Template Fitting Method Research Articles

Exhausting the information: novel Bayesian combination of photometric redshift PDFs

The estimation and utilization of photometric redshift probability density functions (photo-$z$ PDFs) has become increasingly important over the last few years and currently there exist a wide variety of algorithms to compute photo-$z$'s, each with their own strengths and weaknesses. In this paper, we present a novel and efficient Bayesian framework that combines the results from different photo-$z$ techniques into a more powerful and robust estimate by maximizing the information from the photometric data. To demonstrate this we use a supervised machine learning technique based on random forest, an unsupervised method based on self-organizing maps, and a standard template fitting method but can be easily extend to other existing techniques. We use data from the DEEP2 and the SDSS surveys to explore different methods for combining the predictions from these techniques. By using different performance metrics, we demonstrate that we can improve the accuracy of our final photo-$z$ estimate over the best input technique, that the fraction of outliers is reduced, and that the identification of outliers is significantly improved when we apply a Na\"{\i}ve Bayes Classifier to this combined information. Our more robust and accurate photo-$z$ PDFs will allow even more precise cosmological constraints to be made by using current and future photometric surveys. These improvements are crucial as we move to analyze photometric data that push to or even past the limits of the available training data, which will be the case with the Large Synoptic Survey Telescope.

The Revised IRAS-FSC Redshift Catalogue (RIFSCz)

We present a Revised IRAS-FSC Redshift Catalogue (RIFSCz) of 60,303 galaxies selected at 60 microns from the IRAS Faint Source Catalogue (FSC). This revision merges in data from the WISE All-Sky Data Release, the Tenth SDSS Data Release (DR10), the GALEX All-Sky Survey Source Catalog (GASC), the 2MASS Redshift Survey (2MRS) and the Planck Catalogue of Compact Sources (PCCS). The RIFSCz consists of accurate position, ultra-violet (UV), optical, near-, mid- and far-infrared, sub-millimetre (sub-mm) and/or radio identifications, spectroscopic redshift (if available) or photometric redshift (if possible), predicted far-infrared and sub-mm fluxes ranging from 12 to 1380 microns based upon the best-fit infrared template. We also provide stellar masses, star-formation rates and dust masses derived from the optical and infrared template fits, where possible. 56 of the galaxies in the RIFSCz have spectroscopic redshifts and a further 26 have photometric redshifts obtained through the template-fitting method. At S60 > 0.36 Jy, the 90% completeness limit of the FSC, 93 of the sources in the RIFSCz have either spectroscopic or photometric redshifts. An interesting subset of the catalogue is the sources detected by Planck at sub-mm wavelengths. 1200 sources have a detection at better than 5 sigma in at least one Planck band and a further 1186 sources have detections at 3-5 sigma in at least one Planck band.

Newly discovered RR Lyrae stars in the SDSS-Pan-STARRS1-Catalina footprint

We present the detection of 6,371 RR Lyrae (RRL) stars distributed across ~14,000 deg^2 of the sky from the combined data of the Sloan Digital Sky Survey (SDSS), the Panoramic Survey Telescope and Rapid Response System 1 (PS1), and the second photometric catalogue from the Catalina Survey (CSDR2), out of these, ~2,021 RRL stars (~572 RRab and 1,449 RRc) are new discoveries. The RRL stars have heliocentric distances in the 4--28 kpc distance range. RRL-like color cuts from the SDSS and variability cuts from the PS1 are used to cull our candidate list. We then use the CSDR2 multi-epoch data to refine our sample. Periods were measured using the Analysis of Variance technique while the classification process is performed with the Template Fitting Method in addition to the visual inspection of the light curves. A cross-match of our RRL star discoveries with previous published catalogs of RRL stars yield completeness levels of ~50% for both RRab and RRc stars, and an efficiency of ~99% and ~87% for RRab and RRc stars, respectively. We show that our method for selecting RRL stars allows us to recover halo structures. The full lists of all the RRL stars are made publicly available.

A new method to improve photometric redshift reconstruction

In the next decade, the LSST will become a major facility for the astronomical community. However accurately determining the redshifts of the observed galaxies without using spectroscopy is a major challenge. Reconstruction of the redshifts with high resolution and well-understood uncertainties is mandatory for many science goals, including the study of baryonic acoustic oscillations. We investigate different approaches to establish the accuracy that can be reached by the LSST six-band photometry. We construct a realistic mock galaxy catalog, based on the GOODS survey luminosity function, by simulating the expected apparent magnitude distribution for the LSST. To reconstruct the photometric redshifts (photo-z's), we consider a template-fitting method and a neural network method. The photo-z reconstruction from both of these techniques is tested on real CFHTLS data and also on simulated catalogs. We describe a new method to improve photo-z reconstruction that efficiently removes catastrophic outliers via a likelihood ratio statistical test. This test uses the posterior probability functions of the fit parameters and the colors. We show that the photometric redshift accuracy will meet the stringent LSST requirements up to redshift $\sim2.5$ after a selection that is based on the likelihood ratio test or on the apparent magnitude for galaxies with $S/N>5$ in at least 5 bands. The former selection has the advantage of retaining roughly 35% more galaxies for a similar photo-z performance compared to the latter. Photo-z reconstruction using a neural network algorithm is also described. In addition, we utilize the CFHTLS spectro-photometric catalog to outline the possibility of combining the neural network and template-fitting methods. We conclude that the photo-z's will be accurately estimated with the LSST if a Bayesian prior probability and a calibration sample are used.

Data reduction and calibration for LAMOST survey

AbstractThere are three data pipelines for LAMOST survey. The raw data is reduced to one dimension spectra by the data reduction pipeline(2D pipeline), the extracted spectra are classified and measured by the spectral analysis pipeline(1D pipeline), while stellar parameters are measured by LASP pipeline.(a) The data reduction pipeline. The main tasks of the data reduction pipeline include bias calibration, flat field, spectra extraction, sky subtraction, wavelength calibration, exposure merging and wavelength band connection.(b) The spectra analysis pipeline. This pipeline is designed to classify and identify objects from the extracted spectra and to measure their redshift (or radial velocity). The PCAZ (Glazebrooket al. 1998) method is applied to do the classification and redshift measurement.(c) Stellar parameters LASP. Stellar parameters pipeline (LASP) is to estimate stellar atmospheric parameters, e.g. effective temperature Teff, surface gravity log g, and metallicity [Fe/H], for F, G and K type stars. To effectively determine those fundamental stellar measurements, three steps with different methods are employed. The first step utilizes the line indices to approximately define the effective temperature range of the analyzed star. Secondly, a set of the initial approximate values of the three parameters are given based on template fitting method. Finally, we exploit ULySS (Kolevaet al. 2009) to give the final values of parameters through minimizing the χ2value between the observed spectrum and a multidimensional grid of model spectra which is generated by an interpolating of ELODIE library. There are two other classification for A type star and M type star. For A type star, standard MK system is employed (Grayet al. 2009) to give each object temperature class and luminosity type. For M type star, they are classified into subclasses by an improved Hammer method, and metallicity of each objects is also given.During the pilot survey, algorithms were improved and the pipelines were tested. The products of LAMOST survey will include extracted and calibrated spectra in FITS format, a catalog of FGK stars with stellar parameters, a catalog of M dwarf with subclass and metallicity, and a catalog of A type star with MK classification. A part of the pilot survey data, including about 319 000 high quality spectra with SNR > 10, a catalog of stellar parameters of FGK stars and another catalog of a subclass of M type stars have been released to the public in August 2012 (Luoet al. 2012). The general survey started from October 2012, and completed the first year survey. The formal data release one (DR1) is being prepared, which will include both pilot survey and first year general survey, and planed to be released under the LAMOST data policy.

Measurement of muon tagged open heavy flavor production in Pb + Pb collisions at 2.76 TeV with ATLAS

Abstract Heavy flavor quarks are an important probe with which to study the hot, dense medium produced in heavy ion collisions. Charm and bottom quarks are produced at a relatively early stage of the collision and are predicted to have a smaller gluon radiation due to the dead cone effect. Semi-leptonic heavy flavor decays can be identified by the presence of a muon and separated from the light meson background using a template fitting method. In this talk, the R CP for heavy flavor muons in Pb + Pb collisions at s N N = 2.76 TeV with the ATLAS detector at the LHC is presented as a function of muon transverse momentum.

Measuring Stellar Radial Velocity using Markov Chain Monte Carlo(MCMC) Method

AbstractStellar radial velocity is estimated by using template fitting and Markov Chain Monte Carlo(MCMC) methods. This method works on the LAMOST stellar spectra. The MCMC simulation generates a probability distribution of the RV. The RV error can also computed from distribution.

PHOTOMETRIC REDSHIFTS AND SYSTEMATIC VARIATIONS IN THE SPECTRAL ENERGY DISTRIBUTIONS OF LUMINOUS RED GALAXIES FROM SDSS DR7

We describe the construction of a template set of spectral energy distributions (SEDs) for the estimation of photometric redshifts of luminous red galaxies (LRGs) with a Bayesian template fitting method. By examining the color properties of several publicly available SED sets within a redshift range of 0<z<0.5 and comparing them to SDSS DR7 data, we show that only some of the investigated SEDs approximately match the colors of the LRG data throughout the redshift range, however not at the quantitative level required for precise photometric redshifts. We generate new SEDs by superposing model SEDs of composite stellar populations with a burst model, allowing both components to be reddened by dust, in order to match the data in five different redshift bins. We select a set of SEDs which represents the LRG data in color space within five redshift bins, thus defining our new SED template set for photometric redshift estimates. The results we get with the new template set and our Bayesian template fitting photometric redshift code (PhotoZ) are nearly unbiased, with a scatter of \sigma(\Delta z)=0.027 (including outliers), and a fraction of catastrophic outliers (|z_phot-z_spec|/(1+z_spec)>0.15) of 0.12%. We show that templates that optimally describe the brightest galaxies (-24.5<M_R<-22.7) indeed vary from z=0.1 to z=0.5, consistent with aging of the stellar population. Furthermore, we find that templates that optimally describe galaxies at z<0.1 strongly differ as a function of the absolute magnitude of the galaxies, indicating an increase in star formation activity for less luminous galaxies. Our findings based on the photometry of the SDSS LRGs and our SED template fitting are supported by comparison to the average SDSS LRG spectra in different luminosity and redshift bins.

STAR-GALAXY CLASSIFICATION IN MULTI-BAND OPTICAL IMAGING

Ground-based optical surveys such as PanSTARRS, DES, and LSST, will produce large catalogs to limiting magnitudes of r > 24. Star-galaxy separation poses a major challenge to such surveys because galaxies---even very compact galaxies---outnumber halo stars at these depths. We investigate photometric classification techniques on stars and galaxies with intrinsic FWHM < 0.2 arcsec. We consider unsupervised spectral energy distribution template fitting and supervised, data-driven Support Vector Machines (SVM). For template fitting, we use a Maximum Likelihood (ML) method and a new Hierarchical Bayesian (HB) method, which learns the prior distribution of template probabilities from the data. SVM requires training data to classify unknown sources; ML and HB don't. We consider i.) a best-case scenario (SVM_best) where the training data is (unrealistically) a random sampling of the data in both signal-to-noise and demographics, and ii.) a more realistic scenario where training is done on higher signal-to-noise data (SVM_real) at brighter apparent magnitudes. Testing with COSMOS ugriz data we find that HB outperforms ML, delivering ~80% completeness, with purity of ~60-90% for both stars and galaxies, respectively. We find no algorithm delivers perfect performance, and that studies of metal-poor main-sequence turnoff stars may be challenged by poor star-galaxy separation. Using the Receiver Operating Characteristic curve, we find a best-to-worst ranking of SVM_best, HB, ML, and SVM_real. We conclude, therefore, that a well trained SVM will outperform template-fitting methods. However, a normally trained SVM performs worse. Thus, Hierarchical Bayesian template fitting may prove to be the optimal classification method in future surveys.

Multiresolution internal template cleaning: an application to the Wilkinson Microwave Anisotropy Probe 7-yr polarization data

Cosmic microwave background (CMB) radiation data obtained by different experiments contain, besides the desired signal, a superposition of microwave sky contributions. We present a fast and robust method, using a wavelet decomposition on the sphere, to recover the CMB signal from microwave maps. An application to \textit{WMAP} polarization data is presented, showing its good performance particularly in very polluted regions of the sky. The applied wavelet has the advantages of requiring little computational time in its calculations, being adapted to the \textit{HEALPix} pixelization scheme, and offering the possibility of multi-resolution analysis. The decomposition is implemented as part of a fully internal template fitting method, minimizing the variance of the resulting map at each scale. Using a $\chi^2$ characterization of the noise, we find that the residuals of the cleaned maps are compatible with those expected from the instrumental noise. The maps are also comparable to those obtained from the \textit{WMAP} team, but in our case we do not make use of external data sets. In addition, at low resolution, our cleaned maps present a lower level of noise. The E-mode power spectrum $C_{\ell}^{EE}$ is computed at high and low resolution; and a cross power spectrum $C_{\ell}^{TE}$ is also calculated from the foreground reduced maps of temperature given by \textit{WMAP} and our cleaned maps of polarization at high resolution. These spectra are consistent with the power spectra supplied by the \textit{WMAP} team. We detect the E-mode acoustic peak at $\ell \sim 400$, as predicted by the standard $\Lambda CDM$ model. The B-mode power spectrum $C_{\ell}^{BB}$ is compatible with zero.

Unsupervised self-organized mapping: a versatile empirical tool for object selection, classification and redshift estimation in large surveys

We present an application of unsupervised machine learning – the self-organized map (SOM) – as a tool for visualizing, exploring and mining the catalogues of large astronomical surveys. Self-organization culminates in a low-resolution representation of the ‘topology’ of a parameter volume, and this can be exploited in various ways pertinent to astronomy. Using data from the Cosmological Evolution Survey (COSMOS), we demonstrate two key astronomical applications of the SOM: (i) object classification and selection, using galaxies with active galactic nuclei as an example, and (ii) photometric redshift estimation, illustrating how SOMs can be used as totally empirical predictive tools. With a training set of ∼3800 galaxies with zspec≤ 1, we achieve photometric redshift accuracies competitive with other (mainly template fitting) techniques that use a similar number of photometric bands [σ(Δz) = 0.03 with a ∼2 per cent outlier rate when using u* band to 8 m photometry]. We also test the SOM as a photo-z tool using the PHoto-z Accuracy Testing (PHAT) synthetic catalogue of Hildebrandt et al., which compares several different photo-z codes using a common input/training set. We find that the SOM can deliver accuracies that are competitive with many of the established template fitting and empirical methods. This technique is not without clear limitations, which are discussed, but we suggest it could be a powerful tool in the era of extremely large –‘petabyte’– data bases where efficient data mining is a paramount concern.

The rise-time distribution of nearby Type Ia supernovae

We present an analysis of the B-band and V-band rise-time distributions of nearby Type Ia supernovae (SNe Ia). We use a two-stretch template-fitting method to measure the rise and decline of BV light curves. Our analysis of 61 SNe with high-quality light curves indicates that the longer the time between explosion and maximum light (i.e., the rise time), the slower the decline of the light curve after maximum. However, SNe with slower post-maximum decline rates have a faster rise than would be expected from a single-parameter family of light curves, indicating that SN Ia light curves are not a single-parameter family of varying widths. Comparison of the B-band rise-time distribution for spectroscopically normal SNe Ia to those exhibiting high-velocity spectral features indicates that high-velocity (HV) SNe Ia have shorter B-band rise times compared to their spectroscopically normal counterparts. After normalising the B-band light curves to Dm15(B)= 1.1 mag (i.e., correcting the post-maximum decline to have the same shape as our template), we find that spectroscopically normal SNe Ia have a rise time of 18.03 +/- 0.24 d, while HV SNe have a faster B-band rise time of 16.63 +/- 0.29 d. Despite differences in the B band, we find that HV and normal SNe Ia have similar rise times in the V band. The initial rise of a SN Ia B-band light curve follows a power law with index 2.20 +0.27 -0.19, consistent with a parabolic rise in flux predicted by an expanding fireball toy model. We compare our early-time B-band data to models for the predicted signature of companion interaction arising from the single-degenerate progenitor scenario. There is a substantial degree of degeneracy between the adopted power-law index of the SN light-curve template, the rise time, and the amount of shock emission required to match the data.

Template-Based 3D Model Fitting Using Dual-Domain Relaxation

We introduce a template fitting method for 3D surface meshes. A given template mesh is deformed to closely approximate the input 3D geometry. The connectivity of the deformed template model is automatically adjusted to facilitate the geometric fitting and to ascertain high quality of the mesh elements. The template fitting process utilizes a specially tailored Laplacian processing framework, where in the first, coarse fitting stage we approximate the input geometry with a linearized biharmonic surface (a variant of LS-mesh), and then the fine geometric detail is fitted further using iterative Laplacian editing with reliable correspondence constraints and a local surface flattening mechanism to avoid foldovers. The latter step is performed in the dual mesh domain, which is shown to encourage near-equilateral mesh elements and significantly reduces the occurrence of triangle foldovers, a well-known problem in mesh fitting. To experimentally evaluate our approach, we compare our method with relevant state-of-the-art techniques and confirm significant improvements of results. In addition, we demonstrate the usefulness of our approach to the application of consistent surface parameterization (also known as cross-parameterization).

Statistical classification techniques for photometric supernova typing

Future photometric supernova surveys will produce vastly more candidates than can be followed up spectroscopically, highlighting the need for effective classification methods based on lightcurves alone. Here we introduce boosting and kernel density estimation techniques which have minimal astrophysical input, and compare their performance on 20,000 simulated Dark Energy Survey lightcurves. We demonstrate that these methods are comparable to the best template fitting methods currently used, and in particular do not require the redshift of the host galaxy or candidate. However both methods require a training sample that is representative of the full population, so typical spectroscopic supernova subsamples will lead to poor performance. To enable the full potential of such blind methods, we recommend that representative training samples should be used and so specific attention should be given to their creation in the design phase of future photometric surveys.

EFOSC2 spectroscopy of SWIRE-CDFS galaxies

We present the optical spectra of a sample of 34 SWIRE-CDFS sources observed with EFOSC2 on the ESO 3.6m Telescope. We have used the spectra and spectroscopic redshifts to validate our photometric redshift codes and SED template fitting methods. 12 of our sources are Infrared Luminous Galaxies. Of these, five belong to the class of ULIRGs and one to the class of HLIRGs with evidence of both an AGN and starburst component contributing to their extreme infrared luminosity for 3, starburst contributing for 1 and AGN contributing for 2 of them.

A DETAILED STUDY OF PHOTOMETRIC REDSHIFTS FOR GOODS-SOUTH GALAXIES

We use the deepest and the most comprehensive photometric data currently available for GOODS-South galaxies to measure their photometric redshifts. The photometry includes VLT/VIMOS (U-band), HST/ACS (F435W, F606W, F775W, and F850LP bands), VLT/ISAAC (J-, H-, and Ks-bands), and four Spitzer/IRAC channels (3.6, 4.5, 5.8, and 8.0 micron). The catalog is selected in the z-band (F850LP) and photometry in each band is carried out using the recently completed TFIT algorithm, which performs PSF matched photometry uniformly across different instruments and filters, despite large variations in PSFs and pixel scales. Photometric redshifts are derived using the GOODZ code, which is based on the template fitting method using priors. The code also implements "training" of the template SED set, using available spectroscopic redshifts in order to minimize systematic differences between the templates and the SEDs of the observed galaxies. Our final catalog covers an area of 153 sq. arcmin and includes photometric redshifts for a total of 32,505 objects. The scatter between our estimated photometric and spectroscopic redshifts is sigma=0.040 with 3.7% outliers to the full z-band depth of our catalog, decreasing to sigma=0.039 and 2.1% outliers at a magnitude limit m(z)<24.5. This is consistent with the best results previously published for GOODS-S galaxies, however, the present catalog is the deepest yet available and provides photometric redshifts for significantly more objects to deeper flux limits and higher redshifts than earlier works. Furthermore, we show that the photometric redshifts estimated here for galaxies selected as dropouts are consistent with those expected based on the Lyman break technique.

Spectroscopic identifications of SWIRE sources in ELAIS-N1

We present the largest spectroscopic follow-up performed in SWIRE ELAIS-N1. We were able to determine redshifts for 289 extragalactic sources. The values of spectroscopic redshifts of the latter have been compared with the estimated values from our photometric redshift code with very good agreement between the two for both galaxies and quasars. Six of the quasars are hyperluminous infrared galaxies all of which are broad line AGN. We have performed emission line diagnostics for 30 sources in order to classify them into star-forming, Seyferts, composite and LINER and compare the results to the predictions from our SED template fitting methods and mid-IR selection methods.

Cosmic shear requirements on the wavelength dependence of telescope point spread functions

Cosmic shear requires high precision measurement of galaxy shapes in the presence of the observational Point Spread Function (PSF) that smears out the image. The PSF must therefore be known for each galaxy to a high accuracy. However, for several reasons, the PSF is usually wavelength dependent, therefore the differences between the spectral energy distribution of the observed objects introduces further complexity. In this paper we investigate the effect of the wavelength-dependence of the PSF, focusing on instruments in which the PSF size is dominated by the diffraction-limit of the telescope and which use broad-band filters for shape measurement. We first calculate biases on cosmological parameter estimation from cosmic shear when the stellar PSF is used uncorrected. Using realistic galaxy and star spectral energy distributions and populations and a simple three-component circular PSF we find that the colour-dependence must be taken into account for the next generation of telescopes. We then consider two different methods for removing the effect (i) the use of stars of the same colour as the galaxies and (ii) estimation of the galaxy spectral energy distribution using multiple colours and using a telescope model for the PSF. We find that both of these methods correct the effect to levels below the tolerances required for per-cent level measurements of dark energy parameters. Comparison of the two methods favours the template-fitting method because its efficiency is less dependent on galaxy redshift than the broad-band colour method and takes full advantage of deeper photometry.

The ImperialIRAS-FSC Redshift Catalogue

We present a new catalogue, the Imperial IRAS-FSC Redshift Catalogue (IIFSCz), of 60,303 galaxies selected at 60 micron from the IRAS Faint Source Catalogue (FSC). The IIFSCz consists of accurate position, optical, near-infrared and/or radio identifications, spectroscopic redshift (if available) or photometric redshift (if possible), predicted far-infrared (FIR) and submillimetre (submm) fluxes ranging from 12 to 1380 micron based upon the best-fit infrared template. About 55% of the galaxies in the IIFSCz have spectroscopic redshifts and a further 20% have photometric redshifts obtained through either the training set or the template-fitting method. For S(60)>0.36 Jy, the 90% completeness limit of the FSC, 90% of the sources have either spectroscopic or photometric redshifts. Scientific applications of the IIFSCz include validation of current and forthcoming infrared and submm/mm surveys such as AKARI, Planck and Herschel, follow-up studies of rare source populations, large-scale structure and galaxy bias, local multiwavelength luminosity functions and source counts. The catalogue is publicly available from http://astro.imperial.ac.uk/~mrr/fss/

Photometric redshifts for the CFHTLS T0004 deep and wide fields

We compute photometric redshifts based on the template-fitting method in the fourth public release of the Canada-France-Hawaii Telescope Legacy Survey. This unique multi-colour catalogue comprises u*,g',r',i',z' photometry in four deep fields of 1 deg2 each and 35 deg2 distributed over three Wide fields. Our photometric redshifts are calibrated with and compared to 16,983 high-quality spectroscopic redshifts from several surveys. We find a dispersion of 0.028 and an outlier rate of 3.5% in the Deep field at i'AB < 24 and a dispersion of 0.036 and an outlier rate of 2.8% in the Wide field at i'AB < 22.5. Beyond i'AB = 22.5 in the Wide field the number of outliers rises from 5% to 10% at i'AB<23 and i'AB<24 respectively. For the Wide sample, we find the systematic redshift bias keeps below 1% to i'AB < 22.5, whereas we find no significant bias in the Deep field. We investigated the effect of tile-to-tile photometric variations and demonstrate that the accuracy of our photometric redshifts is reduced by at most 21%. We separate stars from galaxies using both the size and colour information, reducing the contamination by stars in our catalogues from 50% to 8% at i'AB < 22.5 in fields with the highest stellar density while keeping a complete galaxy sample. Our CFHTLS T0004 photometric redshifts are distributed to the community. Our release include 592,891 (i'AB < 22.5) and 244,701 (i'AB < 24) reliable galaxy photometric redshifts in the Wide and Deep fields, respectively.