mapp-tricks 0.1.4

Reusable code developed during my PhD in the Medical Applications of Particle Physics group at the University of Bern.

mapp-tricks package

Reusable code developed during my PhD in the Medical Applications of Particle Physics (MAPP) group at the University of Bern.

Use at your own risk!

• Lars Eggimann

Usage of HPGeCalibration

Importing:

from mapp_tricks.spectrometer_calibrations import HPGeCalibration

Basic usage for directly getting the activity at the end of the beam:

calibration = HPGeCalibration(level=1, with_aluminum_foil=True)

A_EoB = calibration.get_activity_for_peak_at_end_of_beam(
    peak_area=...,
    peak_energy=...,
    life_time=...,
    real_time=...,
    cooling_time=...,
    branching_ratio=...,
    half_life=...,
)

print(f"Activity at end of beam: {A_EoB:.3f} Bq")

Or just the efficiency:

efficiency = calibration.evaluate_efficiency_at_energy(
    energy=...
)

print(f"Efficiency: {efficiency:.3f}")

You can also ask for activity of peak at the start of spectra measurement:

A_SoM = calibration.get_activity_for_peak_at_start_of_measurement(
    peak_area=...,
    peak_energy=...,
    life_time=...,
    real_time=...,
    branching_ratio=...,
    half_life=...,
)

print(f"Activity at start of measurement: {A_SoM:.3f} Bq")

You can also show the fit to visually verify the calibration:

calibration.plot_fit()

Usage Orbitos Utils

ORBITOS is a custom software developed to control and acquire data from various beam shaping and monitoring devices.

ElectrometerDataAnalyzer

The orbitos_utils module provides a convenient tool to plot and analyze data from the electrometer in its most simple use case. For more advanced analysis one can still use the util to easily extract the raw data and perform custom analysis.

from mapp_tricks.orbitos_utils import ElectrometerDataAnalyzer

eda = ElectrometerDataAnalyzer(path_to_csv="data/electrometer_data.csv")
electrometer_data = eda.analyze_beam_data()

The electrometer_data is a python object that contains the following attributes:

class BeamData:
    start_of_beam: datetime
    end_of_beam: datetime
    t_irradiation: float
    integrated_charge: ufloat
    plot: go.Figure

i.e. to get the integrated charge (which is a ufloat):

integrated_charge = electrometer_data.integrated_charge
print(f"Integrated charge: {integrated_charge:.3f} C")

To get only the raw data:

eda = ElectrometerDataAnalyzer(path_to_csv="data/electrometer_data.csv")

raw_data = eda.df

The raw data is a pandas dataframe which looks like this

      timestamp       current                   datetime
0  1.756286e+09  1.500000e-12 2025-08-27 11:05:49.924095
1  1.756286e+09 -1.000000e-12 2025-08-27 11:05:50.227460
2  1.756286e+09 -5.000000e-13 2025-08-27 11:05:50.530236
3  1.756286e+09 -8.000000e-13 2025-08-27 11:05:50.834038
4  1.756286e+09 -5.000000e-13 2025-08-27 11:05:51.140312
...

Integrated Correction Factor

It also calculates the integrated correction factor accounting for the decay of a isotope produced during irradiation. It accounts for irregular beam-shape and accurately integrates the needed correction factor to effectively determine i.e. the cross section of a reaction. It is implemented according to the following equation: $$ f(t) = \frac{\int_0^t P(t'),dt'}{e^{-\lambda t}\int_0^t e^{\lambda t'} P(t'),dt'} $$ , where $P(t)$ is the production rate (which is proportional to the beam current for all times $t'$) and $\lambda$ is the decay constant of the isotope. For constant $P(t) = P$ this yields: $$ f(t) = \frac{\lambda t}{1 - e^{-\lambda t}} $$ , which is the well known correction factor we use for i.e. the cross section calculation.

To get the integrated correction factor one can use the following example for a Tc101 peak:

eda = ElectrometerDataAnalyzer(path_to_csv="data/messy_beam_electrometer_data.csv")

electrometer_data = eda.analyze_beam_data()
integrated_correction_factor = eda.get_integrated_correction_factor(
    half_life=14.12 * 60
)

print(f"Integrated correction factor: {integrated_correction_factor:.3f}")