Scan Aggregation

When a measurement contains multiple scans (repetitions of the same spectrum), they must be combined into a single spectrum before further analysis. The aggregate() method on 1D measurements (i.e. Xas and Xes) exposes two mathematically distinct strategies, controlled by the mode argument.

Background

For each scan i, let \(S_i(E)\) be the detector signal and \(M_i(E)\) be the monitor counts at energy point E. The individual normalized spectrum is the ratio \(S_i / M_i\). The two aggregation modes differ in when the monitor division is performed.

Fraction of sums (default)

\[y(E) = \frac{\displaystyle\sum_i S_i(E)}{\displaystyle\sum_i M_i(E)}\]

All signal channels and all monitor channels are accumulated first; only then is the global ratio computed.

When to use: Preferred for most experiments. Because the division is performed once on the totals, high-monitor scans naturally receive more weight. This produces the cleanest final spectrum when beam intensity fluctuates between scans, by giving more influence to the measurements with the best signal-to-noise ratio:

measurement.aggregate()                            # default
measurement.aggregate(mode="fraction of sums")     # explicit

Mean of fractions

\[y(E) = \frac{1}{N}\sum_i \frac{S_i(E)}{M_i(E)}\]

Each scan is normalized by its own monitor before averaging, giving every scan equal weight regardless of how many counts were collected.

When to use: Useful when scans have been recorded under systematically different monitor conditions (e.g. different integration times or attenuator settings) and you want each repetition to contribute equally to the average. Be aware that this mode amplifies noise in low-count scans:

measurement.aggregate(mode="mean of fractions")

Monitor handling

Regardless of the aggregation mode, the stored monitor (measurement.monitor) is always the mean of the monitors across all scans. If no monitor was recorded, the “fraction of sums” mode automatically divides the summed signal by the number of scans to produce a simple mean.

API reference

See aggregate().