Exploratory Information Evaluation: Gamma Spectroscopy in Python (Half 2)

half, I did an exploratory knowledge evaluation of the gamma spectroscopy knowledge. We had been capable of see that utilizing a contemporary scintillation detector, we can’t solely see that the thing is radioactive. With a gamma spectrum, we’re additionally capable of inform why it’s radioactive and how much isotopes the thing accommodates.

On this half, we’ll go additional, and I’ll present the right way to make and practice a machine studying mannequin for detecting radioactive parts.

Earlier than we start, an vital 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 individual danger. I did my checks with sources that may be legally discovered and bought, like classic uranium glass or outdated watches with radium dial paint. Please test your native legal guidelines and skim security tips about dealing with radioactive supplies. Sources used on this take a look at are usually not severely harmful, however nonetheless should be dealt with with care!

Now, let’s get began! I’ll present the right way to gather the information, practice the mannequin, and run it utilizing a Radiacode scintillation detector. For these readers who do not need Radiacode {hardware}, the hyperlink to the datasource is added on the finish of the article.

Methodology

This text will comprise a number of elements:

1. I’ll briefly clarify what a gamma spectrum is and the way we will use it.
2. We are going to gather the information for our ML mannequin. I’ll present the code for gathering the spectra utilizing the Radiacode machine.
3. We are going to practice the mannequin and management its accuracy.
4. Lastly, I’ll make an HTMX-based internet frontend for the mannequin, and we’ll see the leads to real-time.

Let’s get into it!

1. Gamma Spectrum

This can be a quick recap of the first half, and for extra particulars, I extremely suggest studying it first.

Why is the gamma spectrum so attention-grabbing? 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 trendy thoriated tungsten rods. A Geiger counter solely reveals us the variety of radioactive particles that had been detected. A scintillation detector reveals us not solely the variety of particles but additionally 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 might 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 stage is about 1,20 µSv/h, which is 12 instances increased than the background (0,1 µSv/h). It isn’t loopy excessive and akin to a stage on an airplane throughout the flight, however it’s nonetheless statistically vital 😉

Nonetheless, by solely observing the worth, we can’t inform why the thing is radioactive. A gamma spectrum will present us what isotopes are inside the thing:

On this instance, the pendant accommodates 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 we have now discovered this piece of rock:

That is uraninite, a mineral that accommodates quite a lot 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 subject, it’s often not the case 😉 With a gamma spectrum, we will see a picture like this:

By evaluating the peaks with identified isotopes, we will inform that the rock accommodates uranium, however, for instance, not thorium.

A bodily rationalization of the gamma spectrum can also be fascinating. As we will see on the graph under, gamma rays are literally photons and belong to the identical spectrum as seen mild:

When some individuals assume that radioactive objects are glowing in the dead of night, 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 out there just for establishments and large labs (in the perfect case, some used crystals with unknown high quality might 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 undertaking. 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 robotically detect varied parts.

2. Gathering the Information

As readers can guess, our first problem is gathering 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 job, it isn’t required, and a few supplies might be legally discovered and bought. For instance, americium remains to be utilized in smoke detectors; radium was utilized in portray the watch dials earlier than the Nineteen Sixties; uranium was extensively utilized in glass manufacturing earlier than the Nineteen Fifties, and thoriated tungsten rods are nonetheless produced immediately and might be bought from Amazon. Even the pure uranium ore might be bought within the mineral outlets; nonetheless, it requires a bit extra security precautions. And a advantage of gamma-spectroscopy is that we don’t must disassemble or break the objects, and the method is mostly secure.

The second problem is gathering the information. Should you work in e-commerce, then it’s often not an issue, and each SQL request will return tens of millions 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, gathering each spectrum requires 10-20 minutes. For each take a look at object, it might be good to have no less than 10 data. As we will see, the method can take hours, and having tens of millions of data shouldn’t be a sensible choice.

For getting the spectrum knowledge, I might 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 sluggish and tedious. As a substitute, 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 machine """
    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 total 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 reserve it to a JSON file. Now, I solely want to put a Radiacode detector close to the thing and depart the script operating for a number of hours. In consequence, 10-20 JSON information might be saved. I additionally must repeat the method for each pattern I’ve. As a ultimate output, 100-200 information might be collected. It’s nonetheless not tens of millions, however as we’ll see, it’s sufficient for our job.

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 wish to use.

3.1 Information Load

When the information is collected, we should always have some spectrum information saved in JSON format. A person file appears 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. Completely 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] permit us to transform the channel quantity into the vitality in keV (kiloelectronvolt).

First, let’s make a way to load the spectrum from a file:

@dataclass
class Spectrum:
    """ Radiation spectrum measurement knowledge """

    length: int
    a0: float
    a1: float
    a2: float
    counts: checklist[int]

    def channel_to_energy(self, ch: int) -> float:
        """ Convert channel quantity to the vitality stage """
        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: Elective[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("Power, keV")
    plt.legend()
    fig.tight_layout()


sp = load_spectrum_json("thorium-20250617012217.json")
draw_simple_spectrum(sp)

The output appears like this:

What can we see right here?

As was talked about earlier than, from an ordinary Geiger counter, we will get solely the variety of detected particles. It tells us if the thing 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 accommodates the variety of detected particles. This strategy has a easy drawback: the variety of particles shouldn’t be a relentless. It depends upon each the gathering time and the “power” of the supply. It implies 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 get rid of these points, I made a decision to make use of a two-step pipeline. First, I utilized the Savitzky-Golay filter to scale 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 quick assortment instances, the place the peaks are usually not so effectively seen.

Second, I normalized a NumPy array to 0..1 by merely dividing its values by the utmost.

A ultimate “normalize” methodology appears like this:

def normalize(spectrum: Spectrum) -> Spectrum:
    """ Normalize knowledge to the vertical vary of 0..1 """
    # Easy 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
    )

In consequence, spectra from totally different sources now have an identical 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 could have solely 100-200 information in complete. The answer is to enhance 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 attention-grabbing isotopes. Then I added a noise with 50% of the amplitude of that channel. A np.clip name ensures that the information values are usually not adverse (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 appears like this:

As we will see, the noise stage shouldn’t be that large, so it doesn’t distort the peaks. On the similar time, it provides some range to the information.

A extra subtle strategy will also be used. For instance, some radioactive minerals comprise thorium, uranium, or potassium in several proportions. It might be doable to mix spectra of present 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 specifically 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 checklist 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 checklist to a file """
    with open(filename, "w") as f_out:
        json.dump(isotopes, f_out)

Solely spectrum values for these isotopes might be used as enter for the mannequin. I additionally created a way to avoid wasting an inventory 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 checklist on their very own.

Now, let’s create a way that converts a Radiacode spectrum to an inventory 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 checklist of 1024 values to a NumPy array with solely 23 parts, which is an effective 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 depends upon the samples you have got and should appear to be 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 is predicated on gradient tree boosting (authentic paper hyperlink). I can even use a GridSearchCV to seek 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 want to avoid wasting the skilled mannequin:

isotopes_save("../fashions/V1/isotopes.json")
bst.save_model("../fashions/V1/XGBClassifier.json")
np.save("../fashions/V1/LabelEncoder.npy", le.classes_)

Clearly, we want not solely the mannequin itself but additionally the checklist of isotopes and labels. If we alter one thing, the information won’t match anymore, and the mannequin will produce rubbish, so mannequin versioning is our pal!

To confirm the outcomes, I want 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 way 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 further knowledge that isn’t required for our job.

Virtually, that is an instance of information assortment. This Victorian creamer from the Nineties is 130 years outdated, and belief me, you can not get this knowledge through the use of an SQL request 🙂

This uranium glass is barely radioactive (the background stage is about 0,08 µSv/h), however it’s at a secure stage and can’t produce any hurt.

The take a look at code itself is easy:

# 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 no less than for my comparatively small variety of samples, all objects had been detected correctly.

4. Testing

As a ultimate a part of this text, let’s use the mannequin in real-time with a Radiacode machine.

The code is sort of the identical as originally of the article, so I’ll present solely the essential elements. Utilizing the radiacode library, I hook up with the machine, 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(
        stage=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 linked:, 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 machine 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 thing:

This classic watch was made within the Nineteen Fifties, and it has radium paint on the digits. Its radiation stage is ~5 instances the background, however it’s nonetheless inside a secure stage (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 leads to 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 tip 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 might 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 alternative (from not solely monetary or authorized views, but additionally contemplating the free area in my condominium) 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 approach, we lose the details about the radioactivity stage of the objects. For instance, the uranium glass has a a lot decrease radiation stage in comparison with the uranium ore. To tell apart these objects extra successfully, we will add the radioactivity stage as an extra mannequin characteristic.
• Testing different mannequin varieties. It appears promising to make use of a vector search to seek out the closest embeddings. It will also be extra interpretable, and the mannequin can present a number of closest isotopes. A library like FAISS might be helpful for that. One other approach is to make use of a deep studying mannequin, which will also be attention-grabbing to check.

On this article, I used a Radiacode radiation detector. It’s a good machine that enables making some attention-grabbing 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 out there on Kaggle.

The complete supply code for this text is on the market on my Patreon web page. This help helps me to purchase tools or electronics for future checks. And readers are additionally welcome to attach through LinkedIn, the place I periodically publish smaller posts that aren’t sufficiently big for a full article.

Thanks for studying.