Astronomy

Cosmological scenarios wherein the cumulative number of spontaneously formed, cognitively impaired, disembodied transient observers is vastly larger than the corresponding number of atypical ‘ordinary observers’ (OOs) formed in the conventional way—essentially via cosmic evolution and gravitational instability—are disqualified in modern cosmology on the grounds of Cognitive Instability—the untrustworsiness of one own’s reasoning—let alone the atypicality of OOs like us. According to the concordance ΛCDM cosmological model—when described in the (expanding) ‘cosmic frame’—the cosmological expansion is future-eternal. In this frame we are atypical OOs, which are vastly outnumbered by typical Boltzmann Brains (BBs) that spontaneously form via sheer thermal fluctuations in the future-eternal asymptotic de Sitter spacetime. In the case that dark energy (DE) ultimately decays, the cumulative number of transient ‘Freak Observers’ (FOs) formed and destroyed spontaneously by virtue of the quantum uncertainty principle ultimately overwhelms that of OOs. Either possibility is unacceptable. We argue that these unsettling conclusions are artifacts of employing the (default) cosmic frame description in which space expands. When analyzed in the comoving frame, OOs overwhelmingly outnumber both BBs and FOs. This suggests that the dual comoving description is the cognitively stable preferred framework for describing our evolving Universe. In this frame, space is globally static, masses monotonically increase, and the space describing gravitationally bounded objects monotonically contracts.

We consider constraints on the Hubble parameter $H_0$ and the matter density parameter ${\mathrm{\Omega }}_{\mathrm{M}}$ from the following: (i) the age of the Universe based on old stars and stellar populations in the Galactic disc and halo; (ii) the turnover scale in the matter power spectrum, which tells us the cosmological horizon at the epoch of matter-radiation equality; and (iii) the shape of the expansion history from supernovae (SNe) and baryon acoustic oscillations (BAOs) with no absolute calibration of either, a technique known as uncalibrated cosmic standards (UCS). A narrow region is consistent with all three constraints just outside their $1\sigma$ uncertainties. Although this region is defined by techniques unrelated to the physics of recombination and the sound horizon then, the standard Planck fit to the CMB anisotropies falls precisely in this region. This concordance argues against early-time explanations for the anomalously high local estimate of $H_0$ (the ‘Hubble tension’), which can only be reconciled with the age constraint at an implausibly low ${\mathrm{\Omega }}_{\mathrm{M}}$. We suggest instead that outflow from the local KBC supervoid inflates redshifts in the nearby universe and, thus, the apparent local $H_0$. Given the difficulties with solutions in the early universe, we argue that the most promising alternative to a local void is a modification to the expansion history at late times, perhaps due to a changing dark energy density.

Solar flares are among the most powerful and dynamic events in the solar system, resulting from the sudden release of magnetic energy stored in the Sun’s atmosphere. These energetic bursts of electromagnetic radiation can release up to ${10}^{32}$ erg of energy, impacting space weather and posing risks to technological infrastructure and therefore require accurate forecasting of solar flare occurrences and intensities. This study evaluates the predictive performance of three machine learning algorithms—Random Forest (RF), k-Nearest Neighbors (kNN), and Extreme Gradient Boosting (XGBoost)—for classifying solar flares into four categories (B, C, M, X). Using 13 parameters of the SHARP dataset, the effectiveness of the models was evaluated in binary and multiclass classification tasks. The analysis utilized 8 principal components (PCs), capturing 95% of data variance, and 100 PCs, capturing 97.5% of variance. Our approach uniquely combines binary and multiclass classification with different levels of dimensionality reduction, an innovative methodology not previously explored in the context of solar flare prediction. Employing a 10-fold stratified cross-validation and grid search for hyperparameter tuning ensured robust model evaluation. Our findings indicate that RF and XGBoost consistently demonstrate strong performance across all metrics, benefiting significantly from increased dimensionality. The insights of this study enhance future research by optimizing dimensionality reduction techniques and informing model selection for astrophysical tasks. By integrating this newly acquired knowledge into future research, more accurate space weather forecasting systems can be developed, along with a deeper understanding of solar physics.