Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

165
Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
165
Calibration Curves: Correlation Coefficient01:10

Calibration Curves: Correlation Coefficient

1.5K
In a linear calibration curve, there is a value called the calibration coefficient, denoted by 'r,' which measures the strength and the direction of association between two variables. The correlation coefficient value ranges from −1 to +1. A value of +1 indicates a perfect positive linear correlation, −1 denotes a perfect negative correlation, and 0 implies no correlation between the two variables. A positive correlation value establishes that as one variable increases, the...
1.5K
Distance Corrections01:15

Distance Corrections

25
To achieve precise distance measurements, especially in surveying and construction, certain corrections must be applied to account for potential sources of error like the standardization errors, temperature variations, and slope adjustments.Standardization error emerges when measurement equipment undergoes changes, such as wear, repairs, or weather impacts. To address this, surveyors compare the equipment’s readings to a standard. This process identifies any deviation that might lead to...
25
Calculating and Interpreting the Linear Correlation Coefficient01:11

Calculating and Interpreting the Linear Correlation Coefficient

5.9K
The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable, x, and the dependent variable, y. Hence, it is also known as the Pearson product-moment correlation coefficient. It can be calculated using the following equation:
5.9K
Downsampling01:20

Downsampling

126
When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
126
2D NMR: Overview of Heteronuclear Correlation Techniques01:18

2D NMR: Overview of Heteronuclear Correlation Techniques

145
Heteronuclear correlation spectroscopy is an analytical technique that investigates the coupling between different types of nuclei, often a proton and an X-nucleus, such as carbon-13 or nitrogen-15. This method is commonly used in nuclear magnetic resonance (NMR) spectroscopy to gain insights into complex chemical compounds' structural and compositional aspects. A typical heteronuclear correlation spectrum displays X-nucleus chemical shifts on one axis and a proton spectrum on the other...
145

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Clinical Knowledge Representation in Data Science.

Annual review of biomedical data science·2026
Same author

Towards symbolic regression for interpretable clinical decision scores.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences·2026
Same author

Session Introduction: AI and Machine Learning in Clinical Medicine Bridging or Separating Model Intelligence and Human Expertise.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing·2026
Same author

DRIVE-KG: Enhancing variant-phenotype association discovery in understudied complex diseases using heterogeneous knowledge graphs.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing·2026
Same author

CASTER-DTA: equivariant graph neural networks for predicting drug-target affinity.

Briefings in bioinformatics·2025
Same author

Rainer Weiss obituary: Nobel laureate who pioneered the technique that detected gravitational waves.

Nature·2025

Related Experiment Video

Updated: May 28, 2025

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects
10:16

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects

Published on: February 8, 2014

12.2K

Optimal Reconstruction of the Hellings and Downs Correlation.

Bruce Allen1, Joseph D Romano2

  • 1Max Planck Institute for Gravitational Physics (Albert Einstein Institute), Leibniz Universität Hannover, Callinstrasse 38, D-30167, Hannover, Germany.

Physical Review Letters
|February 10, 2025
PubMed
Summary

Pulsar timing arrays (PTAs) detect gravitational waves by reconstructing the Hellings and Downs (HD) curve. This study predicts the expected variance in the HD curve, crucial for confirming gravitational wave signals.

More Related Videos

Lens-free Video Microscopy for the Dynamic and Quantitative Analysis of Adherent Cell Culture
09:04

Lens-free Video Microscopy for the Dynamic and Quantitative Analysis of Adherent Cell Culture

Published on: February 23, 2018

9.4K
Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method
08:42

Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method

Published on: September 3, 2021

3.0K

Related Experiment Videos

Last Updated: May 28, 2025

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects
10:16

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects

Published on: February 8, 2014

12.2K
Lens-free Video Microscopy for the Dynamic and Quantitative Analysis of Adherent Cell Culture
09:04

Lens-free Video Microscopy for the Dynamic and Quantitative Analysis of Adherent Cell Culture

Published on: February 23, 2018

9.4K
Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method
08:42

Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method

Published on: September 3, 2021

3.0K

Area of Science:

  • Astronomy
  • Astrophysics
  • Cosmology

Background:

  • Pulsar timing arrays (PTAs) are key instruments for detecting gravitational waves (GWs).
  • The Hellings and Downs (HD) curve describes the expected correlation between pulsar pairs, serving as a signature for GWs.
  • Deviations from the theoretical HD curve arise from limited pulsar numbers, source interference, and noise.

Purpose of the Study:

  • To predict the variance in the reconstructed Hellings and Downs (HD) correlation curve.
  • To develop an optimal estimator for the HD correlation, considering pulsar locations and GW/noise frequency distributions.

Main Methods:

  • Constructed an optimal estimator for the HD correlation.
  • Accounted for pulsar sky distribution and the frequency characteristics of GWs and pulsar noise.
  • Analyzed variance as a ratio dependent on pulsar locations and signal-to-noise ratio across frequency bins.

Main Results:

  • The variance in the HD correlation is predicted based on an optimal estimator.
  • The variance is shown to be a ratio dependent on pulsar sky locations.
  • The denominator of the variance ratio relates to the effective number of frequency bins where GW signals dominate noise.

Conclusions:

  • The study provides a theoretical prediction for the variance in the HD curve reconstruction.
  • This prediction is essential for accurately interpreting PTA data and confirming GW detection.
  • Each frequency bin where GWs dominate noise contributes an independent estimate of the HD correlation.