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The power spectrum, as a statistic in Fourier space, is commonly numerically calculated using the fast Fourier transform method to efficiently reduce the computational costs. To alleviate the systematic bias known as aliasing due to the…

宇宙学与河外天体物理 · 物理学 2024-09-24 Yipeng Wang , Yu Yu

abridged] A method to rapidly estimate the Fourier power spectrum of a point distribution is presented. This method relies on a Taylor expansion of the trigonometric functions. It yields the Fourier modes from a number of FFTs, which is…

天体物理学 · 物理学 2009-11-13 Stephane Colombi , Andrew H. Jaffe , Dmitri Novikov , Christophe Pichon

In measuring the power spectrum of the distribution of large numbers of dark matter particles in simulations, or galaxies in observations, one has to use Fast Fourier Transforms (FFT) for calculational efficiency. However, because of the…

天体物理学 · 物理学 2009-11-13 Weiguang Cui , Lei Liu , Xiaohu Yang , Yu Wang , Longlong Feng , Volker Springel

Estimators for $n$-point clustering statistics in Fourier-space demand that modern surveys of large-scale structure be transformed to Cartesian coordinates to perform Fast Fourier Transforms (FFTs). In this work, we explore this…

宇宙学与河外天体物理 · 物理学 2024-02-05 Steven Cunnington , Laura Wolz

We present a method to recover the shape and amplitude of the power spectrum of mass fluctuations, P(k), from observations of the high redshift \lya forest. The method is motivated by the physical picture of the \lya forest that has emerged…

天体物理学 · 物理学 2009-10-30 Rupert A. C. Croft , David H. Weinberg , Neal Katz , Lars Hernquist

Efficient estimators of Fourier-space statistics for large number of objects rely on Fast Fourier Transforms (FFTs), which are affected by aliasing from unresolved small scale modes due to the finite FFT grid. Aliasing takes the form of a…

宇宙学与河外天体物理 · 物理学 2017-10-09 Emiliano Sefusatti , Martin Crocce , Roman Scoccimarro , Hugh Couchman

We measure the filling factor, correlation function, and power spectrum of transmitted flux in a large sample of Lya forest spectra, comprised of 30 Keck HIRES spectra and 23 Keck LRIS spectra. We infer the linear matter power spectrum P(k)…

We measure the linear power spectrum of mass density fluctuations at redshift z=2.5 from the \lya forest absorption in a sample of 19 QSO spectra, using the method introduced by Croft et al. (1998). The P(k) measurement covers the range…

天体物理学 · 物理学 2009-10-31 Rupert A. C. Croft , David H. Weinberg , Max Pettini , Lars Hernquist , Neal Katz

Fast Fourier Transform (FFT) is one of the most important tools in digital signal processing. FFT costs O(N \log N) for transforming a signal of length N. Recently, Sparse Fourier Transform (SFT) has emerged as a critical issue addressing…

数据结构与算法 · 计算机科学 2015-05-25 Sung-Hsien Hsieh , Chun-Shien Lu , Soo-Chang Pei

In this paper, we derive a new reconstruction method for real non-harmonic Fourier sums, i.e., real signals which can be represented as sparse exponential sums of the form $f(t) = \sum_{j=1}^{K} \gamma_{j} \, \cos(2\pi a_{j} t + b_{j})$,…

数值分析 · 数学 2020-11-30 Markus Petz , Gerlind Plonka , Nadiia Derevianko

We present a new and highly efficient algorithm for computing a power spectrum made from evenly spaced data which combines the noise-reducing advantages of the weighted fit with the computational advantages of the Fast Fourier Transform…

太阳与恒星天体物理 · 物理学 2015-05-28 S. T. Fletcher , A. -M. Broomhall , W. J. Chaplin , Y. Elsworth , R. New

In single dish neutral hydrogen (HI) intensity mapping, signal separation methods such as principal component analysis (PCA) are used to clean the astrophysical foregrounds. PCA induces a signal loss in the estimated power spectrum, which…

宇宙学与河外天体物理 · 物理学 2025-08-05 Zhaoting Chen

Employing the standard hard-scattering approach and the running coupling method we calculate a class of power-suppressed corrections $\sim 1/Q^{2n},n=1,2,3,...$ to the electromagnetic $\pi^0\gamma$ transition form factor (FF)…

高能物理 - 唯象学 · 物理学 2009-11-10 S. S. Agaev

Precision measurements of the galaxy power spectrum P(k) require a data analysis pipeline that is both fast enough to be computationally feasible and accurate enough to take full advantage of high-quality data. We present a rigorous…

天体物理学 · 物理学 2009-10-07 Max Tegmark , Andrew Hamilton , Michael Strauss , Michael Vogeley , Alexander Szalay

The Fast Fourier Transform (FFT) is the most efficiently known way to compute the Discrete Fourier Transform (DFT) of an arbitrary n-length signal, and has a computational complexity of O(n log n). If the DFT X of the signal x has only k…

信息论 · 计算机科学 2015-01-05 Sameer Pawar , Kannan Ramchandran

Oscillator fluctuations are described as the phase or frequency noise spectrum, or in terms of a wavelet variance as a function of the measurement time. The spectrum is generally approximated by the `power law,' i.e., a Laurent polynomial…

数据分析、统计与概率 · 物理学 2022-01-21 François Vernotte , Siyuan Chen , Enrico Rubiola

Spectra derived from fast Fourier transform (FFT) analysis of time-domain data intrinsically contain statistical fluctuations whose distribution depends on the number of accumulated spectra contributing to a measurement. The tail of this…

太阳与恒星天体物理 · 物理学 2015-06-19 Gelu M. Nita , Gregory D. Fleishman , Dale E. Gary , William Marin , Kristine Boone

The state-of-the-art automotive radars employ multidimensional discrete Fourier transforms (DFT) in order to estimate various target parameters. The DFT is implemented using the fast Fourier transform (FFT), at sample and computational…

信号处理 · 电气工程与系统科学 2018-01-16 Shaogang Wang , Vishal M. Patel , Athina Petropulu

We present the one-dimensional Lyman-$\alpha$ forest power spectrum measurement derived from the data release 1 (DR1) of the Dark Energy Spectroscopic Instrument (DESI). The measurement of the Lyman-$\alpha$ forest power spectrum along the…

To obtain the initial pressure from the collected data on a planar sensor arrangement in photoacoustic tomography, there exists an exact analytic frequency domain reconstruction formula. An efficient realization of this formula needs to…

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