Collider physics has a steep entry ramp: a vocabulary nobody writes down, statistical machinery that arrives fully assembled, and reconstruction algorithms treated as black boxes. These are the explanations I wish had existed when I started — written for students joining the group, but open to anyone. No background is assumed beyond curiosity and a little calculus.
The opening installment of a five-part tour of normalizing flows: a plain-words, poke-the-pictures introduction to what a probability distribution actually is and what it means to reshape one. Read this first if the phrase “generative model” has ever gone by without landing.
The complete tour — take a plain bell curve and bend it, carefully, until it matches your data. Five sections of sliders and live demos carry you from probability conservation through invertible transformations to image generation, closing with a bonus on importance sampling for numerical integration: the same trick, pointed at an integral instead of a picture.
Predict, filter, smooth — the algorithm behind essentially every track in every collider experiment, built up one plane at a time. Covers what a track is numerically, propagation between detector layers, the filter and smoother steps, search windows for hit finding, whether you should believe your covariance matrix, and the failure modes that will bite you. Runs on a toy tracker you can follow end to end.
390 searchable entries across theory, reconstruction, statistics, object identification, software, calorimetry, trigger and DAQ, collaboration practice, beams, Monte Carlo, upgrades, and muon systems — each with a definition and a note on what actually trips people up. The reference for the moment a collaboration meeting turns into acronyms.
Found an error, or something that deserves an entry? Write to saptaparnab@smu.edu — corrections from newcomers are the most useful kind, because you are the one who noticed the gap.