arXiv subjects

Mi Dai

Publications and source records attributed to Mi Dai.

22 records · Page 2Linked to original sources

arXiv2018

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 a SN classifier that uses SN colors to classify LSST SNe with the Random Forest classification algorithm. Our classifier results in an AUC of 0.98 which represents excellent classification. We are able to obtain a photometric SN sample containing 99$\%$ 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 ($\left $) of 0.012 with $σ\left( \frac{z_\mathrm{phot}-z_\mathrm{spec}}{1+z_\mathrm{spec}}\right) = 0.0294$ without using a host-galaxy photo-z prior, and a mean bias ($\left $) of 0.0017 with $σ\left( \frac{z_\mathrm{phot}-z_\mathrm{spec}}{1+z_\mathrm{spec}}\right) = 0.0116$ using a host-galaxy photo-z prior. Assuming a flat $ΛCDM$ model with $Ω_m=0.3$, we obtain $Ω_m$ of $0.305\pm0.008$ (statistical errors only), using the simulated LSST sample of photometric SNe Ia (with intrinsic scatter $σ_\mathrm{int}=0.11$) 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.

astro-ph.CO↗

arXiv2016

Exploring uncertainties in dark energy constraints using current observational data with Planck 2015 distance priors

We present the distance priors that we have derived from the 2015 Planck data, and use these in combination with the latest observational data from Type Ia Supernovae (SNe Ia) and galaxy clustering, to explore the systematic uncertainties in dark energy constraints. We use the Joint Lightcurve Analysis (JLA) set of 740 SNe Ia, galaxy clustering measurements of H(z)s and D_A(z)/s (where s is the sound horizon at the drag epoch) from the Sloan Digital Sky Survey (SDSS) at z=0.35 and z=0.57 (BOSS DR12). We find that the combined dark energy constraints are insensitive to the assumptions made in the galaxy clustering measurements (whether they are for BAO only or marginalized over RSD), which indicates that as the analysis of galaxy clustering data becomes more accurate and robust, the systematic uncertainties are reduced. On the other hand, we find that flux-averaging SNe Ia at z>= 0.5 significantly tightens the dark energy constraints, and excludes a flat universe with a cosmological constant at 68% confidence level, assuming a dark energy equation of state linear in the cosmic scale factor. Flux-averaging has the most significant effect when we allow dark energy density function X(z) to be a free functions given by the cubic spline of its value at z=0, 1/3, 2/3, 1; the measured X(z) deviates from a cosmological constant at more than 95% confidence level for 0.4 < z < 0.7. Since flux-averaging reduces the bias in the SN distance measurements, this may be an indication that we have arrived in the era when the SN distance measurements are limited by systematic uncertainties.

astro-ph.CO↗

arXiv2016

Sampling the Probability Distribution of Type Ia Supernova Lightcurve Parameters in Cosmological Analysis

In order to obtain robust cosmological constraints from Type Ia supernova (SN Ia) data, we have applied Markov Chain Monte Carlo (MCMC) to SN Ia lightcurve fitting. We develop a method for sampling the resultant probability density distributions (pdf) of the SN Ia lightcuve parameters in the MCMC likelihood analysis to constrain cosmological parameters, and validate it using simulated data sets. Applying this method to the Joint Lightcurve Analysis (JLA) data set of SNe Ia, we find that sampling the SN Ia lightcurve parameter pdf's leads to cosmological parameters closer to that of a flat Universe with a cosmological constant, compared to the usual practice of using only the best fit values of the SN Ia lightcurve parameters. Our method will be useful in the use of SN Ia data for precision cosmology.

astro-ph.CO↗

arXiv2013

SZ effect or Not? - Detecting most galaxy clusters' main foreground effect

Galaxy clusters are the most massive objects in the Universe and comprise a high-temperature intracluster medium of about 10^7 K, believed to offer a main foreground effect for cosmic microwave background (CMB) data in the form of the thermal Sunyaev-Zel'dovich (SZ) effect. This assumption has been confirmed by SZ signal detection in hundreds of clusters but, in comparison with the huge numbers of clusters within optically selected samples from Sloan Digital Sky Survey (SDSS) data, this only accounts for a few per cent of clusters. Here we introduce a model-independent new method to confirm the assumption that most galaxy clusters can offer the thermal SZ signal as their main foreground effect. For the Wilkinson Microwave Anisotropy Probe (WMAP) seven-year data (and a given galaxy cluster sample), we introduced a parameter d1 as the nearest-neighbour cluster angular distance of each pixel, then we classified data pixels as 'to be' (d1--> 0 case) or 'not to be' (d1 large enough) affected by the sample clusters. By comparing the statistical results of these two kinds of pixels, we can see how the sample clusters affect the CMB data directly. We find that the Planck Early Sunyaev-Zel'dovich (ESZ) sample and X-ray samples (~10^2 clusters) can lead to obvious temperature depression in the WMAP seven-year data, which confirms the SZ effect prediction. However, each optically selected sample (>10^4 clusters) shows an opposite result: the mean temperature rises to about 10uK. This unexpected qualitative scenario implies that the main foreground effect of most clusters is $not$ always the expected SZ effect. This may be the reason why the SZ signal detection result is lower than expected from the model.

astro-ph.CO↗