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相关论文: From Hubble to Snap Parameters: A Gaussian Process…

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We present a measurement of the Hubble constant ($H_0$) using type Ia supernova (SNe Ia) in the near-infrared (NIR) from the recently updated sample of SNe Ia in nearby galaxies with distances measured via Cepheid period-luminosity…

We present a systematic analysis of the cosmological constraints from the "Pantheon Sample" of 1048 Type Ia Supernovae (SNe Ia) in the redshift range $0.01<z<2.3$ compiled by Scolnic et al. (2018). Applying the flux-averaging method for…

宇宙学与河外天体物理 · 物理学 2019-07-10 Zhongxu Zhai , Yun Wang

We use the newly published 28 observational Hubble parameter data ($H(z)$) and current largest SNe Ia samples (Union2.1) to test whether the universe is transparent. Three cosmological-model-independent methods (nearby SNe Ia method,…

宇宙学与河外天体物理 · 物理学 2013-01-15 Kai Liao , Zhengxiang Li , Jing Ming , Zong-Hong Zhu

We perform model-independent distance measurements on four Type Ia supernovae (SNe Ia) compilations (Pantheon, Pantheon+, DES-Dovekie, Union3) and compress each dataset into the values of $\log r_p(z)$ at eleven redshift knots, where…

宇宙学与河外天体物理 · 物理学 2026-05-20 Zhenyuan Wang , Yun Wang

We analyse the possibility that our Universe could be described by the model recently proposed by Melia & Shevchuk (2012), where the Hubble scale R_h=c/H is at all times equal to the distance ct that light has travelled since the Big Bang.…

宇宙学与河外天体物理 · 物理学 2012-08-28 Maciej Bilicki , Marina Seikel

In this paper, we study the cosmological constraints from the measurements of Hubble parameters---$H(z)$ data. Here, we consider two kinds of $H(z)$ data: the direct $H_0$ probe from the Hubble Space Telescope (HST) observations of Cepheid…

宇宙学与河外天体物理 · 物理学 2014-03-31 Wei Zheng , Hong Li , Jun-Qing Xia , You-Ping Wan , Si-Yu Li , Mingzhe Li

This paper presents a new model-independent constraint on the Hubble constant ($H_0$) by anchoring relative distances from Type Ia supernovae (SNe Ia) observations to absolute distance measurements from time-delay strong Gravitational…

宇宙学与河外天体物理 · 物理学 2025-03-13 L. R. Colaço

We use the redshift Hubble parameter $H(z)$ data derived from relative galaxy ages, distant type Ia supernovae (SNe Ia), the Baryonic Acoustic Oscillation (BAO) peak, and the Cosmic Microwave Background (CMB) shift parameter data, to…

宇宙学与河外天体物理 · 物理学 2011-01-27 Tian Lan , Yan Gong , Hao-Yi Wan , Tong-Jie Zhang

Direct observations of the Hubble rate, from cosmic chronometers and the radial baryon acoustic oscillation scale, can out-perform supernovae observations in understanding the expansion history, because supernovae observations need to be…

宇宙学与河外天体物理 · 物理学 2012-10-26 Marina Seikel , Sahba Yahya , Roy Maartens , Chris Clarkson

In this paper, we show that the expansion history of the Universe in power-law cosmology essentially depends on two crucial parameters, namely the Hubble constant $H_{0}$ and deceleration parameter $q$. We find the constraints on these…

广义相对论与量子宇宙学 · 物理学 2014-08-27 Suresh Kumar

In this article, we introduce an innovative parametric representation of the Hubble parameter, providing a model-independent means to explore the dynamics of an accelerating cosmos. The model's parameters are rigorously constrained through…

宇宙学与河外天体物理 · 物理学 2024-04-16 M. Koussour , N. Myrzakulov , M. K. M. Ali

We present a cosmological analysis of the Lick Observatory Supernova Search (LOSS) Type Ia supernova (SN Ia) photometry sample introduced by Ganeshalingam et al. (2010). These SNe provide an effective anchor point to estimate cosmological…

宇宙学与河外天体物理 · 物理学 2013-07-04 Mohan Ganeshalingam , Weidong Li , Alexei V. Filippenko

Machine learning has become widely used in astronomy. Gaussian Process (GP) regression in particular has been employed a number of times to fit or re-sample supernova (SN) light-curves, however by their nature typical GP models are not…

太阳与恒星天体物理 · 物理学 2022-12-14 H. F. Stevance , A. Lee

This paper builds upon ParamANN's novel approach (S. Pal & R. Saha 2024) of using ANNs to infer cosmological density parameters by determining optimal architecture for varying synthetic Hubble data SNRs in estimating the density parameters…

宇宙学与河外天体物理 · 物理学 2025-10-16 Zijian Jin , Jaehyon Rhee

There is a persistent $H_0$-tension, now at more than $\gtrsim 4\sigma$ level, between the local distance ladder value and the \emph{Planck} cosmic microwave background measurement, in the context of flat $\Lambda$CDM model. We reconstruct…

宇宙学与河外天体物理 · 物理学 2020-01-30 Meng-Zhen Lyu , Balakrishna S. Haridasu , Matteo Viel , Jun-Qing Xia

In the current work, we have implemented an extension of the standard Gaussian Process formalism, namely the Multi-Task Gaussian Process with the ability to perform a joint learning of several cosmological data simultaneously. We have…

宇宙学与河外天体物理 · 物理学 2018-10-15 Balakrishna S. Haridasu , Vladimir V. Luković , Michele Moresco , Nicola Vittorio

The cosmic curvature density parameter has been constrained in the present work independent of any background cosmological model. The reconstruction is performed adopting the non-parametric Gaussian Processes (GP). The constraints on…

宇宙学与河外天体物理 · 物理学 2022-03-18 Purba Mukherjee , Narayan Banerjee

In the $\Lambda$CDM model, cosmological observations from the late and recent universe reveal a puzzling $\sim 4.5\sigma$ tension in the current rate of universe expansion. In addition to the various scenarios suggested to resolve the…

宇宙学与河外天体物理 · 物理学 2022-01-05 Maryam Vazirnia , Ahmad Mehrabi

We continue our presentation of an alternative cosmology based on conformal gravity, following our kinematical approach introduced in a recent paper. In line with the assumptions of our model, which proposes a closed-form expression for the…

天体物理学 · 物理学 2011-09-28 Gabriele U. Varieschi

The errors of cosmological data generated from complex processes, such as the observational Hubble parameter data (OHD) and the Type Ia supernova (SN Ia) data, cannot be accurately modeled by simple analytical probability distributions,…

宇宙学与河外天体物理 · 物理学 2021-06-17 Yu-Chen Wang , Yuan-Bo Xie , Tong-Jie Zhang , Hui-Chao Huang , Tingting Zhang , Kun Liu