Elucidating cosmological model dependence with $$H_0$$

We observe that the errors on the Hubble constant $$H_0$$ H 0 , a universal parameter in any FLRW cosmology, can be larger in specific cosmological models than Gaussian processes (GP) data reconstruction. We comment on the prior mean function and trace the smaller GP errors to stronger correlations, which we show precludes all well studied dynamical dark energy models. We also briefly illustrate cosmographic expansions as another model independent cosmological reconstruction. Our analysis suggests that “cosmological model independence”, especially in the statement of Hubble tension, has become a misnomer.

Testing new physics models with global comparisons to collider measurements: the Contur toolkit

Measurements at particle collider experiments, even if primarily aimed at understanding Standard Model processes, can have a high degree of model independence, and implicitly contain information about potential contributions from physics beyond the Standard Model. The CONTUR package allows users to benefit from the hundreds of measurements preserved in the RIVET library to test new models against the bank of LHC measurements to date. This method has proven to be very effective in several recent publications from the CONTUR team, but ultimately, for this approach to be successful, the authors believe that the CONTUR tool needs to be accessible to the wider high energy physics community. As such, this manual accompanies the first user-facing version: CONTUR v2. It describes the design choices that have been made, as well as detailing pitfalls and common issues to avoid. The authors hope that with the help of this documentation, external groups will be able to run their own CONTUR studies, for example when proposing a new model, or pitching a new search.

Projecting ozone hole recovery using an ensemble of chemistry–climate models weighted by model performance and independence

Calculating a multi-model mean, a commonly used method for ensemble averaging, assumes model independence and equal model skill. Sharing of model components amongst families of models and research centres, conflated by growing ensemble size, means model independence cannot be assumed and is hard to quantify. We present a methodology to produce a weighted-model ensemble projection, accounting for model performance and model independence. Model weights are calculated by comparing model hindcasts to a selection of metrics chosen for their physical relevance to the process or phenomena of interest. This weighting methodology is applied to the Chemistry–Climate Model Initiative (CCMI) ensemble to investigate Antarctic ozone depletion and subsequent recovery. The weighted mean projects an ozone recovery to 1980 levels, by 2056 with a 95 % confidence interval (2052–2060), 4 years earlier than the most recent study. Perfect-model testing and out-of-sample testing validate the results and show a greater projective skill than a standard multi-model mean. Interestingly, the construction of a weighted mean also provides insight into model performance and dependence between the models. This weighting methodology is robust to both model and metric choices and therefore has potential applications throughout the climate and chemistry–climate modelling communities.

What did Mary know? Russellian Monism Without Intrinsics

The Mary thought experiment aims to demonstrate that science cannot capture what experiences feel like. Russellian Monism (RM) avoids this problem by claiming that phenomenality is an intrinsic (non-relational and non-dispositional) property of matter and beyond the scope of science, which is limited to describing extrinsic (relational and dispositional) properties. Against RM, I argue that metaphysical intrinsicality is not compatible with neuroscientific theories where experiences are considered as causal processes. Second, I argue that if intrinsic properties have causal power, they can also affect neuroscientific measuring devices and be scientifically modeled. Thus, intrinsic properties are not inscrutable, as RM holds. In the third part of the article, I sketch the outlines of RM without intrinsics. I propose that the core Kantian thesis of RM about limits of science can be maintained without postulating metaphysical intrinsics. I argue that metaphysical intrinsicality can be replaced with Weak Intrinsicality, meaning model-independence. Science is confined to observations and models, whereas an experience is the concrete, model-independent process that produces observations of its neural mechanisms. On this account, the epistemic gap is difference between a model and the modeled.

Projecting ozone hole recovery using an ensemble of chemistry-climate models weighted by model performance and independence

The current method for averaging model ensembles, which is to calculate a multi model mean, assumes model independence and equal model skill. Sharing of model components amongst families of models and research centres, conflated by growing ensemble size, means model independence cannot be assumed and is hard to quantify. We present a methodology to produce a weighted model ensemble projection, accounting for model performance and model independence. Model weights are calculated by comparing model hindcasts to a selection of metrics chosen for their physical relevance to the process or phenomena of interest. This weighting methodology is applied to the Chemistry-Climate Model Initiative (CCMI) ensemble, to investigate Antarctic ozone depletion and subsequent recovery. The weighted mean projects an ozone recovery to 1980 levels, by 2056 with a 95 % confidence interval (2052–2060), 4 years earlier than the most recent study. Perfect model testing and out-of-sample testing validate the results and show a greater projective skill than a standard multi model mean. Interestingly, the construction of a weighted mean also provides insight into model performance and dependence between the models. This weighting methodology is robust to both model and metric choices and therefore has potential applications throughout the climate and chemistry-climate modelling communities.