Search research articles

Related Concept Videos

The Uncertainty Principle

The Uncertainty Principle

Werner Heisenberg considered the limits of how accurately one can measure properties of an electron or other microscopic particles. He determined that there is a fundamental limit to how accurately one can measure both a particle’s position and its momentum simultaneously. The more accurate the measurement of the momentum of a particle is known, the less accurate the position at that time is known and vice versa. This is what is now called the Heisenberg uncertainty principle. He...

Uncertainty in Measurement: Reading Instruments

Uncertainty in Measurement: Reading Instruments

Counting is the type of measurement that is free from uncertainty, provided the number of objects being counted does not change during the process. Such measurements result in exact numbers. By counting the eggs in a carton, for instance, one can determine exactly how many eggs are there in the carton. Similarly, the numbers of defined quantities are also exact. For example, 1 foot is exactly 12 inches, 1 inch is exactly 2.54 centimeters, and 1 gram is exactly 0.001 kilograms. Quantities...

Gravitation

Gravitation

In the years before Newton, a general belief prevailed that different laws governed objects in the sky than objects on Earth. When Kepler wrote down the three laws of planetary motion, explaining in detail the geometrical properties of the planetary orbits around the Sun, there was no immediate idea to discern their connection with more fundamental laws. It was Isaac Newton who, in 1665–66, figured out the connection between planetary motion, the motion of the moon around the Earth, and...

Uncertainty: Overview

Uncertainty: Overview

In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.

Gravitational Potential Energy

Gravitational Potential Energy

Potential energy is not just a property of each object, but also a property of the interactions between objects in a chosen system. For each type of interaction present in a system, there is a corresponding type of potential energy. The total potential energy of the system is the sum of the potential energies of all the objects. Potential energy can be classified into two major categories: gravitational potential energy and elastic potential energy. The potential energy associated with a...

Newton's Law of Gravitation

Newton's Law of Gravitation

Our everyday observation tells us that all objects close to the Earth naturally tend to fall to the ground. Early philosophers assumed that this downward force was unique to Earth. By the 16th century, Nicolaus Copernicus (1473-1543) put forward the heliocentric theory, which suggested that Earth and other planets orbited the sun, while the Moon orbited the Earth. However, it was Isaac Newton (1642-1727) who linked these two motions together in the 17th century. He reasoned that the force of...

You might also read

Related Articles

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

Sort by

Same author

Gait screening of a population of young, healthy athletes by means of a portable, low-cost device unveils hidden left-right asymmetries in both quadriceps and anterior cruciate ligament forces.

BMC research notes·2019

Same author

Expression of the Parkinson's Disease-Associated Gene Alpha-Synuclein is Regulated by the Neuronal Cell Fate Determinant TRIM32.

Molecular neurobiology·2016

Same author

FastMotif: spectral sequence motif discovery.

Bioinformatics (Oxford, England)·2015

See all related articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Jan 28, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Enhancing Gravitational-Wave Detection: A Machine Learning Pipeline Combination Approach with Robust Uncertainty

Gregory Ashton¹, Ann-Kristin Malz¹, Nicolo Colombo²

¹Royal Holloway, Department of Physics, University of London, Egham Hill, Egham TW20 0EX, United Kingdom.

Physical Review Letters

|January 26, 2026

Summary

We developed a machine learning method to combine gravitational-wave searches, improving signal detection and providing reliable uncertainty estimates. This approach enhances confidence in identifying rare astrophysical events like binary neutron stars.

More Related Videos

A Computational Pipeline for Intergenic/Intragenic Enhancer RNA Quantification in Mouse Embryonic Stem Cells

A Computational Pipeline for Intergenic/Intragenic Enhancer RNA Quantification in Mouse Embryonic Stem Cells

Published on: October 28, 2025

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

Related Experiment Videos

Last Updated: Jan 28, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

A Computational Pipeline for Intergenic/Intragenic Enhancer RNA Quantification in Mouse Embryonic Stem Cells

A Computational Pipeline for Intergenic/Intragenic Enhancer RNA Quantification in Mouse Embryonic Stem Cells

Published on: October 28, 2025

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

Area of Science:

Astrophysics
Data Science
Signal Processing

Background:

Gravitational-wave data contains noise, artifacts, and rare astrophysical signals.
Existing search algorithms for compact binary coalescences have variable performance, complicating data interpretation.

Purpose of the Study:

To present a machine-learning-driven approach for combining gravitational-wave search results.
To provide robust, calibrated uncertainty quantification using conformal prediction.
To improve the detection efficiency and confidence in multipipeline analyses of gravitational-wave events.

Main Methods:

Utilized a machine-learning model to integrate outputs from multiple gravitational-wave search pipelines.
Employed conformal prediction techniques for uncertainty quantification.
Validated the approach using simulated gravitational-wave data.
Applied the model to the Gravitational Wave Transient Catalog 3 (GWTC-3).

Main Results:

Demonstrated improved detection efficiency through simulations.
Enhanced confidence in multipipeline detections of gravitational-wave signals.
Successfully applied the model to GWTC-3 data.
Increased confidence in identifying subthreshold events, such as the binary neutron star candidate GW200311_103121.

Conclusions:

The machine-learning approach offers a robust method for analyzing gravitational-wave data.
Conformal prediction provides reliable uncertainty quantification for gravitational-wave event detection.
This method improves the interpretation of complex gravitational-wave signals and enhances astrophysical discovery potential.