half, I did an exploratory knowledge evaluation of the gamma spectroscopy knowledge. We have been in a position to see that utilizing a contemporary scintillation detector, we can’t solely see that the article is radioactive. With a gamma spectrum, we’re additionally in a position to inform why it’s radioactive and how much isotopes the article incorporates.
On this half, we are going to go additional, and I’ll present make and practice a machine studying mannequin for detecting radioactive components.
Earlier than we start, an essential warning. All knowledge information collected for this text can be found on Kaggle, and readers can practice and take a look at their ML fashions with out having actual {hardware}. If you wish to take a look at actual objects, do it at your personal danger. I did my exams with sources that may be legally discovered and bought, like classic uranium glass or previous watches with radium dial paint. Please examine your native legal guidelines and browse security pointers about dealing with radioactive supplies. Sources used on this take a look at usually are not critically harmful, however nonetheless should be dealt with with care!
Now, let’s get began! I’ll present gather the information, practice the mannequin, and run it utilizing a Radiacode scintillation detector. For these readers who shouldn’t have Radiacode {hardware}, the hyperlink to the datasource is added on the finish of the article.
Methodology
This text will comprise a number of components:
- I’ll briefly clarify what a gamma spectrum is and the way we will use it.
- We’ll gather the information for our ML mannequin. I’ll present the code for amassing the spectra utilizing the Radiacode system.
- We’ll practice the mannequin and management its accuracy.
- Lastly, I’ll make an HTMX-based net frontend for the mannequin, and we are going to see the ends in real-time.
Let’s get into it!
1. Gamma Spectrum
This can be a brief recap of the first half, and for extra particulars, I extremely advocate studying it first.
Why is the gamma spectrum so fascinating? Some objects round us might be barely radioactive. Its sources differ from the naturally occurring radiation of granite within the buildings to the radium in some classic watches or the thorium in fashionable thoriated tungsten rods. A Geiger counter solely reveals us the variety of radioactive particles that have been detected. A scintillation detector reveals us not solely the variety of particles but in addition their energies. This can be a essential distinction—it turned out that totally different radioactive supplies emit gamma rays with totally different energies, and every materials has its personal “footprint.”
As a primary instance, I purchased this pendant within the Chinese language store:
It was marketed as an “ion-generating,” so I already suspected that the pendant could possibly be barely radioactive (an ionizing radiation, as its identify suggests, can produce ions). Certainly, as we will see on the meter display screen, its radioactivity degree is about 1,20 µSv/h, which is 12 instances greater than the background (0,1 µSv/h). It’s not loopy excessive and akin to a degree on an airplane through the flight, however it’s nonetheless statistically important 😉
Nonetheless, by solely observing the worth, we can not inform why the article is radioactive. A gamma spectrum will present us what isotopes are inside the article:
On this instance, the pendant incorporates thorium-232, and a thorium decay chain produces radium and actinium. As we will see on the graph, the actinium-228 peak is effectively seen on the spectrum.
As a second instance, let’s say now we have discovered this piece of rock:
That is uraninite, a mineral that incorporates loads of uranium dioxide. Such specimens might be present in some areas of Germany, the Czech Republic, or the US. If we get it within the mineral store, it in all probability has a label on it. However within the discipline, it’s normally not the case 😉 With a gamma spectrum, we will see a picture like this:
By evaluating the peaks with recognized isotopes, we will inform that the rock incorporates uranium, however, for instance, not thorium.
A bodily rationalization of the gamma spectrum can be fascinating. As we will see on the graph beneath, gamma rays are literally photons and belong to the identical spectrum as seen mild:
When some individuals suppose that radioactive objects are glowing at the hours of darkness, it’s truly true! Each radioactive materials is certainly glowing with its personal distinctive “coloration,” however within the very far and non-visible to the human eye a part of the spectrum.
A second fascinating factor is that solely 10-20 years in the past, gamma-spectroscopy was accessible just for establishments and massive labs (in the very best case, some used crystals with unknown high quality could possibly be discovered on eBay). These days, because of developments in electronics, a scintillation detector might be bought for the worth of a mid-range smartphone.
Now, let’s return to our mission. As we will see from the 2 examples above, the spectra of various objects are totally different. Let’s create a machine studying mannequin that may routinely detect varied components.
2. Gathering the Information
As readers can guess, our first problem is amassing the samples. I’m not a nuclear establishment, and I don’t have entry to the calibrated take a look at sources like cesium or strontium. Nonetheless, for our activity, it’s not required, and a few supplies might be legally discovered and bought. For instance, americium continues to be utilized in smoke detectors; radium was utilized in portray the watch dials earlier than the Nineteen Sixties; uranium was broadly utilized in glass manufacturing earlier than the Fifties, and thoriated tungsten rods are nonetheless produced right this moment and might be bought from Amazon. Even the pure uranium ore might be bought within the mineral retailers; nonetheless, it requires a bit extra security precautions. And a benefit of gamma-spectroscopy is that we don’t have to disassemble or break the objects, and the method is usually secure.
The second problem is amassing the information. For those who work in e-commerce, then it’s normally not an issue, and each SQL request will return hundreds of thousands of data. Alas, within the “actual world,” it may be far more difficult. Particularly if you wish to make a database of the radioactive supplies. In our case, amassing each spectrum requires 10-20 minutes. For each take a look at object, it could be good to have not less than 10 data. As we will see, the method can take hours, and having hundreds of thousands of data will not be a sensible choice.
For getting the spectrum knowledge, I can be utilizing a Radiacode 103G scintillation detector and an open-source radiacode library.
A gamma spectrum might be exported in XML format utilizing the official Radiacode Android app, however the guide course of is simply too gradual and tedious. As an alternative, I created a Python script that collects the spectra utilizing random time intervals:
from radiacode import RadiaCode, RawData, Spectrum
def read_forever(rc: RadiaCode):
""" Learn knowledge from the system """
whereas True:
interval_sec = random.randint(10*60, 30*60)
read_spectrum(rc, interval_sec)
def read_spectrum(rc: RadiaCode, interval: int):
""" Learn and save spectrum """
rc.spectrum_reset()
# Learn
dt = datetime.datetime.now()
filename = dt.strftime("spectrum-%YpercentmpercentdpercentHpercentMpercentS.json")
logging.debug(f"Making spectrum for {interval // 60} min")
# Wait
t_start = time.monotonic()
whereas time.monotonic() - t_start < interval:
show_device_data(rc)
time.sleep(0.4)
# Save
spectrum: Spectrum = rc.spectrum()
spectrum_save(spectrum, filename)
def show_device_data(rc: RadiaCode):
""" Get CPS (counts per second) values """
knowledge = rc.data_buf()
for report in knowledge:
if isinstance(report, RawData):
log_str = f"CPS: {int(report.count_rate)}"
logging.debug(log_str)
def spectrum_save(spectrum: Spectrum, filename: str):
""" Save spectrum knowledge to log """
duration_sec = spectrum.length.total_seconds()
knowledge = {
"a0": spectrum.a0,
"a1": spectrum.a1,
"a2": spectrum.a2,
"counts": spectrum.counts,
"length": duration_sec,
}
with open(filename, "w") as f_out:
json.dump(knowledge, f_out, indent=4)
logging.debug(f"File '{filename}' saved")
rc = RadiaCode()
app.read_forever()Some error dealing with is omitted right here for readability causes. A hyperlink to the complete supply code might be discovered on the finish of the article.
As we will see, I randomly choose the time between 10 and half-hour, gather the gamma spectrum knowledge, and put it aside to a JSON file. Now, I solely want to put a Radiacode detector close to the article and depart the script operating for a number of hours. Consequently, 10-20 JSON information can be saved. I additionally have to repeat the method for each pattern I’ve. As a remaining output, 100-200 information might be collected. It’s nonetheless not hundreds of thousands, however as we are going to see, it’s sufficient for our activity.
3. Coaching the Mannequin
When the information from the earlier step is prepared, we will begin coaching the mannequin. As a reminder, all information can be found on Kaggle, and readers are welcome to make their very own fashions as effectively.
First, let’s preprocess the information and extract the options we need to use.
3.1 Information Load
When the information is collected, we must always have some spectrum information saved in JSON format. A person file seems like this:
{
"a0": 24.524023056030273,
"a1": 2.2699732780456543,
"a2": 0.0004327862989157,
"counts": [ 48, 52, , ..., 0, 35],
"length": 1364.0
}
Right here, the “counts” array is the precise spectrum knowledge. Totally different detectors could have totally different codecs; a Radiacode returns the information within the type of a 1024-channel array. Calibration constants [a0, a1, a2] enable us to transform the channel quantity into the vitality in keV (kiloelectronvolt).
First, let’s make a technique to load the spectrum from a file:
@dataclass
class Spectrum:
""" Radiation spectrum measurement knowledge """
length: int
a0: float
a1: float
a2: float
counts: listing[int]
def channel_to_energy(self, ch: int) -> float:
""" Convert channel quantity to the vitality degree """
return self.a0 + self.a1 * ch + self.a2 * ch**2
def energy_to_channel(self, e: float):
""" Convert vitality to the channel quantity (inverse E = a0 + a1*C + a2 C^2) """
c = self.a0 - e
return int(
(np.sqrt(self.a1**2 - 4 * self.a2 * c) - self.a1) / (2 * self.a2)
)
def load_spectrum_json(filename: str) -> Spectrum:
""" Load spectrum from a json file """
with open(filename) as f_in:
knowledge = json.load(f_in)
return Spectrum(
a0=knowledge["a0"], a1=knowledge["a1"], a2=knowledge["a2"],
counts=knowledge["counts"],
length=int(knowledge["duration"]),
)Now, we will draw it with Matplotlib:
import matplotlib.pyplot as plt
def draw_simple_spectrum(spectrum: Spectrum, title: Non-obligatory[str] = None):
""" Draw spectrum obtained from the Radiacode """
fig, ax = plt.subplots(figsize=(12, 3))
ax.spines["top"].set_color("lightgray")
ax.spines["right"].set_color("lightgray")
counts = spectrum.counts
vitality = [spectrum.channel_to_energy(x) for x in range(len(counts))]
# Bars
ax.bar(vitality, counts, width=3.0, label="Counts")
# X values
ticks_x = [
spectrum.channel_to_energy(ch) for ch in range(0, len(counts), len(counts) // 20)
]
labels_x = [f"{ch:.1f}" for ch in ticks_x]
ax.set_xticks(ticks_x, labels=labels_x)
ax.set_xlim(vitality[0], vitality[-1])
plt.ylim(0, None)
title_str = "Gamma-spectrum" if title is None else title
ax.set_title(title_str)
ax.set_xlabel("Vitality, keV")
plt.legend()
fig.tight_layout()
sp = load_spectrum_json("thorium-20250617012217.json")
draw_simple_spectrum(sp)The output seems like this:
What can we see right here?
As was talked about earlier than, from a normal Geiger counter, we will get solely the variety of detected particles. It tells us if the article is radioactive or not, however no more. From a scintillation detector, we will get the variety of particles grouped by their energies, which is virtually a ready-to-use histogram! A radioactive decay itself is random, so the longer the gathering time, the “smoother” the graph.
3.2 Information Rework
3.2.1 Normalization
Let’s take a look at the spectrum once more:
Right here, the information was collected for about 10 minutes, and the vertical axis incorporates the variety of detected particles. This strategy has a easy drawback: the variety of particles will not be a continuing. It relies on each the gathering time and the “energy” of the supply. It signifies that we could not have 600 particles like on this graph, however 60 or 6000. We will additionally see that the information is a bit noisy. That is particularly seen with a “weak” supply and a brief assortment time.
To remove these points, I made a decision to make use of a two-step pipeline. First, I utilized the Savitzky-Golay filter to cut back the noise:
from scipy.sign import savgol_filter
def smooth_data(knowledge: np.array) -> np.array:
""" Apply 1D smoothing filter to the information array """
window_size = 10
data_out = savgol_filter(
knowledge,
window_length=window_size,
polyorder=2,
)
return np.clip(data_out, a_min=0, a_max=None)It’s particularly helpful for spectra with brief assortment instances, the place the peaks usually are not so effectively seen.
Second, I normalized a NumPy array to 0..1 by merely dividing its values by the utmost.
A remaining “normalize” technique seems like this:
def normalize(spectrum: Spectrum) -> Spectrum:
""" Normalize knowledge to the vertical vary of 0..1 """
# Clean knowledge
counts = np.array(spectrum.counts).astype(np.float64)
counts = smooth_data(counts)
# Normalize
val_norm = counts.max()
return Spectrum(
length=spectrum.length,
a0 = spectrum.a0,
a1 = spectrum.a1,
a2 = spectrum.a2,
counts = counts/val_norm
)Consequently, spectra from totally different sources now have the same scale:
As we will additionally see, the distinction between the 2 samples is kind of seen.
3.2.2 Information Augmentation
Technically, we’re prepared to coach the mannequin. Nonetheless, as we noticed within the “Gathering the information” half, the dataset is fairly small – I’ll have solely 100-200 information in complete. The answer is to reinforce the information by including extra artificial samples.
As a easy strategy, I made a decision so as to add some noise to the unique spectra. However how a lot noise ought to we add? I chosen a 680 keV channel as a reference worth, as a result of this half has no fascinating isotopes. Then I added a noise with 50% of the amplitude of that channel. A np.clip name ensures that the information values usually are not detrimental (for the quantity of detected particles, it doesn’t make bodily sense).
def add_noise(spectrum: Spectrum) -> Spectrum:
""" Add random noise to the spectrum """
counts = np.array(spectrum.counts)
ch_empty = spectrum.energy_to_channel(680.0)
val_norm = counts[ch_empty]
ampl = val_norm / 2
noise = np.random.regular(0, ampl, counts.form)
data_out = np.clip(counts + noise, min=0)
return Spectrum(
length=spectrum.length,
a0 = spectrum.a0,
a1 = spectrum.a1,
a2 = spectrum.a2,
counts = data_out
)
sp = load_spectrum_json("thorium-20250617012217.json")
sp = add_noise(normalize(sp))
draw_simple_spectrum(sp, filename)The output seems like this:
As we will see, the noise degree will not be that massive, so it doesn’t distort the peaks. On the identical time, it provides some range to the information.
A extra refined strategy can be used. For instance, some radioactive minerals comprise thorium, uranium, or potassium in several proportions. It could be doable to mix spectra of current samples to get some “new” ones.
3.2.3 Function Extraction
Technically, we will use all 1024 values “as is” as an enter for our ML mannequin. Nonetheless, this strategy has two issues:
- First, it’s redundant – we’re principally solely particularly isotopes. For instance, on the final graph, there’s a good seen peak at 238 keV, which belongs to Lead-212, and a much less seen peak at 338 keV, which belongs to Actinium-228.
- Second, it’s device-specific. I desire a mannequin to be common. Utilizing solely the energies of the chosen isotopes as enter permits us to make use of any gamma spectrometer mannequin.
Lastly, I created this listing of isotopes:
isotopes = [
# Americium
("Am-241", 59.5),
# Potassium
("K-40", 1460.0),
# Radium
("Ra-226", 186.2),
("Pb-214", 242.0),
("Pb-214", 295.2),
("Pb-214", 351.9),
("Bi-214", 609.3),
("Bi-214", 1120.3),
("Bi-214", 1764.5),
# Thorium
("Pb-212", 238.6),
("Ac-228", 338.2),
("TI-208", 583.2),
("AC-228", 911.2),
("AC-228", 969.0),
# Uranium
("Th-234", 63.3),
("Th-231", 84.2),
("Th-234", 92.4),
("Th-234", 92.8),
("U-235", 143.8),
("U-235", 185.7),
("U-235", 205.3),
("Pa-234m", 766.4),
("Pa-234m", 1000.9),
]
def isotopes_save(filename: str):
""" Save isotopes listing to a file """
with open(filename, "w") as f_out:
json.dump(isotopes, f_out)Solely spectrum values for these isotopes can be used as enter for the mannequin. I additionally created a technique to save lots of a listing into the JSON file – it is going to be used to load the mannequin later. Some isotopes, like Uranium-235, could also be current in minuscule quantities and never be virtually detectable. Readers are welcome to enhance the listing on their very own.
Now, let’s create a technique that converts a Radiacode spectrum to a listing of options:
def get_features(spectrum: Spectrum, isotopes: Listing) -> np.array:
""" Extract options from the spectrum """
energies = [energy for _, energy in isotopes]
knowledge = [spectrum.counts[spectrum.energy_to_channel(energy)] for vitality in energies]
return np.array(knowledge)Virtually, we transformed the listing of 1024 values to a NumPy array with solely 23 components, which is an efficient dimension discount!
3.3 Coaching
Lastly, we’re prepared to coach the ML mannequin.
First, let’s mix all information into one dataset. Virtually, it relies on the samples you’ve got and should seem like this:
all_files = [
("Americium", glob.glob("../data/train/americium*.json")),
("Radium", glob.glob("../data/train/radium*.json")),
("Thorium", glob.glob("../data/train/thorium*.json")),
("Uranium Glass", glob.glob("../data/train/uraniumGlass*.json")),
("Uranium Glaze", glob.glob("../data/train/uraniumGlaze*.json")),
("Uraninite", glob.glob("../data/train/uraninite*.json")),
("Background", glob.glob("../data/train/background*.json")),
]
def prepare_data(augmentation: int) -> Tuple[np.array, np.array]:
""" Put together knowledge for coaching """
x, y = [], []
for identify, information in all_files:
for filename in information:
print(f"Processing {filename}...")
sp = normalize(load_spectrum(filename))
for _ in vary(augmentation):
sp_out = add_noise(sp)
x.append(get_features(sp_out, isotopes))
y.append(identify)
return np.array(x), np.array(y)
X_train, y_train = prepare_data(augmentation=10)As we will see, our y-values comprise names like “Americium.” I’ll use a LabelEncoder to transform them into numeric values:
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
le.match(y_train)
y_train = le.remodel(y_train)
print("X_train:", X_train.form)
#> (1900, 23)
print("y_train:", y_train.form)
#> (1900,)I made a decision to make use of an open-source XGBoost mannequin, which relies on gradient tree boosting (authentic paper hyperlink). I may also use a GridSearchCV to search out optimum parameters:
from xgboost import XGBClassifier
from sklearn.model_selection import GridSearchCV
bst = XGBClassifier(n_estimators=10, max_depth=2, learning_rate=1)
clf = GridSearchCV(
bst,
{
"max_depth": [1, 2, 3, 4],
"n_estimators": vary(2, 20),
"learning_rate": [0.001, 0.01, 0.1, 1.0, 10.0]
},
verbose=1,
n_jobs=1,
cv=3,
)
clf.match(X_train, y_train)
print("best_score:", clf.best_score_)
#> best_score: 0.99474
print("best_params:", clf.best_params_)
#> best_params: {'learning_rate': 1.0, 'max_depth': 1, 'n_estimators': 9}
Final however not least, I would like to save lots of the educated mannequin:
isotopes_save("../fashions/V1/isotopes.json")
bst.save_model("../fashions/V1/XGBClassifier.json")
np.save("../fashions/V1/LabelEncoder.npy", le.classes_)Clearly, we’d like not solely the mannequin itself but in addition the listing of isotopes and labels. If we modify one thing, the information is not going to match anymore, and the mannequin will produce rubbish, so mannequin versioning is our pal!
To confirm the outcomes, I would like knowledge that the mannequin didn’t “see” earlier than. I already collected a number of XML information utilizing the Radiacode Android app, and only for enjoyable, I made a decision to make use of them for testing.
First, I created a technique to load the information:
import xmltodict
def load_spectrum_xml(file_path: str) -> Spectrum:
""" Load the spectrum from a Radiacode Android app file """
with open(file_path) as f_in:
doc = xmltodict.parse(f_in.learn())
end result = doc["ResultDataFile"]["ResultDataList"]["ResultData"]
spectrum = end result["EnergySpectrum"]
cal = spectrum["EnergyCalibration"]["Coefficients"]["Coefficient"]
a0, a1, a2 = float(cal[0]), float(cal[1]), float(cal[2])
length = int(spectrum["MeasurementTime"])
knowledge = spectrum["Spectrum"]["DataPoint"]
return Spectrum(
length=length,
a0=a0, a1=a1, a2=a2,
counts=[int(x) for x in data],
)It has the identical spectra values that I used within the JSON information, with some additional knowledge that isn’t required for our activity.
Virtually, that is an instance of knowledge assortment. This Victorian creamer from the Eighteen Nineties is 130 years previous, and belief me, you can’t get this knowledge by utilizing an SQL request 🙂
This uranium glass is barely radioactive (the background degree is about 0,08 µSv/h), nevertheless it’s at a secure degree and can’t produce any hurt.
The take a look at code itself is straightforward:
# Load mannequin
bst = XGBClassifier()
bst.load_model("../fashions/V1/XGBClassifier.json")
isotopes = isotopes_load("../fashions/V1/isotopes.json")
le = LabelEncoder()
le.classes_ = np.load("../fashions/V1/LabelEncoder.npy")
# Load knowledge
test_data = [
["../data/test/background1.xml", "../data/test/background2.xml"],
["../data/test/thorium1.xml", "../data/test/thorium2.xml"],
["../data/test/uraniumGlass1.xml", "../data/test/uraniumGlass2.xml"],
...
]
# Predict
for group in test_data:
knowledge = []
for filename in group:
spectrum = load_spectrum(filename)
options = get_features(normalize(spectrum), isotopes)
knowledge.append(options)
X_test = np.array(knowledge)
preds = bst.predict(X_test)
preds = le.inverse_transform(preds)
print(preds)
#> ['Background' 'Background']
#> ['Thorium' 'Thorium']
#> ['Uranium Glass' 'Uranium Glass']
#> ...Right here, I additionally grouped the values from totally different samples and used batch prediction.
As we will see, all outcomes are appropriate. I used to be additionally going to make a confusion matrix, however not less than for my comparatively small variety of samples, all objects have been detected correctly.
4. Testing
As a remaining a part of this text, let’s use the mannequin in real-time with a Radiacode system.
The code is nearly the identical as at the start of the article, so I’ll present solely the essential components. Utilizing the radiacode library, I connect with the system, learn the spectra as soon as per minute, and use these values to foretell the isotopes:
from radiacode import RadiaCode, RealTimeData, Spectrum
import logging
le = LabelEncoder()
le.classes_ = np.load("../fashions/V1/LabelEncoder.npy")
isotopes = isotopes_load("../fashions/V1/isotopes.json")
bst = XGBClassifier()
bst.load_model("../fashions/V1/XGBClassifier.json")
def read_spectrum(rc: RadiaCode):
""" Learn spectrum knowledge """
spectrum: Spectrum = rc.spectrum()
logging.debug(f"Spectrum: {spectrum.length} assortment time")
end result = predict_spectrum(spectrum)
logging.debug(f"Predict: {end result}")
def predict_spectrum(sp: Spectrum) -> str:
""" Predict the isotope from a spectrum """
options = get_features(normalize(sp), isotopes)
preds = bst.predict([features])
return le.inverse_transform(preds)[0]
def read_cps(rc: RadiaCode):
""" Learn CPS (counts per second) values """
knowledge = rc.data_buf()
for report in knowledge:
if isinstance(report, RealTimeData):
logging.debug(f"CPS: {report.count_rate:.2f}")
if __name__ == '__main__':
logging.basicConfig(
degree=logging.DEBUG, format="[%(asctime)-15s] %(message)s",
datefmt="%Y-%m-%d %H:%M:%S"
)
rc = RadiaCode()
logging.debug(f"ML mannequin loaded")
fw_version = rc.fw_version()
logging.debug(f"System related:, firmware {fw_version[1]}")
rc.spectrum_reset()
whereas True:
for _ in vary(12):
read_cps(rc)
time.sleep(5.0)
read_spectrum(rc)Right here, I learn the CPS (counts per second) values from the Radiacode each 5 seconds, simply to make sure that the system works. Each minute, I learn the spectrum and use it with the mannequin.
Earlier than operating the app, I positioned the Radiacode detector close to the article:
This classic watch was made within the Fifties, and it has radium paint on the digits. Its radiation degree is ~5 instances the background, however it’s nonetheless inside a secure degree (and it’s truly 2 instances decrease than everybody will get in an airplane throughout a flight).
Now, we will run the code and see the ends in real-time:
As we will see, the mannequin’s prediction is appropriate.
Readers who don’t have a Radiacode {hardware} can use uncooked log information to replay the information. The hyperlink is added to the top of the article.
Conclusion
On this article, I defined the method of making a machine studying mannequin for predicting radioactive isotopes. I additionally examined the mannequin with some radioactive samples that may be legally bought.
I additionally did an interactive HTMX frontend for the mannequin, however this text is already too lengthy. If there’s a public curiosity on this subject, this can be printed within the subsequent half.
As for the mannequin itself, there are a number of methods for enchancment:
- Including extra knowledge samples and isotopes. I’m not a nuclear establishment, and my selection (from not solely monetary or authorized views, but in addition contemplating the free area in my house) is restricted. Readers who’ve entry to different isotopes and minerals are welcome to share their knowledge, and I’ll attempt to add it to the mannequin.
- Including extra options. On this mannequin, I normalized all spectra, and it really works effectively. Nonetheless, on this method, we lose the details about the radioactivity degree of the objects. For instance, the uranium glass has a a lot decrease radiation degree in comparison with the uranium ore. To differentiate these objects extra successfully, we will add the radioactivity degree as an extra mannequin function.
- Testing different mannequin varieties. It seems promising to make use of a vector search to search out the closest embeddings. It can be extra interpretable, and the mannequin can present a number of closest isotopes. A library like FAISS might be helpful for that. One other method is to make use of a deep studying mannequin, which can be fascinating to check.
On this article, I used a Radiacode radiation detector. It’s a good system that permits making some fascinating experiments (disclaimer: I don’t have any revenue or different business curiosity from its gross sales). For these readers who don’t have a Radiacode {hardware}, all collected knowledge is freely accessible on Kaggle.
The complete supply code for this text is offered on my Patreon web page. This help helps me to purchase gear or electronics for future exams. And readers are additionally welcome to attach by way of LinkedIn, the place I periodically publish smaller posts that aren’t large enough for a full article.
Thanks for studying.
