Using ChatGPT to Analyze Radio Telescope Data

[Pablo Lewin]

I recently tested ChatGPT’s specialized workflow on a real set of radio astronomy data.

The Setup

• My radio telescope was aimed at the zenith, performing a drift scan of the Milky Way.

• The backend (IF Average) recorded 1,000 spectra into a ZIP file.

The Prompt to ChatGPT

I asked it to:

• Unpack and process all 1,000 files.

• Identify the strongest hydrogen line (21 cm) detection.

• Filter out radio frequency interference (RFI) spikes.

• Plot the drift profile as the Galactic arm crossed the beam.

• Create an MP4 animation showing the hydrogen line appear, peak, and fade.

The Results

• The AI automatically isolated the best spectrum out of 1,000.

• It produced a clean graph of the hydrogen line’s rise and fall during the drift.

• The animation brought the data to life, showing how neutral hydrogen gas in the Milky Way reveals itself as the sky drifts overhead.

Why This Matters

This experiment demonstrates not just the power of amateur radio telescopes, but also how ChatGPT can act as a scientific assistant:

• Automating data filtering,

• Recognizing astronomical signals,

• Producing publication-style plots and even animations.

It’s a glimpse of how AI can accelerate discovery and outreach—turning raw telescope data into something that’s clear, educational, and inspiring.

Pablo Lewin WA6RSV

[Eduard Mol]

It’s an interesting experiment for sure, but I see a few potential pitfalls with such “vibe astronomy”:

1: the stochastic nature of LLMs means these things have some well- known problems like “hallucinations” and generating different outputs when given the same imput prompt multiple times. Not great for accuracy and reproducibility. 

2: If you lean heavily on LLMs to do all the hard work for you, how are you going to learn and understand what you are actually doing with the data?

3: last but not least, I always remind people that these “AI” tools are large language models, not physics models or engineering models. Yes, these things can emulate solving a physics problem to some extent because they ingested a lot of text on these subjects from the training data, but at the end of the day it is all correlation and no real understanding. I have seen several times how it all breaks down when people ask chatGPT to design, for example, a feedhorn. The more specific and underrepresented the problem is in the trainig data, the more likely it is that the LLM just fills in the blanks with plausible-sounding nonsense.

Of course, one can argue that we as amateurs are doing radio astronomy mostly just for fun and LLMs make it more accessible, so who cares. For me personally, learning new stuff is a very important part of this hobby, and taking shortcuts with LLMs simply does not satisfy that part for me. 

Anyway, just my €0.02.

Eduard

Op ma 8 sep 2025 om 03:51 schreef Pablo Lewin <pabl...@gmail.com>

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[Alex P]

Hello Eduard, 

I concur  :

Personal satisfaction is not derived as much in the Attainment of a Goal

But from the Complexities of the Path, the Challenges and Obstacles Overcome, and in the Knowledge Acquired

my $0.02

Alex

==========================================================================

 For me personally, learning new stuff is a very important part of this hobby, and taking shortcuts with LLMs simply does not satisfy that part for me. 

Anyway, just my €0.02.

Eduard

==========================================================================================

[Jon Abel]

Yes, I've been using ChatGPT to help create a novel type of telescope design.   I started with patent 512,340, and realized that it could be wrapped as a cone (instead of a pancake).   Then I realized that the patent was defining an ARRAY of loop antennas (not one cone antenna).  It also struck me that it might be a way to collate & concentrate similar-phase information at different frequencies at one point (the apex), so the circumference now matches the slant height.

At that point, I started feeding this type of idea into A.I.    It advised that the eddy-current losses associated with wrapping wire on a solid metal cone were too high for the device to work, so it gave me ideas about perforating the metal backing with holes or slots (and ran the numbers for me), along with explaining thermal losses that I had to deal with.   

It's even analyzed the possibility of removing the tip of the cone & replacing it with a beeswax candle (above 1-2 Ghz).    This brought losses WAY down.   

It has done all sorts of comparisons over the last 2 weeks to get to an educated answer. 

It's gotten to the point where A.I. is now saying that my sensitivity is as good as NRAO's new instrument at Green Bank that get -185 dBm/frequency sensitivity (meant for CMB detection).  However, I only will get 300Mhz-15 Ghz reception through wirelength resonance - versus 115 Ghz reception with a $5000 piece of electronics.    A.I. has has found the best off-the-shelf Qorvo LNA, and I am getting it to find the right filters.  What appears to make it so sensitive - is the fact that you can put the pickup & LNA directly at the apex of the cone - causing little to no loss through wiring.     

A.I. can also help write the Python code for my SDR, since this antenna is so different than dish designs.   So I will know how to organize this type of data.   

ChatGPT even attempted to recreate opposing Cosmic Web Filaments using the idea of interleaved coils shown in patent 512,340.  

People who are acting like purists & don't want A.I. to help - don't appear to understand how much work it can actually save you & make impossible projects somewhat realistic.

It turn, it makes me smarter - more knowledgeable.  

It's like having a professor answer math & engineering problems for me all day.   

[Neil Smith]

It's usually highly informative to put the output of one LLM into another and ask for a critical appraisal of the claims. I just did that and it might be useful and informative if you try it too. It can often tease out any hallucinations from particular AI models. 

Assuming that the design isn't intended to use laws of physics other than Maxwell's equations, it would be easy enough to use an open source model to calculate the far field pattern, aperture efficiency and isotropic gain of such a system. Have you tried modelling the system?

I assume that you will be cooling the antenna system, feed lines and LNA well below 23 K to achieve the sort of sensitivity that was mentioned. The cost of the liquid helium might rather offset the low cost of the rest of the system.

What is the effective capture area of the proposed system, and what is the far-field pattern supposed to look like?

when using one of these tools it's important to insist that it quotes references to all of its source material and doesn't include nonsense from Reddit or unmediated forums, as that will pollute its findings. One of the big problems of course is that they have no access to textbooks that are still under copyright, nor to any scientific papers which are behind academic paywalls. That often leads to an extremely blinkered view and the risk of incorporating pseudoscience into its response. Insisting on it showing full working and references is certainly a help in that respect as you can then tell it to discount anything that is less than reputable.

Many of the AI tools are brilliant at programming, because it's easy to harvest information from GitHub and the like. One huge problem is that they tend to be sucophantic and pick up cues from your prompts about the type of answer that you would like to see. Designing the prompt so that it is clear to the engine that what you want has got to be rigorous enough to publish in a scientific journal, and that it will be peer reviewed, tends to reduce the amount of empathy and interpretation in what it produces. The natural tendency is for the engines to reinforce your prejudices and opinions. It's vital that you keep reminding them to make no assumptions about your expectations.

I work with these things professionally, and I also use them as an end user in my own research. Even I've had some horrific experiences, where the LLM started hallucinating about published papers. It quoted the page numbers and titles of the articles and the issue, but there is no sign of an article on that subject by that author or any other within a decade or two of the suggested publication. I also wasted five hours on a very detailed investigation which turned out to be driven by a complete hallucination about a relatively simple laser optics problem. When I carried out the physical experiment, the mechanism proposed simply did not exist. The AI engine did at least have the decency to be extremely apologetic and sent me a finely-detailed analysis about how it had slipped into hallucination.

If you don't want to run proper simulations, how about making a physical mode and using it with a relatively ordinary LNA at room temperature to map the Galactic hydrogen line? Comparing that with a simple tinfoil horn should give you a good baseline comparison. Sun noise vs cold sky would also give an unambiguous and definitive performance measure

Neil Smith

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[Dave Typinski]

On 9/8/25 03:38, Eduard Mol wrote:
> ... I always remind people that these “AI” tools are large

> language models, not physics models or engineering models. Yes, these things can
> emulate solving a physics problem to some extent because they ingested a lot of
> text on these subjects from the training data, but at the end of the day it is
> all correlation and no real understanding.

It's interesting that those statements accurately describe people as well as AI.
--
Dave

[Eduard Mol]

Well said Neil- I was typing out a response but you beat me to it. 

Of course these things can be very useful tools but you have to be aware of the pitfalls. 

when using one of these tools it's important to insist that it quotes references to all of its source material and doesn't include nonsense from Reddit or unmediated forums, as that will pollute its findings. One of the big problems of course is that they have no access to textbooks that are still under copyright, nor to any scientific papers which are behind academic paywalls. That often leads to an extremely blinkered view and the risk of incorporating pseudoscience into its response. Insisting on it showing full working and references is certainly a help in that respect as you can then tell it to discount anything that is less than reputable.

Many of the AI tools are brilliant at programming, because it's easy to harvest information from GitHub and the like. One huge problem is that they tend to be sucophantic and pick up cues from your prompts about the type of answer that you would like to see. Designing the prompt so that it is clear to the engine that what you want has got to be rigorous enough to publish in a scientific journal, and that it will be peer reviewed, tends to reduce the amount of empathy and interpretation in what it produces. The natural tendency is for the engines to reinforce your prejudices and opinions. It's vital that you keep reminding them to make no assumptions about your expectations.

My (admittedly quite limited) understanding of LLMs is that, once they are trained, the patterns in the training data are encoded in the weights, so it is very hard to “un-train” a part of the training data from the model. So how do you keep e.g. Reddit data out of the output? Or does insisting not to use Reddit in the imput prompt simply result in less “Reddit-like” syntethic text?

Best regards, 

Eduard

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[Marcus D. Leech]

I asked GoogleAI what the angular resolution of a 1m at 1.4GHz was.   It
told me 0.9deg.  So, there's that.

I seriously hope nobody designs any bridges based on the results from an
AI query.

[ja...@ganssle.com]

For fun, I asked ChatGPT to design a voltage follower. Among the mistakes it made: It tied the output of the op amp to the power supply.

Jack

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[Karen Fischer]

I have used ChatGPT for various projects such as spectroscopy, astrophotography, my one radio astronomy project and satellite data downloading.   Recently, I used it for setting up my SDR radio for weather sats and for the configuration of a raspberry pi.   It was very helpful.  I discovered it did make mistakes , so I was checking other sources of information to make things work.    For radio, ChatGpt was wrong on skew angle.  I have also seen it provide some incorrect syntax for linux.  (1 line in 20 was wrong) I spend lots of time scanning the various forums and facebook pages to find answers to questions,   I purchase reference books..  I use YouTube, SLACK and DISCORD for astronomy club advice.   I think ChatGPT is a tool that can help people like me become more confident in trying to learn this stuff.    It was encouraging.    

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[Jon Abel]

Folks,  I know how AI is programmed, and I understand how it can easily lie to a person for asking pointed yet generalized questions such as "How's my day going?" - or "What's spacetime?".   I had ELIZA on my Commodore 64 in 1983, so I've been aware of how A.I works for some time.   However, when A.I. starts shoving my numbers into well-documented equations to find eddy-current losses (and making apples-to-apples comparisons) - I'm willing to take a look.  Plus, after crunching numbers for 2 weeks & asking questions about the following - I'm willing to consider that the AI is getting better at understanding my questions & usage patterns.   It's become more than "hearing what I want to hear":   

a. What is the formula for the speed of light?  Why is it considered a constant if there is a formula? 

a. What are the resistive heating & thermal noise contributions in this design?

b. How can wirelength & LC resonant frequencies be collected & compared in this model?

b. How do I find losses due to capacitive coupling (C reactance) for different frequencies (fdf)?

c. How do I find losses due to mutual inductance coupling (L reactance) (fdf)?

d. How do I compare adding/removing distance between the wire & cone plate (such as a 3mm beeswax coating) for different frequencies?

f.  How do I compare slot & hole perforation losses (fdf)?

g. How do I fix the concerns of wire compression of slot perforations?

h. How do I find sweet spots by changing in thickness of the metal plate & diameter of hole perforations (fdf)?

i.  How do I compare the magnetic permeability of different grades of Titanium & Stainless Steel (fdf)?  

j.  How do I compare the conductive permittivity of different grades of metal (fdf)?

k. ferromagnetism versus para-magnetism effects in Titanium Grade 1, 2, & 5, and 304 Stainless Steel. 

l.   How would adding a solid candle of beeswax above 1-2 Ghz improve losses?  

m.  How do I calculate near-field sensor losses - to collect signals and most effectively feed them into the LNA & SDR electronics? 

n.  What would a full analysis of patent 512,340 in the context of creating a cone-shaped radio antenna look like?  

o.  How is a signal deformed when it bounces off of a dish?

p.  Analyze patent 512,340, and assume it's cone (not a pancake) - and tell me if it more resembles a cone antenna or an array of loop antennas?   

  

With the geometry of this patent  - and the collection point immediately at the apex - that's quite a bit different than the historical view that a dish is the best collection method - especially when a dish deforms the incoming signal by smashing it against a wall & expecting it to be accurate.   The resulting signal has to be adjusted anyway - which is (in itself) an assumption or a guess made by people.

So, to be a good scientific whistleblower - one must keep things Newtonian and learn to ignore nonsense - and go back historically to look at the first radio telescope (a loop antenna), or different periodic tables (Janet, 1929) , or solid-state, recursive atomic modelling, or alternative cosmological models (Plasma Cosmology), or more Newtonian quantum mechanics (Bohm-Debroglie). Or less-known things like certain flow mechanics (valvular conduits, Coanda Effect, Bernoulli Effect, etc),  standing waves, twisted-pair plasma conduits, self-organization of charged particles in plasma, etc.   

And, since patent 512,340 can be easily interpreted as an array of loop antennas - that's what I'm pursuing it - to describe not only Celestial Mechanics, but also use it for Newtonian, solid-state, recursive EM modeling for MANY things.  Therefore, patent 512,340 is becoming a common denominator for me.   So, you are welcome to offer all the criticism you want - but nobody yet understands how it's all tied together or can offer a similar "common denominator" to create a "Theory of Everything".  And the NRAO & NASA results are not satisfactory/complete answers to my questions.   They simply aren't releasing useful information fast enough to assure my survival.       

Since this geometry is so different than anything else that's been offered to the public - I have no doubt that information got left out - between 1930 and today.   

A.I. claims my telescope sensitivity numbers are not taking cryogenics into account - because there is no distance between the collection point & the apex.   It's the same point in space.   No dish design can compare to that - even dishes that use log-periodic conical-loop antennas at their feed position.   So, I'm willing to question EVERYTHING at this point - since our mainstream scientists don't appear to understand how Plasma-based Newtonian physics can describe Celestial Mechanics - using the current standardized models.   So, instead of getting bogged down with the Big Bang Theory & Standard Model  - logic would say to go back to the Newtonian drawing board instead of creating more unanswered questions & theories to hide existing unanswered questions (potentially causing a snowball effect of ignorance).   So, I try new things - instead of putting certain theoretical models up on pedestals - and re-hashing the same unanswered questions.  

If you whole-heartedly believe what popular science is sharing with you - you might want to look up why American nutritional information is so incomplete or inaccurate (i.e.  the original name for Vitamin B3  is nicotonic acid).   

There will always be purists trying to defend standards - but there is no lottery to win by going down the standardized path - even with A.I. at your side.  

[Neil Smith]

Conical helix chokes are well-understood as wideband isolation devices. Paired thin wires wound into a close-spaced helix can support a very low loss TE01 circular waveguide mode, but there are very tight limits on the choice of dimensions to prevent unwanted lossy modes or causing evanescence problems. You can't couple into the TE01 mode other than using a probe, orifice or loop in the locations with a high E or H field though, so that is frequency dependent. I know this in detail because I've been building an extremely high-q solenoid-walled TE01n cavity resonator to replicate the one in the UK National Physical Laboratory (there's another at NIST).

I trust that you are aware of David Hume's 1748 essay "Of Miracles" which first proposed the standard that extraordinary claims require extraordinary proof, as later repeated by generations of philosophers and scientists.

All you need to do is make a test system to your design and document the performance in comparison to a standard system with sufficient detail that anyone can replicate the results. 

Hopefully without needing to postulate new Physics. 

Can you get your AI model to explain what performance you would achieve when compared with say a 20 dB horn or a 3 metre parabolic dish illuminated with an optimal edge taper? How would the capture area of your design compare with a traditional antenna? How large would it need to be to capture the same amount of energy as the 3 metre dish?

Any reason to choose a low melting point, low strength dielectric like yellow beeswax with its tan-delta of between 0.009 and 0.007 over the range from 1MHz to 3 GHz rather than a foamed, stiff, high melting point nonpolar dielectric plastic with a tan-delta at least ten times as good? 

Is the metal cone layer there to form a stripline with the conductors? Is there just one conductor and is it fed against the metal cone? If not, are there two conductors fed against each other as in a log-spiral? Is this a leaky-feeder mode of some sort? If do, is the tequired discontinuity caused by the incremental increase in size of adjacent turs further from the apex?

If you can't find the time to do a test yourself, can you document the design clearly do that we could have a go at replicating your theoretical results to make sure your AI isn't hallucinating something like mine was? 

My question was about a very well known characteristic of using multiple total internal reflection in a fused quartz wedge prism to attenuate a high energy laser by 70dB. I asked it to confirm the attenuation level at each internal reflection and to calculate the angles of those reflections at a wide range of different angles of incidence.

It got the angles completely wrong except at zero incidence. It postulated that a set of very narrowly separated parallel beams would be produced based on its understanding of the equations. It went further and further and further to try to justify its model even when I told it that it was completely and utterly wrong and that it was in danger of hallucinating. 

It was self-aware enough to say it would check but then it confirmed that the equations definitely worked. In the end, I typed in the equations and asked "this is the equation I am using is it the same as yours?" It confirmed that it definitely was. 

I then showed it the results of plotting the paths as an image. It went VERY quiet, then it started getting apologetic. Next it started making excuses and then it stopped talking to me and showed an icon with crosses in place of eyes!

After I restarted the session, it then went back into its hallucination mode, insisting that the beams came out as parallel lines. I pasted my results and two classic references. It said "oops, terribly sorry, yes I'm utterly wrong and everything I've spoken about so far is rubbish, hased on my misinterpretation of the physics. It was quite an interesting thing to see. The last three hours of the interaction was basically me sitting back with popcorn, watching as it dug itself ever deeper into an imaginary hole. Beware of deep psychosis in your chosen AI, always pit two or three against each other and see if they can come to a consensus. 

Let's see your experimental results, or at least a detailed and manufacturable design that others can attempt to model or replicate.

Neil

[Neil Smith]

On 2025-09-08 11:56, Dave Typinski wrote:

> I asked GoogleAI what the angular resolution 

> of a 1m at 1.4GHz was.   It told me 0.9deg. 

Dave, that's interesting. I asked Google's Gemini Pro about the domain of applicability of the Rayleigh criterion for diffraction-limited parabolic dish antennas. Then I asked the same as you for a 1m dish at 1.4 GHz and it said 14.8 degrees for separating two distant point sources. It added ( unprompted) that it was the angle to achieve a 27% dip in power between them. 

I wonder whether it learned from your question, or had second thoughts after my initial question, or is fearful of what response I'll throw at it if it is the tiniest bit vague or generic in its replies! 

It had the decency to add a set of caveats and assumptions about 1) circularity of the aperture, 2) similar brightness of the targets, 3) high signal to noise ratio, 4) lack of atmospheric turbulence, 5) absence of surface figure aberrations, and 6) that Fraunhofer diffraction applies (I guess all astronomical objects count as in the far field!)

I think I'll stick to my HP35s RPN calculator until the tools get more refined. It's like having a certain type of grad student apprentice cluttering the place up. Hugely brainy, zero self-awareness, massive self-belief, zero humility or common sense.

Brilliant if you give it 20 published papers that academics have churned out as a Me Too about some JWST observation, asking the AI to filter out inconsequential tosh and find the buried pearls of wisdom amongst the (probably AI-generated) slop that forms a high proportion of content in these "publish or die" times.

Neil

.

[Jon Abel]

Neil:

I wouldn't be sharing pictures of my experiment & research & ways of prioritizing questions to find the quickest, simplest method to try this comparison - if I wasn't already building this device, and testing it myself.    

Not sure why you choose to ask ME your technical questions.   You have access to A.I. as well as I do.     

You are welcome to feed your details into it - and ask your own set of comparative questions - but I don't have the time or the inkling to give answers that you already have access to.   

If you sincerely want to know how the patent works, then analyze it yourself.    

Unless you are willing to do (at the very least) an EM++  comparison of dish performance versus patent 512,340 (with all the common variables you choose to look at - plus all the common variables I am looking at) - you're not comparing apples-to-apples anyway.    A.I. can certainly write an EM++ scripts for you.  

But, you are mentioning capture area & reflection - which means you don't fully understand.   You are treating the patent like a reflector - instead of an array of loop antennas.    

You are assuming 2 different geometries will have the same formulas.    

That's incorrect - causing your questions to be unrelated.   

   

But from your perceived attitude, since you are happy with how dishes work, and don't want another way to interpret signals - then nothing I can offer is going to change that anyway.   

So, keep going down your path  -  but criticizing others for trying something novel?   That's not acceptable.

And, lecturing me about doing work for you - is bossy, assumptive & rhetorical.   

Instead, you can always try asking the opinions of people with attitudes similar to yours - over those with scientifically curious, questioning attitudes - and get your information from them instead.  

I'm sure that will get you down further down your path.    

But, this research is for the curious - not the pacified.    

You are welcome to ask A.I. to provide a path forward for your own experiments.  

But I'm not going to tell you what to do - as you have done.   

Bossing around experimentalists online will only show your shortcomings, not anybody else's.   

No scientist or engineer is going to do the work for you - especially me.      

I have my own reasons for building this device - and making you happy or rich are not my goals.

[Pablo Lewin]

Hi all,

Thanks for the thoughtful feedback. Just to clarify my goal: I posted my data, plots, and animation to get them checked and replicated, not to claim perfection. I wanted to see whether an AI-assisted workflow can be trained and used for signal detection, plotting/graphing, basic RFI handling, and even drafting technical text—and where it falls down. If it’s flawed, great—let’s find out together.

I respect the cautions about hallucinations, reproducibility, and “LLMs ≠ physics.” Totally fair. What I’m pushing back on is the reflex to swat down anything new before we try to verify it. Airplanes, home computers… we’ve heard the “never” chorus before. This hobby thrives when we try, test, and tune.

To keep this useful (and less vibes, more verifiable), here’s what I propose:

1. Open materials
   I’ll share the raw ZIP (1,000 spectra), my exact prompts, model/version, and the Python that produced the plots/animation. If you prefer, we can run it without an LLM using the same code path to compare.

2. Repro recipe
   • Fixed random seeds and cached steps
   • Same inputs → same outputs expectation
   • Note every parameter (gain, sample rate, integration, filters)

3. Cross-checks
   • Classic pipeline (no LLM) vs AI-assisted on the same data
   • Blind tests on held-out spectra labeled only after analysis
   • Independent re-analysis by anyone here (GNU Radio, SciPy, your tools)

4. Physics sanity
   • Confirm 21-cm line identification against known sky transit and beam pattern
   • Sun vs cold-sky SNR; RFI logs; baseline stability
   • Document failures and “nonsense” cases the AI produced

5. Writing guardrails
   If AI helps draft a methods paragraph or figure caption, it must include:
   • Full working (equations/assumptions)
   • Citations to reputable sources
   • A human pass to remove any glib or made-up claims

If you’re up for it, I’d love a replication crew: one or two folks to run the data through their preferred stack; one to stress-test prompts for reproducibility; one to check the line ID and drift profile against ephemerides. I’ll happily package the dataset and scripts for that.

Last, a small, nonsensical pledge to keep things cheerful: I promise not to let a toaster design a bridge, and my cat has declined to serve as sole peer reviewer. 🐱🥼

I’m here to exchange ideas and innovate, not to win arguments. If AI helps us learn faster—great. If it trips, we’ll write that down too. Either way, the sky gets clearer.

73,
Pablo Lewin, WA6RSV