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N. Regnault
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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.
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Many scientific goals for the Dark Energy Survey (DES) require calibration of optical/NIR broadband $b = grizY$ photometry that is stable in time and uniform over the celestial sky to one percent or better. It is also necessary to limit to similar accuracy systematic uncertainty in the calibrated broadband magnitudes due to uncertainty in the spectrum of the source. Here we present a Forward Global Calibration Method (FGCM) for photometric calibration of the DES, and we present results of its application to the first three years of the survey (Y3A1). The FGCM combines data taken with auxiliary instrumentation at the observatory with data from the broad-band survey imaging itself and models of the instrument and atmosphere to estimate the spatial- and time-dependence of the passbands of individual DES survey exposures. Standard passbands are chosen that are typical of the passbands encountered during the survey. The passband of any individual observation is combined with an estimate of the source spectral shape to yield a magnitude $m_b^{mathrm{std}}$ in the standard system. This chromatic correction to the standard system is necessary to achieve sub-percent calibrations. The FGCM achieves reproducible and stable photometric calibration of standard magnitudes $m_b^{mathrm{std}}$ of stellar sources over the multi-year Y3A1 data sample with residual random calibration errors of $sigma=5-6,mathrm{mmag}$ per exposure. The accuracy of the calibration is uniform across the $5000,mathrm{deg}^2$ DES footprint to within $sigma=7,mathrm{mmag}$. The systematic uncertainties of magnitudes in the standard system due to the spectra of sources are less than $5,mathrm{mmag}$ for main sequence stars with $0.5<g-i<3.0$.
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.
We consider the application of relative self-calibration using overlap regions to spectroscopic galaxy surveys that use slit-less spectroscopy. This method is based on that developed for the SDSS by Padmanabhan at al. (2008) in that we consider jointly fitting and marginalising over calibrator brightness, rather than treating these as free parameters. However, we separate the calibration of the detector-to-detector from the full-focal-plane exposure-to-exposure calibration. To demonstrate how the calibration procedure will work, we simulate the procedure for a potential implementation of the spectroscopic component of the wide Euclid survey. We study the change of coverage and the determination of relative multiplicative errors in flux measurements for different dithering configurations. We use the new method to study the case where the flat-field across each exposure or detector is measured precisely and only exposure-to-exposure or detector-to-detector variation in the flux error remains. We consider several base dither patterns and find that they strongly influence the ability to calibrate, using this methodology. To enable self-calibration, it is important that the survey strategy connects different observations with at least a minimum amount of overlap, and we propose an S-pattern for dithering that fulfills this requirement. The final survey strategy adopted by Euclid will have to optimise for a number of different science goals and requirements. The large-scale calibration of the spectroscopic galaxy survey is clearly cosmologically crucial, but is not the only one.
We present photometric properties and distance measurements of 252 high redshift Type Ia supernovae (0.15 < z < 1.1) discovered during the first three years of the Supernova Legacy Survey (SNLS). These events were detected and their multi-colour light curves measured using the MegaPrime/MegaCam instrument at the Canada-France-Hawaii Telescope (CFHT), by repeatedly imaging four one-square degree fields in four bands. Follow-up spectroscopy was performed at the VLT, Gemini and Keck telescopes to confirm the nature of the supernovae and to measure their redshifts. Systematic uncertainties arising from light curve modeling are studied, making use of two techniques to derive the peak magnitude, shape and colour of the supernovae, and taking advantage of a precise calibration of the SNLS fields. A flat LambdaCDM cosmological fit to 231 SNLS high redshift Type Ia supernovae alone gives Omega_M = 0.211 +/- 0.034(stat) +/- 0.069(sys). The dominant systematic uncertainty comes from uncertainties in the photometric calibration. Systematic uncertainties from light curve fitters come next with a total contribution of +/- 0.026 on Omega_M. No clear evidence is found for a possible evolution of the slope (beta) of the colour-luminosity relation with redshift.
We present a measurement of the volumetric Type Ia supernova (SN Ia) rate (SNR_Ia) as a function of redshift for the first four years of data from the Canada-France-Hawaii Telescope (CFHT) Supernova Legacy Survey (SNLS). This analysis includes 286 spectroscopically confirmed and more than 400 additional photometrically identified SNe Ia within the redshift range 0.1<z<1.1. The volumetric SNR_Ia evolution is consistent with a rise to z~1.0 that follows a power-law of the form (1+z)^alpha, with alpha=2.11+/-0.28. This evolutionary trend in the SNLS rates is slightly shallower than that of the cosmic star-formation history over the same redshift range. We combine the SNLS rate measurements with those from other surveys that complement the SNLS redshift range, and fit various simple SN Ia delay-time distribution (DTD) models to the combined data. A simple power-law model for the DTD (i.e., proportional to t^-beta) yields values from beta=0.98+/-0.05 to beta=1.15+/-0.08 depending on the parameterization of the cosmic star formation history. A two-component model, where SNR_Ia is dependent on stellar mass (Mstellar) and star formation rate (SFR) as SNR_Ia(z)=AxMstellar(z) + BxSFR(z), yields the coefficients A=1.9+/-0.1 SNe/yr/M_solar and B=3.3+/-0.2 SNe/yr/(M_solar/yr). More general two-component models also fit the data well, but single Gaussian or exponential DTDs provide significantly poorer matches. Finally, we split the SNLS sample into two populations by the light curve width (stretch), and show that the general behavior in the rates of faster-declining SNe Ia (0.8<s<1.0) is similar, within our measurement errors, to that of the slower objects (1.0<s<1.3) out to z~0.8.
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