Decades-old mystery in particle physics solved

Groundbreaking Discovery by TUM Researchers at CERN Reveals Formation of Deuterons

Technical University of Munich (TUM)

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The result: The protons and neutrons necessary for the formation of deuterons are released during the decay of very short-lived, highly energetic particle states (so-called resonances) and then bind together. The same holds true for their antimatter counterparts. The findings were published in the renowned journal Nature.

In proton collisions at the Large Hadron Collider (LHC) at CERN, temperatures arise that are more than 100,000 times hotter than the center of the Sun. Until now, it had been entirely unclear how fragile particles such as deuterons and antideuterons could survive under these conditions. In such an environment, light atomic nuclei like the deuteron – consisting of just one proton and one neutron – should in fact disintegrate immediately, since the binding force that holds them together is comparatively weak. Yet such nuclei had repeatedly been observed. It is now clear: about 90 percent of the observed (anti)deuterons are produced through this mechanism.

Better understanding of the universe

TUM particle physicist Prof. Laura Fabbietti, a researcher in the ORIGINS Cluster of Excellence and SFB1258, emphasizes: “Our result is an important step toward a better understanding of the ‘strong interaction’ – that fundamental force that binds protons and neutrons together in the atomic nucleus. The measurements clearly show: light nuclei do not form in the hot initial stage of the collision, but later, when the conditions have become somewhat cooler and calmer.”

Dr. Maximilian Mahlein, a researcher at Fabbietti’s Chair for Dense and Strange Hadronic Matter at the TUM School of Natural Sciences, explains: “Our discovery is significant not only for fundamental nuclear physics research. Light atomic nuclei also form in the cosmos – for example in interactions of cosmic rays. They could even provide clues about the still-mysterious dark matter. With our new findings, models of how these particles are formed can be improved and cosmic data interpreted more reliably.”

Further information:

CERN (Conseil Européen pour la Recherche Nucléaire) is the world’s largest research center for particle physics. It is located on the border between Switzerland and France near Geneva. Its centerpiece is the LHC, a 27-kilometer-long underground ring accelerator. In it, protons collide at nearly the speed of light. These collisions recreate conditions similar to those that existed just after the Big Bang – temperatures and energies that do not occur anywhere in everyday life. Researchers can thus investigate how matter is structured at its most fundamental level and which natural laws apply there.

Among the experiments at the LHC, ALICE (A Large Ion Collider Experiment) is specifically designed to study the properties of the so-called strong interaction – the force that holds protons and neutrons together in atomic nuclei. ALICE acts like a giant camera, capable of precisely tracking and reconstructing up to 2000 particles created in each collision. The aim is to reconstruct the conditions of the universe’s earliest fractions of a second – and thereby better understand how a soup of quarks and gluons first gave rise to stable atomic nuclei and ultimately to matter.

The ORIGINS Cluster of Excellence investigates the formation and evolution of the universe and its structures – from galaxies, stars, and planets to the very building blocks of life. ORIGINS traces the path from the smallest particles in the early universe to the emergence of biological systems. Examples include the search for conditions that could enable extraterrestrial life and a deeper understanding of dark matter. In May 2025, the second funding phase of the cluster, jointly proposed by TUM and Ludwig-Maximilians-Universität München (LMU), was approved as part of the highly competitive Excellence Strategy of the German federal and state governments.

The Collaborative Research Center “Neutrinos and Dark Matter in Astro- and Particle Physics” (SFB 1258) focuses on fundamental physics, where the weak interaction, one of the four fundamental forces of nature, is central. The third funding period of the SFB1258 started in January 2025.

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Journal

Nature

DOI

10.1038/s41586-025-09775-5 

Method of Research

Experimental study

Subject of Research

Not applicable

Article Title

Observation of deuteron and antideuteron formation from resonance-decay nucleons

Article Publication Date

11-Dec-2025

 

Summer predictability barrier phenomenon of Indian Ocean Dipole events

Institute of Atmospheric Physics, Chinese Academy of Sciences

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The IOD phenomenon (positive phase) and associated mechanism

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Credit: LIU Da

The Indian Ocean Dipole (IOD) is a dominant mode of interannual variability in the tropical Indian Ocean. It influences temperature and precipitation patterns across the Indian Ocean region and, through the South Asian summer monsoon and planetary waves, also affects weather and climate in other parts of the globe. However, accurate prediction of IOD events remains a major challenge, constrained by limitations in the accuracy of numerical models, deviations in initial field data, and inherent technical bottlenecks in seasonal to annual predictions.

 

To address this scientific issue, Ru Yi from Shanxi Meteorological Information Center, and Dr. Liu Da from the National Meteorological Center, in collaboration with Professor Duan Wansuo from the Institute of Atmospheric Physics, Chinese Academy of Sciences, employed an innovative data analysis method based on predictability dynamics. By examining the evolution of initial errors, they systematically investigated the predictability barrier of both symmetric and asymmetric IOD events, as well as the dynamic mechanisms underlying the growth of prediction errors. Their findings were recently published in Atmospheric and Oceanic Science Letters, in a paper titled “The summer predictability barrier phenomena of symmetric and asymmetric Indian Ocean dipole events.”

 

The study confirms the presence of a summer predictability barrier (SPB) in predictions of both symmetric and asymmetric IOD events. It further demonstrates that interactions between specific spatial patterns of initial errors and the intrinsic asymmetry of IOD events lead to significant differences in the frequency of SPB occurrence, depending on the forecast start time.

Dr. Liu Da, corresponding author, explained that the research focuses on how initial errors influence IOD predictions, and identifies the spatial characteristics of those errors that most strongly trigger the predictability barrier in both types of IOD events. These insights offer a new perspective for understanding the dynamical mechanisms of the IOD predictability barrier. They also help in identifying sensitive regions for targeted observations related to IOD forecasting, thereby providing a scientific basis and practical guidance for improving the prediction of IOD events.

 

 

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Journal

Atmospheric and Oceanic Science Letters

DOI

10.1016/j.aosl.2025.100733 

Fairness in AI: Study shows central role of human decision-making

In addition to helping in a practical way, recommendations based on AI should above all be fair. A new study by researchers at TU Graz, Uni Graz and the Know Center shows how this can be achieved.

Graz University of Technology

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AI-supported recommender systems should provide users with the best possible suggestions for their enquiries. These systems often have to serve different target groups and take other stakeholders into account who also influence the machine’s response: e.g. service providers, municipalities or tourism associations. So how can a fair and transparent recommendation be achieved here? Researchers from Graz University of Technology (TU Graz), the University of Graz and Know Center investigated this using a cycling tour app from the Graz-based start-up Cyclebee. They conducted research into how the diversity of human needs can be taken into account by AI. The study, which was awarded a Mind the Gap research prize for gender and diversity by TU Graz, was funded by the Styrian Future Fund.

Impact on numerous groups

“AI-supported recommender systems can have a major influence on purchasing decisions or the development of guest and visitor numbers,” says Bernhard Wieser from the Institute of Human-Centred Computing at TU Graz. “They provide information on services or places worth visiting and should ideally take individual needs into account. However, there is a risk that certain groups or aspects are under-represented.” In this context, an important finding of the research was that the targeted fairness is a multi-stakeholder problem, as not only end users play a role, but also numerous other actors.

These include service providers such as hotels and restaurants along the routes and third parties such as municipalities and tourism organisations. And then there are stakeholders who don’t even come into contact with the app but are nevertheless affected, such as local residents who could feel the effects of overtourism. According to the study, reconciling all these stakeholders cannot be solved with technology alone. “If the app is to deliver the fairest possible results for everyone, the fairness goals must be clearly defined in advance. And that is a very human process that starts with deciding which target group to serve,” says Bernhard Wieser.

Involving all actors in the design

This target group decision influences the selection of the AI training data, its weighting and further steps in the algorithm design. In order to involve the other stakeholders as well, the researchers propose the use of participatory design, in which all actors are involved in order to harmonise their ideas as well as possible. “Ultimately, however, you have to decide in favour of something, so it’s up to the individual,” says Dominik Kowald from the Fair AI group at the Know Center research centre and the Institute of Digital Humanities at the University of Graz. “Not everything can be optimised at the same time with an AI model. There is always a trade-off.”

Ultimately, it is up to the developers to decide what this trade-off looks like, but according to the researchers, it is important for end users and providers that there is transparency. Users want to be able to adapt or influence the recommendations, and providers want to know the rules according to which routes have been set or providers ranked. “Our study results are intended to support software developers in their work in the form of design guidelines, and we also want to provide guidelines for political decision-makers,” says Bernhard Wieser. “It is important that we make recommender systems increasingly available to smaller, regional players thanks to technological developments. This would make it possible to develop fair solutions and thus create counter-models to multinational corporations, which would sustainably strengthen regional value creation.”

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Journal

Frontiers in Big Data

DOI

10.3389/fdata.2025.1632766 

Method of Research

Case study

Subject of Research

Not applicable

Article Title

Multistakeholder fairness in tourism: what can algorithms learn from tourism management?

Climate research reveals global warming intensifies temperature "roller coasters”

Institute of Atmospheric Physics, Chinese Academy of Sciences

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Global Warming Intensifies Temperature Roller Coasters.

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Credit: Liu Qi

A new study published in Nature Climate Change reveals that rapid, large-scale fluctuations in temperature from one day to the next have intensified under global warming, representing a distinct climate hazard with significant impacts on human health. This increasing volatility creates a weather pattern akin to a climate roller coaster, with populations experiencing more frequent and sharp swings between temperature extremes.

Conducted by scientists from Nanjing University and the Institute of Atmospheric Physics (IAP) at the Chinese Academy of Sciences (CAS), the research defines these events as occurring when the temperature difference between two consecutive days exceeds the 90th percentile of historical records. The analysis finds that such extreme day-to-day temperature changes have become more frequent and intense in low- to mid-latitude regions, with human-driven greenhouse gas emissions confirmed as the primary cause, based on optimal fingerprinting method.

Projections from climate models indicate this trend will continue. Under a high-emission scenario, the frequency and total intensity of these events could increase by approximately 17% and 20% by 2100, affecting regions where over 80% of the global population resides.

The study further explains the physical mechanism based on the composite change index method. "Global warming exacerbates soil drought and intensifies variability in sea-level pressure and soil moisture," said Xu Zhongfeng from IAP/CAS, an author of this study. "These processes reduce the surface's heat capacity and amplify fluctuations in cloud cover and radiation, ultimately leading to more rapid temperature swings."

Critically, the health impact of these sudden roller-coaster swings exceeds that of other temperature variables. Based on mortality data from Jiangsu Province, China, and the United States, the research finds the association with all-cause mortality follows a near-exponential pattern, particularly raising risks for cardiovascular and respiratory diseases.

The authors stress that this phenomenon is distinct from traditional extreme temperature indices. "This study establishes extreme day-to-day temperature change as a distinct and independent category of extreme climate event," said Fu Congbin, a CAS academician from Nanjing University and the corresponding author of this study. "Global warming is systematically intensifying these swings in the world's most populous regions, challenging public health and ecosystem stability. We recommend that relevant international scientific organizations formally recognize it as a new type of extreme weather event."

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Journal

Nature Climate Change

DOI

10.1038/s41558-025-02486-9