What Is Physics?

Last Updated May 28, 2026

Physics is the disciplined study of matter, energy, motion, fields, measurement, and structure across every scale of the material universe. It asks how bodies move, how forces act, how heat flows, how light propagates, how atoms and particles behave, how space and time are structured, how stars and galaxies evolve, and how physical systems can be measured, modeled, predicted, and tested. Yet physics is more than a list of topics. It is a way of knowing: a method for turning observation into measurement, measurement into mathematical structure, mathematical structure into testable models, and testable models into deeper explanations of reality.

The question What is physics? therefore cannot be answered only by naming the branches of the field. Physics is at once a natural science, a measurement practice, a mathematical language, an experimental discipline, a computational enterprise, and a philosophical challenge to ordinary intuition. CERN describes its work as probing the fundamental structure of the particles that make up everything around us using some of the world’s largest and most complex scientific instruments. NIST’s measurement and standards work shows that physics also depends on units, constants, uncertainty, calibration, and shared measurement infrastructure. NASA’s physical-sciences program demonstrates that physics extends into space-based research on fluids, materials, combustion, biophysics, and fundamental physical behavior in microgravity.

This article develops What Is Physics? as the opening foundation piece for the Physics knowledge series. It explains what physics studies, why measurement and mathematics are central to the field, how experiment and theory work together, how major branches of physics connect, and why modern physics increasingly depends on computation. It also introduces the computational identity of the series: physics as a discipline that uses equations, experiments, numerical simulation, statistical analysis, high-performance code, uncertainty modeling, structured data, and reproducible research infrastructure to understand the natural world.

Physics as a Way of Knowing

Physics is best understood not only as a body of knowledge but as a disciplined way of knowing. It seeks general, testable, quantitative descriptions of natural phenomena. Rather than merely recording what happens, physics asks what remains invariant, what changes, what is conserved, what can be measured, what relations govern those measurements, and how those relations can be tested under controlled conditions.

This is why physics often appears more foundational than neighboring sciences. It studies matter, motion, force, energy, fields, space, time, radiation, and interaction at a level of generality that can support chemistry, biology, geology, astronomy, engineering, and technology. That does not mean other sciences are reducible to physics in any simple or dismissive sense. Biological systems, ecosystems, economies, organisms, and institutions exhibit levels of organization that require their own concepts and methods. But physics supplies many of the most basic constraints under which those systems operate: energy cannot be created from nothing, heat flows according to thermodynamic principles, signals propagate through physical media, materials have measurable properties, and every observation depends on instruments that obey physical laws.

Physics therefore has a dual character. It is abstract because it uses mathematics to express general laws. It is concrete because its claims must ultimately survive measurement, experiment, and observation. A physical idea is not mature simply because it is elegant. It must be connected to quantities, units, uncertainties, experimental procedures, and reproducible evidence.

Back to top ↑

What Physics Studies

Physics studies the lawful behavior of the material universe. Its central objects include particles, bodies, waves, fields, fluids, plasmas, materials, radiation, spacetime, stars, galaxies, and the universe as a whole. At small scales, physics investigates atoms, nuclei, quarks, leptons, quantum states, and particle interactions. At human and engineering scales, it studies motion, machines, fluids, heat, light, electricity, magnetism, sound, materials, and instruments. At planetary and cosmic scales, it studies atmospheres, oceans, stars, black holes, galaxies, cosmic expansion, and the large-scale structure of the universe.

The diversity of physics is held together by a common style of explanation. Physics asks for measurable quantities, coherent units, mathematical relations, controlled comparisons, and models that generate predictions. A physicist does not merely ask whether a body falls, whether a material conducts, whether a gas expands, or whether light bends. Physics asks by how much, under what conditions, according to what law, with what uncertainty, and within what domain of validity.

This commitment to quantitative explanation gives physics its unusual range. The same mathematical ideas recur across very different systems: differential equations describe pendulums, circuits, waves, planetary motion, diffusion, quantum states, and statistical systems; conservation laws appear in mechanics, thermodynamics, electromagnetism, relativity, and particle physics; symmetry principles shape everything from crystal structure to the Standard Model. Physics looks for deep structural unity beneath surface difference.

Back to top ↑

Measurement, Standards, and Physical Meaning

Measurement is not an accessory to physics. It is one of the conditions under which physics becomes knowledge. A concept becomes physically meaningful when it can be connected to measurable quantities, expressed in units, compared across observers or laboratories, and assigned uncertainty. To say that something is fast, hot, bright, massive, or energetic is not yet precise physics. Physics asks for velocity in meters per second, temperature in kelvin, luminosity in watts, mass in kilograms, energy in joules, and an uncertainty statement that explains how reliable those quantities are.

Modern physics depends on the International System of Units, or SI. The SI is not merely a convenient convention; it is a global measurement infrastructure. Since the 2019 SI revision, all SI units are defined in relation to constants of nature, including the cesium hyperfine transition frequency, the speed of light in vacuum, the Planck constant, the elementary charge, the Boltzmann constant, the Avogadro constant, and the luminous efficacy of a specified monochromatic source. This creates a deep connection between physics and measurement: the standards used to measure the world are themselves grounded in physical constants.

Measurement also requires uncertainty. Every measured value depends on instruments, calibration, environmental conditions, sampling, model assumptions, and statistical variation. Physics therefore treats uncertainty not as failure, but as disciplined honesty. A result such as \(g = 9.81 \, \text{m/s}^2\) is more useful when accompanied by a statement of how it was measured, over what range, under what assumptions, and with what uncertainty.

Back to top ↑

Mathematics and Physical Law

Mathematics is central to physics because physical laws are relations among quantities. Mathematics provides the language for expressing rate of change, accumulation, symmetry, conservation, probability, geometry, curvature, fields, waves, and dynamical evolution. A derivative describes how a quantity changes. An integral describes accumulated effect. A vector expresses magnitude and direction. A differential equation describes how a system evolves. A tensor expresses relationships that remain meaningful across coordinate systems. A probability amplitude describes quantum behavior in a way ordinary language cannot fully capture.

The point is not that equations replace explanation. Rather, in physics, equations often carry the explanation at its highest precision. A verbal claim such as “force changes motion” becomes physically specific only when expressed in a relation such as \(F = ma\), or more generally as the time rate of change of momentum. A statement such as “energy is conserved” becomes operational when energy terms are defined, measured, and balanced across a system boundary. A claim such as “light has a finite speed” becomes part of a larger structure when connected to electromagnetism, relativity, measurement standards, and spacetime geometry.

Mathematics also reveals assumptions. A linear model assumes proportionality. A small-angle approximation assumes an angle is sufficiently small that \(\sin \theta \approx \theta\). A continuum model assumes microscopic details can be averaged into macroscopic fields. A point-particle model neglects size, internal structure, and deformation. Equations therefore do not eliminate judgment. They make the assumptions precise enough to examine.

Back to top ↑

Theory, Experiment, and Modeling

Physics advances through the interplay of theory, experiment, and modeling. Theory proposes principles, laws, symmetries, mechanisms, and mathematical structures. Experiment tests those structures through controlled intervention, measurement, instrumentation, and statistical analysis. Modeling translates general principles into representations of particular systems, often by simplifying reality in carefully chosen ways.

A pendulum, for example, may be modeled as a point mass attached to a massless string moving without friction at small angular displacement. That model is false in the literal sense: real pendulums have extended masses, air resistance, string stiffness, finite amplitude, support friction, and measurement error. But the model can still be useful because it isolates the dominant structure of the phenomenon. Physics is full of such idealizations. The art lies in knowing when an approximation is valid, when it breaks down, and how to refine it.

Large-scale physics uses the same logic at greater complexity. Particle detectors infer invisible interactions from tracks, energies, timing signals, and statistical patterns. Space telescopes infer astrophysical structure from radiation, spectra, redshifts, and models. Climate and geophysical systems depend on fluid dynamics, thermodynamics, radiation physics, and numerical simulation. Condensed matter physics infers collective behavior from lattice structure, quantum theory, spectroscopy, and statistical mechanics. The cycle remains the same: theory guides measurement, measurement constrains theory, and models mediate between them.

Back to top ↑

Major Branches of Physics

Physics is often divided into branches, though those branches constantly overlap. Classical mechanics studies motion, force, momentum, energy, angular momentum, oscillation, and equilibrium. Thermodynamics studies heat, work, temperature, entropy, and energy transfer at macroscopic scales. Statistical physics connects microscopic states to macroscopic behavior through probability and ensembles. Electromagnetism studies charge, electric fields, magnetic fields, electromagnetic waves, radiation, circuits, and light. Optics studies the behavior of light through reflection, refraction, interference, diffraction, imaging, and wave propagation.

Relativity studies spacetime, high-speed motion, gravity, time dilation, length contraction, mass-energy equivalence, and gravitational curvature. Quantum mechanics studies microscopic systems, wavefunctions, operators, probability amplitudes, measurement, spin, tunneling, entanglement, and nonclassical behavior. Nuclear physics studies nuclei, radioactivity, binding energy, fission, fusion, and nuclear interactions. Particle physics studies fundamental particles and the interactions described by the Standard Model, while also investigating open questions such as neutrino masses, dark matter, and physics beyond current theory.

Condensed matter physics studies solids, liquids, superconductors, semiconductors, magnets, crystals, soft matter, and emergent phases. Plasma physics studies ionized gases, fusion systems, solar and astrophysical plasmas, and electromagnetic collective behavior. Astrophysics applies physics to stars, galaxies, compact objects, cosmic radiation, and planetary systems. Cosmology studies the origin, evolution, expansion, and large-scale structure of the universe. Each branch develops specialized tools, but the underlying commitments remain shared: measurement, mathematics, modeling, experiment, and evidence.

Back to top ↑

Physics and Other Sciences

Physics is deeply connected to neighboring sciences. Chemistry depends on quantum mechanics, atomic structure, thermodynamics, spectroscopy, statistical mechanics, and electromagnetism. Biology depends on energy transfer, molecular forces, diffusion, fluid dynamics, mechanics, imaging, optics, radiation, and increasingly biophysics. Earth science depends on geophysics, fluid mechanics, thermodynamics, radiative transfer, materials physics, seismology, and planetary physics. Astronomy depends on gravitation, nuclear physics, plasma physics, optics, relativity, spectroscopy, and radiation physics.

These relationships should not be understood as a hierarchy in which other sciences are simply “less fundamental.” Complex systems often require concepts that are not visible at lower levels alone. A protein is constrained by quantum chemistry, but biological function requires organization, evolution, environment, and regulation. A hurricane obeys fluid dynamics and thermodynamics, but forecasting it requires data assimilation, Earth-system modeling, and atmospheric science. Physics provides constraints and mechanisms; other sciences develop the organizational concepts needed for their own scales of inquiry.

Back to top ↑

Physics as Foundational and Revisable

Physics is powerful because many of its theories remain extraordinarily reliable within their domains. Newtonian mechanics is still used for bridges, vehicles, projectiles, machines, and planetary approximations. Maxwell’s equations remain central to electricity, magnetism, optics, communication, and electromagnetic radiation. Thermodynamics remains one of the strongest frameworks in science. Quantum mechanics underlies semiconductors, lasers, atomic clocks, chemistry, and modern electronics. Relativity is necessary for precision timing, GPS, astrophysics, and gravitational physics.

Yet physics is also revisable. Newtonian mechanics was not destroyed by relativity; it was reinterpreted as an approximation valid at speeds small compared with the speed of light and gravitational fields weak enough for classical treatment. Classical thermodynamics was not discarded by statistical mechanics; it was deepened by a microscopic explanation. Classical electromagnetism was not made useless by quantum electrodynamics; it became part of a larger structure. The Standard Model is extraordinarily successful, but it does not include gravity and does not by itself explain dark matter, dark energy, or all features of the early universe.

This combination is one of physics’ defining strengths. It builds durable knowledge while preserving the possibility of conceptual transformation. Physics does not claim finality simply because a theory works. It asks where the theory works, why it works, what it leaves out, and what evidence might force a deeper account.

Back to top ↑

Physics and Technology

Physics has shaped the modern technological world. Semiconductors depend on quantum mechanics and solid-state physics. Lasers depend on quantum transitions and optical physics. Medical imaging depends on radiation, magnetism, nuclear physics, signal processing, and detector systems. GPS depends on precise timing and relativistic corrections. Telecommunications depend on electromagnetism, optics, materials science, and information-bearing signals. Renewable energy systems depend on thermodynamics, fluid dynamics, materials, electricity, radiation, and power conversion. Modern computing depends on semiconductor physics, statistical mechanics, electromagnetism, quantum effects, and thermal management.

The technological power of physics comes from its ability to convert natural regularities into controlled systems. Once a phenomenon can be measured, mathematically characterized, reproduced, and engineered, it can become part of a device, instrument, standard, or infrastructure. Physics is therefore not only a science of explanation. It is a science of reliable control under constraint.

Back to top ↑

Physics and the Human Picture of Reality

Physics also matters because it changes humanity’s picture of reality. Classical mechanics altered ideas about motion and causation. Thermodynamics introduced entropy, irreversibility, and limits. Electromagnetism unified electricity, magnetism, and light. Relativity changed the meanings of space, time, simultaneity, mass, and gravity. Quantum theory challenged ordinary assumptions about determinacy, measurement, separability, and probability. Cosmology turned the universe itself into an evolving physical system with a history.

For this reason, physics is not only technical. It is also philosophical and cultural. It reshapes how people understand matter, time, order, randomness, causality, evidence, and the limits of knowledge. The best physics education therefore does not merely teach formulas. It teaches a disciplined way to think about reality through measurement, mathematical structure, uncertainty, experiment, and conceptual humility.

Back to top ↑

Mathematical Lens

A mathematics-first view of physics begins with quantities and relations. A physical system is represented by variables such as position, time, mass, velocity, force, energy, charge, temperature, pressure, wavelength, frequency, and field strength. These variables are connected through equations that describe structure or change.

Velocity is the rate of change of position:

Acceleration is the rate of change of velocity:

Newton’s second law connects force, mass, and acceleration:

Kinetic energy connects mass and speed:

Mass-energy equivalence connects mass and energy through the speed of light:

These equations are elementary, but they reveal the structure of physical reasoning. Physics expresses relationships among measurable quantities, attaches units to those quantities, checks dimensional consistency, estimates uncertainty, and tests whether the relation holds under specified conditions.

Back to top ↑

Variables, Units, and Dimensions

Units are not cosmetic. They are part of the meaning of a physical quantity. The same number can describe radically different things depending on its unit. A value of \(10\) could mean \(10 \, \text{m}\), \(10 \, \text{s}\), \(10 \, \text{kg}\), \(10 \, \text{J}\), or \(10 \, \text{K}\). Physics requires the quantity, the value, the unit, and the uncertainty.

Dimensional analysis is one of the simplest but most powerful checks in physics. If the left and right sides of an equation do not have the same dimensions, the equation cannot be physically correct. This does not prove that a dimensionally consistent equation is true, but it quickly identifies many mistakes in modeling, derivation, and computation.

Back to top ↑

Worked Example: Measurement and Law

A simple pendulum illustrates physics in miniature. One measures the length of the pendulum, records oscillation times, estimates a period, relates the result to a mathematical model, and compares prediction with observation. At small angular amplitudes, the ideal pendulum period is approximately:

where \(T\) is the period in seconds, \(L\) is the pendulum length in meters, and \(g\) is the local gravitational acceleration in \(m/s^2\). The equation says that the period increases with the square root of length. It also says that, within the assumptions of the model, the period does not depend on the mass of the bob.

This example contains much of physics in compressed form: a system, measured quantities, units, an idealized law, a mathematical relationship, experimental data, uncertainty, and a domain of validity. The small-angle formula assumes a simple geometry, negligible friction, a rigid support, a massless string, and angular displacement small enough that \(\sin \theta \approx \theta\). When those assumptions fail, the model must be refined.

Back to top ↑

Computational Modeling

Modern physics is inseparable from computation. Computation helps physicists solve equations that cannot be solved analytically, simulate systems with many interacting parts, estimate uncertainty, analyze experimental data, visualize fields and trajectories, manage instrument logs, conduct parameter sweeps, and reproduce results. The computational identity of physics is not limited to one language. Different tools serve different roles.

• Python is useful for numerical modeling, simulation, plotting, notebooks, symbolic work, and scientific workflows.
• R is useful for statistics, uncertainty analysis, regression, experimental measurement, and data visualization.
• Julia is useful for high-performance scientific computing, differential equations, and numerical simulation.
• C++ is useful for performance-critical simulations, particle models, detector-style loops, and physics engines.
• Fortran remains important in legacy and high-performance scientific computing, especially in fluids, astrophysics, numerical weather, and large simulation codes.
• SQL is useful for experiment logs, simulation metadata, parameter sweeps, sensor data, and reproducible research infrastructure.
• Rust is useful for safe, fast command-line tools and reproducible computational utilities.
• C is useful for embedded instrumentation, microcontrollers, sensors, and low-level acquisition workflows.

The selected article examples below focus on Python and R because this introductory article should remain readable while still showing how physics becomes computational. The full GitHub repository extends the same model into Julia, C++, Fortran, SQL, Rust, and C, with advanced research-style computational resources for reproducibility, metadata, simulation, uncertainty, and performance-oriented numerical methods.

Back to top ↑

Python Workflow: Pendulum Modeling and Residual Analysis

The following Python workflow computes the ideal small-angle pendulum period, compares model predictions with repeated observations, and calculates residuals. This is a compact but realistic pattern in computational physics: state the model, load measurements, compute predictions, compare model and data, and inspect error structure.

"""
Pendulum Modeling and Residual Analysis

This example supports an introductory physics workflow:
1. Define a physical law.
2. Use SI units explicitly.
3. Compare model predictions with measured values.
4. Compute residuals and percent error.

Model:
    T = 2π sqrt(L / g)

where:
    T = period in seconds
    L = pendulum length in meters
    g = gravitational acceleration in meters per second squared

Assumptions:
    - small release angle
    - negligible damping
    - point-mass bob
    - massless, inextensible string
    - fixed local gravitational acceleration
"""

import numpy as np
import pandas as pd


GRAVITY_M_PER_S2 = 9.80665


def ideal_pendulum_period(length_m: np.ndarray) -> np.ndarray:
    """
    Compute the ideal small-angle pendulum period.

    Parameters
    ----------
    length_m:
        Pendulum length in meters.

    Returns
    -------
    np.ndarray
        Predicted pendulum period in seconds.
    """
    return 2.0 * np.pi * np.sqrt(length_m / GRAVITY_M_PER_S2)


def main() -> None:
    """
    Run a small model-data comparison for repeated pendulum measurements.
    """
    measurements = pd.DataFrame(
        {
            "trial": [1, 2, 3, 4, 5, 6, 7, 8, 9],
            "length_m": [0.50, 0.50, 0.50, 0.75, 0.75, 0.75, 1.00, 1.00, 1.00],
            "observed_period_s": [1.420, 1.418, 1.424, 1.739, 1.735, 1.742, 2.006, 2.010, 2.004],
        }
    )

    measurements["predicted_period_s"] = ideal_pendulum_period(
        measurements["length_m"].to_numpy()
    )

    measurements["residual_s"] = (
        measurements["observed_period_s"] - measurements["predicted_period_s"]
    )

    measurements["percent_error"] = (
        measurements["residual_s"] / measurements["predicted_period_s"]
    ) * 100.0

    grouped_summary = (
        measurements
        .groupby("length_m", as_index=False)
        .agg(
            mean_observed_period_s=("observed_period_s", "mean"),
            sd_observed_period_s=("observed_period_s", "std"),
            predicted_period_s=("predicted_period_s", "mean"),
            mean_residual_s=("residual_s", "mean"),
            mean_percent_error=("percent_error", "mean"),
        )
    )

    print("Measurement-level comparison")
    print(measurements.round(5).to_string(index=False))

    print("\nGrouped summary by pendulum length")
    print(grouped_summary.round(5).to_string(index=False))


if __name__ == "__main__":
    main()

The value of this workflow is not the sophistication of the model. Its value lies in the structure of reasoning: a physical law is implemented transparently, variables carry units, observations are compared with predictions, and the residuals expose the difference between idealization and measurement.

Back to top ↑

R Workflow: Repeated Measurement and Uncertainty

R is especially useful when physics is approached through repeated measurement, uncertainty, regression, and experimental data analysis. The following workflow summarizes repeated pendulum measurements by length and estimates the standard error of the mean period.

# Repeated Measurement and Uncertainty Workflow
#
# This example treats physics as an experimental measurement practice.
# It summarizes repeated observations, estimates uncertainty, and compares
# the observed mean period with the ideal small-angle pendulum model.
#
# Model:
#   T = 2 * pi * sqrt(L / g)
#
# Variables:
#   L = pendulum length in meters
#   g = gravitational acceleration in meters per second squared
#   T = period in seconds

library(tibble)
library(dplyr)

gravity_m_per_s2 <- 9.80665

ideal_pendulum_period <- function(length_m) {
  2 * pi * sqrt(length_m / gravity_m_per_s2)
}

measurements <- tibble(
  trial = 1:9,
  length_m = c(0.50, 0.50, 0.50, 0.75, 0.75, 0.75, 1.00, 1.00, 1.00),
  observed_period_s = c(1.420, 1.418, 1.424, 1.739, 1.735, 1.742, 2.006, 2.010, 2.004)
)

summary_by_length <- measurements %>%
  mutate(
    predicted_period_s = ideal_pendulum_period(length_m),
    residual_s = observed_period_s - predicted_period_s
  ) %>%
  group_by(length_m) %>%
  summarise(
    n_trials = n(),
    mean_observed_period_s = mean(observed_period_s),
    sd_observed_period_s = sd(observed_period_s),
    standard_error_s = sd_observed_period_s / sqrt(n_trials),
    predicted_period_s = mean(predicted_period_s),
    mean_residual_s = mean(residual_s),
    .groups = "drop"
  )

print(summary_by_length)

This workflow shows how physics moves from single observations to evidence. Repeated measurement makes uncertainty visible. Statistical summaries help distinguish random variation from systematic mismatch. A physical model becomes more credible when it is not only mathematically plausible but also tested against measured data with explicit uncertainty.

Back to top ↑

GitHub Repository

The article body includes only selected code examples so the argument remains readable. The full repository contains the expanded computational infrastructure: Python modeling and parameter sweeps, R uncertainty analysis, Julia differential-equation simulation, C++ numerical integration, Fortran scientific-computing examples, SQL experiment metadata, Rust utilities, C instrumentation examples, documentation, and reproducible sample data.

Back to top ↑

Why Physics Still Matters

Physics still matters because it remains one of the clearest, strongest, and most generative ways humans have developed for understanding the world. It gives not only explanations, but standards of evidence. It gives not only technologies, but limits and constraints. It gives not only models, but ways of deciding when a model is good. NIST’s measurement mission, CERN’s research program, and NASA’s physical-sciences work all reflect the continuing vitality of physics as both basic inquiry and practical foundation.

That is why What is physics? can be answered most honestly in plural form. Physics is the study of matter and energy. It is the science of measurement and law. It is the search for general patterns in nature. It is the mathematical articulation of physical structure. It is an experimental discipline. It is a technological foundation. It is a computational science. And it is one of the most ambitious human attempts to understand how reality works.

Back to top ↑

Related Articles

• Physics
• Measurement, Mathematics, and the Structure of Physical Inquiry
• Experiment, Instruments, and the Material Practice of Physics
• Motion, Force, and the Foundations of Classical Mechanics
• Symmetry, Law, and the Search for Physical Order
• Physics, Technology, and the Modern World
• Physics and the Philosophy of Reality

Further Reading

• CERN (n.d.) About CERN. Available at: https://home.cern/about (Accessed: 24 April 2026).
• CERN (n.d.) Physics. Available at: https://home.cern/science/physics (Accessed: 24 April 2026).
• NIST (n.d.) SI Units. Available at: https://www.nist.gov/pml/owm/metric-si/si-units (Accessed: 24 April 2026).
• NIST (n.d.) Fundamental Physical Constants. Available at: https://www.nist.gov/pml/fundamental-physical-constants (Accessed: 24 April 2026).
• NIST (n.d.) Constants, Units, and Uncertainty. Available at: https://physics.nist.gov/cuu/ (Accessed: 24 April 2026).
• NASA (2025) Physical Sciences Program. Available at: https://science.nasa.gov/biological-physical/programs/physical-sciences/ (Accessed: 24 April 2026).
• NASA (2023) Fundamental Physics. Available at: https://science.nasa.gov/biological-physical/focus-areas/fundamental-physics/ (Accessed: 24 April 2026).
• OpenStax (2022) University Physics Volume 1. Available at: https://openstax.org/details/books/university-physics-volume-1 (Accessed: 24 April 2026).
• Feynman, R.P., Leighton, R.B. and Sands, M. (1963) The Feynman Lectures on Physics. Available at: https://www.feynmanlectures.caltech.edu/ (Accessed: 24 April 2026).

References

• Bureau International des Poids et Mesures (2025) The International System of Units (SI), 9th edition. Available at: https://www.bipm.org/en/publications/si-brochure (Accessed: 24 April 2026).
• CERN (n.d.) About CERN. Available at: https://home.cern/about (Accessed: 24 April 2026).
• CERN (n.d.) Physics. Available at: https://home.cern/science/physics (Accessed: 24 April 2026).
• CERN (n.d.) The Standard Model. Available at: https://home.cern/science/physics/standard-model (Accessed: 24 April 2026).
• Feynman, R.P., Leighton, R.B. and Sands, M. (1963) The Feynman Lectures on Physics. California Institute of Technology. Available at: https://www.feynmanlectures.caltech.edu/ (Accessed: 24 April 2026).
• NASA (2023) Fundamental Physics. Available at: https://science.nasa.gov/biological-physical/focus-areas/fundamental-physics/ (Accessed: 24 April 2026).
• NASA (2025) Physical Sciences Program. Available at: https://science.nasa.gov/biological-physical/programs/physical-sciences/ (Accessed: 24 April 2026).
• NIST (n.d.) Fundamental Physical Constants. Available at: https://www.nist.gov/pml/fundamental-physical-constants (Accessed: 24 April 2026).
• NIST (n.d.) SI Units. Available at: https://www.nist.gov/pml/owm/metric-si/si-units (Accessed: 24 April 2026).
• OpenStax (2022) University Physics Volume 1. Rice University. Available at: https://openstax.org/details/books/university-physics-volume-1 (Accessed: 24 April 2026).
• Taylor, B.N. and Kuyatt, C.E. (1994) Guidelines for Evaluating and Expressing the Uncertainty of NIST Measurement Results. NIST Technical Note 1297. Available at: https://physics.nist.gov/cuu/pdf/tn1297.pdf (Accessed: 24 April 2026).