We developed a deep convolutional neural network (CNN), used as a classifier, to estimate photometric redshifts and associated probability distribution functions (PDF) for galaxies in the Main Galaxy ...Sample of the Sloan Digital Sky Survey at z < 0.4. Our method exploits all the information present in the images without any feature extraction. The input data consist of 64 × 64 pixel ugriz images centered on the spectroscopic targets, plus the galactic reddening value on the line-of-sight. For training sets of 100k objects or more (≥20% of the database), we reach a dispersion σMAD < 0.01, significantly lower than the current best one obtained from another machine learning technique on the same sample. The bias is lower than 10−4, independent of photometric redshift. The PDFs are shown to have very good predictive power. We also find that the CNN redshifts are unbiased with respect to galaxy inclination, and that σMAD decreases with the signal-to-noise ratio (S/N), achieving values below 0.007 for S/N > 100, as in the deep stacked region of Stripe 82. We argue that for most galaxies the precision is limited by the S/N of SDSS images rather than by the method. The success of this experiment at low redshift opens promising perspectives for upcoming surveys.
Aims. We present photometric properties and distance measurements of 252high 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. Methods. 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. Results. A flat ΛCDM cosmological fit to 231SNLS high redshift type Ia supernovae alone gives ΩM = 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 ΩM. No clear evidence is found for a possible evolution of the slope (β) of the colour-luminosity relation with redshift.
We present spectra and light curves of SNLS 06D4eu and SNLS 07D2bv, two hydrogen-free superluminous supernovae (SNe) discovered by the Supernova Legacy Survey. SNLS 07D2bv does not have a host galaxy ...redshift, but on the basis of the SN spectrum, we estimate it to be at z ~ 1.5. Both SNe have similar observer-frame griz light curves, which map to rest-frame light curves in the U band and UV, rising in ~20 rest-frame days or longer and declining over a similar timescale. We compare the spectra with theoretical models, and we identify lines of C II, C III, Fe III, and Mg II in the spectra of SNLS 06D4eu and SCP 06F6 and find that they are consistent with an expanding explosion of only a few solar masses of carbon, oxygen, and other trace metals. Normal mechanisms of powering core-collapse or thermonuclear SNe do not seem to work for these SNe.
As part of an on-going effort to identify, understand and correct for astrophysics biases in the standardization of Type Ia supernovae (SN Ia) for cosmology, we have statistically classified a large ...sample of nearby SNe Ia into those that are located in predominantly younger or older environments. This classification is based on the specific star formation rate measured within a projected distance of 1 kpc from each SN location (LsSFR). This is an important refinement compared to using the local star formation rate directly, as it provides a normalization for relative numbers of available SN progenitors and is more robust against extinction by dust. We find that the SNe Ia in predominantly younger environments are Δ
Y
= 0.163 ± 0.029 mag (5.7
σ
) fainter than those in predominantly older environments after conventional light-curve standardization. This is the strongest standardized SN Ia brightness systematic connected to the host-galaxy environment measured to date. The well-established step in standardized brightnesses between SNe Ia in hosts with lower or higher total stellar masses is smaller, at Δ
M
= 0.119 ± 0.032 mag (4.5
σ
), for the same set of SNe Ia. When fit simultaneously, the environment-age offset remains very significant, with Δ
Y
= 0.129 ± 0.032 mag (4.0
σ
), while the global stellar mass step is reduced to Δ
M
= 0.064 ± 0.029 mag (2.2
σ
). Thus, approximately 70% of the variance from the stellar mass step is due to an underlying dependence on environment-based progenitor age. Also, we verify that using the local star formation rate alone is not as powerful as LsSFR at sorting SNe Ia into brighter and fainter subsets. Standardization that only uses the SNe Ia in younger environments reduces the total dispersion from 0.142 ± 0.008 mag to 0.120 ± 0.010 mag. We show that as environment-ages evolve with redshift, a strong bias, especially on the measurement of the derivative of the dark energy equation of state, can develop. Fortunately, data that measure and correct for this effect using our local specific star formation rate indicator, are likely to be available for many next-generation SN Ia cosmology experiments.
Aims. We present cosmological constraints from a joint analysis of type Ia supernova (SN Ia) observations obtained by the SDSS-II and SNLS collaborations. The dataset includes several low-redshift ...samples (z< 0.1), all three seasons from the SDSS-II (0.05 <z< 0.4), and three years from SNLS (0.2 <z< 1), and it totals 740 spectroscopically confirmed type Ia supernovae with high-quality light curves. Methods. We followed the methods and assumptions of the SNLS three-year data analysis except for the following important improvements: 1) the addition of the full SDSS-II spectroscopically-confirmed SN Ia sample in both the training of the SALT2 light-curve model and in the Hubble diagram analysis (374 SNe); 2) intercalibration of the SNLS and SDSS surveys and reduced systematic uncertainties in the photometric calibration, performed blindly with respect to the cosmology analysis; and 3) a thorough investigation of systematic errors associated with the SALT2 modeling of SN Ia light curves. Results. We produce recalibrated SN Ia light curves and associated distances for the SDSS-II and SNLS samples. The large SDSS-II sample provides an effective, independent, low-z anchor for the Hubble diagram and reduces the systematic error from calibration systematics in the low-z SN sample. For a flat ΛCDM cosmology, we find Ωm =0.295 ± 0.034 (stat+sys), a value consistent with the most recent cosmic microwave background (CMB) measurement from the Planck and WMAP experiments. Our result is 1.8σ (stat+sys) different than the previously published result of SNLS three-year data. The change is due primarily to improvements in the SNLS photometric calibration. When combined with CMB constraints, we measure a constant dark-energy equation of state parameter w =−1.018 ± 0.057 (stat+sys) for a flat universe. Adding baryon acoustic oscillation distance measurements gives similar constraints: w =−1.027 ± 0.055. Our supernova measurements provide the most stringent constraints to date on the nature of dark energy.
ABSTRACT
We release photometric redshifts, reaching ∼0.7, for ∼14M galaxies at r ≤ 20 in the 11 500 deg2 of the SDSS north and south Galactic caps. These estimates were inferred from a convolution ...neural network (CNN) trained on ugriz stamp images of galaxies labelled with a spectroscopic redshift from the SDSS, GAMA, and BOSS surveys. Representative training sets of ∼370k galaxies were constructed from the much larger combined spectroscopic data to limit biases, particularly those arising from the over-representation of luminous red galaxies. The CNN outputs a redshift classification that offers all the benefits of a well-behaved PDF, with a width efficiently signalling unreliable estimates due to poor photometry or stellar sources. The dispersion, mean bias, and rate of catastrophic failures of the median point estimate are of order σMAD = 0.014, <Δznorm>=0.0015, $\eta (|\Delta z_{\rm norm}|\gt 0.05)=4{{\, \rm per\ cent}}$ on a representative test sample at r < 19.8, outperforming currently published estimates. The distributions in narrow intervals of magnitudes of the redshifts inferred for the photometric sample are in good agreement with the results of tomographic analyses. The inferred redshifts also match the photometric redshifts of the redMaPPer galaxy clusters for the probable cluster members.
We present SiFTO, a new empirical method for modeling Type Ia supernova (SN Ia) light curves by manipulating a spectral template. We make use of high-redshift SN data when training the model, ...allowing us to extend it bluer than rest-frame U. This increases the utility of our high-redshift SN observations by allowing us to use more of the available data. We find that when the shape of the light curve is described using a stretch prescription, applying the same stretch at all wavelengths is not an adequate description. SiFTO therefore uses a generalization of stretch which applies different stretch factors as a function of both the wavelength of the observed filter and the stretch in the rest-frame B band. We compare SiFTO to other published light-curve models by applying them to the same set of SN photometry, and demonstrate that SiFTO and SALT2 perform better than the alternatives when judged by the scatter around the best-fit luminosity distance relationship. We further demonstrate that when SiFTO and SALT2 are trained on the same data set the cosmological results agree.
Precision cosmology with Type Ia supernovae (SNe Ia) makes use of the fact that SN Ia luminosities depend on their light-curve shapes and colours. Using Supernova Legacy Survey (SNLS) and other data, ...we show that there is an additional dependence on the global characteristics of their host galaxies: events of the same light-curve shape and colour are, on average, 0.08 mag (≃4.0σ) brighter in massive host galaxies (presumably metal-rich) and galaxies with low specific star formation rates (sSFR). These trends do not depend on any assumed cosmological model, and are independent of the SN light-curve width: both fast and slow-declining events show the same trends. SNe Ia in galaxies with a low sSFR also have a smaller slope (‘β’) between their luminosities and colours with ∼2.7σ significance, and a smaller scatter on SN Ia Hubble diagrams (at 95 per cent confidence), though the significance of these effects is dependent on the reddest SNe. SN Ia colours are similar between low-mass and high-mass hosts, leading us to interpret their luminosity differences as an intrinsic property of the SNe and not of some external factor such as dust. If the host stellar mass is interpreted as a metallicity indicator using galaxy mass–metallicity relations, the luminosity trends are in qualitative agreement with theoretical predictions. We show that the average stellar mass, and therefore the average metallicity, of our SN Ia host galaxies decreases with redshift. The SN Ia luminosity differences consequently introduce a systematic error in cosmological analyses, comparable to the current statistical uncertainties on parameters such as w, the equation of state of dark energy. We show that the use of two SN Ia absolute magnitudes, one for events in high-mass (metal-rich) galaxies and the other for events in low-mass (metal-poor) galaxies, adequately corrects for the differences. Cosmological fits incorporating these terms give a significant reduction in χ2 (3.8σ–4.5σ); linear corrections based on host parameters do not perform as well. We conclude that all future SN Ia cosmological analyses should use a correction of this (or similar) form to control demographic shifts in the underlying galaxy population.
This paper exploits the gravitational magnification of Type Ia supernovae (SNe Ia) to measure properties of dark matter haloes. Gravitationally magnified and de-magnified SNe Ia should be brighter ...and fainter than average, respectively. The magnification of individual SNe Ia can be computed using observed properties of foreground galaxies and dark matter halo models. We model the dark matter haloes of the galaxies as truncated singular isothermal spheres with velocity dispersion and truncation radius obeying luminosity-dependent scaling laws. A homogeneously selected sample of 175 SNe Ia from the first 3 yr of the Supernova Legacy Survey (SNLS) in the redshift range 0.2 ≲z≲ 1 is used to constrain models of the dark matter haloes associated with foreground galaxies. The best-fitting velocity dispersion scaling law agrees well with galaxy–galaxy lensing measurements. We further find that the normalization of the velocity dispersion of passive and star-forming galaxies are consistent with empirical Faber–Jackson and Tully–Fisher relations, respectively. If we make no assumption on the normalization of these relations, we find that the data prefer gravitational lensing at the 92 per cent confidence level. Using recent models of dust extinction, we deduce that the impact of this effect on our results is very small. We also investigate the brightness scatter of SNe Ia due to gravitational lensing, which has implications for SN Ia cosmology. The gravitational lensing scatter is approximately proportional to the SN Ia redshift. We find the constant of proportionality to be B≃ 0.055+0.039−0.041 mag (B≲ 0.12 mag at the 95 per cent confidence level). If this model is correct, the contribution from lensing to the intrinsic brightness scatter of SNe Ia is small for the SNLS sample. According to the best-fitting dark matter model, gravitational lensing should, however, contribute significantly to the brightness scatter at z≳ 1.6.
We present observational constraints on the nature of dark energy using the Supernova Legacy Survey three-year sample (SNLS3) of Guy et al. and Conley et al. We use the 472 Type Ia supernovae (SNe ...Ia) in this sample, accounting for recently discovered correlations between SN Ia luminosity and host galaxy properties, and include the effects of all identified systematic uncertainties directly in the cosmological fits. Combining the SNLS3 data with the full WMAP7 power spectrum, the Sloan Digital Sky Survey luminous red galaxy power spectrum, and a prior on the Hubble constant H 0 from SHOES, in a flat universe we find Delta *W m = 0.269 ? 0.015 and w = --1.061+0.069 -- 0.068 (where the uncertainties include all statistical and SN Ia systematic errors)--a 6.5% measure of the dark energy equation-of-state parameter w. The statistical and systematic uncertainties are approximately equal, with the systematic uncertainties dominated by the photometric calibration of the SN Ia fluxes--without these calibration effects, systematics contribute only a ~2% error in w. When relaxing the assumption of flatness, we find Delta *W m = 0.271 ? 0.015, Delta *W k = --0.002 ? 0.006, and w = --1.069+0.091 -- 0.092. Parameterizing the time evolution of w as w(a) = w 0 + wa (1 -- a) gives w 0 = --0.905 ? 0.196, wa = --0.984+1.094 -- 1.097 in a flat universe. All of our results are consistent with a flat, w = --1 universe. The size of the SNLS3 sample allows various tests to be performed with the SNe segregated according to their light curve and host galaxy properties. We find that the cosmological constraints derived from these different subsamples are consistent. There is evidence that the coefficient, Delta *b, relating SN Ia luminosity and color, varies with host parameters at >4 Delta *s significance (in addition to the known SN luminosity-host relation); however, this has only a small effect on the cosmological results and is currently a subdominant systematic.
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