Physics / research
From particle collisions to evidence
Follow detector-level events through calibration, reconstruction, unfolding, likelihood fits, and statistical interpretation in particle physics measurements.
Your route through physics
Follow the path or begin with a question. Every lesson is open.
Your place, at your pace. Progress is optional and saved only in this browser when available.
Read / explore / reason
Inside this module
Module 01 research
From particle collisions to evidence
Follow detector-level events through calibration, reconstruction, unfolding, likelihood fits, and statistical interpretation in particle physics measurements.
- 01
Event generation and detector simulation
Separate collision models, detector transport, and reconstruction, then normalize a finite simulated sample without confusing it with observed evidence.
27 min - 02
Tracking curvature and momentum resolution
Derive momentum from magnetic bending, propagate sagitta errors, and separate measurement precision from scattering and alignment bias.
27 min - 03
Calorimeter response and energy calibration
Separate average energy scale from event resolution, interpret stochastic/noise/constant terms, and propagate a shared calibration uncertainty correctly.
27 min - 04
Particle identification and misidentification
Connect detector likelihoods to efficiency, fake rates, and sample purity while accounting for population composition and calibration transport.
27 min - 05
Trigger efficiency and selection bias
Model trigger inclusion probabilities, calculate an induced spectrum distortion, and state the independence and support requirements for data-based correction.
28 min - 06
Acceptance, efficiency, and fiducial cross sections
Define a particle-level target, separate acceptance from detector correction, and extract a cross section with consistent exposure, branching, and migration conventions.
28 min - 07
Background control regions and transfer factors
Use auxiliary counts to constrain signal-region backgrounds while preserving Poisson fluctuations, transfer uncertainty, and possible signal contamination.
28 min - 08
Response matrices and unfolding regularization
Define migration and efficiency in a forward response, diagnose amplified inverse fluctuations, and quantify a simple regularization bias.
29 min - 09
Profile likelihoods and nuisance parameters
Construct a joint likelihood with auxiliary data, profile shared nuisance parameters, and distinguish asymptotic uncertainty from guaranteed coverage.
29 min - 10
Discovery significance and look-elsewhere effects
Interpret one-sided discovery tests, distinguish local and global tail probabilities, and calibrate the full search procedure when locations or selections are scanned.
29 min