Abstract
Spectroscopic surveys require fast and efficient analysis methods to maximize their scientific impact. Here, we apply a deep neural network architecture to analyse both SDSS-III APOGEE DR13 ...and synthetic stellar spectra. When our convolutional neural network model (StarNet) is trained on APOGEE spectra, we show that the stellar parameters (temperature, gravity, and metallicity) are determined with similar precision and accuracy as the APOGEE pipeline. StarNet can also predict stellar parameters when trained on synthetic data, with excellent precision and accuracy for both APOGEE data and synthetic data, over a wide range of signal-to-noise ratios. In addition, the statistical uncertainties in the stellar parameter determinations are comparable to the differences between the APOGEE pipeline results and those determined independently from optical spectra. We compare StarNet to other data-driven methods; for example, StarNet and the Cannon 2 show similar behaviour when trained with the same data sets; however, StarNet performs poorly on small training sets like those used by the original Cannon. The influence of the spectral features on the stellar parameters is examined via partial derivatives of the StarNet model results with respect to the input spectra. While StarNet was developed using the APOGEE observed spectra and corresponding ASSET synthetic data, we suggest that this technique is applicable to other wavelength ranges and other spectral surveys.
We present a fully consistent catalog of local and global properties of host galaxies of 882 Type Ia supernovæ (SNIa) that were selected based on their light-curve properties, spanning the redshift ...range 0.01 < z < 1. This catalog corresponds to a preliminary version of the compilation sample and includes Supernova Legacy Survey (SNLS) 5-year data, Sloan Digital Sky Survey (SDSS), and low-redshift surveys. We measured low- and moderate-redshift host galaxy photometry in SDSS stacked and single-epoch images and used spectral energy distribution fitting techniques to derive host properties such as stellar mass and U − V rest-frame colors; the latter are an indicator of the luminosity-weighted age of the stellar population in a galaxy. We combined these results with high-redshift host photometry from the SNLS survey and thus obtained a consistent catalog of host stellar masses and colors across a wide redshift range. We also estimated the local observed fluxes at the supernova location within a proper distance radius of 3 kpc, corresponding to the SNLS imaging resolution, and transposed them into local U − V rest-frame colors. This is the first time that local environments surrounding SNIa have been measured at redshifts spanning the entire Hubble diagram. Selecting SNIa based on host photometry quality, we then performed cosmological fits using local color as a third standardization variable, for which we split the sample at the median value. We find a local color step significance of − 0.091 ± 0.013 mag (7σ), which effect is as significant as the maximum mass step effect. This indicates that the remaining luminosity variations in SNIa samples can be reduced with a third standardization variable that takes the environment into account. Correcting for the maximum mass step correction of − 0.094 ± 0.013 mag, we find a local color effect of − 0.057 ± 0.012 mag (5σ), which shows that additional information is provided by the close environment of SNIa. Departures from the initial choices were investigated and showed that the local color effect is still present, although less pronounced. We discuss the possible implications for cosmology and find that using the local color in place of the stellar mass results in a change in the measured value of the dark energy equation-of-state parameter of 0.6%. Standardization using local U − V color in addition to stretch and color reduces the total dispersion in the Hubble diagram from 0.15 to 0.14 mag. This will be of tremendous importance for the forthcoming SNIa surveys, and in particular for the Large Synoptic Survey Telescope (LSST), for which uncertainties on the dark energy equation of state will be comparable to the effects reported here.
We report the discovery of five new doubly imaged lensed quasars from the first 2500 square degrees of the ongoing Canada-France Imaging Survey (CFIS), which is a component of the Ultraviolet Near ...Infrared Optical Northern Survey. The systems are preselected in the initial catalogues of either
Gaia
pairs or MILLIQUAS quasars. We then take advantage of the deep, 0.6″median-seeing
r
-band imaging of CFIS to confirm the presence of multiple point sources with similar colour of
u
−
r
via convolution of the Laplacian of the point spread function. Requiring point sources of similar colour and with flux ratios of less than 2.5 mag in
r
-band, we reduce the number of candidates from 256 314 to 7815. After visual inspection, we obtain 30 high-grade candidates, and prioritise a spectroscopic follow-up analysis for those showing signs of a lensing galaxy upon subtraction of the point sources. We obtain long-slit spectra for 18 candidates with ALFOSC on the 2.56-m Nordic Optical Telescope, confirming five new doubly lensed quasars with 1.21 <
z
< 3.36 and angular separations from 0.8″ to 2.5″. One additional system is a probable lensed quasar based on the CFIS imaging and existing SDSS spectrum. We further classify six objects as nearly identical quasars, that is, possible lenses but without the detection of a lensing galaxy. Given our recovery rate (83%) of existing optically bright lenses within the CFIS footprint, we expect that a similar strategy, coupled with
u
−
r
colour-selection from CFIS alone, will provide an efficient and complete discovery of small-separation lensed quasars of source redshifts below
z
= 2.7 within the CFIS
r
-band magnitude limit of 24.1 mag.
Aims. Strong lensing by massive galaxy clusters can provide magnification of the flux and even multiple images of the galaxies that lie behind them. This phenomenon facilitates observations of ...high-redshift supernovae (SNe) that would otherwise remain undetected. Type Ia supernovae (SNe Ia) detections are of particular interest because of their standard brightness, since they can be used to improve either cluster lensing models or cosmological parameter measurements. Methods. We present a ground-based, near-infrared search for lensed SNe behind the galaxy cluster Abell 370. Our survey was based on 15 epochs of J-band observations with the HAWK-I instrument on the Very Large Telescope (VLT). We use Hubble Space Telescope (HST) photometry to infer the global properties of the multiply-imaged galaxies. Using a recently published lensing model of Abell 370, we also present the predicted magnifications and time delays between the images. Results. In our survey, we did not discover any live SNe from the 13 lensed galaxies with 47 multiple images behind Abell 370. This is consistent with the expectation of 0.09 ± 0.02 SNe calculated based on the measured star formation rate. We compare the expectations of discovering strongly lensed SNe in our survey and that performed with HST during the Hubble Frontier Fields (HFF) programme. We also show the expectations of search campaigns that can be conducted with future facilities, such as the James Webb Space Telescope (JWST) or the Wide-Field Infrared Survey Telescope (WFIRST). We show that the NIRCam instrument aboard the JWST will be sensitive to most SN multiple images in the strongly lensed galaxies and thus will be able to measure their time delays if observations are scheduled accordingly.
We report on work to increase the number of well-measured Type Ia supernovae (SNe Ia) at high redshifts. Light curves, including high signal-to-noise Hubble Space Telescope data, and spectra of six ...SNe Ia that were discovered during 2001, are presented. Additionally, for the two SNe with z > 1, we present ground-based J-band photometry from Gemini and the Very Large Telescope. These are among the most distant SNe Ia for which ground-based near-IR observations have been obtained. We add these six SNe Ia together with other data sets that have recently become available in the literature to the Union compilation. We have made a number of refinements to the Union analysis chain, the most important ones being the refitting of all light curves with the SALT2 fitter and an improved handling of systematic errors. We call this new compilation, consisting of 557 SNe, the Union2 compilation. The flat concordance ΛCDM model remains an excellent fit to the Union2 data with the best-fit constant equation-of-state parameter w = -0.997+0.050 -0.054(stat)+0.077 -0.082(stat + sys together) for a flat universe, or w = -1.038+0.056 -0.059(stat)+0.093 -0.097(stat + sys together) with curvature. We also present improved constraints on w(z). While no significant change in w with redshift is detected, there is still considerable room for evolution in w. The strength of the constraints depends strongly on redshift. In particular, at z >~ 1, the existence and nature of dark energy are only weakly constrained by the data. Based in part on observations made with the NASA/ESA Hubble Space Telescope, obtained from the data archive at the Space Telescope Science Institute (STScI). STScI is operated by the Association of Universities for Research in Astronomy (AURA), Inc. under the NASA contract NAS 5-26555. The observations are associated with programs HST-GO-08585 and HST-GO-09075. Based, in part, on observations obtained at the ESO La Silla Paranal Observatory (ESO programs 67.A-0361 and 169.A-0382). Based, in part, on observations obtained at the Cerro-Tololo Inter-American Observatory (CTIO), National Optical Astronomy Observatory (NOAO). Based on observations obtained at the Canada-France-Hawaii Telescope (CFHT). Based, in part, on observations obtained at the Gemini Observatory (Gemini programs GN-2001A-SV-19 and GN-2002A-Q-31). Based, in part on observations obtained at the Subaru Telescope. Based, in part, on data that were obtained at the W. M. Keck Observatory.
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.
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 a search for galaxy-scale strong gravitational lenses in the initial 2500 square degrees of the Canada-France Imaging Survey (CFIS). We designed a convolutional neural network (CNN) ...committee that we applied to a selection of 2 344 002 exquisite-seeing
r
-band images of color-selected luminous red galaxies. Our classification uses a realistic training set where the lensing galaxies and the lensed sources are both taken from real data, namely the CFIS
r
-band images themselves and the
Hubble
Space Telescope (HST). A total of 9460 candidates obtain a score above 0.5 with the CNN committee. After a visual inspection of the candidates, we find a total of 133 lens candidates, of which 104 are completely new. The set of false positives mainly contains ring, spiral, and merger galaxies, and to a lesser extent galaxies with nearby companions. We classify 32 of the lens candidates as secure lenses and 101 as maybe lenses. For the 32 highest quality lenses, we also fit a singular isothermal ellipsoid mass profile with external shear along with an elliptical Sersic profile for the lens and source light. This automated modeling step provides distributions of properties for both sources and lenses that have Einstein radii in the range 0.5″ <
θ
E
< 2.5″. Finally, we introduce a new lens and/or source single-band deblending algorithm based on auto-encoder representation of our candidates. This is the first time an end-to-end lens-finding and modeling pipeline is assembled together, in view of future lens searches in a single band, as will be possible with
Euclid
.
Context.
In June 2022,
Gaia
DR3 provided the astronomy community with about one million spectra from the Radial Velocity Spectrometer (RVS) covering the CaII triplet region. In the next
Gaia
data ...releases, we anticipate the number of RVS spectra to successively increase from several 10 million spectra to eventually more than 200 million spectra. Thus, stellar spectra are projected to be produced on an ‘industrial scale’, with numbers well above those for current and anticipated ground-based surveys. However, one-third of the published spectra have 15 ≤
S /N
≤ 25 per pixel such that they pose problems for classical spectral analysis pipelines, and therefore, alternative ways to tap into these large datasets need to be devised.
Aims.
We aim to leverage the versatility and capabilities of machine learning techniques for supercharged stellar parametrisation by combining
Gaia
-RVS spectra with the full set of
Gaia
products and high-resolution, high-quality ground-based spectroscopic reference datasets.
Methods.
We developed a hybrid convolutional neural network (CNN) that combines the
Gaia
DR3 RVS spectra, photometry (G, G_BP, G_RP), parallaxes, and XP coefficients to derive atmospheric parameters (
T
eff
, log(g) as well as overall M/H) and chemical abundances (Fe/H and
α
/M). We trained the CNN with a high-quality training sample based on APOGEE DR17 labels.
Results.
With this CNN, we derived homogeneous atmospheric parameters and abundances for 886 080 RVS stars that show remarkable precision and accuracy compared to external datasets (such as GALAH and asteroseismology). The CNN is robust against noise in the RVS data, and we derive very precise labels down to S/N =15. We managed to characterise the
α
/M - M/H bimodality from the inner regions to the outer parts of the Milky Way, which has never been done using RVS spectra or similar datasets.
Conclusions.
This work is the first to combine machine learning with such diverse datasets and paves the way for large-scale machine learning analysis of
Gaia
-RVS spectra from future data releases. Large, high-quality datasets can be optimally combined thanks to the CNN, thereby realising the full power of spectroscopy, astrometry, and photometry.
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