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Comment by JamesTRexx | Hacker News Reader

"Makes it so clear what it is."

Well.., I've been more busy with writing code lately so that the first question coming to mind was, how many bytes is an array of one square kilometer? And I assume it's a two-dimensional array.

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I'm thinking that you'd need to print out the array to be able to properly measure this. So we'd need to decide on the print itself. Do we use A4 paper, old school green bar? What size font is used? We'd also need to decide on the contents of each element in the array. Let's say they are floats with a set limit of precision. So an A4 is 210mm x 297mm. 1km / 210mm = 4761.9 1km/297mm = 3367.0. If we go with 4bytes per float, that's 4761.9 * 3367.0 * 4 = 64,133,269.2 bytes => 61.16 MB

It's also before coffee, so my logic might not be right for basic math yet

It's a radio telescope, how would you imagine translating that to bytes?

Here's an article mentioning the data transmission rates in SKA, up to 20 terabits per second:

https://www.skao.int/en/explore/big-data

Every sensor in the array is sampling at frequency, so - first order - you can use that sampling frequency and the sample size, you get an idea of the input bandwidth in bytes/second. There are of course bandwidth reduction steps (filtering, downsampling, beamforming)...

Aren't they sampling broadband for later processing?

On SKA from what I understand they're sampling broadband but quickly beamform and downsample as the datarates would be unsustainable to store over the whole array.

Right, that makes sense, you'd be looking at an insane amount of data across the ranges that these sensors can look at. But they would still need to preserve phase information if they want to use the array for what it is best at and that alone is a massive amount of data.

I think they preserve timestamped I,Q data. Know some people looking at down-sampling, preselecting those signals for longer term storage and deeper reprocessing and they seem to have a 24h window to 'analyze and keep what you need'.

We're still in technological phase where ADCs are far more advanced than storage and online processing systems, which means throwing away a lot. But I have high hopes for a system where you upgrade computing, network, storage (and maybe ADCs...) and you get an improved sensor. Throw man-hours at some GPU kernel developers and you get new science. The limit seems more now about enough people and compute to fully exploit the data than technological...

Fascinating stuff, thank you for the details and the view of a possible path forward.

BTW if you're interested in the concept of upgrading a sensor without retooling the RF part, and the impact of 'just' putting new COTS racked server hardware and engineering man-hours to get a 'new' sensor with new capabilities, have a look at Julien Plante's work on NenuFAR (which isn't like the SKA at all :-) : https://cnrs.hal.science/USN/obspm-04273804v1 . Damien Gratadour, his PhD supervisor is an amazing technologist, dedicated to improving astronomy instruments, and I was very lucky to work with him and his team... the things the French can string together with small teams and thin budgets...

Too late to edit: any idea of the resolution that the I,Q data is sampled at (bandwidth, bit depth)? I've been in one of these installations a while ago and the tourguide had really no clue about any of the details (I think he was the son of one of the scientists)?

Remembering now that SKA is many, many things and I was talking of the higher-bands array still being installed, but many infos on the -low and -mid arrays are available, for example https://www.skao.int/sites/default/files/documents/Year_In_T...

That's an incredible resource, thank you again. I've been reading for the last hour and it is only getting better.

This is also an interesting development:

https://en.wikipedia.org/wiki/Low-Frequency_Array

This makes no sense though? Given the Nyquist theorem, simply increasing sampling frequency past a certain step doesn't change the outcome.

Actually, it does. You can decimate the higher sample rate to increase dynamic range and S/N ratio.

Also, for direct down conversion, you can get better mirror frequency rejection by oversampling and filtering in software.

None of this changes the actual real amount of data you have at the end of the day though after all is said and done, that's what I mean, so long as you don't botch it and capture too little. In computing terms, the amount of real data in a compressed archive and the uncompressed original is the same, even if the file size is larger for the latter.

Sorry, not sure I follow from what I said (explaining how much data sensors produce) to 'increasing the sampling frequency' ? You're usually sampling at larger width to then put specifically taylored pass-band filter and removing aliasing effects and then downsampling. This is a classic signal acquisition pattern : https://dsp.stackexchange.com/questions/63359/obtain-i-q-com...

Are you deliberately obtuse to the play on words of an array being used from a programmer's use of the word in contrast to an array of antennas?

If you have to ask, you know the answer. :-p

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