precision radial velocity (RV) estimates, we investigated the RV distributions of sample and identified spectroscopic binaries (SBs). Most of the investigated stars belong to a physically linked population located at d = 2.5$pm$0.4 kpc. We identify 79 confirmed or likely members, and 5 member candidates. No important differences are detected in the distribution of parallaxes for stars in h and X Persei or the full sample. On the contrary, most O-type stars seem to be part of a differentiated population in terms of kinematical properties. In particular, the percentage of runaways among them (45%) is considerable higher than for the more evolved targets (that is below 5% in all cases). A similar tendency is also found for the percentage of clearly detected SBs, which already decreases from 15% to 10% when comparing the O star and B Sg samples, respectively, and practically vanishes in the cooler Sgs. All but 4 stars in our working sample can be considered as part of the same (interrelated) population. However, any further attempt to describe the empirical properties of this sample of massive stars in an evolutionary context must take into account that an important fraction of the O stars is - or has likely been - part of a binary/multiple system. In addition, some of the other more evolved targets may have also been affected by binary evolution.
The nature of dark matter, dark energy and large-scale gravity pose some of the most pressing questions in cosmology today. These fundamental questions require highly precise measurements, and a number of wide-field spectroscopic survey instruments are being designed to meet this requirement. A key component in these experiments is the development of a simulation tool to forecast science performance, define requirement flow-downs, optimize implementation, demonstrate feasibility, and prepare for exploitation. We present SPOKES (SPectrOscopic KEn Simulation), an end-to-end simulation facility for spectroscopic cosmological surveys designed to address this challenge. SPOKES is based on an integrated infrastructure, modular function organization, coherent data handling and fast data access. These key features allow reproducibility of pipeline runs, enable ease of use and provide flexibility to update functions within the pipeline. The cyclic nature of the pipeline offers the possibility to make the science output an efficient measure for design optimization and feasibility testing. We present the architecture, first science, and computational performance results of the simulation pipeline. The framework is general, but for the benchmark tests, we use the Dark Energy Spectrometer (DESpec), one of the early concepts for the upcoming project, the Dark Energy Spectroscopic Instrument (DESI). We discuss how the SPOKES framework enables a rigorous process to optimize and exploit spectroscopic survey experiments in order to derive high-precision cosmological measurements optimally.
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