University Physics IV · unit UPIV-02
Special Relativity I
Connect the ideas, make your working visible, and test the result. Use the hints when you need a starting point; open the full solution after your attempt.
Extra challenging questions
A selection from this pathway. Open a topic below for its complete practice and challenge set.
Inertial frames and the Galilean group: infer an input with uncertainty
An inertial frame is one in which a body free of net force keeps a constant velocity, and the Galilean group is the ten-parameter set of coordinate changes — boosts, rotations, spatial displacements and clock shifts — that carries one such frame to another. Use the supplied topic model Y_out = (30-v-w)*te, where Y_out is x″ OF EVENT E IN S″ (m). The output symbol Y_out is distinct from all input symbols. ^ denotes a power; π = 3.141592653589793. te is Time t of event E, measured in s. Keep v (Boost v of S′ relative to S) = 12 m s⁻¹; w (Further boost w of S″ vs S′) = 8 m s⁻¹ fixed. For this exercise use only 0.871875 s ≤ te ≤ 3.62812 s on this continuous branch. A grid of test settings is an input restriction for this exercise, not by itself a claim of physical quantisation. This calculation retains the topic model's stated assumptions; it does not extend them to a different physical setup. A reading gives Y_out = 25.531875 m, with a bounded reading uncertainty ±1.6124 m. Treat the model and all fixed inputs as exact for this exercise; the bound is not a standard deviation. First estimate the unknown te by straight-line interpolation between the two endpoint predictions. Then solve the actual model on the stated branch to an absolute input tolerance of 0.000275625 s, using a numerical bracket if required. Find the full input interval consistent with the bounded reading uncertainty and compare it with a symmetric input-error assumption. Do not assume that the supplied calibration points prove monotonicity outside this branch.
x″ OF EVENT E IN S″ against te
Predictions from the supplied Inertial frames and the Galilean group model, with all other inputs fixed as stated in the question. 5 calibration points support a trend comparison; they are not measured data or a straight-line law.
Vertical axis (y): x″ OF EVENT E IN S″ / m
Horizontal axis (x): te: Time t of event E / s
Axes show the supplied calibration interval; the origin may lie outside the plot.
Read the exact data as a table
| te: Time t of event E / s | x″ OF EVENT E IN S″ / m |
|---|---|
| 0.871875 | 8.71875 |
| 1.5609374999999999 | 15.609374999999998 |
| 2.25 | 22.5 |
| 2.9390624999999995 | 29.390624999999993 |
| 3.628125 | 36.28125 |
These are the supplied values, not a worked answer. No fitted trend is drawn.
Show a hint
The model's endpoint predictions bracket the reading. Solve Y_out(te) = Y_measured and then solve again at Y_measured − u and Y_measured + u; a nonlinear inverse can give unequal input deviations.
Reveal the worked solution
- Endpoint predictions are Y_out(0.871875) = 8.71875 m and Y_out(3.62812) = 36.2813 m. The target lies between them. On the selected branch the response is strictly increasing.
- Endpoint interpolation gives te_line = 0.871875 + (25.5319 − 8.71875)(3.62812 − 0.871875)/(36.2813 − 8.71875) = 2.55319 s. This estimates the inverse of the endpoint chord, rather than assuming that chord is the physical model.
- Use the actual equation, not the chord, and bracket its root. Evaluating the midpoint and retaining the sign-changing half-interval gives te = 2.55319 s; substituting back gives Y_out = 25.531875 m.
- The interpolation error in the input is |te_line − te| = 0 s. This is within the requested numerical tolerance.
- Invert both limits Y_out = 23.919475 m and 27.144275 m. Sort the roots even if the response decreases: 2.39195 s ≤ te ≤ 2.71443 s.
- The lower and upper input deviations are 0.16124 s and 0.16124 s. They agree on this branch; this does not establish linearity for changes in other inputs or outside the interval. This interval includes reading uncertainty only, not model error or uncertainty in fixed inputs.
Answer te ≈ 2.55319 s; compatible interval [2.39195, 2.71443] s. Endpoint interpolation gives 2.55319 s.
Compare the assumptions, method and units with your attempt. Explain any correction before trying another question.
Electrodynamics under a Galilean boost: infer an input with uncertainty
Maxwell's equations are not Galilean-covariant: substituting x′ = x − vt, t′ = t into the electromagnetic wave equation adds a cross term the original does not contain, so the equation can hold in its standard form in at most one frame. Use the supplied topic model Y_out = (b+w)^2/max(1-(b+w)^2,0.04), where Y_out is ROUND-TRIP EXCESS β²/(1−β²) (dimensionless). The output symbol Y_out is distinct from all input symbols. ^ denotes a power; π = 3.141592653589793; max(a,b) selects the larger value. w is Extra Galilean boost w/c, measured in c. Keep b (Frame speed through the aether v/c) = 0.3 c fixed. For this exercise use only -0.273 c ≤ w ≤ 0.315 c on this continuous branch. A grid of test settings is an input restriction for this exercise, not by itself a claim of physical quantisation. This calculation retains the topic model's stated assumptions; it does not extend them to a different physical setup. A reading gives Y_out = 0.17474173, with a bounded reading uncertainty ±0.026102. Treat the model and all fixed inputs as exact for this exercise; the bound is not a standard deviation. First estimate the unknown w by straight-line interpolation between the two endpoint predictions. Then solve the actual model on the stated branch to an absolute input tolerance of 0.0000588 c, using a numerical bracket if required. Find the full input interval consistent with the bounded reading uncertainty and compare it with a symmetric input-error assumption. Do not assume that the supplied calibration points prove monotonicity outside this branch.
ROUND-TRIP EXCESS β²/(1−β²) against w
Predictions from the supplied Electrodynamics under a Galilean boost model, with all other inputs fixed as stated in the question. 5 calibration points support a trend comparison; they are not measured data or a straight-line law.
Vertical axis (y): ROUND-TRIP EXCESS β²/(1−β²)
Horizontal axis (x): w: Extra Galilean boost w/c / c
Axes show the supplied calibration interval; the origin may lie outside the plot.
Read the exact data as a table
| w: Extra Galilean boost w/c / c | ROUND-TRIP EXCESS β²/(1−β²) |
|---|---|
| -0.273 | 0.0007295318287031229 |
| -0.12600000000000003 | 0.031221254707525015 |
| 0.020999999999999963 | 0.11487816054022532 |
| 0.16799999999999993 | 0.2804490791010222 |
| 0.31499999999999995 | 0.608298821921113 |
These are the supplied values, not a worked answer. No fitted trend is drawn.
Show a hint
The model's endpoint predictions bracket the reading. Solve Y_out(w) = Y_measured and then solve again at Y_measured − u and Y_measured + u; a nonlinear inverse can give unequal input deviations.
Reveal the worked solution
- Endpoint predictions are Y_out(-0.273) = 0.000729532 and Y_out(0.315) = 0.608299. The target lies between them. On the selected branch the response is strictly increasing.
- Endpoint interpolation gives w_line = -0.273 + (0.174742 − 0.000729532)(0.315 − -0.273)/(0.608299 − 0.000729532) = -0.104593 c. This estimates the inverse of the endpoint chord, rather than assuming that chord is the physical model.
- Use the actual equation, not the chord, and bracket its root. Evaluating the midpoint and retaining the sign-changing half-interval gives w = 0.08568 c; substituting back gives Y_out = 0.17474173.
- The interpolation error in the input is |w_line − w| = 0.190273 c. This is larger than the requested numerical tolerance.
- Invert both limits Y_out = 0.14863973 and 0.20084373. Sort the roots even if the response decreases: 0.0597291 c ≤ w ≤ 0.108965 c.
- The lower and upper input deviations are 0.0259509 c and 0.0232848 c. They differ because the inverse response is nonlinear; a symmetric error bar would only be a local approximation. This interval includes reading uncertainty only, not model error or uncertainty in fixed inputs.
Answer w ≈ 0.08568 c; compatible interval [0.0597291, 0.108965] c. Endpoint interpolation gives -0.104593 c.
Compare the assumptions, method and units with your attempt. Explain any correction before trying another question.
The postulates and clock synchronisation: infer an input with uncertainty
Einstein synchronisation defines two separated clocks to agree when the outward and return legs of a light signal's round trip between them are assigned equal durations — the ε = ½ choice — which makes the one-way speed of light c by stipulation rather than by measurement. Use the supplied topic model Y_out = 1/(2*max(eps,0.05)), where Y_out is OUTWARD c₊ (c). The output symbol Y_out is distinct from all input symbols. ^ denotes a power; π = 3.141592653589793; max(a,b) selects the larger value. eps is Synchronisation choice ε, dimensionless. Keep sep (Baseline A to B) = 240 m fixed. For this exercise use only 0.26375 ≤ eps ≤ 0.73625 on this continuous branch. A grid of test settings is an input restriction for this exercise, not by itself a claim of physical quantisation. This calculation retains the topic model's stated assumptions; it does not extend them to a different physical setup. A reading gives Y_out = 0.90583813 c, with a bounded reading uncertainty ±0.034008 c. Treat the model and all fixed inputs as exact for this exercise; the bound is not a standard deviation. First estimate the unknown eps by straight-line interpolation between the two endpoint predictions. Then solve the actual model on the stated branch to an absolute input tolerance of 0.00004725, using a numerical bracket if required. Find the full input interval consistent with the bounded reading uncertainty and compare it with a symmetric input-error assumption. Do not assume that the supplied calibration points prove monotonicity outside this branch.
OUTWARD c₊ against eps
Predictions from the supplied The postulates and clock synchronisation model, with all other inputs fixed as stated in the question. 5 calibration points support a trend comparison; they are not measured data or a straight-line law.
Vertical axis (y): OUTWARD c₊ / c
Horizontal axis (x): eps: Synchronisation choice ε
Axes show the supplied calibration interval; the origin may lie outside the plot.
Read the exact data as a table
| eps: Synchronisation choice ε | OUTWARD c₊ / c |
|---|---|
| 0.26375000000000004 | 1.895734597156398 |
| 0.3818750000000001 | 1.3093289689034366 |
| 0.5 | 1 |
| 0.618125 | 0.8088978766430738 |
| 0.7362500000000001 | 0.6791171477079796 |
These are the supplied values, not a worked answer. No fitted trend is drawn.
Show a hint
The model's endpoint predictions bracket the reading. Solve Y_out(eps) = Y_measured and then solve again at Y_measured − u and Y_measured + u; a nonlinear inverse can give unequal input deviations.
Reveal the worked solution
- Endpoint predictions are Y_out(0.26375) = 1.89573 c and Y_out(0.73625) = 0.679117 c. The target lies between them. On the selected branch the response is strictly decreasing.
- Endpoint interpolation gives eps_line = 0.26375 + (0.905838 − 1.89573)(0.73625 − 0.26375)/(0.679117 − 1.89573) = 0.648198. This estimates the inverse of the endpoint chord, rather than assuming that chord is the physical model.
- Use the actual equation, not the chord, and bracket its root. Evaluating the midpoint and retaining the sign-changing half-interval gives eps = 0.551975; substituting back gives Y_out = 0.90583813 c.
- The interpolation error in the input is |eps_line − eps| = 0.0962229. This is larger than the requested numerical tolerance.
- Invert both limits Y_out = 0.87183013 c and 0.93984613 c. Sort the roots even if the response decreases: 0.532002 ≤ eps ≤ 0.573506.
- The lower and upper input deviations are 0.019973 and 0.0215312. They differ because the inverse response is nonlinear; a symmetric error bar would only be a local approximation. This interval includes reading uncertainty only, not model error or uncertainty in fixed inputs.
Answer eps ≈ 0.551975; compatible interval [0.532002, 0.573506]. Endpoint interpolation gives 0.648198.
Compare the assumptions, method and units with your attempt. Explain any correction before trying another question.
Deriving the boost from symmetry: infer an input with uncertainty
The Lorentz boost is the unique linear map between inertial frames allowed by homogeneity, isotropy and group closure, fixed up to one universal constant K whose value only experiment supplies. Use the supplied topic model Y_out = 1/sqrt(max(1-k*b*b,0.0001)), where Y_out is γ (dimensionless). The output symbol Y_out is distinct from all input symbols. ^ denotes a power; π = 3.141592653589793; sqrt(x) means √x; max(a,b) selects the larger value. b is Speed β = v/c, dimensionless. Keep k (Constant k = Kc²) = 1 fixed. For this exercise use only 0.145625 ≤ b ≤ 0.854375 on this continuous branch. A grid of test settings is an input restriction for this exercise, not by itself a claim of physical quantisation. This calculation retains the topic model's stated assumptions; it does not extend them to a different physical setup. A reading gives Y_out = 1.2253951, with a bounded reading uncertainty ±0.032193. Treat the model and all fixed inputs as exact for this exercise; the bound is not a standard deviation. First estimate the unknown b by straight-line interpolation between the two endpoint predictions. Then solve the actual model on the stated branch to an absolute input tolerance of 0.000070875, using a numerical bracket if required. Find the full input interval consistent with the bounded reading uncertainty and compare it with a symmetric input-error assumption. Do not assume that the supplied calibration points prove monotonicity outside this branch.
γ against b
Predictions from the supplied Deriving the boost from symmetry model, with all other inputs fixed as stated in the question. 5 calibration points support a trend comparison; they are not measured data or a straight-line law.
Vertical axis (y): γ
Horizontal axis (x): b: Speed β = v/c
Axes show the supplied calibration interval; the origin may lie outside the plot.
Read the exact data as a table
| b: Speed β = v/c | γ |
|---|---|
| 0.145625 | 1.010775002632917 |
| 0.32281249999999995 | 1.0565654096071393 |
| 0.49999999999999994 | 1.1547005383792517 |
| 0.6771874999999999 | 1.359045572071094 |
| 0.8543749999999999 | 1.9243463878411997 |
These are the supplied values, not a worked answer. No fitted trend is drawn.
Show a hint
The model's endpoint predictions bracket the reading. Solve Y_out(b) = Y_measured and then solve again at Y_measured − u and Y_measured + u; a nonlinear inverse can give unequal input deviations.
Reveal the worked solution
- Endpoint predictions are Y_out(0.145625) = 1.01078 and Y_out(0.854375) = 1.92435. The target lies between them. On the selected branch the response is strictly increasing.
- Endpoint interpolation gives b_line = 0.145625 + (1.2254 − 1.01078)(0.854375 − 0.145625)/(1.92435 − 1.01078) = 0.312128. This estimates the inverse of the endpoint chord, rather than assuming that chord is the physical model.
- Use the actual equation, not the chord, and bracket its root. Evaluating the midpoint and retaining the sign-changing half-interval gives b = 0.577962; substituting back gives Y_out = 1.2253951.
- The interpolation error in the input is |b_line − b| = 0.265835. This is larger than the requested numerical tolerance.
- Invert both limits Y_out = 1.1932021 and 1.2575881. Sort the roots even if the response decreases: 0.545546 ≤ b ≤ 0.606383.
- The lower and upper input deviations are 0.0324167 and 0.0284202. They differ because the inverse response is nonlinear; a symmetric error bar would only be a local approximation. This interval includes reading uncertainty only, not model error or uncertainty in fixed inputs.
Answer b ≈ 0.577962; compatible interval [0.545546, 0.606383]. Endpoint interpolation gives 0.312128.
Compare the assumptions, method and units with your attempt. Explain any correction before trying another question.