🌌 Decoding Pulsar Signals with Python: From Raw Data to Pulsar Profiles

Introduction

Pulsars are some of the universe’s most extraordinary objects. These spinning neutron stars emit powerful beams of radio waves that sweep across the cosmos like cosmic lighthouses. When their beams cross Earth, we detect a series of precisely timed pulses.

Imagine being in a room filled with chatter, the noise of air conditioners and fans, and trying to pick out a faint humming sound in the background. It’s nearly impossible to hear it clearly without filtering out all that surrounding noise. The same challenge exists in astronomy: besides the periodic signals from pulsars, there’s plenty of interference from the atmosphere and other sources that we need to eliminate. That’s exactly what we’ve achieved in this case study—isolating the pulsar’s rhythm from all the background noise.

In this case study, I share how I analysed real pulsar signal data using Python, extracting its periodicity, visualising its power spectrum, and producing a clean pulse profile. This project combined astrophysics, signal processing, and data science into a single fascinating challenge.

**Full notebook and code**

🔗Pulsar Signal Analysis on Github

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Why Study Pulsars?

Pulsars are more than cosmic curiosities—they’re:

• Precise cosmic clocks (some more stable than atomic clocks!)

• Natural laboratories for extreme physics

• Tools to probe the interstellar medium

• Key to testing Einstein’s theories under extreme gravity

Analysing their signals helps scientists:

✅ Measure their rotational properties
✅ Study the interstellar plasma between stars
✅ Search for gravitational waves via pulsar timing arrays

But before any astrophysical discoveries can happen, we must first detect the pulsar’s periodic pulse hidden in noisy radio data. That was my mission.

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Project Goals

I wanted to answer:

“How can we extract meaningful, scientifically useful insights from raw radio signals recorded from a pulsar?”

My specific goals were:

• Detect the pulsar’s rotation frequency

• Visualise how the signal’s frequency content varies over time

• Correct for interstellar dispersion

• Build a high signal-to-noise folded pulse profile

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Data Description

The data consisted of:

• Voltage time series from a radio telescope

• Two channels (often representing different polarisations)

• Sampled at high frequencies

• Signals buried in significant noise and possible radio interference

The raw data captured everything the telescope “heard” during the observation—including:

• The pulsar’s faint periodic pulses

• Random thermal noise

• Artificial radio frequency interference (RFI)

My job was to filter out the pulsar’s signature from this noisy chaos.

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Tools and Libraries

My entire analysis was built using:

• Python

• NumPy

• Pandas

• Matplotlib

• SciPy

These libraries made it straightforward to handle large arrays, perform fast Fourier transforms, and generate high-quality plots.

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Step 1: Visualising the Raw Signal

I started by simply plotting the time-series data.

At first glance, it looked purely random. No obvious pulses were visible. That’s normal—pulsar signals are faint and only emerge after careful processing.

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Step 2: Power Spectrum Analysis

Pulsars emit pulses at precise intervals, so their signature appears as a spike in the frequency domain. I computed the power spectrum via the Fast Fourier Transform (FFT). Because running an FFT on over 150 MB of data wasn’t feasible, I split the dataset into 100 segments of 512 points each, computed the FFT for every chunk, and then plotted the averaged power spectrum.

This step alone was thrilling and the key catch because it revealed the pulsar’s “heartbeat” hidden inside the noise.

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Why FFT Matters in Pulsar Astronomy

This is not just a mathematical trick. Detecting frequency peaks:

• Confirms the pulsar’s periodicity

• Allows precise period measurements

Astronomers scan millions of data streams, and FFTs are crucial to spot these cosmic lighthouses.

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Step 3: Dynamic Spectrum (Spectrogram)

To investigate how the pulsar signal evolves over time, I computed a dynamic spectrum (spectrogram):

This 2D plot helps visualise:

• Fluctuations in signal strength

• Possible bursts of RFI

This spectra also shows that lower frequencies arrive later than the higher frequencies. This is the core principle behind dispersion in pulsar signals.

While my dataset showed relatively stable emission, real observations often reveal fascinating patterns.

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Step 4: Dedispersion

One major challenge in radio astronomy is dispersion. As pulsar signals travel through interstellar plasma, lower frequencies arrive later than higher frequencies. This smears out the pulses and makes them hard to detect.

Dedispersion corrects this effect. Conceptually:

• Calculate the time delay for each frequency channel

• Shift each channel accordingly

• Sum across frequencies to reconstruct sharp pulses

For my analysis:

• I used a known Dispersion Measure (DM)

• Aligned the pulses to improve clarity

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Step 5: Pulse Folding

Even after dedispersion, individual pulses remain faint. To increase signal-to-noise ratio, astronomers “fold” the signal:

Fold = break the time series into segments equal to the pulsar’s period, then average them.

This sums hundreds or thousands of faint pulses into a single high quality pulse profile, basically it amplifies the signal.

The resulting profile displayed a sharp peak—the pulsar’s “fingerprint.”

Results

✅ FFT Analysis: Confirmed pulsar frequency
✅ Spectrogram: Showed stable emission with no significant RFI
✅ Dedispersion: Sharpened pulses significantly
✅ Folding: Revealed the characteristic pulse profile

Seeing the folded pulse appear out of noise was incredibly satisfying—a cosmic heartbeat revealed through code!

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Challenges Faced

• Managing large datasets during FFTs

• Choosing optimal parameters for spectrogram resolution

• Handling potential radio frequency interference (RFI)

• Interpreting noise levels in low-SNR observations

These are the same challenges astronomers face when scanning the sky for new pulsars.

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Scientific Significance

This analysis is more than a technical exercise. It’s how astronomers:

• Search for new pulsars in massive data surveys

• Monitor pulsar timing to detect gravitational waves

• Study the interstellar medium

Pulsars act as natural cosmic laboratories, helping test fundamental physics like General Relativity under extreme conditions.

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Conclusion

Analysing pulsar signals taught me how astronomy, physics, and data science intersect in fascinating ways. It’s extraordinary that a distant neutron star spinning dozens of times per second can whisper its secrets across space—and that we can decode those whispers with Python.

This project deepened my appreciation for signal processing, astrophysics, and the power of data analysis to reveal hidden truths.

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Full notebook & code:
🔗 Pulsar Data Analysis on GitHub

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About Me

I’m Arpita Garg, passionate about analysing fresh datasets and captivated by how science and technology reveal the hidden secrets of the cosmos.