1.2.2(k)-(l) - ICT, data logging and ionising radiation

Digital Tools in Practical Physics

Outcome 1.2.2(k) covers three closely linked ideas: computer modelling, data logging with sensors, and software processing of data. They all use ICT, but they do different jobs.

A data logger collects measurements from sensors. Software then stores, displays and processes the readings. A computer model is different: it uses assumptions and equations to predict what should happen, so that predictions can be compared with real measurements.

Data logging can reduce some human errors, such as reaction-time delay or reading a scale at the wrong instant. It does not remove all uncertainty. A poorly calibrated sensor, unsuitable range, slow sampling rate or wrong spreadsheet formula can produce precise-looking but poor data.

Logging Data with Sensors

A sensor converts a physical quantity into an electrical signal that the data logger can record. Examples include temperature probes, light gates, position sensors, force sensors, microphones, pressure sensors and radiation counters. In this lesson the important general skill is choosing and configuring the digital system, not learning the detailed physics of every sensor.

Before collecting data, decide:

• the physical quantity to measure and the unit to record
• the sensor range, so expected values do not exceed the sensor limit
• the resolution, so small changes are not hidden by coarse readings
• the sampling interval, so readings are frequent enough for the event
• whether a trigger is needed to start logging at the right instant
• how the sensor is zeroed or calibrated
• what raw data and metadata must be stored in the lab book

For a slow experiment, such as cooling over 20 minutes, one reading every second might be more than enough. For a fast motion or a rapidly changing signal, one reading every second may miss the important part of the change.

Worked Example: choosing a sampling rate

A sensor is used to record an event lasting $4.0\,\mathrm{s}$. The important change happens over about $0.20\,\mathrm{s}$. A student suggests logging at 5 samples per second.

At 5 samples per second, the time between readings is:

That gives only about one reading during the important change. This is too sparse to determine the shape or gradient with confidence. A better choice might be 50 samples per second:

That gives about 10 readings during the $0.20\,\mathrm{s}$ change, which is more likely to show the trend clearly.

The sensor itself must respond quickly enough as well: faster recording cannot recover a peak that a slow sensor never detects. Keep the raw timestamps and settings so the sampling interval can be checked. The final answer in a practical question should not simply say "use a faster data logger". It should link the setting to the timescale of the event.

Processing Data with Software

Software processing is part of this boundary. It includes spreadsheets, graphing software, data-logger software and reporting tools. The physics mark is usually for using the software intelligently, not for saying the name of the program.

Good software processing keeps a clear chain:

1. keep the raw readings with units
2. calculate processed quantities in clearly named columns
3. plot the correct variables on labelled axes
4. use a line of best fit, trend line, gradient or area only when the physics model justifies it
5. quote calculated values to sensible significant figures
6. keep enough information for another person to repeat the processing

For example, a spreadsheet might turn a column of counts $N$ and count times $t$ into count rate $R=N/t$. It might also subtract a background count rate from every measurement. That is useful because it applies the same formula consistently to a whole data set, but it is only as good as the formula and units entered.

Computer modelling is different from processing measured data. A model can predict the effect of changing one variable while keeping others fixed. It is useful for planning or interpreting an experiment, but the model assumptions must be stated. In exam-style answers, a model should normally be checked against real data where possible.

A simple numerical model makes repeated predictions from a stated rule. Suppose the excess temperature above a constant room temperature is $\theta$, with the model $\Delta\theta=-k\theta\Delta t$, where $k=0.10\,\mathrm{s}^{-1}$. For $\Delta t=1.0\,\mathrm{s}$ and initial $\theta=20.0\,\mathrm{K}$, the next value is $20.0-0.10(20.0)(1.0)=18.0\,\mathrm{K}$; the following value is $18.0-0.10(18.0)(1.0)=16.2\,\mathrm{K}$. Each row uses the previous row, not the initial value again.

Test the model by reducing the time step and checking whether predictions change appreciably. Compare its predicted temperatures with measurements at matching times, including uncertainty. The model assumes a constant room temperature and a constant cooling coefficient; numerical agreement alone cannot establish those assumptions. Check one spreadsheet row manually, and lock any cell reference that should stay constant when a formula is copied.

Worked Example: a spreadsheet formula

A detector records 150 counts in $30\,\mathrm{s}$. The spreadsheet column A contains counts and column B contains time in seconds. The count rate column should use:

For this row:

The unit is count per second. Do not label this value as becquerel unless the detector and geometry have been calibrated to infer the activity of the source.

Using Ionising Radiation Detectors

Outcome 1.2.2(l) is about the practical use of ionising radiation, including detectors. The later nuclear-physics lessons teach the detailed theory of alpha, beta, gamma radiation and radioactive decay. Here the focus is the practical technique: using a detector safely, collecting counts, and interpreting count-rate data carefully.

A common school detector is a Geiger-Muller tube connected to a counter, rate meter or data logger. Ionising radiation entering the detector can produce an electrical pulse. The counter records pulses as counts.

[DIAGRAM: asset_slug: 1_02_2c_ict_data_logging_and_ionising_radiation__diagram_01; caption: A sealed source, absorber and GM detector are kept at fixed separation when comparing corrected count rates.; description: Sealed source, absorber and GM detector at fixed separation; corrected count rate and safe handling reminders.]

Several practical details matter:

• background radiation is always present, so measure background count rate first with the same detector settings and a sufficiently long interval
• counts fluctuate because individual decays are random: equal intervals need not give equal counts, even when the mean rate is steady. Longer count intervals and repeated readings reduce the percentage random fluctuation in a mean count rate
• the source-detector distance and alignment must be fixed when comparing absorbers
• the detector window should face the source consistently
• the detector has limitations, so very high count rates or wrong geometry can give misleading data
• a simple GM detector counts ionising events but does not by itself identify the radiation type or energy

Here $N$ is number of counts and $t$ is the time interval. If the times are in seconds, the corrected rate is in $\mathrm{count}\,\mathrm{s}^{-1}$.

Worked Example: correcting for background

A detector records 312 counts in $120\,\mathrm{s}$ with a source present. With the source removed and safely stored, it records 84 background counts in $180\,\mathrm{s}$.

Source-present count rate:

Background count rate:

Corrected count rate:

The corrected rate is about $2.1\ \text{count s}^{-1}$ to 2 significant figures. This is a count rate at the detector, not the source activity.

Safe Radiation Methods

Safe use is part of the outcome. A strong practical answer gives named precautions and links them to reducing exposure or improving data quality.

The core radiation safety ideas are:

• keep exposure time as short as reasonably possible
• keep as much distance as practical between people and the source
• use shielding, source holders and storage containers
• use the specified handling tool so that the source stays at least $10\,\mathrm{cm}$ from the hand; never touch it directly
• point the source away from people and return it to storage promptly
• follow local written instructions and teacher or technician supervision

PAG7-style work can involve observing the random nature of decay, comparing absorption by materials, or collecting counts over time for a half-life investigation. For this lesson, the common practical structure is the important part:

1. Measure background count rate with no source in the measuring position.
2. Place the sealed source in a holder.
3. Align the detector at a fixed distance from the source.
4. Choose a count interval long enough to reduce random fluctuation.
5. Record counts for each condition, such as each absorber material or thickness.
6. Repeat readings where possible and calculate mean corrected count rates.
7. Keep the independent variable, dependent variable and control variables clear.

In an absorption investigation, the independent variable might be absorber material or absorber thickness. The dependent variable is the corrected count rate. Key control variables include source identity, source-detector distance and alignment, detector orientation, and absorber position. If material is the independent variable, absorber thickness should also be controlled. Equal count intervals are needed if raw counts are compared; count rates can be calculated from unequal intervals, although different intervals give different percentage random fluctuations.