scholarly journals Band-Limited Peak-Finding Method for a Noisy Frequency Spectrum

2021 ◽  
Author(s):  
Xiangcheng Chen ◽  
Qian Wang ◽  
Meng Wang ◽  
Yuhu Zhang
Keyword(s):  
Frequency Spectrum ◽  
Peak Finding ◽  
Band Limited ◽  
Finding Method

A vision-based hybrid particle tracking velocimetry (PTV) technique using a modified cascade correlation peak-finding method

2012 ◽  
Vol 53 (5) ◽  
pp. 1251-1268 ◽  
Author(s):  
Y.-C. Lei ◽  
W.-H. Tien ◽  
J. Duncan ◽  
M. Paul ◽  
N. Ponchaut ◽  
...  
Keyword(s):  
Particle Tracking ◽  
Particle Tracking Velocimetry ◽  
Correlation Peak ◽  
Hybrid Particle ◽  
Peak Finding ◽  
Cascade Correlation ◽  
Finding Method

scholarly journals Gaussian Process Classification for Galaxy Blend Identification in LSST

2022 ◽  
Vol 924 (2) ◽  
pp. 94
Author(s):  
James J. Buchanan ◽  
Michael D. Schneider ◽  
Robert E. Armstrong ◽  
Amanda L. Muyskens ◽  
Benjamin W. Priest ◽  
...  
Keyword(s):  
Neural Network ◽  
Gaussian Process ◽  
Classification Accuracy ◽  
Process Model ◽  
Line Of Sight ◽  
High Fidelity ◽  
Current Standard ◽  
Peak Finding ◽  
Process Classification ◽  
Finding Method

Abstract A significant fraction of observed galaxies in the Rubin Observatory Legacy Survey of Space and Time (LSST) will overlap at least one other galaxy along the same line of sight, in a so-called “blend.” The current standard method of assessing blend likelihood in LSST images relies on counting up the number of intensity peaks in the smoothed image of a blend candidate, but the reliability of this procedure has not yet been comprehensively studied. Here we construct a realistic distribution of blended and unblended galaxies through high-fidelity simulations of LSST-like images, and from this we examine the blend classification accuracy of the standard peak-finding method. Furthermore, we develop a novel Gaussian process blend classifier model, and show that this classifier is competitive with both the peak finding method as well as with a convolutional neural network model. Finally, whereas the peak-finding method does not naturally assign probabilities to its classification estimates, the Gaussian process model does, and we show that the Gaussian process classification probabilities are generally reliable.

scholarly journals An automatic peak finding method for LC-MS data using Gaussian second derivative filtering

2009 ◽  
Vol 32 (22) ◽  
pp. 3906-3918 ◽  
Author(s):  
Mattias J. Fredriksson ◽  
Patrik Petersson ◽  
Bengt-Olof Axelsson ◽  
Dan Bylund
Keyword(s):  
Second Derivative ◽  
Peak Finding ◽  
Finding Method

scholarly journals Unequal-mass mergers of dark matter haloes with rare and frequent self-interactions

2021 ◽  
Author(s):  
Moritz S Fischer ◽  
Marcus Brüggen ◽  
Kai Schmidt-Hoberg ◽  
Klaus Dolag ◽  
Antonio Ragagnin ◽  
...  
Keyword(s):  
Dark Matter ◽  
Cold Dark Matter ◽  
Large Angle ◽  
Space Distribution ◽  
Unequal Mass ◽  
Peak Finding ◽  
The Galaxy ◽  
Minor Mergers ◽  
The Impact ◽  
Finding Method

Abstract Dark matter self-interactions have been proposed to solve problems on small length scales within the standard cold dark matter cosmology. Here we investigate the effects of dark matter self-interactions in merging systems of galaxies and galaxy clusters with equal and unequal mass ratios. We perform N-body dark matter-only simulations of idealised setups to study the effects of dark matter self-interactions that are elastic and velocity-independent. We go beyond the commonly adopted assumption of large-angle (rare) dark matter scatterings, paying attention to the impact of small-angle (frequent) scatterings on astrophysical observables and related quantities. Specifically, we focus on dark matter-galaxy offsets, galaxy-galaxy distances, halo shapes, morphology and the phase-space distribution. Moreover, we compare two methods to identify peaks: one based on the gravitational potential and one based on isodensity contours. We find that the results are sensitive to the peak finding method, which poses a challenge for the analysis of merging systems in simulations and observations, especially for minor mergers. Large dark matter-galaxy offsets can occur in minor mergers, especially with frequent self-interactions. The subhalo tends to dissolve quickly for these cases. While clusters in late merger phases lead to potentially large differences between rare and frequent scatterings, we believe that these differences are non-trivial to extract from observations. We therefore study the galaxy/star populations which remain distinct even after the dark matter haloes have coalesced. We find that these collisionless tracers behave differently for rare and frequent scatterings, potentially giving a handle to learn about the micro-physics of dark matter.

Method of estimation of hidden correlation of narrowband noise signals

2019 ◽  
pp. 34-39 ◽  
Author(s):  
E.I. Chernov ◽  
N.E. Sobolev ◽  
A.A. Bondarchuk ◽  
L.E. Aristarhova
Keyword(s):  
Signal Processing ◽  
Digital Signal Processing ◽  
New Technology ◽  
Digital Signal ◽  
Measuring Instruments ◽  
Central Frequency ◽  
Pearson Criterion ◽  
Useful Signal ◽  
Band Limited ◽  
Narrowband Noise

The concept of hidden correlation of noise signals is introduced. The existence of a hidden correlation between narrowband noise signals isolated simultaneously from broadband band-limited noise is theoretically proved. A method for estimating the latent correlation of narrowband noise signals has been developed and experimentally investigated. As a result of the experiment, where a time frag ent of band-limited noise, the basis of which is shot noise, is used as the studied signal, it is established: when applying the Pearson criterion, there is practically no correlation between the signal at the Central frequency and the sum of signals at mirror frequencies; when applying the proposed method for the analysis of the same signals, a strong hidden correlation is found. The proposed method is useful for researchers, engineers and metrologists engaged in digital signal processing, as well as developers of measuring instruments using a new technology for isolating a useful signal from noise – the method of mirror noise images.

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