Record labels dig their own grave, and the shovel is called TikTok

Each fixed system has a noise floor, but there is no noise floor forced on all systems. You can design a system to have as low of a noise floor as you desire, digital or analog. Physics experiments routinely do it to detect extremely rare events.

As to thermo, it's almost like you ignored that I already wrote this, which I repeat: "both can be run easily down to thermal background radiation noise floor, then both need things like liquid cooling and other techniques if you want to go below". In common audio, thermal noise is often the biggest component of the noise floor in a system.

Hence the point to cool them. There is no floor except zero if you want to engineer such a system. And it works for analog and digital.

>If you can push the artifacts of your output below that then they will be indistinguishable from the noise.

Nope, you can signal below the noise floor using many techniques, such as spread spectrum techniques. This is used routinely for military and other covert operations. As usual, this is even Wikipedia level knowledge: "Signals that are below the noise floor can be detected by using different techniques of spread spectrum communications, where signal of a particular information bandwidth is deliberately spread in the frequency domain resulting in a signal with a wider occupied bandwidth." [1]

Heck, even everyday GPS signals are vastly below the noise floor, yet we use those signals all the time with cheap hardware. "The strength of received GPS signal transmitted from a satellite to the ground users is about −157 dBm below the noise floor of −138.5 dBm" [2]. This is all common knowledge among people doing even basic signal processing. Tons of everyday tech signals below their respective noise floors. It's not a rare thing to do.

You make many claims that are not true.

So, as to your claim that "the noise floor of a digital system is still way lower than what’s achievable with an analog one" - have a citation in a paper stating this? Have a theorem in a textbook? I'd like to see why you make this claim.

[1] https://en.wikipedia.org/wiki/Noise_floor

[2] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3292143/

In this context, we were talking about digital audio & the perception thereof by human listeners. Spread spectrum signal processing techniques are not relevant. Driving the noise floor down to the quantum limit by using liquid helium & a bunch of other abstruse techniques is not relevant. The fact that GPS signals are below the noise floor in their radio spectrum is, once again, not relevant.

Do you argue like this in all your interactions with other people?

Actually, this thread was on sampling theory and whether or not Nyquist perfectly recreated original signals. The only mention of anything about human perception in the chain you're replying to is me writing that good engineering can push errors outside human hearing, while pointing out that it is not the original signal. All the other posts in the thread you claim is about human perception are not about human perception. They're about signal reconstruction with one mention of how that applies to hearing.

>Do you argue like this in all your interactions with other people?

No, most of my friends don't make incorrect claims then double down on them when better information is pointed out. They also don't tend to claim an entire conversation is about a different topic than it was.

You: "Up to the Nyquist limit, a digital signal will completely recreate the original signal". No, as explained.

You: "Digitisation does not result in square wave output anywhere in the output chain" - again false, even for audio, as explained above. Thinking that output is a nice set of pure sine waves making the original band limited (which is also an approximation) leads people to flawed thinking. Realizing the how and why of what actually comes out is useful.

You imply pushing things below a noise floor makes them indistinguishable from noise - this isn't true in general, and is not even true in audio. There are even audio products that use signaling below the noise floor. Repeated misunderstanding of what a noise floor is (and isn't) is extremely useful, even in audio. The other examples were to make it clear there is useful knowledge here.

Understanding these nuances is useful for even audio to understand that reconstruction is always a design tradeoff on where you want to put the errors - and that you never are reconstructing the original signal.