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+---
+jupyter:
+  jupytext:
+    text_representation:
+      extension: .md
+      format_name: markdown
+      format_version: '1.3'
+      jupytext_version: 1.17.2
+  kernelspec:
+    display_name: base
+    language: python
+    name: python3
+---
+
+<!-- #region id="a6fb405c-18f7-4224-9ec8-24f0a5c3c825" -->
+<a href="https://colab.research.google.com/github/project-ida/arpa-e-experiments/blob/neutrons-background-2/tutorials/He3-Background-Characterization.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a href="https://nbviewer.org/github/project-ida/arpa-e-experiments/blob/neutrons-background-2/tutorials/He3-Background-Characterization.ipynb" target="_parent"><img src="https://nbviewer.org/static/img/nav_logo.svg" alt="Open In nbviewer" width="100"/></a>
+<!-- #endregion -->
+
+<!-- #region id="_WZ7vK7sctla" -->
+# Helium-3 Detector Background Characterization
+
+### Prequel - What's a Helium-3 detector ?
+
+Before we jump into background characterization, let's recall how these nuclear particle detectors work.
+
+Helium-3 (³He) detectors are gas-filled proportional counters widely used for detecting thermal neutrons. When a thermal neutron enters the detector, it interacts with a ³He nucleus via the reaction:
+
+$$
+n + {}^3\text{He} \rightarrow p + {}^3\text{H} + 764\,\text{keV}
+$$
+
+This reaction produces a proton and a triton (³H), which ionize the surrounding gas. The resulting charge is collected by an anode wire, and the induced current is amplified to produce a measurable signal. The signal amplitude is proportional to the energy deposited and provides a clear signature of neutron interactions.
+
+
+## Experimental Setup - Our Helium-3 Detectors
+
+We are running experiments in which we are looking out for the production of neutrons. In order to characterize these bursts, we need to be certain of the background levels against which we are comparing our experimental data.
+
+In the lab, we have access to Helium-3 tubes. In this notebook, our goal is to set up a precedure for characterizing the background of one of these detectors from a "long" (~1 month) background measurement. More specifically, we would like to characterize the probabilistic distribution of the background counts picked up by a single Helium-3 detector and set up a protocol to use statistical tests to determine whether certain counts or bursts are background or events of significance in  experiments.
+
+In order to do so, we began by running the detector in question throughout December 2024 and January 2025. On December 17th, we introduced a neutron source, ²⁵²Cf to collect some callibration data. We will now characterize this background, which will be useful for future analysis.
+
+The data panel describing this background measurement can be found [here](https://lenr.mit.edu/data/load-panel.php?filename=he3-detectors-background).
+<!-- #endregion -->
+
+```python colab={"base_uri": "https://localhost:8080/"} id="PxFHoPsndN0t" outputId="19ead881-1d66-42e9-ac2a-a0c2cf481193"
+# RUN THIS IF YOU ARE USING GOOGLE COLAB
+import sys
+import os
+!git clone https://github.com/project-ida/arpa-e-experiments.git
+sys.path.insert(0,'/content/arpa-e-experiments')
+os.chdir('/content/arpa-e-experiments')
+```
+
+```python
+# RUN THIS IF YOU ARE LOCAL.
+# It makes sure we can import helpers from libs which is one level up
+
+import sys
+import os
+
+# Get the parent directory (one level up from the current directory)
+project_root = os.path.abspath(os.path.join(os.getcwd(), '..'))
+
+# Add the parent directory to sys.path
+sys.path.insert(0, project_root)
+```
+
+```python id="EKZ_R9o2dRek"
+# Libraries and helper functions
+
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import scipy.stats as stats
+from scipy.stats import norm
+from scipy.stats import poisson
+from IPython.display import display
+
+from IPython.display import Image
+from IPython.display import Video
+from IPython.display import HTML
+
+# Use our custom helper functions
+# - process_data
+# - plot_panels
+# - plot_panels_with_scatter
+# - print_info
+from libs.helpers import *
+```
+
+```python
+meta = {
+    "descriptor" : "He-3 12-2024" # This will go into the title of all plots
+}
+```
+
+<!-- #region id="lzKFEAF6dagV" -->
+## Step 1 – Data Collection
+
+Let’s begin by collecting raw data from the Helium-3 neutron detectors.
+
+We have collected long-term time-resolved background data using our Helium-3 detectors, covering the period from December 14th, 2024 at 00:01:01 to January 23rd, 2025 at 23:58:59. These data consist of neutron counts recorded at regular time intervals.
+
+We store the data as pandas DataFrames, which allows us to easily manipulate, visualize, and analyze the results in the steps that follow.
+<!-- #endregion -->
+
+```python id="fdZ7r-YJdcrc"
+he3_dec = pd.read_csv(
+    'http://nucleonics.mit.edu/csv-files/he3-detectors-background2-2.csv',
+    parse_dates=['time'],
+    date_format="ISO8601",
+    index_col='time'
+)
+
+he3_jan = pd.read_csv(
+    'http://nucleonics.mit.edu/csv-files/he3-detectors-background-2.csv',
+    parse_dates=['time'],
+    date_format="ISO8601",
+    index_col='time'
+)
+
+# To facilitate our analysis, let's concatenate december and january
+he3_all = pd.concat([he3_dec, he3_jan])
+he3_all = he3_all.sort_index()
+
+```
+
+```python
+he3_all
+```
+
+```python
+# We will also begin numbering our figures here for easier reference later in the notebook
+fig_counter = 0
+```
+
+<!-- #region id="PzugPnB5d5JT" -->
+## Step 2 - Visualizing Neutron counts
+
+Now that we have collected the raw data (i.e. electric signal history) that interests us, let us have a look at the measured neutron and gamma counts.
+<!-- #endregion -->
+
+```python colab={"base_uri": "https://localhost:8080/", "height": 463} id="bUOQ2fYdd712" outputId="21c11446-99c1-413f-c816-eac991ffd815"
+plt.figure(figsize=(8, 4))
+plt.plot(he3_all['Counts ch50-1000'])
+plt.xlabel('Time')
+plt.ylabel('Counts')
+plt.xticks(rotation=45)
+plt.title(f"He3 counts December 2024 and January 2025")
+plt.show()
+# plt.savefig("He3-counts-dec.png", dpi=600)
+```
+
+We know from the statistical analysis of our 2" Eljen detector that a Cf-252 source was introduced in the lab on December 17- 18th 2024. To the naked eye, this is not necessarily visible. 
+
+
+## Step 3 - Resampling Data and Removing Neutron Burst from Background Data
+
+The current data is taken about once per second. We'll now aggregate this data to present counts in 1 minute intervals.
+
+This is an arbitrary choice, but will allow us to develop some intuition about count binning. Indeed, larger time intervals will necessarily include more counts, so it will be easier to distinguish by eye any significant events. This is not nessecarily the method we will keep for further analysis, given its dependence on an arbitrary bin choice, but it remains useful in our intuition building. In the future, we hope to remove the arbitrarity of binning all together (see later notebook introduced in last section of this notebook).
+
+Furthermore, we commented earlier on a neutron and gamma burst begining on December 17th. This corresponded to a time-period in which we brought a ²⁵²Cf neutron source into the lab (i.e. the bursts that the detectors are picking up). Hence, in order to define a clear background time, we will start collecting data from December 19th.
+
+So, in sum, our next step is to :
+
+1. **Aggregate** the raw second-by-second counts into 1 minute bins.  
+2. **Exclude** the burst period when the ²⁵²Cf source was in the lab, and begin our background analysis on December 19th.
+
+```python
+he3_df_1_minute = he3_all.resample('1min').sum()
+
+# Assuming neutron_df_1_minute has a datetime index
+start_time = "2024-12-19 00:00:00"
+end_time = "2025-01-23 23:59:00"
+
+he3_df_1_minute_background = he3_df_1_minute.loc[start_time:end_time]
+he3_df_1_minute_background.rename(columns={"Counts ch50-1000": "Counts"}, inplace=True)
+he3_df_1_minute_background.index = pd.to_datetime(he3_df_1_minute_background.index)
+
+```
+
+<!-- #region -->
+## Step 4 - Analyzing the Measured Background Counts
+
+Now that we have excluded the time before and when the neutron source was introduced, let us take a closer look at our background neutron counts.
+
+Here are the different steps we will take:
+
+**Side-step 4.1 Expected Poisson Distribution**
+- *Fitting the experimental data to a Poisson distribution*
+- *Checking quantitatively the goodness of the fit*
+
+**Side-Step 4.2 Stability of the Background Rate and Normality of Daily Means**
+- *Fitting the experimental data to a Normal distribution*
+- *Checking quantitatively the goodness of the fit*
+
+**Side-Step 4.3 Comparing our Background Rates with the Literature**
+- *Reference Neutron Background Flux*
+
+
+We will start by building a daily histogram of the background neutron counts per minute. Each line in the plot will represent one day’s worth of 1 minute bins, normalized to form a probability distribution. This lets us see how the shape of the count distribution varies from day to day.
+<!-- #endregion -->
+
+```python
+fig_counter += 1 
+
+# Ensure datetime index
+he3_all.index = pd.to_datetime(he3_all.index)
+
+# Temporarily rename the column for ease
+counts_series = he3_all["Counts ch50-1000"]
+
+# Group by day
+grouped_by_day = counts_series.groupby(he3_all.index.date)
+
+# Define bins
+bins = np.arange(counts_series.min(), counts_series.max() + 1, 1)
+
+# Plot histograms as line plots
+plt.figure(figsize=(10, 6))
+
+for day, group in grouped_by_day:
+    hist_values, bin_edges = np.histogram(group, bins=bins, density=True)
+    plt.plot(bin_edges[:-1], hist_values, alpha=0.1, label=str(day))
+
+plt.xlabel("Counts per Minute")
+plt.ylabel("Normalized Frequency")
+plt.title(f"Daily Histograms of He3 Counts (Fig. {fig_counter})")
+plt.show()
+
+```
+
+Let us quickly comment on how to read this plot:
+
+- Each colored line corresponds to one calendar day’s distribution of neutron counts per minute.
+- Horizontal axis: number of counts detected in a 1 min bin (i.e. $n$ counts per minute, with $n$ the numbers on the horizontal axis).
+- Vertical axis: normalized frequency (so that areas under each curve sum to 1).
+- The shading/transparency helps you see where multiple days’ distributions overlap. Each color corresponds to the data from a different day.
+
+<!-- #region id="zxgDMmwGfr8K" -->
+## Side-step 4.1 Expected Poisson Distribution
+
+In order to conduct a statistical analysis on these background counts, we need to have an idea of what qualifies as a "significant" deviation from background. This will be of interest when trying to determine whether or not we have detected a "significant" number of neutron counts.
+<!-- #endregion -->
+
+## Fitting to a Poisson distribution
+
+Neutron background counts are typically modeled by a Poisson distribution because they arise from random, independent events which occur at a constant average rate over time. Our experimental setup aligns with the conditions under which the Poisson distribution is valid:
+
+- Rare Events: Background neutrons are detected infrequently and individually; each detection is a discrete event.
+
+- Statistical Independence: The arrival of one neutron does not affect the probability of another arriving.
+
+- Constant Rate: Over short timescales (like 1-minute bins), the average background rate is approximately constant.
+
+- Fixed Observation Interval: Counts are measured over uniform time intervals (i.e. counted over fixed 1 minute intervals).
+
+Under these conditions, the number of neutrons detected in a fixed time interval should follow a Poisson distribution with mean $\lambda$, where $\lambda$ is the expected number of events (neutrons) per interval.
+
+The standard deviation of the ditribution would thus be $\sigma = \sqrt{λ}$.
+
+Furthermore, in the literature, we will typically consider that count is "significantly high" if it exceeds $\lambda + \sqrt{\lambda}\cdot Z$
+
+where $Z = 3$ corresponds to a $3\sigma$ threshold (confidence level ~$99.7\%$)
+
+Let us now have a closer look at how close our experimental background measurements are to a Poisson distribution, and extract its key statistical features.
+
+```python
+from scipy import stats
+
+fig_counter += 1  
+
+# Ensure the index is datetime
+he3_all.index = pd.to_datetime(he3_all.index)
+
+# Extract counts column
+counts_series = he3_all["Counts ch50-1000"]
+
+# Group by day
+grouped_by_day = counts_series.groupby(he3_all.index.date)
+
+# Define histogram bins
+bins = np.arange(counts_series.min(), counts_series.max() + 1, 1)
+
+# Compute histograms per day
+histograms = []
+for day, group in grouped_by_day:
+    hist_values, bin_edges = np.histogram(group, bins=bins, density=True)
+    histograms.append(hist_values)
+
+# Convert to array and compute mean and standard deviation
+histograms = np.array(histograms)
+mean_histogram = np.mean(histograms, axis=0)
+std_histogram = np.std(histograms, axis=0)
+
+# Estimate Poisson background mean
+lambda_ = counts_series.mean()
+threshold_3sigma = lambda_ + 3 * np.sqrt(lambda_)
+
+# Compute Poisson fit
+k_values = bin_edges[:-1]
+poisson_pmf = stats.poisson.pmf(k_values, mu=lambda_)
+
+# Normalize Poisson PMF to match area of mean histogram
+poisson_pmf_normalized = poisson_pmf / np.sum(poisson_pmf)
+poisson_pmf_normalized *= np.sum(mean_histogram)
+
+# Plotting
+plt.figure(figsize=(10, 6))
+
+# Plot all daily histograms
+for hist_values in histograms:
+    plt.plot(bin_edges[:-1], hist_values, alpha=0.2, color='gray')
+
+# Plot mean histogram
+plt.plot(bin_edges[:-1], mean_histogram, color='black', linewidth=2, label='Mean Histogram')
+
+# Plot 3σ band
+plt.fill_between(bin_edges[:-1],
+                 np.maximum(mean_histogram - 3 * std_histogram, 0),
+                 mean_histogram + 3 * std_histogram,
+                 color='black', alpha=0.1, label='3σ Band')
+
+# Plot Poisson fit
+plt.plot(k_values, poisson_pmf_normalized, 'r--', linewidth=2, label=f'Poisson Fit (λ = {lambda_:.2f})')
+
+plt.xlabel("Counts per Minute")
+plt.ylabel("Normalized Frequency")
+plt.title(f"Daily Histograms of He3 Counts with 3σ Band (Fig. {fig_counter})")
+plt.legend()
+plt.grid(True)
+plt.show()
+
+```
+
+Let's take a step back to understand the graph we are looking at above.
+
+The black line corresponds to the average distribution of neutron counts across all days.
+
+The grey shaded area shows the spread of day-to-day variation, with upper and lower bounds at $3$ standard deviations above and below the mean. Days that would lie outside this band would be statistically rare under normal conditions (probability < $0.3\%$). Hence, we may identify neutron bursts in future runs by looking at "outliers" of this grey shaded area.
+
+The red dashed line corresponds to the theoretical distribution assuming that neutron counts follow a Poisson process. We plotted this normalized Poisson ditribution assuming the Poisson paramter $\lambda$ to me the mean of our background data i.e. $\lambda \approx 11.19$.
+
+
+## Quantitative goodness-of-fit
+
+In order to test more rigorously whether our background truly follows a Poisson process, we can perform a $χ^2$ (chi-square) goodness-of-fit test comparing the observed mean histogram to the expected Poisson probabilities:
+
+1. Compute the test statistic  
+   $$
+     \chi^2 = \sum_{k} \frac{(O_k - E_k)^2}{E_k},
+   $$
+   where $O_k$ are the observed counts in bin $k$ (from the mean histogram) and $E_k = N_{\rm tot}\,P_{\rm Poisson}(k;\lambda)$.  
+2. Under the null hypothesis (data ∼ Poisson\($\lambda)$, $\chi^2$ follows a $\chi^2$ distribution with $\text{Degrees of Freedom} = \text{number of bins} - 1 - 1$ (subtracting one for the estimated $\lambda$ and 1 for normalization).  
+3. A large $p$-value $(p>0.05$) implies we cannot reject the Poisson hypothesis at the $5 \%$ level.
+
+In the code below, we conduct this goodness of fit analysis and find a p value of $p = 1$ so we cannot reject the null-hypothesis. Hence, for our purposes, we are in a good position to say that background follows a Poisson process.
+
+```python
+# Aggregate observed counts across all days
+O_counts = histograms.sum(axis=0)       # observed total counts per bin
+N_tot = O_counts.sum()                         # total number of 1-min intervals
+
+# Expected counts under Poisson(λ)
+pk = stats.poisson.pmf(k_values, mu=lambda_)
+E_counts = N_tot * pk
+
+# Compute χ² statistic and p-value
+chi2_stat = np.sum((O_counts - E_counts)**2 / E_counts)
+dof = len(k_values) - 2  # degrees of freedon = bins minus 1 (normalization) minus 1 (λ estimated)
+p_value = stats.chi2.sf(chi2_stat, dof)
+
+print(f"Chi-square statistic: {chi2_stat:.2f}")
+print(f"Degrees of freedom: {dof}")
+print(f"p-value: {p_value:.10f}")
+
+if p_value > 0.05:
+    print("Cannot reject Poisson(λ) at the 5% significance level.")
+else:
+    print("Data significantly deviate from Poisson(λ).")
+
+```
+
+## Side-Step 4.2 Stability of the Background Rate and Normality of Daily Means
+
+
+## Fitting to a Normal Distribution
+
+Furthermore, before trusting the aformentioned single global $\lambda$, we should check how much the daily average neutron count per minute varies over our measurement period, and whether those daily means themselves follow an approximately normal distribution (by the Central Limit Theorem, if $\lambda$ is truly constant).
+
+```python
+fig_counter += 1
+
+# Ensure datetime index
+he3_all.index = pd.to_datetime(he3_all.index)
+
+# Extract counts
+counts_series = he3_all["Counts ch50-1000"]
+
+# Group by day
+grouped_by_day = counts_series.groupby(he3_all.index.date)
+
+# 1. Compute daily means
+daily_means = [group.mean() for _, group in grouped_by_day]
+days = list(grouped_by_day.groups.keys())
+
+# 2. Summary statistics
+mu_daily = np.mean(daily_means)
+sigma_daily = np.std(daily_means)
+print(f"Daily mean counts per minute: μ = {mu_daily:.2f}, σ = {sigma_daily:.2f}")
+
+# 3. Histogram of daily means with Normal fit overlay
+plt.figure(figsize=(8, 4))
+
+# Histogram
+vals, edges, _ = plt.hist(daily_means, bins='auto', density=True, alpha=0.6, label='Empirical')
+
+# Normal PDF
+x = np.linspace(min(edges), max(edges), 200)
+pdf = stats.norm.pdf(x, loc=mu_daily, scale=sigma_daily)
+plt.plot(x, pdf, 'r--', label=f'Normal Fit\nμ = {mu_daily:.2f}, σ = {sigma_daily:.2f}')
+
+plt.xlabel('Daily Mean Counts per Minute')
+plt.ylabel('Density')
+plt.title(f'Distribution of Daily Means (Fig. {fig_counter})')
+plt.legend()
+plt.show()
+
+```
+
+## Quantitative goodness-of-fit
+
+The above plot does not shed enough light on how close our mean distribution is to a normal distribution. In order to determine the quantitative goodness of our fit, we may start with a graphical check: the QQ-plot. This plot sample quantiles vs theoretical normal quantiles; and deviations from the straight line highlight non-normality.
+
+```python
+fig_counter += 1
+
+# 4. QQ-plot for normality check
+plt.figure(figsize=(6, 6))
+stats.probplot(daily_means, dist="norm", plot=plt)
+plt.title(f'QQ Plot of Daily Means (Fig. {fig_counter})')
+plt.show()
+```
+
+The plot above shows us that the experimental distribution is fairly "linearly" close to a normal distribution. However, is not a good enough "quantitative" measure of the goodness of our fit. For this, we will perform the Shapiro-Wilk test.
+
+The Shapiro–Wilk test computes a statistic  
+$$
+W \;=\; \frac{\bigl(\sum_{i=1}^n a_i\,x_{(i)}\bigr)^2}
+                {\sum_{i=1}^n\bigl(x_i - \bar x\bigr)^2}\,,
+$$  
+
+where the $x_{(i)}$ are the ordered sample values, the $a_i$ are constants derived from the means and covariances of order statistics of a normal distribution, and $\bar x$ is the sample mean.  Under the null hypothesis that the data come from a normal distribution, $W$ is close to 1; values substantially below 1 indicate departure from normality.
+
+In practice, we obtain from `scipy.stats.shapiro(daily_means)` both the test statistic $W$ and a $p$-value.  We then compare the $p$-value to our significance level (commonly $\alpha=0.05$):
+
+- If $p > 0.05$, we **fail to reject** the null hypothesis: there is no strong evidence against normality.
+- If $p \le 0.05$, we **reject** the null hypothesis: the daily means significantly deviate from a normal distribution.
+
+```python
+# 5. Shapiro–Wilk test for normality
+W, p_value = stats.shapiro(daily_means)
+print(f"Shapiro–Wilk test: W = {W:.4f}, p-value = {p_value:.4f}")
+
+if p_value > 0.05:
+    print("Fail to reject H₀: data are consistent with a normal distribution")
+else:
+    print("Reject H₀: data significantly deviate from normality")
+```
+
+By combining the visual Q–Q plot and the Shapiro–Wilk test, we obtain both qualitative and quantitative assurance that our daily means are well-approximated by a normal distribution. Hence, we may me confident in using a single global $\lambda$ for the background rate.  
+
+So, it is a reasonable assumption to claim that our $\lambda$ is essentially constant over the January-December background collection period.
+
+<!-- #region -->
+## Side-Step 4.3 Comparing our Background Rates with the Literature
+
+
+### Reference Neutron Background Flux
+
+For benchmarking our Eljen detector measurements against a well-established baseline, we will start by adopting the sea-level cosmic-ray neutron flux measured by [Gordon et al. (2004)](https://ieeexplore.ieee.org/document/1369506) on the roof of the IBM T. J. Watson Research Center in Yorktown Heights, NY:
+
+> **Φ<sub>ref</sub> = 0.0134 n cm<sup>−2</sup> s<sup>−1</sup>**  
+
+This value was Measured at ∼20 m a.s.l., geomagnetic cutoff ≃ 3 GV, and mid-level solar activity. In their paper, Gordon et al. provided corrections for different coordinates, altitudes, geomagnetic cutoffs and solar activity.
+
+Hence, we will follow the equations given in the paper, and adapt to our experimental conditions, adjusting for location, environmental parameters, and detector efficiency in order to define a value for the expected background neutron flux from cosmic-rays.
+
+Let us begin by applying a site-adjustment,  i.e.
+   - Apply the altitude-dependence correction ([Gordon et al. (2004)](https://ieeexplore.ieee.org/document/1369506) Eq. (4)) to account for our [lab elevation]( https://elevation.maplogs.com/poi/massachusetts_institute_of_technology_77_massachusetts_ave_cambridge_ma_usa.197544.html).  
+   - Apply [geomagnetic-rigidity](https://www.spenvis.oma.be/models.php) and [solar-modulation](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2016JA023819) corrections ([Gordon et al. (2004)](https://ieeexplore.ieee.org/document/1369506) Eqs. (3) & (5)) for our latitude and current solar cycle.
+
+   
+NB: *The hyper-links above correspond to references for the sources of the figures we will use for adaptiation to our specific experimental environment.*
+
+<!-- #endregion -->
diff --git a/tutorials/He3-Background-Characterization.ipynb b/tutorials/He3-Background-Characterization.ipynb
new file mode 100644
index 0000000..92f3ac8
--- /dev/null
+++ b/tutorials/He3-Background-Characterization.ipynb
@@ -0,0 +1,889 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "a6fb405c-18f7-4224-9ec8-24f0a5c3c825"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/project-ida/arpa-e-experiments/blob/neutrons-background-2/tutorials/He3-Background-Characterization.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a href=\"https://nbviewer.org/github/project-ida/arpa-e-experiments/blob/neutrons-background-2/tutorials/He3-Background-Characterization.ipynb\" target=\"_parent\"><img src=\"https://nbviewer.org/static/img/nav_logo.svg\" alt=\"Open In nbviewer\" width=\"100\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_WZ7vK7sctla"
+      },
+      "source": [
+        "# Helium-3 Detector Background Characterization\n",
+        "\n",
+        "### Prequel - What's a Helium-3 detector ?\n",
+        "\n",
+        "Before we jump into background characterization, let's recall how these nuclear particle detectors work.\n",
+        "\n",
+        "Helium-3 (³He) detectors are gas-filled proportional counters widely used for detecting thermal neutrons. When a thermal neutron enters the detector, it interacts with a ³He nucleus via the reaction:\n",
+        "\n",
+        "$$\n",
+        "n + {}^3\\text{He} \\rightarrow p + {}^3\\text{H} + 764\\,\\text{keV}\n",
+        "$$\n",
+        "\n",
+        "This reaction produces a proton and a triton (³H), which ionize the surrounding gas. The resulting charge is collected by an anode wire, and the induced current is amplified to produce a measurable signal. The signal amplitude is proportional to the energy deposited and provides a clear signature of neutron interactions.\n",
+        "\n",
+        "\n",
+        "## Experimental Setup - Our Helium-3 Detectors\n",
+        "\n",
+        "We are running experiments in which we are looking out for the production of neutrons. In order to characterize these bursts, we need to be certain of the background levels against which we are comparing our experimental data.\n",
+        "\n",
+        "In the lab, we have access to Helium-3 tubes. In this notebook, our goal is to set up a precedure for characterizing the background of one of these detectors from a \"long\" (~1 month) background measurement. More specifically, we would like to characterize the probabilistic distribution of the background counts picked up by a single Helium-3 detector and set up a protocol to use statistical tests to determine whether certain counts or bursts are background or events of significance in  experiments.\n",
+        "\n",
+        "In order to do so, we began by running the detector in question throughout December 2024 and January 2025. On December 17th, we introduced a neutron source, ²⁵²Cf to collect some callibration data. We will now characterize this background, which will be useful for future analysis.\n",
+        "\n",
+        "The data panel describing this background measurement can be found [here](https://lenr.mit.edu/data/load-panel.php?filename=he3-detectors-background)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "PxFHoPsndN0t",
+        "outputId": "19ead881-1d66-42e9-ac2a-a0c2cf481193"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Cloning into 'arpa-e-experiments'...\n",
+            "remote: Enumerating objects: 356, done.\u001b[K\n",
+            "remote: Counting objects: 100% (91/91), done.\u001b[K\n",
+            "remote: Compressing objects: 100% (64/64), done.\u001b[K\n",
+            "remote: Total 356 (delta 59), reused 45 (delta 27), pack-reused 265 (from 1)\u001b[K\n",
+            "Receiving objects: 100% (356/356), 30.15 MiB | 6.22 MiB/s, done.\n",
+            "Resolving deltas: 100% (193/193), done.\n",
+            "Updating files: 100% (54/54), done.\n"
+          ]
+        }
+      ],
+      "source": [
+        "# RUN THIS IF YOU ARE USING GOOGLE COLAB\n",
+        "import sys\n",
+        "import os\n",
+        "!git clone https://github.com/project-ida/arpa-e-experiments.git\n",
+        "sys.path.insert(0,'/content/arpa-e-experiments')\n",
+        "os.chdir('/content/arpa-e-experiments')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# RUN THIS IF YOU ARE LOCAL.\n",
+        "# It makes sure we can import helpers from libs which is one level up\n",
+        "\n",
+        "import sys\n",
+        "import os\n",
+        "\n",
+        "# Get the parent directory (one level up from the current directory)\n",
+        "project_root = os.path.abspath(os.path.join(os.getcwd(), '..'))\n",
+        "\n",
+        "# Add the parent directory to sys.path\n",
+        "sys.path.insert(0, project_root)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "EKZ_R9o2dRek"
+      },
+      "outputs": [],
+      "source": [
+        "# Libraries and helper functions\n",
+        "\n",
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "import scipy.stats as stats\n",
+        "from scipy.stats import norm\n",
+        "from scipy.stats import poisson\n",
+        "from IPython.display import display\n",
+        "\n",
+        "from IPython.display import Image\n",
+        "from IPython.display import Video\n",
+        "from IPython.display import HTML\n",
+        "\n",
+        "# Use our custom helper functions\n",
+        "# - process_data\n",
+        "# - plot_panels\n",
+        "# - plot_panels_with_scatter\n",
+        "# - print_info\n",
+        "from libs.helpers import *"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "meta = {\n",
+        "    \"descriptor\" : \"He-3 12-2024\" # This will go into the title of all plots\n",
+        "}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lzKFEAF6dagV"
+      },
+      "source": [
+        "## Step 1 – Data Collection\n",
+        "\n",
+        "Let’s begin by collecting raw data from the Helium-3 neutron detectors.\n",
+        "\n",
+        "We have collected long-term time-resolved background data using our Helium-3 detectors, covering the period from December 14th, 2024 at 00:01:01 to January 23rd, 2025 at 23:58:59. These data consist of neutron counts recorded at regular time intervals.\n",
+        "\n",
+        "We store the data as pandas DataFrames, which allows us to easily manipulate, visualize, and analyze the results in the steps that follow."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "fdZ7r-YJdcrc"
+      },
+      "outputs": [],
+      "source": [
+        "he3_dec = pd.read_csv(\n",
+        "    'http://nucleonics.mit.edu/csv-files/he3-detectors-background2-2.csv',\n",
+        "    parse_dates=['time'],\n",
+        "    date_format=\"ISO8601\",\n",
+        "    index_col='time'\n",
+        ")\n",
+        "\n",
+        "he3_jan = pd.read_csv(\n",
+        "    'http://nucleonics.mit.edu/csv-files/he3-detectors-background-2.csv',\n",
+        "    parse_dates=['time'],\n",
+        "    date_format=\"ISO8601\",\n",
+        "    index_col='time'\n",
+        ")\n",
+        "\n",
+        "# To facilitate our analysis, let's concatenate december and january\n",
+        "he3_all = pd.concat([he3_dec, he3_jan])\n",
+        "he3_all = he3_all.sort_index()\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Counts ch50-1000</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>time</th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>2024-12-19 00:02:16</th>\n",
+              "      <td>16.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2024-12-19 00:03:31</th>\n",
+              "      <td>13.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2024-12-19 00:04:47</th>\n",
+              "      <td>13.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2024-12-19 00:06:02</th>\n",
+              "      <td>13.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2024-12-19 00:07:17</th>\n",
+              "      <td>10.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2025-01-15 23:47:05</th>\n",
+              "      <td>14.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2025-01-15 23:49:24</th>\n",
+              "      <td>6.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2025-01-15 23:51:42</th>\n",
+              "      <td>8.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2025-01-15 23:53:55</th>\n",
+              "      <td>10.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2025-01-15 23:56:11</th>\n",
+              "      <td>14.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>19478 rows × 1 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                     Counts ch50-1000\n",
+              "time                                 \n",
+              "2024-12-19 00:02:16              16.0\n",
+              "2024-12-19 00:03:31              13.0\n",
+              "2024-12-19 00:04:47              13.0\n",
+              "2024-12-19 00:06:02              13.0\n",
+              "2024-12-19 00:07:17              10.0\n",
+              "...                               ...\n",
+              "2025-01-15 23:47:05              14.0\n",
+              "2025-01-15 23:49:24               6.0\n",
+              "2025-01-15 23:51:42               8.0\n",
+              "2025-01-15 23:53:55              10.0\n",
+              "2025-01-15 23:56:11              14.0\n",
+              "\n",
+              "[19478 rows x 1 columns]"
+            ]
+          },
+          "execution_count": 9,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "he3_all"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# We will also begin numbering our figures here for easier reference later in the notebook\n",
+        "fig_counter = 0"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "PzugPnB5d5JT"
+      },
+      "source": [
+        "## Step 2 - Visualizing Neutron counts\n",
+        "\n",
+        "Now that we have collected the raw data (i.e. electric signal history) that interests us, let us have a look at the measured neutron and gamma counts."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 463
+        },
+        "id": "bUOQ2fYdd712",
+        "outputId": "21c11446-99c1-413f-c816-eac991ffd815"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 576x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          },
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "plt.figure(figsize=(8, 4))\n",
+        "plt.plot(he3_all['Counts ch50-1000'])\n",
+        "plt.xlabel('Time')\n",
+        "plt.ylabel('Counts')\n",
+        "plt.xticks(rotation=45)\n",
+        "plt.title(f\"He3 counts December 2024 and January 2025\")\n",
+        "plt.show()\n",
+        "# plt.savefig(\"He3-counts-dec.png\", dpi=600)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We know from the statistical analysis of our 2\" Eljen detector that a Cf-252 source was introduced in the lab on December 17- 18th 2024. To the naked eye, this is not necessarily visible. "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Step 3 - Resampling Data and Removing Neutron Burst from Background Data\n",
+        "\n",
+        "The current data is taken about once per second. We'll now aggregate this data to present counts in 1 minute intervals.\n",
+        "\n",
+        "This is an arbitrary choice, but will allow us to develop some intuition about count binning. Indeed, larger time intervals will necessarily include more counts, so it will be easier to distinguish by eye any significant events. This is not nessecarily the method we will keep for further analysis, given its dependence on an arbitrary bin choice, but it remains useful in our intuition building. In the future, we hope to remove the arbitrarity of binning all together (see later notebook introduced in last section of this notebook).\n",
+        "\n",
+        "Furthermore, we commented earlier on a neutron and gamma burst begining on December 17th. This corresponded to a time-period in which we brought a ²⁵²Cf neutron source into the lab (i.e. the bursts that the detectors are picking up). Hence, in order to define a clear background time, we will start collecting data from December 19th.\n",
+        "\n",
+        "So, in sum, our next step is to :\n",
+        "\n",
+        "1. **Aggregate** the raw second-by-second counts into 1 minute bins.  \n",
+        "2. **Exclude** the burst period when the ²⁵²Cf source was in the lab, and begin our background analysis on December 19th."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "he3_df_1_minute = he3_all.resample('1min').sum()\n",
+        "\n",
+        "# Assuming neutron_df_1_minute has a datetime index\n",
+        "start_time = \"2024-12-19 00:00:00\"\n",
+        "end_time = \"2025-01-23 23:59:00\"\n",
+        "\n",
+        "he3_df_1_minute_background = he3_df_1_minute.loc[start_time:end_time]\n",
+        "he3_df_1_minute_background.rename(columns={\"Counts ch50-1000\": \"Counts\"}, inplace=True)\n",
+        "he3_df_1_minute_background.index = pd.to_datetime(he3_df_1_minute_background.index)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Step 4 - Analyzing the Measured Background Counts\n",
+        "\n",
+        "Now that we have excluded the time before and when the neutron source was introduced, let us take a closer look at our background neutron counts.\n",
+        "\n",
+        "Here are the different steps we will take:\n",
+        "\n",
+        "**Side-step 4.1 Expected Poisson Distribution**\n",
+        "- *Fitting the experimental data to a Poisson distribution*\n",
+        "- *Checking quantitatively the goodness of the fit*\n",
+        "\n",
+        "**Side-Step 4.2 Stability of the Background Rate and Normality of Daily Means**\n",
+        "- *Fitting the experimental data to a Normal distribution*\n",
+        "- *Checking quantitatively the goodness of the fit*\n",
+        "\n",
+        "**Side-Step 4.3 Comparing our Background Rates with the Literature**\n",
+        "- *Reference Neutron Background Flux*\n",
+        "\n",
+        "\n",
+        "We will start by building a daily histogram of the background neutron counts per minute. Each line in the plot will represent one day’s worth of 1 minute bins, normalized to form a probability distribution. This lets us see how the shape of the count distribution varies from day to day."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 720x432 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          },
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "fig_counter += 1 \n",
+        "\n",
+        "# Ensure datetime index\n",
+        "he3_all.index = pd.to_datetime(he3_all.index)\n",
+        "\n",
+        "# Temporarily rename the column for ease\n",
+        "counts_series = he3_all[\"Counts ch50-1000\"]\n",
+        "\n",
+        "# Group by day\n",
+        "grouped_by_day = counts_series.groupby(he3_all.index.date)\n",
+        "\n",
+        "# Define bins\n",
+        "bins = np.arange(counts_series.min(), counts_series.max() + 1, 1)\n",
+        "\n",
+        "# Plot histograms as line plots\n",
+        "plt.figure(figsize=(10, 6))\n",
+        "\n",
+        "for day, group in grouped_by_day:\n",
+        "    hist_values, bin_edges = np.histogram(group, bins=bins, density=True)\n",
+        "    plt.plot(bin_edges[:-1], hist_values, alpha=0.1, label=str(day))\n",
+        "\n",
+        "plt.xlabel(\"Counts per Minute\")\n",
+        "plt.ylabel(\"Normalized Frequency\")\n",
+        "plt.title(f\"Daily Histograms of He3 Counts (Fig. {fig_counter})\")\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Let us quickly comment on how to read this plot:\n",
+        "\n",
+        "- Each colored line corresponds to one calendar day’s distribution of neutron counts per minute.\n",
+        "- Horizontal axis: number of counts detected in a 1 min bin (i.e. $n$ counts per minute, with $n$ the numbers on the horizontal axis).\n",
+        "- Vertical axis: normalized frequency (so that areas under each curve sum to 1).\n",
+        "- The shading/transparency helps you see where multiple days’ distributions overlap. Each color corresponds to the data from a different day."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "zxgDMmwGfr8K"
+      },
+      "source": [
+        "## Side-step 4.1 Expected Poisson Distribution\n",
+        "\n",
+        "In order to conduct a statistical analysis on these background counts, we need to have an idea of what qualifies as a \"significant\" deviation from background. This will be of interest when trying to determine whether or not we have detected a \"significant\" number of neutron counts."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Fitting to a Poisson distribution\n",
+        "\n",
+        "Neutron background counts are typically modeled by a Poisson distribution because they arise from random, independent events which occur at a constant average rate over time. Our experimental setup aligns with the conditions under which the Poisson distribution is valid:\n",
+        "\n",
+        "- Rare Events: Background neutrons are detected infrequently and individually; each detection is a discrete event.\n",
+        "\n",
+        "- Statistical Independence: The arrival of one neutron does not affect the probability of another arriving.\n",
+        "\n",
+        "- Constant Rate: Over short timescales (like 1-minute bins), the average background rate is approximately constant.\n",
+        "\n",
+        "- Fixed Observation Interval: Counts are measured over uniform time intervals (i.e. counted over fixed 1 minute intervals).\n",
+        "\n",
+        "Under these conditions, the number of neutrons detected in a fixed time interval should follow a Poisson distribution with mean $\\lambda$, where $\\lambda$ is the expected number of events (neutrons) per interval.\n",
+        "\n",
+        "The standard deviation of the ditribution would thus be $\\sigma = \\sqrt{λ}$.\n",
+        "\n",
+        "Furthermore, in the literature, we will typically consider that count is \"significantly high\" if it exceeds $\\lambda + \\sqrt{\\lambda}\\cdot Z$\n",
+        "\n",
+        "where $Z = 3$ corresponds to a $3\\sigma$ threshold (confidence level ~$99.7\\%$)\n",
+        "\n",
+        "Let us now have a closer look at how close our experimental background measurements are to a Poisson distribution, and extract its key statistical features."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 720x432 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          },
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "from scipy import stats\n",
+        "\n",
+        "fig_counter += 1  \n",
+        "\n",
+        "# Ensure the index is datetime\n",
+        "he3_all.index = pd.to_datetime(he3_all.index)\n",
+        "\n",
+        "# Extract counts column\n",
+        "counts_series = he3_all[\"Counts ch50-1000\"]\n",
+        "\n",
+        "# Group by day\n",
+        "grouped_by_day = counts_series.groupby(he3_all.index.date)\n",
+        "\n",
+        "# Define histogram bins\n",
+        "bins = np.arange(counts_series.min(), counts_series.max() + 1, 1)\n",
+        "\n",
+        "# Compute histograms per day\n",
+        "histograms = []\n",
+        "for day, group in grouped_by_day:\n",
+        "    hist_values, bin_edges = np.histogram(group, bins=bins, density=True)\n",
+        "    histograms.append(hist_values)\n",
+        "\n",
+        "# Convert to array and compute mean and standard deviation\n",
+        "histograms = np.array(histograms)\n",
+        "mean_histogram = np.mean(histograms, axis=0)\n",
+        "std_histogram = np.std(histograms, axis=0)\n",
+        "\n",
+        "# Estimate Poisson background mean\n",
+        "lambda_ = counts_series.mean()\n",
+        "threshold_3sigma = lambda_ + 3 * np.sqrt(lambda_)\n",
+        "\n",
+        "# Compute Poisson fit\n",
+        "k_values = bin_edges[:-1]\n",
+        "poisson_pmf = stats.poisson.pmf(k_values, mu=lambda_)\n",
+        "\n",
+        "# Normalize Poisson PMF to match area of mean histogram\n",
+        "poisson_pmf_normalized = poisson_pmf / np.sum(poisson_pmf)\n",
+        "poisson_pmf_normalized *= np.sum(mean_histogram)\n",
+        "\n",
+        "# Plotting\n",
+        "plt.figure(figsize=(10, 6))\n",
+        "\n",
+        "# Plot all daily histograms\n",
+        "for hist_values in histograms:\n",
+        "    plt.plot(bin_edges[:-1], hist_values, alpha=0.2, color='gray')\n",
+        "\n",
+        "# Plot mean histogram\n",
+        "plt.plot(bin_edges[:-1], mean_histogram, color='black', linewidth=2, label='Mean Histogram')\n",
+        "\n",
+        "# Plot 3σ band\n",
+        "plt.fill_between(bin_edges[:-1],\n",
+        "                 np.maximum(mean_histogram - 3 * std_histogram, 0),\n",
+        "                 mean_histogram + 3 * std_histogram,\n",
+        "                 color='black', alpha=0.1, label='3σ Band')\n",
+        "\n",
+        "# Plot Poisson fit\n",
+        "plt.plot(k_values, poisson_pmf_normalized, 'r--', linewidth=2, label=f'Poisson Fit (λ = {lambda_:.2f})')\n",
+        "\n",
+        "plt.xlabel(\"Counts per Minute\")\n",
+        "plt.ylabel(\"Normalized Frequency\")\n",
+        "plt.title(f\"Daily Histograms of He3 Counts with 3σ Band (Fig. {fig_counter})\")\n",
+        "plt.legend()\n",
+        "plt.grid(True)\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Let's take a step back to understand the graph we are looking at above.\n",
+        "\n",
+        "The black line corresponds to the average distribution of neutron counts across all days.\n",
+        "\n",
+        "The grey shaded area shows the spread of day-to-day variation, with upper and lower bounds at $3$ standard deviations above and below the mean. Days that would lie outside this band would be statistically rare under normal conditions (probability < $0.3\\%$). Hence, we may identify neutron bursts in future runs by looking at \"outliers\" of this grey shaded area.\n",
+        "\n",
+        "The red dashed line corresponds to the theoretical distribution assuming that neutron counts follow a Poisson process. We plotted this normalized Poisson ditribution assuming the Poisson paramter $\\lambda$ to me the mean of our background data i.e. $\\lambda \\approx 11.19$."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Quantitative goodness-of-fit\n",
+        "\n",
+        "In order to test more rigorously whether our background truly follows a Poisson process, we can perform a $χ^2$ (chi-square) goodness-of-fit test comparing the observed mean histogram to the expected Poisson probabilities:\n",
+        "\n",
+        "1. Compute the test statistic  \n",
+        "   $$\n",
+        "     \\chi^2 = \\sum_{k} \\frac{(O_k - E_k)^2}{E_k},\n",
+        "   $$\n",
+        "   where $O_k$ are the observed counts in bin $k$ (from the mean histogram) and $E_k = N_{\\rm tot}\\,P_{\\rm Poisson}(k;\\lambda)$.  \n",
+        "2. Under the null hypothesis (data ∼ Poisson\\($\\lambda)$, $\\chi^2$ follows a $\\chi^2$ distribution with $\\text{Degrees of Freedom} = \\text{number of bins} - 1 - 1$ (subtracting one for the estimated $\\lambda$ and 1 for normalization).  \n",
+        "3. A large $p$-value $(p>0.05$) implies we cannot reject the Poisson hypothesis at the $5 \\%$ level.\n",
+        "\n",
+        "In the code below, we conduct this goodness of fit analysis and find a p value of $p = 1$ so we cannot reject the null-hypothesis. Hence, for our purposes, we are in a good position to say that background follows a Poisson process."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 24,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Chi-square statistic: 0.11\n",
+            "Degrees of freedom: 24\n",
+            "p-value: 1.0000000000\n",
+            "Cannot reject Poisson(λ) at the 5% significance level.\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Aggregate observed counts across all days\n",
+        "O_counts = histograms.sum(axis=0)       # observed total counts per bin\n",
+        "N_tot = O_counts.sum()                         # total number of 1-min intervals\n",
+        "\n",
+        "# Expected counts under Poisson(λ)\n",
+        "pk = stats.poisson.pmf(k_values, mu=lambda_)\n",
+        "E_counts = N_tot * pk\n",
+        "\n",
+        "# Compute χ² statistic and p-value\n",
+        "chi2_stat = np.sum((O_counts - E_counts)**2 / E_counts)\n",
+        "dof = len(k_values) - 2  # degrees of freedon = bins minus 1 (normalization) minus 1 (λ estimated)\n",
+        "p_value = stats.chi2.sf(chi2_stat, dof)\n",
+        "\n",
+        "print(f\"Chi-square statistic: {chi2_stat:.2f}\")\n",
+        "print(f\"Degrees of freedom: {dof}\")\n",
+        "print(f\"p-value: {p_value:.10f}\")\n",
+        "\n",
+        "if p_value > 0.05:\n",
+        "    print(\"Cannot reject Poisson(λ) at the 5% significance level.\")\n",
+        "else:\n",
+        "    print(\"Data significantly deviate from Poisson(λ).\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Side-Step 4.2 Stability of the Background Rate and Normality of Daily Means"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Fitting to a Normal Distribution\n",
+        "\n",
+        "Furthermore, before trusting the aformentioned single global $\\lambda$, we should check how much the daily average neutron count per minute varies over our measurement period, and whether those daily means themselves follow an approximately normal distribution (by the Central Limit Theorem, if $\\lambda$ is truly constant)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 27,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Daily mean counts per minute: μ = 11.30, σ = 0.75\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 576x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          },
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "fig_counter += 1\n",
+        "\n",
+        "# Ensure datetime index\n",
+        "he3_all.index = pd.to_datetime(he3_all.index)\n",
+        "\n",
+        "# Extract counts\n",
+        "counts_series = he3_all[\"Counts ch50-1000\"]\n",
+        "\n",
+        "# Group by day\n",
+        "grouped_by_day = counts_series.groupby(he3_all.index.date)\n",
+        "\n",
+        "# 1. Compute daily means\n",
+        "daily_means = [group.mean() for _, group in grouped_by_day]\n",
+        "days = list(grouped_by_day.groups.keys())\n",
+        "\n",
+        "# 2. Summary statistics\n",
+        "mu_daily = np.mean(daily_means)\n",
+        "sigma_daily = np.std(daily_means)\n",
+        "print(f\"Daily mean counts per minute: μ = {mu_daily:.2f}, σ = {sigma_daily:.2f}\")\n",
+        "\n",
+        "# 3. Histogram of daily means with Normal fit overlay\n",
+        "plt.figure(figsize=(8, 4))\n",
+        "\n",
+        "# Histogram\n",
+        "vals, edges, _ = plt.hist(daily_means, bins='auto', density=True, alpha=0.6, label='Empirical')\n",
+        "\n",
+        "# Normal PDF\n",
+        "x = np.linspace(min(edges), max(edges), 200)\n",
+        "pdf = stats.norm.pdf(x, loc=mu_daily, scale=sigma_daily)\n",
+        "plt.plot(x, pdf, 'r--', label=f'Normal Fit\\nμ = {mu_daily:.2f}, σ = {sigma_daily:.2f}')\n",
+        "\n",
+        "plt.xlabel('Daily Mean Counts per Minute')\n",
+        "plt.ylabel('Density')\n",
+        "plt.title(f'Distribution of Daily Means (Fig. {fig_counter})')\n",
+        "plt.legend()\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Quantitative goodness-of-fit\n",
+        "\n",
+        "The above plot does not shed enough light on how close our mean distribution is to a normal distribution. In order to determine the quantitative goodness of our fit, we may start with a graphical check: the QQ-plot. This plot sample quantiles vs theoretical normal quantiles; and deviations from the straight line highlight non-normality."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 28,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 432x432 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          },
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "fig_counter += 1\n",
+        "\n",
+        "# 4. QQ-plot for normality check\n",
+        "plt.figure(figsize=(6, 6))\n",
+        "stats.probplot(daily_means, dist=\"norm\", plot=plt)\n",
+        "plt.title(f'QQ Plot of Daily Means (Fig. {fig_counter})')\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The plot above shows us that the experimental distribution is fairly \"linearly\" close to a normal distribution. However, is not a good enough \"quantitative\" measure of the goodness of our fit. For this, we will perform the Shapiro-Wilk test.\n",
+        "\n",
+        "The Shapiro–Wilk test computes a statistic  \n",
+        "$$\n",
+        "W \\;=\\; \\frac{\\bigl(\\sum_{i=1}^n a_i\\,x_{(i)}\\bigr)^2}\n",
+        "                {\\sum_{i=1}^n\\bigl(x_i - \\bar x\\bigr)^2}\\,,\n",
+        "$$  \n",
+        "\n",
+        "where the $x_{(i)}$ are the ordered sample values, the $a_i$ are constants derived from the means and covariances of order statistics of a normal distribution, and $\\bar x$ is the sample mean.  Under the null hypothesis that the data come from a normal distribution, $W$ is close to 1; values substantially below 1 indicate departure from normality.\n",
+        "\n",
+        "In practice, we obtain from `scipy.stats.shapiro(daily_means)` both the test statistic $W$ and a $p$-value.  We then compare the $p$-value to our significance level (commonly $\\alpha=0.05$):\n",
+        "\n",
+        "- If $p > 0.05$, we **fail to reject** the null hypothesis: there is no strong evidence against normality.\n",
+        "- If $p \\le 0.05$, we **reject** the null hypothesis: the daily means significantly deviate from a normal distribution."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 29,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Shapiro–Wilk test: W = 0.9704, p-value = 0.6773\n",
+            "Fail to reject H₀: data are consistent with a normal distribution\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 5. Shapiro–Wilk test for normality\n",
+        "W, p_value = stats.shapiro(daily_means)\n",
+        "print(f\"Shapiro–Wilk test: W = {W:.4f}, p-value = {p_value:.4f}\")\n",
+        "\n",
+        "if p_value > 0.05:\n",
+        "    print(\"Fail to reject H₀: data are consistent with a normal distribution\")\n",
+        "else:\n",
+        "    print(\"Reject H₀: data significantly deviate from normality\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "By combining the visual Q–Q plot and the Shapiro–Wilk test, we obtain both qualitative and quantitative assurance that our daily means are well-approximated by a normal distribution. Hence, we may me confident in using a single global $\\lambda$ for the background rate.  \n",
+        "\n",
+        "So, it is a reasonable assumption to claim that our $\\lambda$ is essentially constant over the January-December background collection period."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Side-Step 4.3 Comparing our Background Rates with the Literature\n",
+        "\n",
+        "\n",
+        "### Reference Neutron Background Flux\n",
+        "\n",
+        "For benchmarking our Eljen detector measurements against a well-established baseline, we will start by adopting the sea-level cosmic-ray neutron flux measured by [Gordon et al. (2004)](https://ieeexplore.ieee.org/document/1369506) on the roof of the IBM T. J. Watson Research Center in Yorktown Heights, NY:\n",
+        "\n",
+        "> **Φ<sub>ref</sub> = 0.0134 n cm<sup>−2</sup> s<sup>−1</sup>**  \n",
+        "\n",
+        "This value was Measured at ∼20 m a.s.l., geomagnetic cutoff ≃ 3 GV, and mid-level solar activity. In their paper, Gordon et al. provided corrections for different coordinates, altitudes, geomagnetic cutoffs and solar activity.\n",
+        "\n",
+        "Hence, we will follow the equations given in the paper, and adapt to our experimental conditions, adjusting for location, environmental parameters, and detector efficiency in order to define a value for the expected background neutron flux from cosmic-rays.\n",
+        "\n",
+        "Let us begin by applying a site-adjustment,  i.e.\n",
+        "   - Apply the altitude-dependence correction ([Gordon et al. (2004)](https://ieeexplore.ieee.org/document/1369506) Eq. (4)) to account for our [lab elevation]( https://elevation.maplogs.com/poi/massachusetts_institute_of_technology_77_massachusetts_ave_cambridge_ma_usa.197544.html).  \n",
+        "   - Apply [geomagnetic-rigidity](https://www.spenvis.oma.be/models.php) and [solar-modulation](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2016JA023819) corrections ([Gordon et al. (2004)](https://ieeexplore.ieee.org/document/1369506) Eqs. (3) & (5)) for our latitude and current solar cycle.\n",
+        "\n",
+        "   \n",
+        "NB: *The hyper-links above correspond to references for the sources of the figures we will use for adaptiation to our specific experimental environment.*\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "base",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.7"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}