## Trinity College Dublin, The University of Dublin

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# PYU11T10 Physics for Theoretical Physics

Semester 1 – MT – lectures, practical laboratory, online & small group tutorials– 10 credits
(G. Cross, N Caffrey, L Bradley, M Möbius)

Introduction to Physics – 1 lecture
Special Relativity – 15 lectures
Waves and Optics I – 20 lectures
Statistics – 10 lectures

Learning Outcomes
On successful completion of this module, students should be able to:

• Express relativistic effects concerning the motion of a body, as observed in different inertial reference frames
• Describe wave motion and relate it to basic phenomena in light and sound
• Understand sources of errors in measurements and calculate their propagation.
• Prepare a brief report, including error analysis, on a simple physical experiment

Syllabus
What is Physics – 1 lecture
An introduction to the School of Physics and the JF Physics course.

Special Relativity – 15 lectures
Galilean transformations, Michelson-Morley experiment. Lorentz transformations, time dilation, length contraction, simultaneity. Doppler effect. Transformation of velocities and forces. Relativistic dynamics. Pair creation, fission, fusion. Collisions. Compton effect. Relativity and electromagnetism.

Waves and Optics I – 20 lectures
Resonance, harmonic oscillators, SHM, frequency.
Waves: standing, travelling, wavelength, wave velocity.
Sound: music, vibrations of a string and of a column of air, harmonics, Doppler effect.
Light: Rayleigh scattering, refraction, reflection, dispersion, index of refraction, polarization, polarized reflection, Malus' law, birefringence, total internal reflection, colour vision, gas discharges, lasers.
Optics: refracting optics, lenses, real images, focus, focal length,
f-numbers, lens equation, cameras, reflecting optics, curved mirrors, telescopes.
Interference: superposition of waves, beating, 2 beam interference, anti-reflection coating.
Diffraction: Huygen's principle, diffraction by a slit and grating, X-ray diffraction.

Statistics – 10 lectures
Systematic and random errors. Discrete and continuous distributions such as binomial, Poisson, Gaussian and Lorentzian. Moments of a distribution. Histograms and probability densities. Estimation of mean and standard deviation in a measurement. Error propagation and transformation of variables in probability distributions. Linear regression analysis, method of least squares, goodness of fit (Chi squared) and plotting techniques. Introduction to programming basics in Python.

 Assessment Weighting Examination 60% Laboratory Practical work 30% Online tutorials 10%