A new method to determine large scale structure from the luminosity distance
Bibliographic record
Abstract
The luminosity distance can be used to determine the properties of large scale structure around the observer. To this purpose we develop a new inversion method to map luminosity distance to a LTB metric based on the use of the exact analytical solution for Einstein equations. The main advantages of this approach are an improved numerical accuracy and stability, an exact analytical setting of the initial conditions for the differential equations which need to be solved and the validity for any sign of the functions determining the LTB geometry. Given the fully analytical form of the differential equations, this method also simplifies the calculation of the red-shift expansion around the apparent horizon point where the numerical solution becomes unstable. We test the method by inverting the supernovae Ia luminosity distance function corresponding to the the best fit $\Lambda CDM$ model. We find that only a limited range of initial conditions is compatible with observations, or a transition from red to blue shift can occur at relatively low redshift. Despite LTB solutions without a cosmological constant have been shown not to be compatible with all different set of available observational data, those studies normally fit data assuming a special functional ansatz for the inhomogeneity profile, which often depend only on few parameters. Inversion methods on the contrary are able to fully explore the freedom in fixing the functions which determine a LTB solution. Another important possible application is not about LTB solutions as cosmological models, but rather as tools to study the effects on the observations made by a generic observer located in an inhomogeneous region of the Universe where a fully non perturbative treatment involving exact solutions of Einstein equations is required.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".