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Here’s an extended comment by another astrophysicst: https://telescoper.blog/2025/01/02/timescape-versus-dark-energy/ https://telescoper.blog/2025/01/02/timesca

Here’s an extended comment by another astrophysicst: https://telescoper.blog/2025/01/02/timescape-versus-dark-energy/ https://telescoper.blog/2025/01/02/timescape-versus-dark-ene... The most important bit: > The new papers under discussion focus entirely on supernovae measurements. It must be recognized that these provide just one of the pillars supporting the standard cosmology. Over the years, many alternative models have been suggested that claim to “fix” some alleged problem with cosmology only to find that it makes other issues worse. That’s not a reason to ignore departures from the standard framework, but it is an indication that we have a huge amount of data and we’re not allowed to cherry-pick what we want.

throwawaymaths 2y ago
the thing is, this is not really an alternative model. it's rather actually bothering to do the hard math based on existing principles (GR) and existing observations, dropping the fairly convincingly invalidated assumption of large scale uniformity in the mass distribution of the universe. if anything the standard model of cosmology should at this point be considered alternative as it introduces extra parameters that might be unnecessary. so yeah it's one calculation. but give it time. the math is harder.
- sandgiant 2y ago
  This has the same number of free parameters as LambdaCDM. Also this result only looks supernovae, i.e. low redshift sources. LambdaCDM is tested on cosmological scales. Very interesting, but “more work is needed”.
  - throwawaymaths 2y ago
    thats not the case, if, as is increasingly speculated, the lambda is not constant over time. you figure two parameters for linear and three for a quadratic experience
- User23 2y ago
  Calculation is harder in a world of functionally limitless compute is sort of interesting. Where do we go from here?
- bsder 2y ago
  > dropping the fairly convincingly invalidated assumption of large scale uniformity in the mass distribution of the universe. The problem with that is then you need a mechanism that creates non-uniformly distributed mass. Otherwise, you are simply invoking the anthropic principle: "The universe is the way it is because we are here."
  - zmgsabst 2y ago
    You don’t need a mechanism to point out a fact contradicts an assumption, eg, our measurements show non-uniform mass at virtually all scales (including billions of light years). There simply is no observable scale with uniform mass. Obviously there’s some mechanism which causes that, but the mere existence of multi-billion light year structures invalidates the modeling assumption — that assumption doesn’t correspond to reality.
    - throwawaymaths 2y ago
      yeah the ~1b ly nonuniformity is pretty much there. the ~10b ly uniformity is still early days but looking more and more likely as more data roll in (unless there is a systematic problem)
  - marcyb5st 2y ago
    I think that can be mitigated in three ways: our understanding of inflation is flawed, there were more "nucleation" sites where our universe came to be, and there are the already theorized baryonic acoustic oscillations that could introduce heterogeneity in the universe. Maybe is a combination of these, maybe something else. If nothing else, the uniformity is less probable than a mass distribution with variance (unless there is a phenomenon like inflation that smoothen things out, but also that was introduced to explain the assumption of a homogeneous universe). I concede that explaining the little variance in the CMB with our current understanding is hard when dropping homogeneity assumption however.
  - jcarreiro 2y ago
    > The problem with that is then you need a mechanism that creates non-uniformly distributed mass. The mechanism is gravity; and we have good observational evidence that the mass distribution of the universe is not uniform, at least at the scales we can observe (we can see galaxy clusters and voids).
austin-cheney 2y ago
That sounds like regression. If this problem of regression occurs as regularly as your quote implies then the fault is not in these proposed alternatives, or even in the likely faulty existing model, but in the gaping wide holes for testing these things quickly and objectively. That is why us dumb software guys have test automation.
- bubblyworld 2y ago
  I think automated hypothesis testing against new data in science is itself an incredibly difficult problem. Every experiment has its own methodology and particular interpretation, often you need to custom build models for your experimental setup to test a given hypothesis, there are lots of data cleanup and aggregation steps that don't generalise, etc. My partner is in neuroscience, for instance, and merging another lab's data into their own workflows is a whole project unto itself. Test automation in the software context is comparatively trivial. Formal systems make much better guarantees than the universe. (not to say I think it's a bad idea - it would be incredible! - but perhaps the juice isn't worth the squeeze?)
  - austin-cheney 2y ago
    > Every experiment has its own methodology That is bias. Bias is always an implicit default in any initiative and requires a deliberate concerted effort to identify. None of what you said is unique to any form of science or engineering. Perhaps the only thing about this unique to this field of science, as well as microbiology, is the shear size and diversity of the data. From an objective perspective test automation is not more or less trivial to any given subject. The triviality of testing is directly determined by the tests written and their quality (speed and reproducibility). The juice is always worth the squeeze. Its a business problem that can be answered with math in consideration of risk, velocity, and confidence.
    - bubblyworld 2y ago
      Respectfully I disagree - the situation is far more complex in science than software engineering disciplines. I agree that different tests require different amounts of effort (obviously), but even the simplest "unit tests" you could conceive of for scientific domains are very complex, as there's no standard (or even unique) way to translate a scientific problem into a formally checkable system. Theories are frameworks within which experiments can be judged, but this is rarely unambiguous, and often requires a great deal of domain-specific knowledge - in analogy to programming it would be like the semantics of your language changing with every program you write. On the other hand, any programmer in a modern language can add useful tests to a codebase with (relatively) little effort. We are talking hours versus months or even years here! The experiment informs the ontology which informs the experiment. I don't think this is reducible to bias, although that certainly exists. Rather to me it's inherent uncertainty in the domain that experiments seek to address. Business practice, as you use the term, evolved to serve very different needs. Automated testing is useful for building software, but that effort may be better spent in science developing new experiments and hypotheses. It's very much an open problem whether the juice is worth the squeeze - in fact the lack of such efforts is (weak) evidence that it might not be. Scientists are not stupid.

throwawaymaths 2y ago

the thing is, this is not really an alternative model. it's rather actually bothering to do the hard math based on existing principles (GR) and existing observations, dropping the fairly convincingly invalidated assumption of large scale uniformity in the mass distribution of the universe. if anything the standard model of cosmology should at this point be considered alternative as it introduces extra parameters that might be unnecessary. so yeah it's one calculation. but give it time. the math is harder.

sandgiant 2y ago
This has the same number of free parameters as LambdaCDM. Also this result only looks supernovae, i.e. low redshift sources. LambdaCDM is tested on cosmological scales. Very interesting, but “more work is needed”.
- throwawaymaths 2y ago
  thats not the case, if, as is increasingly speculated, the lambda is not constant over time. you figure two parameters for linear and three for a quadratic experience
User23 2y ago
Calculation is harder in a world of functionally limitless compute is sort of interesting. Where do we go from here?
bsder 2y ago
> dropping the fairly convincingly invalidated assumption of large scale uniformity in the mass distribution of the universe. The problem with that is then you need a mechanism that creates non-uniformly distributed mass. Otherwise, you are simply invoking the anthropic principle: "The universe is the way it is because we are here."
- zmgsabst 2y ago
  You don’t need a mechanism to point out a fact contradicts an assumption, eg, our measurements show non-uniform mass at virtually all scales (including billions of light years). There simply is no observable scale with uniform mass. Obviously there’s some mechanism which causes that, but the mere existence of multi-billion light year structures invalidates the modeling assumption — that assumption doesn’t correspond to reality.
  - throwawaymaths 2y ago
    yeah the ~1b ly nonuniformity is pretty much there. the ~10b ly uniformity is still early days but looking more and more likely as more data roll in (unless there is a systematic problem)
- marcyb5st 2y ago
  I think that can be mitigated in three ways: our understanding of inflation is flawed, there were more "nucleation" sites where our universe came to be, and there are the already theorized baryonic acoustic oscillations that could introduce heterogeneity in the universe. Maybe is a combination of these, maybe something else. If nothing else, the uniformity is less probable than a mass distribution with variance (unless there is a phenomenon like inflation that smoothen things out, but also that was introduced to explain the assumption of a homogeneous universe). I concede that explaining the little variance in the CMB with our current understanding is hard when dropping homogeneity assumption however.
- jcarreiro 2y ago
  > The problem with that is then you need a mechanism that creates non-uniformly distributed mass. The mechanism is gravity; and we have good observational evidence that the mass distribution of the universe is not uniform, at least at the scales we can observe (we can see galaxy clusters and voids).

austin-cheney 2y ago

That sounds like regression. If this problem of regression occurs as regularly as your quote implies then the fault is not in these proposed alternatives, or even in the likely faulty existing model, but in the gaping wide holes for testing these things quickly and objectively. That is why us dumb software guys have test automation.

bubblyworld 2y ago
I think automated hypothesis testing against new data in science is itself an incredibly difficult problem. Every experiment has its own methodology and particular interpretation, often you need to custom build models for your experimental setup to test a given hypothesis, there are lots of data cleanup and aggregation steps that don't generalise, etc. My partner is in neuroscience, for instance, and merging another lab's data into their own workflows is a whole project unto itself. Test automation in the software context is comparatively trivial. Formal systems make much better guarantees than the universe. (not to say I think it's a bad idea - it would be incredible! - but perhaps the juice isn't worth the squeeze?)
- austin-cheney 2y ago
  > Every experiment has its own methodology That is bias. Bias is always an implicit default in any initiative and requires a deliberate concerted effort to identify. None of what you said is unique to any form of science or engineering. Perhaps the only thing about this unique to this field of science, as well as microbiology, is the shear size and diversity of the data. From an objective perspective test automation is not more or less trivial to any given subject. The triviality of testing is directly determined by the tests written and their quality (speed and reproducibility). The juice is always worth the squeeze. Its a business problem that can be answered with math in consideration of risk, velocity, and confidence.
  - bubblyworld 2y ago
    Respectfully I disagree - the situation is far more complex in science than software engineering disciplines. I agree that different tests require different amounts of effort (obviously), but even the simplest "unit tests" you could conceive of for scientific domains are very complex, as there's no standard (or even unique) way to translate a scientific problem into a formally checkable system. Theories are frameworks within which experiments can be judged, but this is rarely unambiguous, and often requires a great deal of domain-specific knowledge - in analogy to programming it would be like the semantics of your language changing with every program you write. On the other hand, any programmer in a modern language can add useful tests to a codebase with (relatively) little effort. We are talking hours versus months or even years here! The experiment informs the ontology which informs the experiment. I don't think this is reducible to bias, although that certainly exists. Rather to me it's inherent uncertainty in the domain that experiments seek to address. Business practice, as you use the term, evolved to serve very different needs. Automated testing is useful for building software, but that effort may be better spent in science developing new experiments and hypotheses. It's very much an open problem whether the juice is worth the squeeze - in fact the lack of such efforts is (weak) evidence that it might not be. Scientists are not stupid.