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Reid, B. A. et al, & de Putter, R. (2012). The clustering of galaxies in the SDSS-III Baryon Oscillation Spectroscopic Survey: measurements of the growth of structure and expansion rate at z=0.57 from anisotropic clustering. Mon. Not. Roy. Astron. Soc., 426(4), 2719–2737.
Abstract: We analyse the anisotropic clustering of massive galaxies from the Sloan Digital Sky Survey III Baryon Oscillation Spectroscopic Survey (BOSS) Data Release 9 (DR9) sample, which consists of 264-283 galaxies in the redshift range 0.43 < z < 0.7 spanning 3275 deg(2). Both peculiar velocities and errors in the assumed redshiftdistance relation (AlcockPaczynski effect) generate correlations between clustering amplitude and orientation with respect to the line of sight. Together with the sharp baryon acoustic oscillation (BAO) standard ruler, our measurements of the broad-band shape of the monopole and quadrupole correlation functions simultaneously constrain the comoving angular diameter distance (2190 +/- 61 Mpc) to z = 0.57, the Hubble expansion rate at z = 0.57 (92.4 +/- 4.5 km s(-1) Mpc(-1)) and the growth rate of structure at that same redshift (d(sigma 8)/d ln a = 0.43 +/- 0.069). Our analysis provides the best current direct determination of both DA and H in galaxy clustering data using this technique. If we further assume a cold dark matter expansion history, our growth constraint tightens to d(sigma 8)/d ln a = 0.415 +/- 0.034. In combination with the cosmic microwave background, our measurements of D-A,H and d(sigma 8)/d ln a all separately require dark energy at z > 0.57, and when combined imply Omega(A) = 0.74 +/- 0.016, independent of the Universe's evolution at z < 0.57. All of these constraints assume scale-independent linear growth, and assume general relativity to compute both O(10 per cent) non-linear model corrections and our errors. In our companion paper, Samushia et al., we explore further cosmological implications of these observations.
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Stoppa, F., Bhattacharyya, S., Ruiz de Austri, R., Vreeswijk, P., Caron, S., Zaharijas, G., et al. (2023). AutoSourceID-Classifier Star-galaxy classification using a convolutional neural network with spatial information. Astron. Astrophys., 680, A109–16pp.
Abstract: Aims. Traditional star-galaxy classification techniques often rely on feature estimation from catalogs, a process susceptible to introducing inaccuracies, thereby potentially jeopardizing the classification's reliability. Certain galaxies, especially those not manifesting as extended sources, can be misclassified when their shape parameters and flux solely drive the inference. We aim to create a robust and accurate classification network for identifying stars and galaxies directly from astronomical images.Methods. The AutoSourceID-Classifier (ASID-C) algorithm developed for this work uses 32x32 pixel single filter band source cutouts generated by the previously developed AutoSourceID-Light (ASID-L) code. By leveraging convolutional neural networks (CNN) and additional information about the source position within the full-field image, ASID-C aims to accurately classify all stars and galaxies within a survey. Subsequently, we employed a modified Platt scaling calibration for the output of the CNN, ensuring that the derived probabilities were effectively calibrated, delivering precise and reliable results.Results. We show that ASID-C, trained on MeerLICHT telescope images and using the Dark Energy Camera Legacy Survey (DECaLS) morphological classification, is a robust classifier and outperforms similar codes such as SourceExtractor. To facilitate a rigorous comparison, we also trained an eXtreme Gradient Boosting (XGBoost) model on tabular features extracted by SourceExtractor. While this XGBoost model approaches ASID-C in performance metrics, it does not offer the computational efficiency and reduced error propagation inherent in ASID-C's direct image-based classification approach. ASID-C excels in low signal-to-noise ratio and crowded scenarios, potentially aiding in transient host identification and advancing deep-sky astronomy.
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