Probing Coronal Activity Using Radio Signals Based on the 2021 superior conjunction of Mars: the Downlink Data from Tianwen-1

This paper demonstrates that analyzing Doppler frequency scintillation in the Tianwen-1 downlink signal during its 2021 superior conjunction effectively probes and spatially localizes solar coronal activities, such as streamers and coronal mass ejections, by revealing strong spatio-temporal correlations with data from SOHO and SDO.

The Gist

Imagine the Sun as a giant, chaotic lighthouse in the middle of a stormy ocean. Usually, when we send a radio message from a spaceship (like China's Tianwen-1 probe) to Earth, the signal travels through the calm, empty dark of space. But sometimes, the Sun, Earth, and Mars line up perfectly in a row. This is called a Superior Conjunction.

When this happens, the radio signal has to swim right through the Sun's "atmosphere" (the corona) to get to us. This atmosphere isn't empty; it's a boiling soup of charged particles (plasma) and magnetic storms.

Here is a simple breakdown of what this paper discovered, using some everyday analogies:

1. The Experiment: Listening to the "Static"

Think of the radio signal from Tianwen-1 as a pure, clear whistle sent from Mars. As this whistle travels through the Sun's stormy atmosphere, the turbulence in the air makes the whistle wobble, crackle, and change pitch slightly.

• The Problem: Usually, scientists want a clear signal. But here, they wanted the static.
• The Method: They used a giant 70-meter radio dish in Wuqing, China, to catch these wobbling signals. They treated the "crackles" (called scintillation) not as noise, but as a treasure map. By analyzing how much the signal jittered, they could figure out what kind of storm was happening near the Sun.

2. The "Ruler" for Solar Storms

The scientists created a special "ruler" (a mathematical parameter called $\sigma_{FM}$) to measure how wild the signal was getting.

• The Rule of Thumb: The closer the signal gets to the Sun, the more turbulent the atmosphere is, and the more the signal should jitter. It's like walking closer to a waterfall; the mist gets heavier and the noise gets louder.
• The Surprise: On most days, the signal got louder as it got closer to the Sun, just as expected. But on October 5, 13, and 15, the signal went crazy—much louder and more chaotic than the distance alone should have caused.

3. The Three Culprits (What caused the noise?)

The team compared their radio data with images from solar telescopes (like SOHO and SDO) to see what was actually happening on the Sun. They found three different "weather events" causing the radio chaos:

• The "Waterfall" (Coronal Streamers): Imagine a giant, slow-moving curtain of water falling from a dam. On October 5, a massive stream of slow solar wind (a streamer) was flowing right through the signal's path. It was like the signal had to push through a thick, slow-moving fog.
• The "Firehose" (High-Speed Solar Wind): On October 13, a jet of solar wind was shooting out at high speed (over 400 km/s). This is like a firehose blasting water at the signal. The signal had to dodge a fast-moving bullet stream, causing a sudden spike in noise.
• The "Explosion" (Coronal Mass Ejection - CME): On October 15, the Sun let out a massive burp of plasma—a CME. This is like a giant wave crashing over the signal. The radio data showed a huge spike in noise exactly when this wave passed through.

4. The "Time Lag" Mystery

One of the coolest findings was about timing.

• The telescopes saw the solar storm happen first.
• The radio signal on Earth reacted later.
• Why? Imagine you are at the bottom of a canyon (Earth) and a rock falls from a cliff (the Sun). You hear the sound (the radio noise) a few seconds after you see the rock fall.
• The scientists calculated that the solar wind takes time to travel from the Sun to the point where the signal passes, and then the signal takes another 20 minutes to travel from Mars to Earth. By doing the math, they proved that the "delay" they saw in the radio data perfectly matched the travel time of the solar wind. It was like solving a puzzle where the pieces fit perfectly.

5. The "False Alarm" (Spatial Accuracy)

To prove their method was accurate, they looked at October 2.

• The Scene: A huge explosion (CME) happened on the right side of the Sun.
• The Signal: The radio signal from Tianwen-1 was passing on the left side of the Sun.
• The Result: The radio signal was perfectly calm.
• The Lesson: This proved that their method isn't just guessing; it can tell exactly where the storm is. If the storm isn't in the signal's path, the signal doesn't care. It's like standing in the rain: if you are under an umbrella (the signal path), you stay dry even if it's pouring next to you.

Why Does This Matter?

This paper is a big deal for two reasons:

1. Space Weather Forecasting: Just as we need to know about hurricanes to protect ships, we need to know about solar storms to protect satellites and astronauts. This method gives us a new way to "see" solar storms using radio waves, even when we can't see them with cameras.
2. Better Communication: As we plan to send humans to Mars, we need to know when the "radio static" will be too loud to talk. This research helps us build better systems to keep the line open, even when the Sun is throwing a tantrum.

In short: The scientists used a radio signal from Mars as a "flashlight" to shine through the Sun's atmosphere. By watching how the light flickered, they could identify different types of solar storms, measure their speed, and even pinpoint exactly where they were happening, all without needing to be there in person.

Technical Summary

1. Problem Statement

Deep-space communication signals between Earth and planetary probes are significantly disturbed by the solar wind plasma, particularly during superior conjunctions (when the Sun, Earth, and probe align).

• Challenge: As the signal path approaches the Sun (specifically within 10 solar radii, $R_\odot$), the electron density in the solar corona increases drastically ($10^9 - 10^{12} \text{cm}^{-3}$), causing severe scattering, refraction, and delay. This leads to amplitude and frequency scintillation, often disrupting communication links.
• Scientific Gap: While the terrestrial and Martian ionospheres can be modeled and corrected, the dynamic, turbulent nature of the solar corona near the Sun is difficult to observe in situ. There is a need for robust methods to retrieve solar wind parameters and characterize coronal activity (CMEs, high-speed streams, streamers) using remote sensing via radio signals, especially during the active Solar Cycle 25.

2. Methodology

The study utilizes downlink telemetry, tracking, and command (TT&C) data from the Tianwen-1 Mars probe received by the Wuqing 70-m Radio Telescope (WRT70) in China during the superior conjunction in October 2021.

A. Data Acquisition

• Observation Window: October 1–15, 2021 (Ingress and Egress phases).
• Geometry: The signal path passed as close as 4.53 $R_\odot$ (Solar Offset, SO) to the Sun.
• Signal Type: X-band downlink signals transmitted via a low-gain antenna (scientific data was suspended for safety).
• Data Volume: 746 GB of raw data with sampling rates of 1.25 MHz and 6.25 MHz.

B. Signal Processing & Algorithm

To extract solar wind parameters from the noisy, scintillated signal, the authors employed a multi-step processing pipeline:

1. Doppler Trend Removal: A multi-level iterative correction algorithm based on Kalman filtering was developed. This method effectively removes the long-term Doppler shift caused by relative motion while correcting for phase jumps induced by solar activity. It outperforms simple polynomial fitting in high-interference environments.
2. Frequency Scintillation Extraction: The residual frequency fluctuations ($\delta f$) were isolated.
3. Power Spectral Density (PSD) Analysis: The frequency fluctuation power spectrum was computed.
4. Integration & Normalization:
   • The Normalized RMS Frequency Fluctuation Measure ($\sigma_{FM}$) was calculated by integrating the PSD over specific frequency bands (2 mHz to 28 mHz and 2 mHz to 100 mHz).
   • The integration limits were carefully selected (lower limit 2 mHz) to avoid low-frequency trend errors and high-frequency system noise.
   • Gauss–Kronrod quadrature was used for numerical integration to handle the Kolmogorov spectrum characteristics accurately.

C. Correlative Analysis

• Spatial Verification: The "O point" (the point on the line of sight closest to the Sun) was tracked.
• Multi-instrument Comparison: The $\sigma_{FM}$ anomalies were cross-referenced with:
  • SOHO (LASCO C2/C3): For coronal mass ejections (CMEs) and solar wind velocity estimation.
  • SDO (AIA 193 Å): For coronal streamers and high-temperature plasma structures.
  • STEREO-A: For coronal hole identification.

3. Key Contributions

1. Algorithmic Innovation: Development and validation of a multi-level iterative trend removal algorithm specifically for deep-space signals under extreme solar scintillation. This allows for stable frequency estimation where standard Phase-Locked Loops (PLL) fail.
2. First Chinese Deep-Space IPS Observation: This is the first time China has used a deep-space probe (outside the Earth-Moon system) to observe and study the interplanetary medium and solar wind during Solar Cycle 25.
3. Quantitative Correlation Model: Established a robust quantitative link between the normalized frequency fluctuation measure ($\sigma_{FM}$) and specific solar phenomena (CMEs, high-speed streams, streamers) within 10 $R_\odot$.
4. Spatio-Temporal Localization: Demonstrated that $\sigma_{FM}$ anomalies are not only temporally correlated with solar events but also spatially dependent. The study proved that a strong CME occurring outside the signal path does not cause an anomaly, validating the method's ability to localize activity.

4. Key Results

• Turbulence Characteristics: The study confirmed that $\sigma_{FM}$ generally increases as the Solar Offset (SO) decreases, consistent with theoretical turbulent spectrum models and empirical formulas (e.g., $\sigma_{FM} \propto n_e \sqrt{r}$).
• Anomaly Detection: Three specific dates showed significant deviations from the expected trend:
  • October 5: Anomaly attributed to a Coronal Streamer.
  • October 13: Anomaly attributed to High-Speed Solar Wind (originating from a coronal hole).
  • October 15: Anomaly attributed to a Coronal Mass Ejection (CME).
• Temporal Delay Analysis: A distinct time lag was observed between the peak solar wind velocity (measured by SOHO) and the peak $\sigma_{FM}$ anomaly.
  • October 15 Event: Lag of ~38 mins to 1 hr 26 mins.
  • October 13 Event: Lag of ~40 mins to 52 mins.
  • Validation: These lags were quantitatively explained by the propagation time of the solar wind from the source region (observed by SOHO) to the signal path's O point, plus the signal travel time from Mars to Earth. The theoretical calculations matched the observed delays.
• Spatial Specificity (Counterexample): On October 2, a strong CME occurred on the right side of the Sun (from Earth's view), but the Tianwen-1 signal path was on the left. Consequently, no $\sigma_{FM}$ anomaly was detected, proving the method's spatial precision.

5. Significance

• Solar Physics: Provides a new remote sensing tool to probe the solar corona and solar wind dynamics at high latitudes and close distances (4.5–10 $R_\odot$) where in situ measurements are impossible. It validates the use of radio scintillation to detect CMEs and high-speed streams.
• Deep Space Communication: Offers critical insights for modeling communication channels during superior conjunctions. Understanding these disturbances is vital for maintaining link stability for future missions (e.g., Mars sample return, manned missions) and developing compensation technologies.
• Modeling Future: The data supports the construction of strong solar scintillation models for Solar Cycle 25 and beyond, aiding in the prediction of space weather impacts on interplanetary communication.
• Technological Milestone: Marks a significant advancement in China's deep space navigation and radio science capabilities, transitioning from Earth-Moon observations to interplanetary medium studies.