Sea-floor samples for the study were taken with this deep-sea submersible vehicle (Alvin) from inactive as well as active hydrothermal systems in several thousands of meters of water. Credit: Woods Hole Oceanographic Institution, National Deep Submergence Facility, National Science Foundation.

Under certain conditions microbial communities can grow and thrive, even in places that are seemingly uninhabitable. This is the case at inactive hydrothermal vents on the sea floor. An international team that includes researchers from MARUM—Center for Marine Environmental Sciences at the University of Bremen, is presently working to accurately quantify how much inorganic carbon can be bound in these environments.

With its high pressure, darkness, and nutrient deficiency, the deep sea is generally not a hospitable place. But in the presence of heat and a rich influx of energy-rich fluids, as is the case at active hydrothermal vents, numerous fish, shellfish, and microorganisms are able to settle there. But what happens to these biotic communities when the source of hot fluids is exhausted?

The chimneys form over long time periods when seawater seeps through cracks into the Earth’s crust, is warmed there, then dissolves and takes up minerals on its way back up to the ocean floor. This hot, mineral-rich, and often smokey water seeks the most pervious path through the Earth’s crust and encounters cold, oxygen-rich water at the sea floor.

This results in the precipitation of minerals, which are deposited as chimneys. These hydrothermal vents are energy-rich habitats based on chemosynthesis where microorganisms from the base of the food webs. Depending on the region, chimneys at hydrothermal seeps contain minerals like copper, zinc, gold, or silver. As a result, there is a growing interest in exploiting inactive smokers in deep-sea mining activities.

When the flow of mineral-rich fluids dries up, the black smokers become inactive. Larger organisms migrate away to the next vent, but the microbial communities have ways to adapt to the new conditions.

“Even forty years after the discovery of the first hydrothermal fields, we constantly learn new things about how these ecosystems work,” says Dr. Florence Schubotz of MARUM, “particularly relating to the amount of CO₂ bound up in inactive smokers, but also with regard to the volume of microbial life, its activity, and rates of production.”

Determining how densely inactive smokers are colonized is the central focus of a research project in which Schubotz is working. The work involves sampling at the exact area where the first hydrothermal vents were discovered in the eastern Pacific around four decades ago.

“The initial results indicate that even inactive smokers are important locations for microbial activity and the production of organic carbon on the sea floor. We are just beginning to understand how the carbon cycle functions in the deep sea. It is certain that carbon is fixed at such hotspots.

“But,” according to Schubotz, “we do not yet understand these ecosystems well enough to estimate the magnitudes involved.” Broad areas of the ocean floor have not yet been investigated and still unknown hydrothermal systems await discovery.

Every plate-boundary spreading center is a potential colonization area. The samples from the eastern Pacific will provide a good starting point because there is already a good understanding of the extent of microbial communities at this location. The international team has therefore investigated samples from active and inactive smokers and compared them with each other.

The team obtained the samples during three expeditions in 2019 and 2021, in part with the help of the manned submersible research vehicle Alvin, from the East Pacific Rise (9 degrees north), an oceanic ridge at a Pacific plate boundary. Their objective is to understand better the deep-sea ecosystem and the interactions between various organisms and to calculate how metabolic rates change from active to inactive systems for the first time.

“Without this kind of data,” according to the publication, “our understanding of the element cycles in the inactive-chimney ecosystem and their possible influence on the biochemistry of the deep sea remains incomplete.” The team emphasizes that such investigations are essential before any decisions can be made about deep-sea mining.

The biogeochemistry at the sea floor and the interactions of marine ecosystems with the environment are also some of the core research themes within the Cluster of Excellence, “The Ocean Floor—Earth’s Uncharted Interface.”

The findings are published in the journal Nature Microbiology.

A map of dark matter from 2021 using weak gravitational lensing data set. Credit: Dark Energy Survey. darkenergysurvey.org/des-year-3-cosmology-results-papers/.

New research in Physical Review Letters (PRL) has proposed a novel method to detect light dark matter candidates using laser interferometry to measure the oscillatory electric fields generated by these candidates.

Dark matter is one of the most pressing challenges in modern physics, with dark matter particles being elusive and hard to detect. This has prompted scientists to come up with new and innovative ways to look for these particles.

There are several candidates for dark matter particles, such as WIMPs, light dark matter particles (axions), and the hypothetical gravitino. Light dark matter, including bosonic particles like the QCD (quantum chromo dynamics) axion, has become a point of interest in recent years.

These particles typically have suppressed interactions with the standard model, making them challenging to detect. However, knowing their characteristics, including their wave-like behavior and coherent nature at galactic scales, helps to design more efficient experiments.

In the new PRL study, researchers from the University of Maryland and Johns Hopkins University have proposed Galactic Axion Laser Interferometer Leveraging Electro-Optics or GALILEO, a new approach to detect both axion and dark photon dark matter over a wide mass range.

Lead researcher Reza Ebadi, a graduate student at the Quantum Technology Center (QTC) at the University of Maryland, spoke to Phys.org about the research and their motivation for developing this new approach, “Although the standard model provides successful explanations of phenomena ranging from sub-nuclear distances to the size of the universe, it is not a complete explanation of nature.”

“It fails to account for cosmological observations from which the existence of dark matter is inferred. We aspire to gain insight into the physical theories operating on galactic scales using small-scale lab experiments.”

Axions and axionlike particles

Axions and axionlike particles were initially proposed to solve problems in particle physics, such as the strong charge-parity (CP) problem. This problem arises from the observation that the strong force doesn’t seem to exhibit a particular type of symmetry violation, called CP violation, as much as theory predicts it should.

This theoretical framework naturally gives rise to axionlike particles, which share similar properties to axions, with both being bosons.

Axions and axionlike particles are predicted to have very low masses, typically ranging from microelectronvolts to millielectronvolts. This makes them suitable candidates for light dark matter, as they can exhibit wave-like behavior at galactic scales.

In addition to their low mass, axions and axionlike particles interact very weakly with ordinary matter, making them difficult to detect using conventional means.

These are some reasons the researchers have chosen to detect these particles in their experimental setup. However, the method hinges on oscillatory electric fields produced by these particles.

In regions with significant dark matter density, axions and ALPs can undergo coherent oscillations. These coherent oscillations can give rise to detectable signals, such as oscillatory electric fields, which the proposed GALILEO experiment aims to measure.

Projected sensitivities of the GALILEO experiment for axion (Left) and dark photon (Right) dark matter searches. Credit: Physical Review Letters (2024). DOI: 10.1103/PhysRevLett.132.101001

GALILEO

“Light dark matter candidates behave as waves in the solar neighborhood. Such dark matter waves are predicted to induce very weak oscillating electric fields with magnetic fields because of their minuscule interactions with electromagnetism.”

“We focused on the detection of the electric field rather than the magnetic field, which is the target signal in most current and proposed experiments,” explained Ebadi.

Light dark matter-induced electric fields can be detected using electro-optical materials, where the external electric field modifies the material’s properties, such as refractive index.

GALILEO utilizes an asymmetric Michelson interferometer, a device that can measure the changes in refractive index. One arm of the interferometer contains the electro-optical material.

When a probe laser beam is split and sent through the two arms of the interferometer, the arm containing the electro-optical material introduces a variable refractive index. This change in refractive index affects the phase of the laser beam, resulting in an oscillating signal when the beams are merged back together.

By measuring the differential phase velocity between the two arms of the interferometer, GALILEO can detect the frequency of oscillation induced by light dark matter. This oscillatory signal serves as the signature of the presence of dark matter particles.

The sensitivity of the method can be increased by incorporating Fabry-Perot cavities (which increase the length of the interferometer arm, allowing for greater precision) and taking repeated independent measurements.

Laser interferometry and implementing GALILEO

The research relies on precision measurements by laser interferometry.

Ebadi explained, “A prime example of how laser interferometers can be used for precision measurements is LIGO, the ground-based gravitational wave detector.”

“Our proposal uses similar technological advancements as LIGO, such as Fabry-Perot cavities or squeezed light to suppress the quantum noise limit. However, unlike LIGO, the proposed GALILEO interferometer is a tabletop-scale device.”

Even though the work is theoretical, the researchers already have plans to implement the experimental program step-by-step.

Importantly, they want to determine the technical parameters required for an optimized experimental setup, which they plan to use for conducting scientific experiments to search for light dark matter.

Additionally, Ebadi highlights the importance of operating high-finesse Fabry-Perot cavities alongside electro-optical material within the cavity, as well as characterizing the noise budget and setup systematics, which are crucial aspects of the experimental process.

“GALILEO has the potential to be a significant component of the bigger mission of exploring the vast theoretically viable space of dark matter candidates,” concluded Ebadi.

There are many things we don’t know about how history unfolds. The process might be impersonal, even inevitable, as some social scientists have suggested; human societies might be doomed to decline. Or, individual actions and environmental conditions might influence our communities’ trajectories. Social scientists have struggled to find a consensus on such fundamental issues.

A new framework by SFI faculty and others suggests a way to unify these perspectives. In a new paper in the Journal of Computer Applications in Archaeology, multidisciplinary researchers describe how using stochastic processes to analyze historical datasets could reveal surprising patterns that have gone unnoticed in previous models.

A stochastic model, which incorporates uncertainty and randomness, would treat historical shifts not as deterministic but instead as probabilistic. Stochastic models have previously been used to study systems in a range of fields, from biology to physics to information theory, but have remained under-explored in the study of history and archaeology.

Taking this approach to studying historical data doesn’t only potentially unite previous ideas; it may also yield new ideas about how historical systems change over time. “Adopting a stochastic process also forces us to be precise and explicit in our thinking about the dynamics underlying any particular historical data set,” says physicist and SFI Professor David Wolpert, senior author on the study.

Stochastic processes let the data speak for itself and can remove potential interpretive biases, says SFI External Professor and co-author Stefani Crabtree (Utah State University), who in her work uses diverse methodologies to model systems in social sciences and ecology.

Where previous approaches begin with a preconceived idea and then find data to support it, a stochastic framework instead can “allow the data itself to identify how human social groups evolve, possibly possible unanticipated revelations,” she says. “It could lead to the detection of sometimes surprising patterns that would be missed in more traditional analyses.”

The diverse forces that shape the evolution of history are complex, notes biologist and SFI External Professor Manfred Laubichler (Arizona State University), another co-author on the paper. Laubichler’s work focuses on evolution in its many guises, from genes to knowledge systems. Instead of treating historical evolution as a deterministic process, he says the new framework allows for probabilities of different events to evolve.

Using randomness, the new approach suggests a structured way to identify the causes of historical shifts. Those might be individuals in society who take dramatic actions that change the course of history, or they might be natural forces external to society, like volcanoes or climate change, that nonetheless are critical forcing factors. The proposed model also provides a new way to compare cases from different points in history.

The researchers say their framework could be used to find patterns in archaeological data. (Or, in cases where data is missing from the historical record, it might be used in tandem with machine learning systems.) It might also help elucidate drivers of the Great Acceleration—the exponential growth, ongoing since the beginning of the 1950s, in many areas of earth and social systems. It could help differentiate random occurrences from truly transformative events, says Wolpert.

He notes that the new framework isn’t an end-all solution; rather, by grounding investigations of social dynamics in stochastic models, the researchers hope to unearth new, data-driven tools for finding patterns in historical records.

“Not only does this perspective allow us to unify the analyses of computational history,” Wolpert says, “it also allows us to align how we investigate human history with how it is done in the other historical sciences.”

Pouya Hosseinzadeh, left, a USU doctoral student in computer science, with faculty mentor Soukaina Filali Boubrahimi, right, assistant professor in the Department of Computer Science, published a description of a machine learning method to enhance water data collected by satellites in an AGU journal. He presents the research at USU’s 2024 Spring Runoff Conference March 26–27.

Satellites encircling the Earth collect a bounty of water data about our planet, yet distilling usable information from these sources about our oceans, lakes, rivers and streams can be a challenge.

“Water managers need accurate data for water resource management tasks, including lake coastal zone monitoring, rising seas border shift detection and erosion monitoring,” says Utah State University computer scientist Pouya Hosseinzadeh. “But they face a trade-off when reviewing data from currently deployed satellites, which yield complementary data that are either of high spatial or high temporal resolutions. We’re trying to integrate the data to provide more accurate information.”

Varied data fusion approaches present limitations, including sensitivity to atmospheric disturbances and other climatic factors that can result in noise, outliers and missing data.

A proposed solution, say Hosseinzadeh, a doctoral student, and his faculty mentor Soukaina Filali Boubrahimi, is the Hydrological Generative Adversarial Network—known as Hydro-GAN. The scientists developed the Hydro-GAN model with USU colleagues Ashit Neema, Ayman Nassar and Shah Muhammad Hamdi, and describe this tool in the online issue of Water Resources Research.

Hydro-GAN, says Filali Boubrahimi, assistant professor in USU’s Department of Computer Science, is a novel machine learning-based method that maps the available satellite data at low resolution to a high-resolution data counterpart.

“In our paper, we describe integrating data collected by MODIS, a spectroradiometer aboard the Terra Earth Observing System satellite, and the Landsat 8 satellite, both of which have varied spatial and temporal resolutions,” she says. “We’re trying to bridge the gap by generating new data samples from images collected by these satellites that improve the resolution of the shape of water boundaries.”

The dataset used in this research consists of image data collected during a seven-year span (2015–2021) of 20 reservoirs in the United States, Australia, Mexico and other countries. The authors present a case study of Lake Tharthar, a salt water lake in Iraq, comparable in size to Great Salt Lake and facing similar climate and usage pressures.

“Using seven years of data from MODIS and Landsat 8, we evaluated our proposed Hydro-GAN model on Lake Tharthar’s shrinking and expansion behaviors,” Hosseinzadeh says. “Using Hydro-GAN, we were able to improve our predictions about the lake’s changing area.”

Such information is critical for the region’s hydrologists and environmental scientists, he says, who need to monitor seasonal dynamics and make decisions about how to sustain the lake’s water supply.

The scientists demonstrate Hydro-GAN can generate high-resolution data at historical time steps, which is otherwise unavailable, for situations where a large amount of historical data is needed for accurate forecasting.

“We think this will be a valuable tool for water managers and, moving forward with similar models, we can employ a multi-modal approach to provide data in addition to images, including information about topology, snow data amounts, streamflow, precipitation, temperature and other climate variables,” says Hosseinzadeh, who presents the research during USU’s 2024 Spring Runoff Conference March 26–27 in Logan, Utah.

Finds from Vaakunakylä, including wartime bullet casings, high-end porcelain and a child’s doll.

Archaeologists have excavated the former working-class neighborhood of Vaakunakylä near Oulu, west-central Finland and interviewed its previous inhabitants, revealing the rich heritage of this marginalized community.

Vaakunakylä was initially established by German troops stationed in Finland during the Second World War and was abandoned once they retreated from Finland between 1944–45.

Finns left homeless as a result of the conflict moved into the barracks in the late 1940s, forming a community that existed largely outside of the emerging Finnish welfare state.

As a result, the neighborhood was labeled as “criminal and restless,” leading to the marginalization of Vaakunakylä’s populace and the eventual demolition of the settlement against the residents’ wishes during the late 1980s.

“The outside perception of what might be referred to as ‘bad’ neighborhoods can be markedly different from the ways the communities see themselves,” says lead author of the research Dr. Oula Seitsonen. “Archaeology can offer a tool to investigate the realities of life in such places.”

To investigate Vaakunakylä from the point of view of its residents, Dr. Seitsonen and a team of researchers from the University of Oulu excavated at Vaakunakylä and spoke with former inhabitants of the community to collect their memories. Their results are published in the journal Antiquity.

3D model of the foundation of a German barrack turned into family housing in the post-war years.

“Archaeologies of 20th-century working-class communities and conflicts have been little-studied in Finland, and the Vaakunakylä project combines these both,” states Dr. Seitsonen. “Material heritage of the Vaakunakylä area was practically unknown before our research, and by studying a former Nazi military camp turned into a Finnish working-class neighborhood we can probe various neglected societal themes.”

Remains of buildings uncovered at the site highlight the efforts made by residents to improve the facilities at Vaakunakylä. Barracks were refurbished as family housing, and one was even transformed into a sauna.

Furthermore, material culture such as waste uncovered from rubbish pits reveals a higher standard of living than previously believed, with some households owning high-end porcelain sets.

The discovery of toys, children’s medication and dummies suggests that children at Vaakunakylä also enjoyed a good quality of life. In this way, the project gives a glimpse into the often-silenced lives of women and children in the past.

Interviews with former residents returned a generally positive view of the community, with many stating that life in Vaakunakylä was “good enough.”

Importantly, this means that the poor reputation of Vaakunakylä is largely unfounded and highlights the value of archaeological research in giving a voice to marginalized communities.

“Both the finds and the collected oral histories give a different and more nuanced picture of the Vaakunakylä community than the popular image of the area as a restless and criminal slum-like shantytown,” says Dr. Seitsonen. “We hope that this can have a healing aspect when the pent-up feelings are brought to the surface and discussed in public.”