We perform a model-independent reconstruction of the cosmic distances using the Multi-Task Gaussian Process (MTGP) framework as well as knot-based spline techniques with DESI-DR2 BAO and DES-SN5YR datasets. We calibrate the comoving sound horizon at the baryon drag epoch $r_d$ to the Planck value, ensuring consistency with early-universe physics. With the reconstructed cosmic distances and their derivatives, we obtain seven characteristic redshifts in the range $0.3 \leq z \leq 1.7$. We derive the normalized expansion rate of the Universe $E(z)$ at these redshifts. Our findings reveal significant deviations of approximately $4$ to $5σ$ from the Planck 2018 $Λ$CDM predictions, particularly pronounced in the redshift range $z \sim 0.35-0.55$. These anomalies are consistently observed across both reconstruction methods and combined datasets, indicating robust late-time tensions in the expansion rate of the Universe and which are distinct from the existing "Hubble Tension". This could signal new physics beyond the standard cosmological framework at this redshift range. Our findings underscore the role of characteristic redshifts as sensitive indicators of expansion rate anomalies and motivate further scrutiny with forthcoming datasets from DESI-5YR BAO, Euclid, and LSST. These future surveys will tighten constraints and will confirm whether these late-time anomalies arise from new fundamental physics or unresolved systematics in the data.

This means that galaxies had to be closer to each other in the past. In 1964, Wilson and Penzias discovered the cosmic background radiation, which is a like a fossil of radiation emitted during the beginning of the universe, when it was hot and dense. The cosmic background radiation is observable everywhere in the universe. The composition of the universe - that is, the the number of atoms of different elements - is consistent with the Big Bang Theory. So far, this theory is the only one that can explain why we observe an abundance of primordial elements in the universe.

Breakthroughs, discoveries, and DIY tips sent every weekday. While the Hubble and James Webb Space Telescopes continue to offer astronomers revolutionary glimpses of our universe, their upcoming sibling may very well upstage them. Scheduled to launch in 2027, NASA's Nancy Grace Roman Space Telescope is designed with a field of view at least 100 times larger than Hubble's, with the potential to document light from over a billion galaxies over its career. Combined with timelapse recording capabilities, Roman will help researchers to better understand exoplanets, infrared astrophysics, and the nature of dark matter. According to a study published on July 15 in The Astrophysics Journal, Roman is poised to eventually capture an estimated 100,000 celestial explosions over its lifetime.

We perform a model-independent reconstruction of the angular diameter distance ($D_{A}$) using the Multi-Task Gaussian Process (MTGP) framework with DESI-DR1 BAO and DES-SN5YR datasets. We calibrate the comoving sound horizon at the baryon drag epoch $r_d$ to the Planck best-fit value, ensuring consistency with early-universe physics. With the reconstructed $D_A$ at two key redshifts, $z\sim 1.63$ (where $D_{A}^{\prime} =0$) and at $z\sim 0.512$ (where $D_{A}^{\prime} = D_{A}$), we derive the expansion rate of the Universe $H(z)$ at these redshifts. Our findings reveal that at $z\sim 1.63$, the $H(z)$ is fully consistent with the Planck-2018 $\Lambda$CDM prediction, confirming no new physics at that redshift. However, at $z \sim 0.512$, the derived $H(z)$ shows a more than $5\sigma$ discrepancy with the Planck-2018 $\Lambda$CDM prediction, suggesting a possible breakdown of the $\Lambda$CDM model as constrained by Planck-2018 at this lower redshift. This emerging $\sim 5\sigma$ tension at $z\sim 0.512$, distinct from the existing ``Hubble Tension'', may signal the first strong evidence for new physics at low redshifts.

Finding the best parametrization for cosmological models in the absence of first-principle theories is an open question. We propose a data-driven parametrization of cosmological models given by the disentangled 'latent' representation of a variational autoencoder (VAE) trained to compress cosmic microwave background (CMB) temperature power spectra. We consider a broad range of $\Lambda$CDM and beyond-$\Lambda$CDM cosmologies with an additional early dark energy (EDE) component. We show that these spectra can be compressed into 5 ($\Lambda$CDM) or 8 (EDE) independent latent parameters, as expected when using temperature power spectra alone, and which reconstruct spectra at an accuracy well within the Planck errors. These latent parameters have a physical interpretation in terms of well-known features of the CMB temperature spectrum: these include the position, height and even-odd modulation of the acoustic peaks, as well as the gravitational lensing effect. The VAE also discovers one latent parameter which entirely isolates the EDE effects from those related to $\Lambda$CDM parameters, thus revealing a previously unknown degree of freedom in the CMB temperature power spectrum. We further showcase how to place constraints on the latent parameters using Planck data as typically done for cosmological parameters, obtaining latent values consistent with previous $\Lambda$CDM and EDE cosmological constraints. Our work demonstrates the potential of a data-driven reformulation of current beyond-$\Lambda$CDM phenomenological models into the independent degrees of freedom to which the data observables are sensitive.

We present DE-VAE, a variational autoencoder (VAE) architecture to search for a compressed representation of dynamical dark energy (DE) models in observational studies of the cosmic large-scale structure. DE-VAE is trained on matter power spectra boosts generated at wavenumbers $k\in(0.01-2.5) \ h/\rm{Mpc}$ and at four redshift values $z\in(0.1,0.48,0.78,1.5)$ for the most typical dynamical DE parametrization with two extra parameters describing an evolving DE equation of state. The boosts are compressed to a lower-dimensional representation, which is concatenated with standard cold dark matter (CDM) parameters and then mapped back to reconstructed boosts; both the compression and the reconstruction components are parametrized as neural networks. Remarkably, we find that a single latent parameter is sufficient to predict 95% (99%) of DE power spectra generated over a broad range of cosmological parameters within $1\sigma$ ($2\sigma$) of a Gaussian error which includes cosmic variance, shot noise and systematic effects for a Stage IV-like survey. This single parameter shows a high mutual information with the two DE parameters, and these three variables can be linked together with an explicit equation through symbolic regression. Considering a model with two latent variables only marginally improves the accuracy of the predictions, and adding a third latent variable has no significant impact on the model's performance. We discuss how the DE-VAE architecture can be extended from a proof of concept to a general framework to be employed in the search for a common lower-dimensional parametrization of a wide range of beyond-$\Lambda$CDM models and for different cosmological datasets. Such a framework could then both inform the development of cosmological surveys by targeting optimal probes, and provide theoretical insight into the common phenomenological aspects of beyond-$\Lambda$CDM models.

Elon Musk has officially introduced his xAI team to the masses with a live Twitter Spaces event - after years of claiming the tech will be the demise of humanity. The Twitter boss laid out his plans to make an artificial general intelligence (AGI) that will be'maximally curious and truth-seeking' and'won't be politically correct.' 'People will be offended,' Musk said. 'Our AI can give answers that they might find controversial even though they might be true.' But beyond the AI culture wars, Musk expressed ambitious hopes to produce an AGI with deep analytical reasoning capable of solving higher order math and science problems, including many that have eluded mankind's best thinkers. The billionaire suggested that xAI could answer questions about the nature of dark matter and dark energy: theorized but difficult to confirm components of the known universe which astrophysicists estimate constitute 95 percent of the cosmos. Musk also said he hoped xAI could help resolve the'Fermi paradox' -- a theoretical question that asks why humans have not yet encountered extraterrestrial life in a universe that is over 13 billion years old and ripe with the conditions supporting life.

Brian Nord first fell in love with physics when he was a teenager growing up in Wisconsin. His high school physics program wasn't exceptional, and he sometimes struggled to keep up with class material, but those difficulties did nothing to dampen his interest in the subject. In addition to the main curriculum, students were encouraged to independently study topics they found interesting, and Nord quickly developed a fascination with the cosmos. "A touchstone that I often come back to is space," he says. Nord was an avid reader of comic books, and astrophysics appealed to his desire to become a part of something bigger.

The current accelerated expansion of the universe is driven by mysterious dark energy. Upcoming astronomical imaging surveys, such as LSST at Rubin Observatory, are set to provide unprecedented precise measurements of cosmological parameters, including this dark energy, using measurements such as weak gravitational lensing. Weak lensing is measured by looking for coherent patterns in galaxy shapes, which can be caused by the fact that the cosmic matter distribution coherently distorts spacetime, affecting the appearances of nearby galaxy images in similar ways. However, there are still challenges facing cosmologists on their path from data to science. One of these challenges is the effect of intrinsic alignments – where galaxies are not oriented randomly in the sky, but rather tend to point towards other galaxies.

The current accelerated expansion of the Universe is driven by mysterious dark energy. Upcoming astronomical imaging surveys, such as LSST at Rubin Observatory, are set to provide unprecedented precise measurements of cosmological parameters, including this dark energy, using measurements such as weak gravitational lensing. Weak lensing is measured by looking for coherent patterns in galaxy shapes, which can be caused by the fact that the cosmic matter distribution coherently distorts spacetime, affecting the appearances of nearby galaxy images in similar ways. However, there are still challenges facing cosmologists on their path from data to science. One of these challenges is the effect of intrinsic alignments – where galaxies are not oriented randomly in the sky, but rather tend to point towards other galaxies.