A streamlined method for chiral fermions on the lattice

We discuss the use of renormalization counterterms to restore the chiral gauge symmetry in a lattice theory of Wilson fermions. We show that a large class of counterterms can be implemented automatically by making a simple modification to the fermion determinant.Comment: 4 pages, ANL-HEP-CP-92-10

Photometric classification and redshift estimation of LSST Supernovae

Supernova (SN) classification and redshift estimation using photometric data only have become very important for the Large Synoptic Survey Telescope (LSST), given the large number of SNe that LSST will observe and the impossibility of spectroscopically following up all the SNe. We investigate the performance of an SN classifier that uses SN colours to classify LSST SNe with the Random Forest classification algorithm. Our classifier results in an area-under-the-curve of 0.98 which represents excellent classification. We are able to obtain a photometric SN sample containing 99 per cent SNe Ia by choosing a probability threshold. We estimate the photometric redshifts (photo-z) of SNe in our sample by fitting the SN light curves using the SALT2 model with nested sampling. We obtain a mean bias (⟨z_(phot) − z_(spec)⟩) of 0.012 with σ((z_(phot) − z_(spec))/(1+z_(spec)) = 0.0294 without using a host-galaxy photo-z prior, and a mean bias (⟨z_(phot) − z_(spec)⟩) of 0.0017 with σ((z_(phot) − z_(spec))/(1 + z_(spec)) = 0.0116 using a host-galaxy photo-z prior. Assuming a flat ΛCDM model with Ωm = 0.3, we obtain Ωm of 0.305 ± 0.008 (statistical errors only), using the simulated LSST sample of photometric SNe Ia (with intrinsic scatter σ_(int) = 0.11) derived using our methodology without using host-galaxy photo-z prior. Our method will help boost the power of SNe from the LSST as cosmological probes

Photometric classification and redshift estimation of LSST Supernovae

Supernova (SN) classification and redshift estimation using photometric data only have become very important for the Large Synoptic Survey Telescope (LSST), given the large number of SNe that LSST will observe and the impossibility of spectroscopically following up all the SNe. We investigate the performance of an SN classifier that uses SN colours to classify LSST SNe with the Random Forest classification algorithm. Our classifier results in an area-under-the-curve of 0.98 which represents excellent classification. We are able to obtain a photometric SN sample containing 99 per cent SNe Ia by choosing a probability threshold. We estimate the photometric redshifts (photo-z) of SNe in our sample by fitting the SN light curves using the SALT2 model with nested sampling. We obtain a mean bias (⟨z_(phot) − z_(spec)⟩) of 0.012 with σ((z_(phot) − z_(spec))/(1+z_(spec)) = 0.0294 without using a host-galaxy photo-z prior, and a mean bias (⟨z_(phot) − z_(spec)⟩) of 0.0017 with σ((z_(phot) − z_(spec))/(1 + z_(spec)) = 0.0116 using a host-galaxy photo-z prior. Assuming a flat ΛCDM model with Ωm = 0.3, we obtain Ωm of 0.305 ± 0.008 (statistical errors only), using the simulated LSST sample of photometric SNe Ia (with intrinsic scatter σ_(int) = 0.11) derived using our methodology without using host-galaxy photo-z prior. Our method will help boost the power of SNe from the LSST as cosmological probes

Type Ia Supernovae Selection and Forecast of Cosmology Constraints for the Dark Energy Survey

We present the results of a study of selection criteria to identify Type Ia supernovae photometrically in a simulated mixed sample of Type Ia supernovae and core collapse supernovae. The simulated sample is a mockup of the expected results of the Dark Energy Survey. Fits to the MLCS2k2 and SALT2 Type Ia supernova models are compared and used to help separate the Type Ia supernovae from the core collapse sample. The Dark Energy Task Force Figure of Merit (modified to include core collapse supernovae systematics) is used to discriminate among the various selection criteria. This study of varying selection cuts for Type Ia supernova candidates is the first to evaluate core collapse contamination using the Figure of Merit. Different factors that contribute to the Figure of Merit are detailed. With our analysis methods, both SALT2 and MLCS2k2 Figures of Merit improve with tighter selection cuts and higher purities, peaking at 98% purity.Comment: submitted to JCAP, 23 pages, 36 picture

Scaling, asymptotic scaling and Symanzik improvement. Deconfinement temperature in SU(2) pure gauge theory

We report on a high statistics simulation of SU(2) pure gauge field theory at finite temperature, using Symanzik action. We determine the critical coupling for the deconfinement phase transition on lattices up to 8 x 24, using Finite Size Scaling techniques. We find that the pattern of asymptotic scaling violation is essentially the same as the one observed with conventional, not improved action. On the other hand, the use of effective couplings defined in terms of plaquette expectation values shows a precocious scaling, with respect to an analogous analysis of data obtained by the use of Wilson action, which we interpret as an effect of improvement.Comment: 43 pages ( REVTeX 3.0, self-extracting shell archive, 13 PostScript figs.), report IFUP-TH 21/93 (2 TYPOS IN FORMULAS CORRECTED,1 CITATION UPDATED,CITATIONS IN TEXT ADDED

Simulating image coaddition with the Nancy Grace Roman Space Telescope: I. Simulation methodology and general results

The upcoming Nancy Grace Roman Space Telescope will carry out a wide-area survey in the near infrared. A key science objective is the measurement of cosmic structure via weak gravitational lensing. Roman data will be undersampled, which introduces new challenges in the measurement of source galaxy shapes; a potential solution is to use linear algebra-based coaddition techniques such as Imcom that combine multiple undersampled images to produce a single oversampled output mosaic with a desired "target" point spread function (PSF). We present here an initial application of Imcom to 0.64 square degrees of simulated Roman data, based on the Roman branch of the Legacy Survey of Space and Time (LSST) Dark Energy Science Collaboration (DESC) Data Challenge 2 (DC2) simulation. We show that Imcom runs successfully on simulated data that includes features such as plate scale distortions, chip gaps, detector defects, and cosmic ray masks. We simultaneously propagate grids of injected sources and simulated noise fields as well as the full simulation. We quantify the residual deviations of the PSF from the target (the "leakage"), as well as noise properties of the output images; we discuss how the overall tiling pattern as well as Moir\'e patterns appear in the final leakage and noise maps. We include appendices on interpolation algorithms and the interaction of undersampling with image processing operations that may be of broader applicability. The companion paper ("Paper II") explores the implications for weak lensing analyses.Comment: 28 pages, 19 figures, matches version accepted by MNRA