The puzzle comes from a deep tension within statistical physics. A key foundation for understanding why time seems to move in one direction is Boltzmann’s H theorem, a central principle in statistical mechanics tied to the second law of thermodynamics. That law explains why entropy tends to increase over time, giving us a sense of past and future.

However, the H theorem itself is time-symmetric, meaning it does not prefer one direction of time over another. This creates a surprising implication. From a strictly formal standpoint, it is more probable for the patterns that make up our memories and observations to arise from random entropy fluctuations than from a real sequence of past events. Put simply, physics appears to allow the possibility that our memories are not reliable records but instead detailed illusions produced by chance. This unsettling idea is what defines the Boltzmann brain hypothesis.

How Assumptions About Time Shape the Debate

To better understand this problem, the researchers built a formal framework that examines how different assumptions affect conclusions about entropy and memory. Their work connects the Boltzmann brain hypothesis, the second law of thermodynamics, and the related “past hypothesis,” which assumes the universe began in a state of low entropy.

A crucial issue is which points in time are treated as fixed when analyzing how entropy evolves. Some approaches take the current state of the universe as given and work outward from there. Others assume a low-entropy starting point at the Big Bang. Importantly, the laws of physics do not specify which of these perspectives is correct, leaving room for interpretation.

Circular Reasoning in Entropy and Memory Arguments

The study introduces what the authors call the “entropy conjecture” to highlight a key problem in many existing arguments. They show that discussions about entropy, time, and memory often rely on subtle circular reasoning. In these cases, assumptions about the past are used to support conclusions, such as the reliability of memory or the direction in which entropy increases. Those same conclusions are then used to justify the original assumptions.

Rather than settling the debate, the researchers focus on making these hidden structures clear. By separating the role of physical laws from the assumptions we use to interpret them, the study provides a more transparent way to think about long-standing questions surrounding time, entropy, and the nature of memory.

Their findings, published in MNRAS, are based on a detailed analysis of observations from more than 2.2 million stars gathered during TESS’s first four years. The researchers focused on planets that orbit very close to their stars, completing a full orbit in less than 16 days. This approach has produced one of the most precise measurements yet of how common these short-period planets are.

“Using our newly developed RAVEN pipeline, we were able to validate 118 new planets, and over 2,000 high-quality planet candidates, nearly 1,000 of them entirely new,” said first author Dr. Marina Lafarga Magro, Postdoctoral Researcher at the University of Warwick. “This represents one of the best characterized samples of close in planets and will help us identify the most promising systems for future study.”

Rare and Extreme Planet Types Identified

The newly confirmed planets include several especially interesting categories. Some are ultra-short-period planets that circle their stars in under 24 hours. Others belong to the so-called ‘Neptunian desert,’ a region where few planets are expected to exist based on current theories. The study also revealed tightly packed multi-planet systems, including previously unknown pairs of planets orbiting the same star.

How RAVEN Improves Planet Detection

Modern planet-hunting missions often flag thousands of possible planets, but determining which signals are genuine remains difficult. Many false signals can mimic planets, including eclipsing binary stars.

“The challenge lies in identifying if the dimming is indeed caused by a planet in orbit around the star or by something else, like eclipsing binary stars, which is what RAVEN tries to answer. Its strength stems from our carefully created dataset of hundreds of thousands of realistically simulated planets and other astrophysical events that can masquerade as planets. We trained machine learning models to identify patterns in the data that can tell us the type of event we have detected, something that AI models excel at.” said Warwick’s Dr. Andreas Hadjigeorghiou, who led the development of the pipeline.

“In addition, RAVEN is designed to handle the whole process in one go, from detecting the signal, to vetting it with machine learning and statistically validating it. This gives the pipeline an additional edge over contemporary tools that only focus on specific parts of the workflow.”

Dr. David Armstrong, Associate Professor at Warwick and senior co-author on the RAVEN studies, added: “RAVEN allows us to analyse enormous datasets consistently and objectively. Because the pipeline is well-tested and carefully validated, this is not just a list of potential planets — it is also reliable enough use as a sample to map the prevalence of distinct types of planets around Sun-like stars.”

Measuring How Common Planets Really Are

With this carefully validated dataset, the researchers were able to go beyond individual discoveries and examine broader patterns. In a companion MNRAS study, they measured how often close-in planets occur around Sun-like stars, mapping results by orbital period and planet size with an unprecedented level of detail.

The results show that about 9-10% of Sun-like stars host a close-in planet. This aligns with earlier findings from NASA’s Kepler mission — a space telescope that previously measured planet occurrence rates, but the new analysis reduces uncertainties by up to a factor of ten.

The team also made the first direct measurement of how rare ‘Neptunian desert’ planets are, finding that they appear around just 0.08% of Sun-like stars.

“For the first time, we can put a precise number on just how empty this ‘desert’ is,” said Dr. Kaiming Cui, Postdoctoral Researcher at Warwick and first author of the population study. “These measurements show that TESS can now match, and in some cases surpass, Kepler for studying planetary populations.”

A New Era for Planet Discovery

Together, these studies highlight how advances in artificial intelligence are transforming astronomy. By combining massive datasets with machine learning, researchers can uncover new planets while also improving the tools themselves through challenging real-world data.

The team has also released interactive catalogs and tools so other scientists can explore the results and identify promising targets for follow-up observations using ground-based telescopes and future missions such as ESA’s PLATO.

What Is RAVEN

RAVEN is an automated system designed to address one of astronomy’s biggest challenges, turning enormous volumes of space telescope data into reliable discoveries. It scans data from millions of stars to find the tiny drops in brightness caused by planets passing in front of them. The system then uses artificial intelligence trained on realistic simulations to filter out false signals such as binary stars or instrument noise, before statistically confirming the strongest candidates.

Importantly, RAVEN also evaluates which types of planets are easier or harder to detect, helping researchers correct for hidden biases. This means it not only speeds up the discovery of new worlds but also produces cleaner, more reliable datasets that can be used to answer larger questions about how common different kinds of planets are across the galaxy.

The original idea came from Sir William Hamilton, British ambassador to Naples and Sicily from 1765 to 1800, who was also deeply interested in volcanology. His concept blended artistic expression with mechanical design to capture the dramatic visual effects of a volcanic eruption.

Inspired by the 1771 watercolor ‘Night view of a current of lava’ by British-Italian artist Pietro Fabris, the device was designed to use light and movement to mimic flowing lava and explosive bursts from Vesuvius. It remains uncertain whether Hamilton ever constructed the mechanism, but a detailed sketch preserved in the Bordeaux Municipal Library served as the foundation for its modern recreation.

Reconstructing the Historic Vesuvius Device

Dr. Richard Gillespie, Senior Curator in the Faculty of Engineering and Information Technology, launched the project and guided its development.

“It is fitting that after 250 years exactly, our students have brought this dormant project to life,” he said.

“It is a wonderful piece of science communication. People around the world have always been fascinated by the immense power of volcanoes.”

Modern Engineering Meets 18th-Century Design

Master of Mechatronics student Xinyu (Jasmine) Xu and Master of Mechanical Engineering student Yuji (Andy) Zeng spent three months building the device in The Creator Space student workshop. Using modern materials and technologies, including laser-cut timber and acrylic, programmable LED lighting, and electronic control systems, they adapted Hamilton’s clockwork-based design for today’s audience.

“The project offered a wealth of learning opportunities. I’ve extended many skills, including programming, soldering and physics applications,” Ms. Xu said.

Mr. Zeng said the experience gave him a deeper understanding of mechanical engineering in practice.

“It was a fantastic way to build my hands-on problem-solving skills,” he said. “We still faced some of the challenges that Hamilton faced. The light had to be designed and balanced so the mechanisms were hidden from view.”

Hands-On Learning and Engineering Skills

Research engineer Mr. Andrew Kogios, who supervised the students, highlighted the growth they achieved through the project.

“From selecting materials and 3D printing, to troubleshooting electronics and satisfying requirements, working collaboratively with Yuji and Xinyu has been extremely rewarding,” Mr. Kogios said. “Experiences like these, supplementing their university studies, position them well for their future endeavors.”

On Display at The Grand Tour Exhibition

The completed device is now the centerpiece of The Grand Tour, an exhibition at the University’s Baillieu Library, where it will be on display until June 28, 2026.

The team found that increasing levels of Sox9, a protein that plays a major role in regulating astrocyte activity during aging, significantly improved these cells’ ability to remove amyloid plaques. The findings, published in Nature Neuroscience, point to a potential treatment strategy that focuses on boosting the brain’s own support system to slow cognitive decline in neurodegenerative disease.

Astrocytes and Brain Function

“Astrocytes perform diverse tasks that are essential for normal brain function, including facilitating brain communications and memory storage. As the brain ages, astrocytes show profound functional alterations; however, the role these alterations play in aging and neurodegeneration is not yet understood,” said first author Dr. Dong-Joo Choi, who conducted the work while at Baylor’s Center for Cell and Gene Therapy and Department of Neurosurgery. Choi is now an assistant professor at the Center for Neuroimmunology and Glial Biology, Institute of Molecular Medicine at the University of Texas Health Science Center at Houston.

Sox9 and Aging Astrocytes

In this study, researchers set out to better understand how astrocytes change with age and how those changes are linked to Alzheimer’s disease. They focused on Sox9 because it controls the activity of many genes in aging astrocytes.

“We manipulated the expression of the Sox9 gene to assess its role in maintaining astrocyte function in the aging brain and in Alzheimer’s disease models,” said corresponding author Dr. Benjamin Deneen, professor and Dr. Russell J. and Marian K. Blattner Chair in the Department of Neurosurgery, director of the Center for Cancer Neuroscience, a member of the Dan L Duncan Comprehensive Cancer Center at Baylor and a principal investigator at the Jan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital.

Testing in Mice With Established Symptoms

“An important point of our experimental design is that we worked with mouse models of Alzheimer’s disease that had already developed cognitive impairment, such as memory deficits, and had amyloid plaques in the brain,” Choi said. “We believe these models are more relevant to what we see in many patients with Alzheimer’s disease symptoms than other models in which these types of experiments are conducted before the plaques form.”

To test their approach, the researchers either increased or eliminated Sox9 in these mice and tracked their cognitive performance over six months. The animals were evaluated on their ability to recognize familiar objects and environments. At the end of the study, the team measured how much plaque had accumulated in the brain.

Boosting Sox9 Improves Plaque Clearance and Memory

The results revealed a clear contrast. Lower Sox9 levels led to faster plaque buildup, simpler astrocyte structure and reduced ability to clear amyloid deposits. Increasing Sox9 produced the opposite outcome, enhancing astrocyte activity, improving their structural complexity and promoting plaque removal.

Importantly, mice with higher Sox9 levels maintained better cognitive function, suggesting that activating astrocytes to clear plaques can help slow the mental decline associated with Alzheimer’s disease.

“We found that increasing Sox9 expression triggered astrocytes to ingest more amyloid plaques, clearing them from the brain like a vacuum cleaner,” Deneen said. “Most current treatments focus on neurons or try to prevent the formation of amyloid plaques. This study suggests that enhancing astrocytes’ natural ability to clean up could be just as important.”

A New Direction for Alzheimer’s Treatment

The researchers emphasize that more work is needed to understand how Sox9 functions in the human brain over time. Even so, the findings open the door to new therapies that aim to harness astrocytes as a natural defense against neurodegenerative disease.

Research Team and Funding

Additional contributors to the study from Baylor College of Medicine include Sanjana Murali, Wookbong Kwon, Junsung Woo, Eun-Ah Christine Song, Yeunjung Ko, Debo Sardar, Brittney Lozzi, Yi-Ting Cheng, Michael R. Williamson, Teng-Wei Huang, Kaitlyn Sanchez and Joanna Jankowsky.

The research was supported by National Institutes of Health grants (R35-NS132230, R01- AG071687, R01-CA284455, K01-AG083128, R56-MH133822). Additional funding came from the David and Eula Wintermann Foundation, the Eunice Kennedy Shriver National Institute of Child Health & Human Development of the National Institutes of Health under Award Number P50HD103555 and shared resources from Houston Methodist and Baylor College of Medicine.