Related Experiment Video
Updated: Feb 1, 2026

Indirect Fabrication of Lattice Metals with Thin Sections Using Centrifugal Casting
Published on: May 14, 2016
Precise Predictions of Charmed-Bottom Hadrons from Lattice QCD
Nilmani Mathur1, M Padmanath2, Sourav Mondal1
1Department of Theoretical Physics, Tata Institute of Fundamental Research, Homi Bhabha Road, Mumbai 400005, India.
We calculated the masses of exotic hadrons with charm and bottom quarks using lattice quantum chromodynamics. Our predictions guide the experimental discovery of these new heavy quark particles.
Area of Science:
- Particle Physics
- Quantum Chromodynamics
- Hadron Spectroscopy
Background:
- Understanding the properties of hadrons containing heavy quarks is crucial for testing the Standard Model.
- Lattice Quantum Chromodynamics (LQC) provides a non-perturbative approach to studying these systems.
Purpose of the Study:
- To calculate the ground state masses of hadrons with at least one charm and one bottom quark.
- To predict the masses of yet undiscovered heavy quark hadrons for experimental guidance.
Main Methods:
- Utilizing lattice quantum chromodynamics (LQC) simulations.
- Calculating the ground state masses for various meson and baryon configurations.
Main Results:
- Reported ground state masses for mesons (J^P: 0^-, 1^-, 1^+, 0^+) and baryons (spin 1/2, 3/2).
- Only the 0^- meson ground state is experimentally known; predictions cover all other states.
Conclusions:
- The calculated masses provide essential predictions for the experimental search of novel heavy quark hadrons.
- This work advances the understanding of Quantum Chromodynamics in the non-perturbative regime.
Related Concept Videos
Lattice Centering and Coordination Number
Types of Unit Cells
Imagine taking a large number of identical...
Trends in Lattice Energy: Ion Size and Charge
Bewley Lattice Diagram
Uncertainty in Measurement: Accuracy and Precision
Predicting Molecular Geometry
Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

