Frontiers of Science

What makes a total solar eclipse so spectacular? As daylight fades to a coppery hue, familiar images of people gazing skyward through protective glasses fill newspapers and social media. Soon, the Moon appears to take its first bite from the Sun: the show has begun.

This post lays the groundwork for the Redshift series. Instead of focusing on a single nebula, galaxy, star cluster, or other celestial object, it introduces the conventions astronomers use to describe, identify, and navigate the night sky.

At the center of this image burns a star so extreme it barely belongs to the vocabulary of ordinary stellar physics. WR~136 --- classified as a Wolf-Rayet star, the rarest and most violent class of star known --- is roughly 21 times more massive than our Sun, approximately 600,000 times brighter, and so hot that its surface temperature approaches 70,000 degrees Kelvin.

Few experiments are as famous — or as misunderstood — as the Michelson–Morley experiment of 1887. Often portrayed as a simple refutation of the luminiferous ether and a direct precursor to Einstein’s special relativity, its true history is considerably more subtle.

In our earlier Redshift post, we showcased the Crescent Nebula — an emission nebula located in the Cygnus constellation, approximately 5,000 light-years from Earth. We now revisit Cygnus, but from a closer perspective: the North America Nebula is at about 2,600 light-years, comfortably within the same span of the Swan’s wings. Although these two nebulae share a constellation, galaxy, and spiral arm, they tell remarkably different stories.

The search for Earth-like planets around nearby stars is one of the most ambitious goals in modern astronomy, driven by the hope of discovering life beyond Earth. Yet these worlds are extraordinarily difficult to detect because they lie extremely close to their much brighter parent stars, whose overwhelming glare can conceal the faint light reflected by an orbiting planet. As a result, astronomers must develop highly sophisticated telescopes and imaging techniques capable of suppressing starlight and revealing the hidden planets nearby. This article explores the nature of this challenge and the innovative technologies that may soon allow us to directly observe Earth-like worlds around other stars, bringing us closer to answering the age-old question of whether we are alone in the Universe.

This article presents an extended dialogue between Angshuman Guha, a researcher with hands-on experience in neural networks dating back to 1993, and Google’s large language model (LLM), Gemini. It explores a set of closely connected questions at the intersection of AI and cognitive science. Are modern LLMs genuine reasoning systems, or are they sophisticated Stochastic Parrots that recombine language without understanding? Do the so-called “emergent abilities” of large models reflect a real shift in machine capability, or are they artifacts of how we measure performance? And perhaps most fundamentally: can a system trained entirely on human-generated text be said to produce anything like “original thought”?

Gravity‑assist flybys, or gravitational slingshots, are essential to modern deep‑space exploration because they allow spacecraft to gain heliocentric energy without expending propellant. By passing behind a moving planet, a probe undergoes an exchange of linear momentum that increases its Sun‑centered velocity while leaving its speed relative to the planet nearly unchanged. This fuel‑free transfer of orbital energy is indispensable for reaching the outer Solar System, where vast travel distances and the prohibitive propellant demands of direct propulsion make conventional trajectories unworkable. Gravity assists provide the momentum needed to access high‑value astrobiological targets such as Europa and Enceladus — icy moons whose subsurface oceans, sustained by tidal heating, make them prime candidates in the search for extraterrestrial life. By reshaping trajectories through planetary and lunar encounters, mission designers can conserve fuel for complex scientific operations, including plume sampling and close‑range reconnaissance, thereby enabling ambitious exploration that would otherwise be impossible.

Most intelligent tasks we perform in our lives, we learn those skills through examples rather than being told step-by-step how to do them. For example, no one told us how to recognize the numbers, but showed us many examples, and our minds figured out some subconscious rules to distinguish a “1” from a “2” and all the other digits. Scientists quickly realized that if machines must do complex tasks, they cannot be taught algorithmically, with step-by-step instructions, as we ourselves may not know these logical steps, but rather by showing many examples of the correct behavior. Although that was the holy grail of AI, machine learning was a hard task.

This article is an overview of the oldest electromagnetic radiation in the Universe, known as the Cosmic Microwave Background (CMB), representing a snapshot of the cosmos approximately 380,000 years after the Big Bang. The author discusses how precise measurements from COBE, WMAP, and Planck telescopes have given us clues about when and how the CMB formed, and helped us refine our understanding of the Universe's composition, age, and geometry.