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Carbon-enhanced metal-poor (CEMP) stars comprise almost a third of stars with [Fe/H] < −2, although their origins are still poorly understood. It is highly likely that one sub-class (CEMP-s stars) is tied to mass-transfer events in binary stars, while another sub-class (CEMP-no stars) are enriched by the nucleosynthetic yields of the first generations of stars. Previous studies of CEMP stars have primarily concentrated on the Galactic halo, but more recently they have also been detected in the thick disc and bulge components of the Milky Way. Gaia DR3 has provided an unprecedented sample of over 200 million low-resolution (R ≈ 50) spectra from the BP and RP photometers. Training on the CEMP catalogue from the SDSS/SEGUE database, we use XGBoost to identify the largest all-sky sample of CEMP candidate stars to date. In total, we find 58 872 CEMP star candidates, with an estimated contamination rate of 12 per cent. When comparing to literature high-resolution catalogues, we positively identify 60–68 per cent of the CEMP stars in the data, validating our results and indicating a high completeness rate. Our final catalogue of CEMP candidates spans from the inner to outer Milky Way, with distances as close as r ∼ 0.8 kpc from the Galactic centre, and as far as r > 30 kpc. Future higher resolution spectroscopic follow-up of these candidates will provide validations of their classification and enable investigations of the frequency of CEMP-s and CEMP-no stars throughout the Galaxy, to further constrain the nature of their progenitors.

 

ABSTRACT The Sunyaev–Zel’dolvich (SZ) effect is expected to be instrumental in measuring velocities of distant clusters in near future telescope surveys. We simplify the calculation of peculiar velocities of galaxy clusters using deep learning frameworks trained on numerical simulations to avoid the independent estimation of the optical depth. Images of distorted photon backgrounds are generated for idealized observations using one of the largest cosmological hydrodynamical simulations, the Magneticum simulations. The model is tested to determine its ability of estimating peculiar velocities from future kinetic SZ observations under different noise conditions. The deep learning algorithm displays robustness in estimating peculiar velocities from kinetic SZ effect by an improvement in accuracy of about 17 per cent compared to the analytical approach. 

ABSTRACT We explore unsupervised machine learning for galaxy morphology analyses using a combination of feature extraction with a vector-quantized variational autoencoder (VQ-VAE) and hierarchical clustering (HC). We propose a new methodology that includes: (1) consideration of the clustering performance simultaneously when learning features from images; (2) allowing for various distance thresholds within the HC algorithm; (3) using the galaxy orientation to determine the number of clusters. This set-up provides 27 clusters created with this unsupervised learning that we show are well separated based on galaxy shape and structure (e.g. Sérsic index, concentration, asymmetry, Gini coefficient). These resulting clusters also correlate well with physical properties such as the colour–magnitude diagram, and span the range of scaling relations such as mass versus size amongst the different machine-defined clusters. When we merge these multiple clusters into two large preliminary clusters to provide a binary classification, an accuracy of $\sim 87{{\ \rm per\ cent}}$ is reached using an imbalanced data set, matching real galaxy distributions, which includes 22.7 per cent early-type galaxies and 77.3 per cent late-type galaxies. Comparing the given clusters with classic Hubble types (ellipticals, lenticulars, early spirals, late spirals, and irregulars), we show that there is an intrinsic vagueness in visual classification systems, in particular galaxies with transitional features such as lenticulars and early spirals. Based on this, the main result in this work is not how well our unsupervised method matches visual classifications and physical properties, but that the method provides an independent classification that may be more physically meaningful than any visually based ones. 

ABSTRACT Large pristine samples of red clump stars are highly sought after given that they are standard candles and give precise distances even at large distances. However, it is difficult to cleanly select red clumps stars because they can have the same Teff and log g as red giant branch stars. Recently, it was shown that the asteroseismic parameters, $\rm {\Delta }$P and $\rm {\Delta \nu }$, which are used to accurately select red clump stars, can be derived from spectra using the change in the surface carbon to nitrogen ratio ([C/N]) caused by mixing during the red giant branch. This change in [C/N] can also impact the spectral energy distribution. In this study, we predict the $\rm {\Delta }$P, $\rm {\Delta \nu }$, Teff, and log g using 2MASS, AllWISE, Gaia, and Pan-STARRS data in order to select a clean sample of red clump stars. We achieve a contamination rate of ∼20 per cent, equivalent to what is achieved when selecting from Teff and log g derived from low-resolution spectra. Finally, we present two red clump samples. One sample has a contamination rate of ∼20 per cent and ∼405 000 red clump stars. The other has a contamination of ∼33 per cent and ∼2.6 million red clump stars that includes ∼75 000 stars at distances >10 kpc. For |b| > 30 deg, we find ∼15 000 stars with contamination rate of ∼9 per cent. The scientific potential of this catalogue for studying the structure and formation history of the Galaxy is vast, given that it includes millions of precise distances to stars in the inner bulge and distant halo where astrometric distances are imprecise.