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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.

Extra challenging · 01

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.

Explore the question data

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

x″ OF EVENT E IN S″ against tePredictions 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. Horizontal axis: te: Time t of event E / s. Vertical axis: x″ OF EVENT E IN S″ / m. Exact values are available in the data table below.7.61615.0622.529.9437.380.76161.5062.252.9943.738xy

Horizontal axis (x): te: Time t of event E / s

Axes show the supplied calibration interval; the origin may lie outside the plot.

Reading 1te: Time t of event E / s: 0.8719x″ OF EVENT E IN S″ / m: 8.719
Read the exact data as a table
x″ OF EVENT E IN S″ against te — supplied values
te: Time t of event E / sx″ OF EVENT E IN S″ / m
0.8718758.71875
1.560937499999999915.609374999999998
2.2522.5
2.939062499999999529.390624999999993
3.62812536.28125

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
  1. 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.
  2. 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.
  3. 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.
  4. The interpolation error in the input is |te_line − te| = 0 s. This is within the requested numerical tolerance.
  5. 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.
  6. 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.

Extra challenging · 02

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.

Explore the question data

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)

ROUND-TRIP EXCESS β²/(1−β²) against wPredictions 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. Horizontal axis: w: Extra Galilean boost w/c / c. Vertical axis: ROUND-TRIP EXCESS β²/(1−β²). Exact values are available in the data table below.-0.023570.14050.30450.46860.6326-0.2965-0.13780.0210.17980.3385xy

Horizontal axis (x): w: Extra Galilean boost w/c / c

Axes show the supplied calibration interval; the origin may lie outside the plot.

Reading 1w: Extra Galilean boost w/c / c: -0.273ROUND-TRIP EXCESS β²/(1−β²): 7.30e-4
Read the exact data as a table
ROUND-TRIP EXCESS β²/(1−β²) against w — supplied values
w: Extra Galilean boost w/c / cROUND-TRIP EXCESS β²/(1−β²)
-0.2730.0007295318287031229
-0.126000000000000030.031221254707525015
0.0209999999999999630.11487816054022532
0.167999999999999930.2804490791010222
0.314999999999999950.608298821921113

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
  1. 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.
  2. 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.
  3. 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.
  4. The interpolation error in the input is |w_line − w| = 0.190273 c. This is larger than the requested numerical tolerance.
  5. 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.
  6. 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.

Extra challenging · 03

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.

Explore the question data

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

OUTWARD c₊ against epsPredictions 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. Horizontal axis: eps: Synchronisation choice ε. Vertical axis: OUTWARD c₊ / c. Exact values are available in the data table below.0.63050.95891.2871.6161.9440.24490.37240.50.62760.7552xy

Horizontal axis (x): eps: Synchronisation choice ε

Axes show the supplied calibration interval; the origin may lie outside the plot.

Reading 1eps: Synchronisation choice ε: 0.2638OUTWARD c₊ / c: 1.896
Read the exact data as a table
OUTWARD c₊ against eps — supplied values
eps: Synchronisation choice εOUTWARD c₊ / c
0.263750000000000041.895734597156398
0.38187500000000011.3093289689034366
0.51
0.6181250.8088978766430738
0.73625000000000010.6791171477079796

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
  1. 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.
  2. 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.
  3. 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.
  4. The interpolation error in the input is |eps_line − eps| = 0.0962229. This is larger than the requested numerical tolerance.
  5. 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.
  6. 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.

Extra challenging · 04

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.

Explore the question data

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

γ against bPredictions 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. Horizontal axis: b: Speed β = v/c. Vertical axis: γ. Exact values are available in the data table below.0.97421.2211.4681.7141.9610.11730.30860.50.69140.8827xy

Horizontal axis (x): b: Speed β = v/c

Axes show the supplied calibration interval; the origin may lie outside the plot.

Reading 1b: Speed β = v/c: 0.1456γ: 1.011
Read the exact data as a table
γ against b — supplied values
b: Speed β = v/cγ
0.1456251.010775002632917
0.322812499999999951.0565654096071393
0.499999999999999941.1547005383792517
0.67718749999999991.359045572071094
0.85437499999999991.9243463878411997

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
  1. 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.
  2. 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.
  3. 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.
  4. The interpolation error in the input is |b_line − b| = 0.265835. This is larger than the requested numerical tolerance.
  5. Invert both limits Y_out = 1.1932021 and 1.2575881. Sort the roots even if the response decreases: 0.545546 ≤ b ≤ 0.606383.
  6. 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.

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