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Register a new userThe ALHAMBRA photometric system
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Added by
Teresa Aparicio Villegas
Publication date
2010
fields
Physics
and research's language is
English
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This paper presents the characterization of the optical range of the ALHAMBRA photometric system, a 20 contiguous, equal-width, medium-band CCD system with wavelength coverage from 3500A to 9700A. The photometric description of the system is done by presenting the full response curve as a product of the filters, CCD and atmospheric transmission curves, and using some first and second order moments of this response function. We also introduce the set of standard stars that defines the system, formed by 31 classic spectrophotometric standard stars which have been used in the calibration of other known photometric systems, and 288 stars, flux calibrated homogeneously, from the Next Generation Spectral Library (NGSL). Based on the NGSL, we determine the transformation equations between Sloan Digital Sky Survey (SDSS) ugriz photometry and the ALHAMBRA photometric system, in order to establish some relations between both systems. Finally we develop and discuss a strategy to calculate the photometric zero points of the different pointings in the ALHAMBRA project.
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The Pan-STARRS1 survey is collecting multi-epoch, multi-color observations of the sky north of declination -30 deg to unprecedented depths. These data are being photometrically and astrometrically calibrated and will serve as a reference for many other purposes. In this paper we present our determination of the Pan-STARRS photometric system: gp1, rp1, ip1, zp1, yp1, and wp1. The Pan-STARRS photometric system is fundamentally based on the HST Calspec spectrophotometric observations, which in turn are fundamentally based on models of white dwarf atmospheres. We define the Pan-STARRS magnitude system, and describe in detail our measurement of the system passbands, including both the instrumental sensitivity and atmospheric transmission functions. Byproducts, including transformations to other photometric systems, galactic extinction, and stellar locus are also provided. We close with a discussion of remaining systematic errors.
Machine learning (ML) is a standard approach for estimating the redshifts of galaxies when only photometric information is available. ML photo-z solutions have traditionally ignored the morphological information available in galaxy images or partly included it in the form of hand-crafted features, with mixed results. We train a morphology-aware photometric redshift machine using modern deep learning tools. It uses a custom architecture that jointly trains on galaxy fluxes, colors and images. Galaxy-integrated quantities are fed to a Multi-Layer Perceptron (MLP) branch while images are fed to a convolutional (convnet) branch that can learn relevant morphological features. This split MLP-convnet architecture, which aims to disentangle strong photometric features from comparatively weak morphological ones, proves important for strong performance: a regular convnet-only architecture, while exposed to all available photometric information in images, delivers comparatively poor performance. We present a cross-validated MLP-convnet model trained on 130,000 SDSS-DR12 galaxies that outperforms a hyperoptimized Gradient Boosting solution (hyperopt+XGBoost), as well as the equivalent MLP-only architecture, on the redshift bias metric. The 4-fold cross-validated MLP-convnet model achieves a bias $delta z / (1+z) =-0.70 pm 1 times 10^{-3} $, approaching the performance of a reference ANNZ2 ensemble of 100 distinct models trained on a comparable dataset. The relative performance of the morphology-aware and morphology-blind models indicates that galaxy morphology does improve ML-based photometric redshift estimation.
We study the clustering of galaxies as a function of spectral type and redshift in the range $0.35 < z < 1.1$ using data from the Advanced Large Homogeneous Area Medium Band Redshift Astronomical (ALHAMBRA) survey. The data cover 2.381 deg$^2$ in 7 fields, after applying a detailed angular selection mask, with accurate photometric redshifts [$sigma_z < 0.014(1+z)$] down to $I_{AB} < 24$. From this catalog we draw five fixed number density, redshift-limited bins. We estimate the clustering evolution for two different spectral populations selected using the ALHAMBRA-based photometric templates: quiescent and star-forming galaxies. For each sample, we measure the real-space clustering using the projected correlation function. Our calculations are performed over the range $[0.03,10.0] h^{-1}$ Mpc, allowing us to find a steeper trend for $r_p lesssim 0.2 h^{-1}$ Mpc, which is especially clear for star-forming galaxies. Our analysis also shows a clear early differentiation in the clustering properties of both populations: star-forming galaxies show weaker clustering with evolution in the correlation length over the analysed redshift range, while quiescent galaxies show stronger clustering already at high redshifts, and no appreciable evolution. We also perform the bias calculation where similar segregation is found, but now it is among the quiescent galaxies where a growing evolution with redshift is clearer. These findings clearly corroborate the well known colour-density relation, confirming that quiescent galaxies are mainly located in dark matter halos that are more massive than those typically populated by star-forming galaxies.
We present the photometric calibration of the Supernova Legacy Survey (SNLS) fields. The SNLS aims at measuring the distances to SNe Ia at (0.3<z<1) using MegaCam, the 1 deg^2 imager on the Canada-France-Hawaii Telescope (CFHT). The uncertainty affecting the photometric calibration of the survey dominates the systematic uncertainty of the key measurement of the survey, namely the dark energy equation of state. The photometric calibration of the SNLS requires obtaining a uniform response across the imager, calibrating the science field stars in each survey band (SDSS-like ugriz bands) with respect to standards with known flux in the same bands, and binding the calibration to the UBVRI Landolt standards used to calibrate the nearby SNe from the literature necessary to produce cosmological constraints. The spatial non-uniformities of the imager photometric response are mapped using dithered observations of dense stellar fields. Photometric zero-points against Landolt standards are obtained. The linearity of the instrument is studied. We show that the imager filters and photometric response are not uniform and publish correction maps. We present models of the effective passbands of the instrument as a function of the position on the focal plane. We define a natural magnitude system for MegaCam. We show that the systematics affecting the magnitude-to-flux relations can be reduced if we use the spectrophotometric standard star BD +17 4708 instead of Vega as a fundamental flux standard. We publish ugriz catalogs of tertiary standards for all the SNLS fields.
Automated photometric supernova classification has become an active area of research in recent years in light of current and upcoming imaging surveys such as the Dark Energy Survey (DES) and the Large Synoptic Survey Telescope, given that spectroscopic confirmation of type for all supernovae discovered will be impossible. Here, we develop a multi-faceted classification pipeline, combining existing and new approaches. Our pipeline consists of two stages: extracting descriptive features from the light curves and classification using a machine learning algorithm. Our feature extraction methods vary from model-dependent techniques, namely SALT2 fits, to more independent techniques fitting parametric models to curves, to a completely model-independent wavelet approach. We cover a range of representative machine learning algorithms, including naive Bayes, k-nearest neighbors, support vector machines, artificial neural networks and boosted decision trees (BDTs). We test the pipeline on simulated multi-band DES light curves from the Supernova Photometric Classification Challenge. Using the commonly used area under the curve (AUC) of the Receiver Operating Characteristic as a metric, we find that the SALT2 fits and the wavelet approach, with the BDTs algorithm, each achieves an AUC of 0.98, where 1 represents perfect classification. We find that a representative training set is essential for good classification, whatever the feature set or algorithm, with implications for spectroscopic follow-up. Importantly, we find that by using either the SALT2 or the wavelet feature sets with a BDT algorithm, accurate classification is possible purely from light curve data, without the need for any redshift information.
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