Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userOn the cosmological performance of photometrically classified supernovae with machine learning
178
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
The efficient classification of different types of supernova is one of the most important problems for observational cosmology. However, spectroscopic confirmation of most objects in upcoming photometric surveys, such as the The Rubin Observatory Legacy Survey of Space and Time (LSST), will be unfeasible. The development of automated classification processes based on photometry has thus become crucial. In this paper we investigate the performance of machine learning (ML) classification on the final cosmological constraints using simulated lightcurves from The Supernova Photometric Classification Challenge, released in 2010. We study the use of different feature sets for the lightcurves and many different ML pipelines based on either decision tree ensembles or automated search processes. To construct the final catalogs we propose a threshold selection method, by employing a emph{Bias-Variance tradeoff}. This is a very robust and efficient way to minimize the Mean Squared Error. With this method we were able to get very strong cosmological constraints, which allowed us to keep $sim 75%$ of the total information in the type Ia SNe when using the SALT2 feature set and $sim 33%$ for the other cases (based on either the Newling model or on standard wavelet decomposition).
rate research
Read More
We present the cosmological analysis of 752 photometrically-classified Type Ia Supernovae (SNe Ia) obtained from the full Sloan Digital Sky Survey II (SDSS-II) Supernova (SN) Survey, supplemented with host-galaxy spectroscopy from the SDSS-III Baryon Oscillation Spectroscopic Survey (BOSS). Our photometric-classification method is based on the SN typing technique of Sako et al. (2011), aided by host galaxy redshifts (0.05<z<0.55). SNANA simulations of our methodology estimate that we have a SN Ia typing efficiency of 70.8%, with only 3.9% contamination from core-collapse (non-Ia) SNe. We demonstrate that this level of contamination has no effect on our cosmological constraints. We quantify and correct for our selection effects (e.g., Malmquist bias) using simulations. When fitting to a flat LambdaCDM cosmological model, we find that our photometric sample alone gives omega_m=0.24+0.07-0.05 (statistical errors only). If we relax the constraint on flatness, then our sample provides competitive joint statistical constraints on omega_m and omega_lambda, comparable to those derived from the spectroscopically-confirmed three-year Supernova Legacy Survey (SNLS3). Using only our data, the statistics-only result favors an accelerating universe at 99.96% confidence. Assuming a constant wCDM cosmological model, and combining with H0, CMB and LRG data, we obtain w=-0.96+0.10-0.10, omega_m=0.29+0.02-0.02 and omega_k=0.00+0.03-0.02 (statistical errors only), which is competitive with similar spectroscopically confirmed SNe Ia analyses. Overall this comparison is re-assuring, considering the lower redshift leverage of the SDSS-II SN sample (z<0.55) and the lack of spectroscopic confirmation used herein. These results demonstrate the potential of photometrically-classified SNe Ia samples in improving cosmological constraints.
The Pan-STARRS (PS1) Medium Deep Survey discovered over 5,000 likely supernovae (SNe) but obtained spectral classifications for just 10% of its SN candidates. We measured spectroscopic host galaxy redshifts for 3,147 of these likely SNe and estimate that $sim$1,000 are Type Ia SNe (SNe Ia) with light-curve quality sufficient for a cosmological analysis. We use these data with simulations to determine the impact of core-collapse SN (CC SN) contamination on measurements of the dark energy equation of state parameter, $w$. Using the method of Bayesian Estimation Applied to Multiple Species (BEAMS), distances to SNe Ia and the contaminating CC SN distribution are simultaneously determined. We test light-curve based SN classification priors for BEAMS as well as a new classification method that relies upon host galaxy spectra and the association of SN type with host type. By testing several SN classification methods and CC SN parameterizations on large SN simulations, we estimate that CC SN contamination gives a systematic error on $w$ ($sigma_w^{CC}$) of 0.014, 29% of the statistical uncertainty. Our best method gives $sigma_w^{CC} = 0.004$, just 8% of the statistical uncertainty, but could be affected by incomplete knowledge of the CC SN distribution. This method determines the SALT2 color and shape coefficients, $alpha$ and $beta$, with $sim$3% bias. However, we find that some variants require $alpha$ and $beta$ to be fixed to known values for BEAMS to yield accurate measurements of $w$. Finally, the inferred abundance of bright CC SNe in our sample is greater than expected based on measured CC SN rates and luminosity functions.
The present paper analyses the quasar clustering using the two-point correlation function (2pCF) and the largest existing sample of photometrically selected quasars: the SDSS NBCKDE catalogue (from the SDSS DR6). A new technique of random catalogue generation was developed for this purpose, that allows to take into account the original homogeneity of the survey without knowledge of its imaging mask. When averaged over photometrical redshifts 0.8<z_phot<2.2 the 2pCF of photometrically selected quasars is found to be approximated well with the power law w(theta)=(theta/theta_0)^{-alpha} with theta_0=4.5+/-1.4, alpha=0.94+/-0.06 over the range 1<theta<40. It agrees well with previous results by Myers et al. (2006,2007), obtained for samples of NBCKDE quasars with similar mean z_phot, but averaged over broader z_phot range. The parameters of the deprojected 2pCF averaged over the same z_phot range and modelled with a power law xi(r)=(r/r_0)^{-gamma}, are r_0=7.81^{+1.18}_{-1.16} Mpc/h, gamma=1.94+/-0.06, which are in perfect agreement with previous results from spectroscopic surveys. We confirm the evidence for an increase of the clustering amplitude with z, and find no evidence for luminosity dependence of the quasar clustering. The latter is consistent with the models of the quasar formation, in which bright and faint quasars are assumed to be similar sources, hosted by dark matter halos of similar masses, but observed at different stages of their evolution. Comparison of our results with studies of the X-ray selected AGNs with similar z shows that the clustering amplitude of optically selected quasars is similar to that of X-ray selected quasars, but lower than that of samples of all X-ray selected AGNs. As the samples of all X-ray selected AGNs contain AGNs of both types, our result serves as an evidence for different types of AGNs to reside in different environments.
Recent cosmological analyses (e.g., JLA, Pantheon) of Type Ia Supernova (SNIa) have propagated systematic uncertainties into a covariance matrix and either binned or smoothed the systematic vectors in redshift space. We demonstrate that systematic error budgets of these analyses can be improved by a factor of $sim1.5times$ with the use of unbinned and unsmoothed covariance matrices. To understand this, we employ a separate approach that simultaneously fits for cosmological parameters and additional self-calibrating scale parameters that constrain the size of each systematic. We show that the covariance-matrix approach and scale-parameter approach yield equivalent results, implying that in both cases the data can self-calibrate certain systematic uncertainties, but that this ability is hindered when information is binned or smoothed in redshift space. We review the top systematic uncertainties in current analyses and find that the reduction of systematic uncertainties in the unbinned case depends on whether a systematic is consistent with varying the cosmological model and whether or not the systematic can be described by additional correlations between SN properties and luminosity. Furthermore, we show that the power of self-calibration increases with the size of the dataset, which presents a tremendous opportunity for upcoming analyses of photometrically classified samples, like those of Legacy Survey of Space and Time (LSST) and the Nancy Grace Roman Telescope (NGRST). However, to take advantage of self-calibration in large, photometrically-classified samples, we must first address the issue that binning is required in currently-used photometric methodologies.
We explore the use of random forest and gradient boosting, two powerful tree-based machine learning algorithms, for the detection of cosmic strings in maps of the cosmic microwave background (CMB), through their unique Gott-Kaiser-Stebbins effect on the temperature anisotropies.The information in the maps is compressed into feature vectors before being passed to the learning units. The feature vectors contain various statistical measures of processed CMB maps that boost the cosmic string detectability. Our proposed classifiers, after training, give results improved over or similar to the claimed detectability levels of the existing methods for string tension, $Gmu$. They can make $3sigma$ detection of strings with $Gmu gtrsim 2.1times 10^{-10}$ for noise-free, $0.9$-resolution CMB observations. The minimum detectable tension increases to $Gmu gtrsim 3.0times 10^{-8}$ for a more realistic, CMB S4-like (II) strategy, still a significant improvement over the previous results.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
