First time-resolved measurement of infrared scintillation light in gaseous xenon

Repository with supplemental data to:
First time-resolved measurement of infrared scintillation light in gaseous xenon. Piotter, M., Cichon, D., Hammann, R., Jörg, F., Hötzsch, L., Marrodán Undagoitia, T. Eur. Phys. J. C 83, 482 (2023).
A pre-print of the article is available on arXiv: 2303.09344

Note: When re-using the data, please make sure to cite the article (and not only the dataset)

The files contain all data related to the observed IR scintillation in gaseous xenon presented in the paper. This comprises the IR time profiles obtained via single photon counting and the measured pressure dependence of the IR light yield for the three extrapolation methods:

waveform_before.csv, waveform_during.csv, waveform_after.csv: These files contain the IR time profiles before, during, and after the purification of the gas (presented in figure 8 in the publication). The column dt is given in nanoseconds relative to the UV pulse and counts corresponds to counts per nanosecond per 100 UV events.

light_yield_ir.csv: This file contains the IR light yield as a function of pressure obtained with the three extrapolation models together with the respective statistical and systematic uncertainties. The data is presented in figure 9 in the publication and all values are given in units of photons per MeV.

waveform_495.csv, waveform_742.csv, waveform_1047.csv: These files contain the IR time profiles for xenon gas pressures of 495.0 mbar, 742.5 mbar, and 1047.0 mbar, respectively (presented in figure 10 in the publication). The column dt is given in nanoseconds relative to the UV pulse and counts corresponds to counts per nanosecond per 100 UV events.

Code examples for plotting the data:

The following Python code reproduces figure 9 in the publication:

import pandas as pd
import matplotlib.pyplot as plt

if __name__ == '__main__':
df = pd.read_csv("light_yield_ir.csv")
color_pairs = [("#365898", "#B7D0FF"),
("#AB123B", "#F0B5C5"),
("#E1992E", "#F1DAB9")]
fig, ax = plt.subplots(1, figsize=(4, 3))
for fit_func_str, cs in zip(["Recombination model fit", "Exponential fit", "Linear fit"],
color_pairs):
# Plot systematic error
ax.errorbar(df["Pressure"],
df[f"IR Light Yield ({fit_func_str} fit)"],
yerr=df[f"Syst. uncertainty ({fit_func_str} fit)"],
ls="", elinewidth=3, capsize=0, ecolor=cs[1]
)
# Plot estimator with statistical error
ax.errorbar(df["Pressure"],
df[f"IR Light Yield ({fit_func_str} fit)"],
yerr=df[f"Stat. uncertainty ({fit_func_str} fit)"],
ls="", c=cs[0], ecolor=cs[0], elinewidth=1, capsize=1,
marker=".", label=fit_func_str)
# Cosmetics
ax.set_xlabel("Pressure [mbar]")
ax.set_ylabel("IR light yield [ph / MeV]")
ax.set_ylim(1200, 12_500)

ax.legend(frameon=False, loc="upper left")
plt.show()

The IR time response of figure 8 can be redrawn as follows:

import pandas as pd
import matplotlib.pyplot as plt

if __name__ == '__main__':
fig, ax = plt.subplots(1, figsize=(4, 3))
for label in ["before", "during", "after"]:
df = pd.read_csv(f"waveform_{label}.csv")
ax.step(df["dt"], df["counts"],
label=label)
# Cosmetics
ax.set_xlabel("$\Delta t$ between IR and UV signal [ns]")
ax.set_ylabel("Counts per 1 ns per 100 UV events")

ax.legend(frameon=False, loc="upper right")
plt.show()