- 标题
- 摘要
- 关键词
- 实验方案
- 产品
-
Denoising, deconvolving, and decomposing multi-domain photon observations
摘要： Astronomical imaging based on photon count data is a non-trivial task. In this context we show how to denoise, deconvolve, and decompose multi-domain photon observations. The primary objective is to incorporate accurate and well motivated likelihood and prior models in order to give reliable estimates about morphologically different but superimposed photon flux components present in the data set. Thereby we denoise and deconvolve photon counts, while simultaneously decomposing them into diffuse, point-like and uninteresting background radiation fluxes. The decomposition is based on a probabilistic hierarchical Bayesian parameter model within the framework of information field theory (IFT). In contrast to its predecessor D3PO, D4PO reconstructs multi-domain components. Thereby each component is defined over its own direct product of multiple independent domains, for example location and energy. D4PO has the capability to reconstruct correlation structures over each of the sub-domains of a component separately. Thereby the inferred correlations implicitly define the morphologically different source components, except for the spatial correlations of the point-like flux. Point-like source fluxes are spatially uncorrelated by definition. The capabilities of the algorithm are demonstrated by means of a synthetic, but realistic, mock data set, providing spectral and spatial information about each detected photon. D4PO successfully denoised, deconvolved, and decomposed a photon count image into diffuse, point-like and background flux, each being functions of location as well as energy. Moreover, uncertainty estimates of the reconstructed fields as well as of their correlation structure are provided employing their posterior density function and accounting for the manifolds the domains reside on.
关键词： gamma rays: general,methods: data analysis,methods: statistical,X-rays: general,methods: numerical,techniques: image processing
更新于2025-09-23 15:23:52
-
Measuring dark energy with the <i>E</i> <sub/>iso</sub> – <i>E</i> <sub/>p</sub> correlation of gamma-ray bursts using model-independent methods
摘要： We use two model-independent methods to standardize long gamma-ray bursts (GRBs) using the Eiso ? Ep correlation (log Eiso = a + b log Ep), where Eiso is the isotropic-equivalent gamma-ray energy and Ep is the spectral peak energy. We update 42 long GRBs and attempt to constrain the cosmological parameters. The full sample contains 151 long GRBs with redshifts from 0.0331 to 8.2. The first method is the simultaneous fitting method. We take the extrinsic scatter σext into account and assign it to the parameter Eiso. The best-fitting values are a = 49.15 ± 0.26, b = 1.42 ± 0.11, σext = 0.34 ± 0.03 and Ωm = 0.79 in the flat ΛCDM model. The constraint on Ωm is 0.55 < Ωm < 1 at the 1σ confidence level. If reduced χ2 method is used, the best-fit results are a = 48.96 ± 0.18, b = 1.52 ± 0.08, and Ωm = 0.50 ± 0.12. The second method uses type Ia supernovae (SNe Ia) to calibrate the Eiso ? Ep correlation. We calibrate 90 high-redshift GRBs in the redshift range from 1.44 to 8.1. The cosmological constraints from these 90 GRBs are Ωm = 0.23+0.06?0.04 for flat ΛCDM and Ωm = 0.18 ± 0.11 and ΩΛ = 0.46 ± 0.51 for non-flat ΛCDM. For the combination of GRB and SNe Ia sample, we obtain Ωm = 0.271 ± 0.019 and h = 0.701 ± 0.002 for the flat ΛCDM and the non-flat ΛCDM, and the results are Ωm = 0.225 ± 0.044, ΩΛ = 0.640 ± 0.082, and h = 0.698 ± 0.004. These results from calibrated GRBs are consistent with that of SNe Ia. Meanwhile, the combined data can improve cosmological constraints significantly, compared to SNe Ia alone. Our results show that the Eiso ? Ep correlation is promising to probe the high-redshift Universe.
关键词： cosmological parameters,dark energy,gamma rays: general
更新于2025-09-04 15:30:14