Praxis Archives – Page 34 of 181

What’s in the box?

A neural network probing a black box of complex final states — **Magnifying the unexpected** Artist’s impression of a neural network probing a black box of complex final states at the LHC. Credit: B Nachman (inspired by arxiv:0811.4622)

The need for innovation in machine learning (ML) transcends any single experimental collaboration, and requires more in-depth work than can take place at a workshop. Data challenges, wherein simulated “black box” datasets are made public, and contestants design algorithms to analyse them, have become essential tools to spark interdisciplinary collaboration and innovation. Two have recently concluded. In both cases, contestants were challenged to use ML to figure out “what’s in the box?”

LHC Olympics

The LHC Olympics (LHCO) data challenge was launched in autumn 2019, and the results were presented at the ML4Jets and Anomaly Detection workshops in spring and summer 2020. A final report summarising the challenge was posted to arXiv earlier this year, written by around 50 authors from a variety of backgrounds in theory, the ATLAS and CMS experiments, and beyond. The name of this community effort was inspired by the first LHC Olympics that took place more than a decade ago, before the start of the LHC. In those olympics, researchers were worried about being able to categorise all of the new particles that would be discovered when the machine turned on. Since then, we have learned a great deal about nature at TeV energy scales, with no evidence yet for new particles or forces of nature. The latest LHC Olympics focused on a different challenge – being able to find new physics in the first place. We now know that new physics must be rare and not exactly like what we expected.

In order to prepare for rare and unexpected new physics, organisers Gregor Kasieczka (University of Hamburg), Benjamin Nachman (Lawrence Berkeley National Laboratory) and David Shih (Rutgers University) provided a set of black-box datasets composed mostly of Standard Model (SM) background events. Contestants were charged with identifying any anomalous events that would be a sign of new physics. These datasets focused on resonant anomaly detection, whereby the anomaly is assumed to be localised – a “bump hunt”, in effect. This is a generic feature of new physics produced from massive new particles: the reconstructed parent mass is the resonant feature. By assuming that the signal is localised, one can use regions away from the signal to estimate the background. The LHCO provided one R&D dataset with labels and three black boxes to play with: one with an anomaly decaying into two two-pronged resonances, one without an anomaly, and one with an anomaly featuring two different decay modes (a dijet decay X → qq and a trijet decay X → gY, Y → qq). There are currently no dedicated searches for these signals in LHC data.

No labels

About 20 algorithms were deployed on the LHCO datasets, including supervised learning, unsupervised learning, weakly supervised learning and semi-supervised learning. Supervised learning is the most widely used method across science and industry, whereby each training example has a label: “background” or “signal”. For this challenge, the data do not have labels as we do not know exactly what we are looking for, and so strategies trained with labels from a different dataset often did not work well. By contrast, unsupervised learning generally tries to identify events that are rarely or never produced by the background; weakly supervised methods use some context from data to provide noisy labels; and semi-supervised methods use some simulation information in order to have a partial set of labels. Each method has its strengths and weaknesses, and multiple approaches are usually needed to achieve a broad coverage of possible signals.

The Dark Machines data challenge focused on developing algorithms broadly sensitive to non-resonant anomalies

The best performance on the first black box in the LHCO challenge, as measured by finding and correctly characterising the anomalous signals, was by a team of cosmologists at Berkeley (George Stein, Uros Seljak and Biwei Dai) who compared the phase-space density between a sliding signal region and sidebands (see “Olympian algorithm” figure). Overall, the algorithms did well on the R&D dataset, and some also did well on the first black box, with methods that made use of likelihood ratios proving particularly effective. But no method was able to detect the anomalies in the third black box, and many teams reported a false signal for the second black box. This “placebo effect’’ illustrates the need for ML approaches to have an accurate estimation of the background and not just a procedure for identifying signals. The challenge for the third black box, however, required algorithms to identify multiple clusters of anomalous events rather than a single cluster. Future innovation is needed in this department.

Dark Machines

A second data challenge was launched in June 2020 within the Dark Machines initiative. Dark Machines is a research collective of physicists and data scientists who apply ML techniques to understand the nature of dark matter – as we don’t know the nature of dark matter, it is critical to search broadly for its anomalous signatures. The challenge was organised by Sascha Caron (Radboud University), Caterina Doglioni (University of Lund) and Maurizio Pierini (CERN), with notable contributions from Bryan Ostidiek (Harvard University) in the development of a common software infrastructure, and Melissa van Beekveld (University of Oxford) for dataset generation. In total, 39 participants arranged in 13 teams explored various unsupervised techniques, with each team submitting multiple algorithms.

The anomaly score — **Olympian algorithm** The “anomaly score”, α, as a function of the invariant mass of the leading two jets of events in “black box 1” of the LHCO data challenge, in the analysis of Stein, Seljak and Dai, who used an early form of a technique that is now called “Gaussianising iterative slicing”. A number of anomalous events are seen near 3750 GeV. Credit: arXiv:2101.08320

By contrast with LHCO, the Dark Machines data challenge focused on developing algorithms broadly sensitive to non-resonant anomalies. Good examples of non-resonant new physics include many supersymmetric models and models of dark matter – anything where “invisible” particles don’t interact with the detector. In such a situation, resonant peaks become excesses in the tails of the missing-transverse-energy distribution. Two datasets were provided: R&D datasets including a concoction of SM processes and many signal samples for contestants to develop their approaches on; and a black-box dataset mixing SM events with events from unspecified signal processes. The challenge has now formally concluded, and its outcome was posted on arXiv in May, but the black-box has not been opened to allow the community to continue to test ideas on it.

A wide variety of unsupervised methods have been deployed so far. The algorithms use diverse representations of the collider events (for example, lists of particle four-momenta, or physics quantities computed from them), and both implicit and explicit approaches for estimating the probability density of the background (for example, autoencoders and “normalising flows”). While no single method universally achieved the highest sensitivity to new-physics events, methods that mapped the background to a fixed point and looked for events that were not described well by this mapping generally did better than techniques that had a so-called dynamic embedding. A key question exposed by this challenge that will inspire future innovation is how best to tune and combine unsupervised machine-learning algorithms in a way that is model independent with respect to the new physics describing the signal.

The enthusiastic response to the LHCO and Dark Machines data challenges highlights the important future role of unsupervised ML at the LHC and elsewhere in fundamental physics. So far, just one analysis has been published – a dijet-resonance search by the ATLAS collaboration using weakly-supervised ML – but many more are underway, and these techniques are even being considered for use in the level-one triggers of LHC experiments (see Hunting anomalies with an AI trigger). And as the detection of outliers also has a large number of real-world applications, from fraud detection to industrial maintenance, fruitful cross-talk between fundamental research and industry is possible.

The LHCO and Dark Machines data challenges are a stepping stone to an exciting experimental programme that is just beginning.

Stealing theorists’ lunch

John Ellis and Anima Anandkumar — **John Ellis** is Clerk Maxwell Professor of Theoretical Physics at King’s College London, and **Anima Anandkumar** is Bren professor at Caltech and a director of machine-learning research at NVIDIA. Credit: J Guillaune/CERN-PHOTO-201410-199-5/A Anandkumar

How might artificial intelligence make an impact on theoretical physics?

John Ellis (JE): To phrase it simply: where do we go next? We have the Standard Model, which describes all the visible matter in the universe successfully, but we know dark matter must be out there. There are also puzzles, such as what is the origin of the matter in the universe? During my lifetime we’ve been playing around with a bunch of ideas for tackling those problems, but haven’t come up with solutions. We have been able to solve some but not others. Could artificial intelligence (AI) help us find new paths towards attacking these questions? This would be truly stealing theoretical physicists’ lunch.

Anima Anandkumar (AA): I think the first steps are whether you can understand more basic physics and be able to come up with predictions as well. For example, could AI rediscover the Standard Model? One day we can hope to look at what the discrepancies are for the current model, and hopefully come up with better suggestions.

JE: An interesting exercise might be to take some of the puzzles we have at the moment and somehow equip an AI system with a theoretical framework that we physicists are trying to work with, let the AI loose and see whether it comes up with anything. Even over the last few weeks, a couple of experimental puzzles have been reinforced by new results on B-meson decays and the anomalous magnetic moment of the muon. There are many theoretical ideas for solving these puzzles but none of them strike me as being particularly satisfactory in the sense of indicating a clear path towards the next synthesis beyond the Standard Model. Is it imaginable that one could devise an AI system that, if you gave it a set of concepts that we have, and the experimental anomalies that we have, then the AI could point the way?

AA: The devil is in the details. How do we give the right kind of data and knowledge about physics? How do we express those anomalies while at the same time making sure that we don’t bias the model? There are anomalies suggesting that the current model is not complete – if you are giving that prior knowledge then you could be biasing the models away from discovering new aspects. So, I think that delicate balance is the main challenge.

JE: I think that theoretical physicists could propose a framework with boundaries that AI could explore. We could tell you what sort of particles are allowed, what sort of interactions those could have and what would still be a well-behaved theory from the point of view of relativity and quantum mechanics. Then, let’s just release the AI to see whether it can come up with a combination of particles and interactions that could solve our problems. I think that in this sort of problem space, the creativity would come in the testing of the theory. The AI might find a particle and a set of interactions that would deal with the anomalies that I was talking about, but how do we know what’s the right theory? We have to propose some other experiments that might test it – and that’s one place where the creativity of theoretical physicists will come into play.

AA: Absolutely. And many theories are not directly testable. That’s where the deeper knowledge and intuition that theoretical physicists have is so critical.

Is human creativity driven by our consciousness, or can contemporary AI be creative?

AA: Humans are creative in so many ways. We can dream, we can hallucinate, we can create – so how do we build those capabilities into AI? Richard Feynman famously said “What I cannot create, I do not understand.” It appears that our creativity gives us the ability to understand the complex inner workings of the universe. With the current AI paradigm this is very difficult. Current AI is geared towards scenarios where the training and testing distributions are similar, however, creativity requires extrapolation – being able to imagine entirely new scenarios. So extrapolation is an essential aspect. Can you go from what you have learned and extrapolate new scenarios? For that we need some form of invariance or understanding of the underlying laws. That’s where physics is front and centre. Humans have intuitive notions of physics from early childhood. We slowly pick them up from physical interactions with the world. That understanding is at the heart of getting AI to be creative.

JE: It is often said that a child learns more laws of physics than an adult ever will! As a human being, I think that I think. I think that I understand. How can we introduce those things into AI?

Could AI rediscover the Standard Model?

AA: We need to get AI to create images, and other kinds of data it experiences, and then reason about the likelihood of the samples. Is this data point unlikely versus another one? Similarly to what we see in the brain, we recently built feedback mechanisms into AI systems. When you are watching me, it’s not just a free-flowing system going from the retina into the brain; there’s also a feedback system going from the inferior temporal cortex back into the visual cortex. This kind of feedback is fundamental to us being conscious. Building these kinds of mechanisms into AI is the first step to creating conscious AI.

JE: A lot of the things that you just mentioned sound like they’re going to be incredibly useful going forward in our systems for analysing data. But how is AI going to devise an experiment that we should do? Or how is AI going to devise a theory that we should test?

AA: Those are the challenging aspects for an AI. A data-driven method using a standard neural network would perform really poorly. It will only think of the data that it can see and not about data that it hasn’t seen – what we call “zero-short generalisation”. To me, the past decade’s impressive progress is due to a trinity of data, neural networks and computing infrastructure, mainly powered by GPUs [graphics processing units], coming together: the next step for AI is a wider generalisation to the ability to extrapolate and predict hitherto unseen scenarios.

Across the many tens of orders of magnitude described by modern physics, new laws and behaviours “emerge” non-trivially in complexity (see Emergence). Could intelligence also be an emergent phenomenon?

JE: As a theoretical physicist, my main field of interest is the fundamental building blocks of matter, and the roles that they play very early in the history of the universe. Emergence is the word that we use when we try to capture what happens when you put many of these fundamental constituents together, and they behave in a way that you could often not anticipate if you just looked at the fundamental laws of physics. One of the interesting developments in physics over the past generation is to recognise that there are some universal patterns that emerge. I’m thinking, for example, of phase transitions that look universal, even though the underlying systems are extremely different. So, I wonder, is there something similar in the field of intelligence? For example, the brain structure of the octopus is very different from that of a human, so to what extent does the octopus think in the same way that we do?

AA: There’s a lot of interest now in studying the octopus. From what I learned, its intelligence is spread out so that it’s not just in its brain but also in its tentacles. Consequently, you have this distributed notion of intelligence that still works very well. It can be extremely camouflaged – imagine being in a wild ocean without a shell to protect yourself. That pressure created the need for intelligence such that it can be extremely aware of its surroundings and able to quickly camouflage itself or manipulate different tools.

JE: If intelligence is the way that a living thing deals with threats and feeds itself, should we apply the same evolutionary pressure to AI systems? We threaten them and only the fittest will survive. We tell them they have to go and find their own electricity or silicon or something like that – I understand that there are some first steps in this direction, computer programs competing with each other at chess, for example, or robots that have to find wall sockets and plug themselves in. Is this something that one could generalise? And then intelligence could emerge in a way that we hadn’t imagined?

Similarly to what we see in the brain, we recently built feedback mechanisms into AI systems

AA: That’s an excellent point. Because what you mentioned broadly is competition – different kinds of pressures that drive towards good, robust objectives. An example is generative adversarial models, which can generate very realistic looking images. Here you have a discriminator that challenges the generator to generate images that look real. These kinds of competitions or games are getting a lot of traction and we have now passed the Turing test when it comes to generating human faces – you can no longer tell very easily whether it is generated by AI or if it is a real person. So, I think those kinds of mechanisms that have competition built into the objective they optimise are fundamental to creating more robust and more intelligent systems.

JE: All this is very impressive – but there are still some elements that I am missing, which seem very important to theoretical physics. Take chess: a very big system but finite nevertheless. In some sense, what I try to do as a theoretical physicist has no boundaries. In some sense, it is infinite. So, is there any hope that AI would eventually be able to deal with problems that have no boundaries?

AA: That’s the difficulty. These are infinite-dimensional spaces… so how do we decide how to move around there? What distinguishes an expert like you from an average human is that you build your knowledge and develop intuition – you can quickly make judgments and find which narrow part of the space you want to work on compared to all the possibilities. That’s the aspect that is so difficult for AI to figure out. The space is enormous. On the other hand, AI does have a lot more memory, a lot more computational capacity. So can we create a hybrid system, with physicists and machine learning in tandem, to help us harness the capabilities of both AI and humans together? We’re currently exploring theorem provers: can we use the theorems that humans have proven, and then add reinforcement learning on top to create very fast theorem solvers? If we can create such fast theorem provers in pure mathematics, I can see them being very useful for understanding the Standard Model and the gaps and discrepancies in it. It is much harder than chess, for example, but there are exciting programming frameworks and data sets available, with efforts to bring together different branches of mathematics. But I don’t think humans will be out of the loop, at least for now.

Quantum gravity in the Vatican

Gabriele Gionti with Pope Francis — **Missionary of science** Gabriele Gionti presents Pope Francis with a journal containing a recent publication on black holes, gravitational waves and spacetime singularities. Credit: Vatican Observatory

“Our job is to be part of the scientific community and show that there can be religious people and priests who are scientists,” says Gabriele Gionti, a Roman Catholic priest and theoretical physicist specialising in quantum gravity who is resident at the Vatican Observatory.

“Our mission is to do good science,” agrees Guy Consolmagno, a noted planetary scientist, Jesuit brother and the observatory’s director. “I like to say we are missionaries of science to the believers.”

Not only missionaries of faith, then, but also of science. And there are advantages.

“At the Vatican Observatory, we don’t have to write proposals, we don’t have to worry about tenure and we don’t have to have results in three years to get our money renewed,” says Consolmagno, who is directly appointed by the Pope. “It changes the nature of the research that is available to us.”

“Here I have had time to just study,” says Gionti, who explains that he was able to extend his research to string theory as a result of this extra freedom. “If you are a postdoc or under tenure, you don’t have this opportunity.”

“I remember telling a friend of mine that I don’t have to write grant proposals, and he said, ‘how do I get in on this?’” jokes Consolmagno, a native of Detroit. “I said that he needed to take a vow of celibacy. He replied, ‘it’s worth it!’.”

Cannonball moment

Clad in T-shirts, Gionti and Consolmagno don’t resemble the priests and monks seen in movies. They are connected to monastic tradition, but do not withdraw from the world. As well as being full-time physicists, both are members of the Society of Jesus – a religious order that traces its origin to 1521, when Saint Ignatius of Loyola was struck in the leg by a cannonball at the Battle of Pamplona. Today they help staff at an institution that was founded in 1891, though its origins arguably date back to attempts to fix the date for Easter in 1582.

“It was at the end of the 19th century that the myth began that the church was anti-science, and they would use Galileo as the excuse,” says Consolmagno, explaining that the Pope at the time, Pope Leo XIII, wanted to demonstrate that faith and science were fully compatible. “The first thing that the Vatican Observatory did was to take part in the Carte du Ciel programme,” he says, hinting at a secondary motivation. “Every national observatory was given a region of the sky. Italy was given one region and the Vatican was given another. So, de facto, the Vatican became seen as an independent nation state.”

Guy Consolmagno poses with a summer student — **The Pope’s astronomer** Guy Consolmagno, the director of the Vatican Observatory, poses with a summer student. Credit: Vatican Observatory

The observatory quickly established itself as a respected scientific organisation. Though it is staffed by priests and brothers, there is an absolute rule that science comes first, says Consolmagno, and the stereotypical work of a priest or monk is actually a temptation to be resisted. “Day-to-day life as a scientist can be tedious, and it can be a long time until you see a reward, but pastoral life can be rewarding immediately,” he explains.

Consolmagno was a planetary scientist for 20 years before becoming a Jesuit. By contrast, Gionti, who hails from Capua in Italy, joined after his first postdoc at UC Irvine in California. Neither reports encountering professional prejudice as a result of their vocation. “I think that’s a generational thing,” says Consolmagno. “Scientists working in the 1970s and 1980s were more likely to be anti-religious, but nowadays it’s not the case. You are looked on as part of the multicultural nature of the field.”

And besides, antagonism between science and religion is largely based on a false dichotomy, says Consolmagno. “The God that many atheists don’t believe in is a God that we also don’t believe in.”

The observatory’s director pushes back hard on the idea that faith is incompatible with physics. “It doesn’t tell me what science to do. It doesn’t tell me what the questions and answers are going to be. It gives me faith that I can understand the universe using reason and logic.”

Surprised by CERN

Due to light pollution in Castel Gandolfo, a new outpost of the Vatican Observatory was established in Tucson, Arizona, in 1980. A little later in the day, when the Sun was rising there, I spoke to Paul Gabor – an astrophysicist, Jesuit priest and deputy director for the Tucson observatory. Born in Košice, Slovakia, Gabor was a summer student at CERN in 1992, working on the development of the electromagnetic calorimeter of the ATLAS experiment, a project he later continued in Grenoble, thanks to winning a scholarship at the university. “We were making prototypes and models and software. We tested the actual physical models in a couple of test-beam runs – that was fun,” he recalls.

Gabor was surprised at how he found the laboratory. “It was an important part of my journey, because I was quite surprised that I found CERN to be full of extremely nice people. I was expecting everyone to be driven, ambitious, competitive and not necessarily collaborative, but people were very open,” he says. “It was a really good human experience for me.”

“When I finally caved in and joined the Jesuit order in 1995, I always thought, well, these scientists definitely are a group that I got to know and love, and I would like to, in one way or another, be a minister to them and be involved with them in some way.”

“Something that I came to realise, in a beginning, burgeoning kind of way at CERN, is the idea of science being a spiritual journey. It forms your personality and your soul in a way that any sustained effort does.”

Scientific athletes

“Experimental science can be a journey to wisdom,” says Gabor. “We are subject to constant frustration, failure and errors. We are confronted with our limitations. This is something that scientists have in common with athletes, for example. These long labours tend to make us grow as human beings. I think this point is quite important. In a way it explains my experience at CERN as a place full of nice, generous people.”

Surprisingly, however, despite being happy with life as a scientific religious and religious scientist, Gabor is not recruiting.

“There is a certain tendency to abandon science to join the priesthood or religious life,” he says. “This is not necessarily the best thing to do, so I urge a little bit of restraint. Religious zeal is a great thing, but if you are in the third year of a doctorate, don’t just pack up your bags and join a seminary. That is not a very prudent thing to do. That is to nobody’s benefit. This is a scenario that is all too common unfortunately.”

Consolmagno also offers words of caution. “50% of Jesuits leave the order,” he notes. “But this is a sign of success. You need to be where you belong.”

But Gionti, Consolmagno and Gabor all agree that, if properly discerned, the life of a scientific religious is a rewarding one in a community like the Vatican Observatory. They describe a close-knit group with a common purpose and little superficiality.

“Faith gives us the belief that the universe is good and worth studying,” says Consolmagno. “If you believe that the universe is good, then you are justified in spending your life studying things like quarks, even if it is not useful. Believing in God gives you a reason to study science for the sake of science.”

Jürgen Hans Garlef Körner 1939–2021

Jürgen Körner in discussion with Carlo Becchi at the 1996 Triangle School on Particle Physics, Prague. Credit: S Groote

Jürgen G Körner, a well-known German theoretical physicist at the Johannes Gutenberg University in Mainz, passed away after a brief illness on 16 July 2021 at the age of 82.

Jürgen was born in Hong Kong in 1939, as the fourth child of a Hamburg merchant’s family. After the family returned to Germany in 1949, he attended the secondary school in Blankenese and studied physics at the Technical University of Berlin and the University of Hamburg. He received his PhD from Northwestern University, Illinois, in 1966 under Richard Capps. He then held research positions at Imperial College London, Columbia University, the University of Heidelberg and DESY. He completed his habilitation at the University of Hamburg in 1976.

In 1982 Jürgen became a professor of theoretical particle physics at Johannes Gutenberg University, where he remained for the rest of his career. His research interests included the phenomenology of elementary particles, heavy-quark physics, spin physics, radiative corrections and exclusive decay processes. He made pioneering contributions to the heavy-quark effective theory with applications to exclusive hadron decays. He also studied mass and spin effects in inclusive and exclusive processes in the Standard Model, and developed the helicity formalism describing angular distributions in exclusive hadron decays. Jürgen’s other notable contributions include the Körner–Pati–Woo theorem providing selection rules for baryon transitions and a relativistic formalism for electromagnetic excitations of nucleon resonances.

Jürgen collaborated with theoretical physicists worldwide and published about 250 papers in leading physics journals, including several influential reviews on the physics of baryons. He also contributed to the development of strong relations between German and Russian particle physicists. Together with colleagues from the Joint Institute for Nuclear Research, Dubna, and leading German and Russian universities he initiated a series of international workshops on problems in heavy-quark physics (Dubna: 1993–2019, Bad Honnef: 1994 and Rostock: 1997).

Jürgen was a cheerful person, attentive to the needs of his colleagues and friends, and always ready to help. He liked to travel and was actively involved in sports, especially football and cycling. Despite various commitments, he always found time for discussions. He cherished good conversations about physics and made a lasting impact on our lives. We will always remember him.

Steven Weinberg 1933–2021

Steven Weinberg, one of the greatest theoretical physicists of all time, passed away on 23 July, aged 88. He revolutionised particle physics, quantum field theory and cosmology with conceptual breakthroughs that still form the foundation of our understanding of physical reality.

Weinberg is well known for the unified theory of weak and electromagnetic forces, which earned him the Nobel Prize in Physics in 1979, jointly awarded with Sheldon Glashow and Abdus Salam, and led to the prediction of the Z and W vector bosons, later discovered at CERN in 1983. His breakthrough was the realisation that some new theoretical ideas, initially believed to play a role in the description of nuclear strong interactions, could instead explain the nature of the weak force. “Then it suddenly occurred to me that this was a perfectly good sort of theory, but I was applying it to the wrong kind of interaction. The right place to apply these ideas was not to the strong interactions, but to the weak and electromagnetic interactions,” as he later recalled. With his work, Weinberg had made the next step in the unification of physical laws, after Newton understood that the motion of apples on Earth and planets in the sky are governed by the same gravitational force, and Maxwell understood that electric and magnetic phenomena are the expression of a single force.

In my life, I have built only one model
Steven Weinberg

In his research, Weinberg always focused on an overarching vision of physics and not on a model description of any single phenomenon. At a lunch among theorists, when a colleague referred to him as a model builder, he jokingly retorted: “I am not a model builder. In my life, I have built only one model.” Indeed, Weinberg’s greatest legacy is his visionary approach to vast areas of physics, in which he starts from complex theoretical concepts, reinterprets them in original ways, and applies them to the description of the physical world. A good example is his construction of effective field theories, which are still today the basic tool to understand the Standard Model of particle interactions. His inimitable way of thinking has been the inspiration and guidance for generations of physicists, and it will certainly continue to serve future generations.

Steven Weinberg is among the very few individuals who, during the course of the history of civilisation, have radically changed the way we look at the universe.

Latvia to become Associate Member of CERN

Lativan prime minister Krišjānis Kariņš and Fabiola Gianotti — **Strengthening ties** Lativan prime minister Krišjānis Kariņš and CERN Director-General Fabiola Gianotti on the occasion of the remote signature of the agreement. Credit: CERN-PHOTO-202104-049-17

On 14 April, representatives of CERN and the Republic of Latvia gathered in a virtual ceremony to sign an agreement admitting Latvia as an Associate Member State.

Latvia, which is the third of the Baltic States to join CERN in recent years after Lithuania and Estonia, first became involved with CERN activities in the early 1990s. Latvian researchers have since participated in many CERN projects, including contributions to the CMS hadron calorimeter and, more recently, participation in the Future Circular Collider study.

“As we become CERN’s newest Associate Member State, we look forward to enhancing our contribution to the Organization’s major scientific endeavours, as well as to investing the unparalleled scientific and technological excellence gained by this membership in further building the economy and well-being of our societies,” said Latvian prime minister Krišjānis Kariņš.

As an Associate Member State, Latvia will be entitled to appoint representatives to attend meetings of the CERN Council and Finance Committee. Its nationals will be eligible for staff positions, fellowships and studentships, and its industries will be entitled to bid for CERN contracts, increasing opportunities for collaboration in advanced technologies.

“We are delighted to welcome Latvia as a new Associate Member State,” said CERN Director-General Fabiola Gianotti. “The present agreement contributes to strengthening the ties between CERN and Latvia, thereby offering opportunities for the further growth of particle physics in Latvia through partnership in research, technological development and education.”

Sustainable high-energy physics

**Sustainable HEP** Before the pandemic, a typical high-energy physics researcher crossed the globe several times a year for conferences, collaboration meetings and detector shifts. Credit: SustHEP 2021

COVID-19 put the community on a steep learning curve regarding new forms of online communication and collaboration. Before the pandemic, a typical high-energy physics (HEP) researcher was expected to cross the world several times a year for conferences, collaboration meetings and detector shifts, at the cost of thousands of dollars and a sizeable carbon footprint. The online workshop Sustainable HEP — a new initiative this year — attracted more than 300 participants from 45 countries from 28 to 30 June to discuss how the lessons learned in the past two years might help HEP transition to a more sustainable future.

The first day of the workshop focused on how new forms of online interaction could change our professional travel culture. Shaun Hotchkiss (University of Auckland) stressed in a session dedicated to best-practice examples that the purpose of online meetings should not simply be to emulate traditional 20th-century in-person conferences and collaboration meetings. Instead, the community needs to rethink what virtual scientific exchange could look like in the 21st century. This might, for instance, include replacing traditional live presentations by pre-recorded talks that are pre-watched by the audience at their own convenience, leaving more precious conference time for in-depth discussions and interactions among the participants.

Social justice

The second day highlighted social-justice issues, and the potential for greater inclusivity using online formats. Alice Gathoni (British Institute in Eastern Africa) powerfully described the true meaning of online meetings to her: everyone wants to belong. It was only during the first online meetings during the pandemic that she truly felt a real sense of belonging to the global scientific community.

The third day was dedicated to existing sustainability initiatives and new technologies. Mike Seidel (PSI) presented studies on energy-recovery linacs and discussed energy-management concepts for future colliders, including daily “standby modes”. Other options include beam dynamics explicitly designed to maximise the ratio of luminosity to power, more efficient radio-frequency cavities, the use of permanent magnets, and high-temperature superconductor cables and cavities. He concluded his talk by asking thought-provoking questions such as whether the HEP community should engage with its international networks to help establish sustainable energy-supply solutions.

The workshop ended by drafting a closing statement that calls upon the HEP community to align its activities with the Paris Climate Agreement and the goal of limiting global warming to 1.5 degrees. This statement can be signed by members of the HEP community until 20 August.

Resistive Gaseous Detectors: Designs, Performance, and Perspectives

The first truly resistive gaseous detector was invented by Rinaldo Santonico and Roberto Cardarelli in 1981. A kind of parallel-plate detector with electrodes made of resistive materials such as Bakelite and thin-float glass, the design is sometimes also known as a resistive-plate chamber (RPC). Resistive gaseous detectors use electronegative gases and electric fields that typically exceed 10 kV/cm. When a charged particle is incident in the gas gap, the working operational gas is ionised, and primary electrons cause an avalanche as a result of the high electric field. The induced charge is then obtained on the readout pad as a signal. RPCs have several unique and important practical features, combining good spatial resolution with a time resolution comparable to that of scintillators. They are therefore well suited for fast spacetime particle tracking, as a cost-effective way to instrument large volumes of a detector, for example in muon systems at collider experiments.

Resistive gaseous detectors use electronegative gases and electric fields which typically exceed 10 kV/cm

Resistive Gaseous Detectors: Designs, Performance, and Perspectives, a new book by Marcello Abbrescia, Vladimir Peskov and Paulo Fonte, covers the basic principles of their operation, historical development, the latest achievements and their growing applications in various fields from hadron colliders to astrophysics. This book is not only a summary of numerous scientific publications on many different examples of RPCs, but also a detailed description of their design, operation and performance.

The book has nine chapters. The operational principle of gaseous detectors and some of their limitations, most notably the efficiency drop in a high-particle-rate environment, are described. This is followed by a history of parallel-plate detectors, the first classical Bakelite RPC, double-gap RPCs and glass-electrode multi-gap timing RPCs. A modern design of double-gap RPCs and examples for the muon systems like those at ATLAS and CMS at the LHC, the STAR detector at the Relativistic Heavy-Ion Collider at Brookhaven and the multi-gap timing RPC for the time-of-flight system of the HADES experiment at GSI are detailed. Advanced designs with new materials for electrodes for high-rate detectors are then introduced, and ageing and longevity are elaborated upon. A new generation of gaseous detectors with resistive electrodes that can be made with microelectronic technology is then introduced: these large-area electrodes can easily be manufactured while still achieving high spatial resolutions up to 12 microns.

Homeland security

The final chapter covers applications outside particle physics such as those in medicine exploiting positron-emission tomography. For homeland security, RPCs can be used in muon-scattering tomography with cosmic-ray muons to scan spent nuclear fuel containers without opening them, or to quickly scan incoming cargo trucks without disrupting the traffic of logistics. A key subject not covered in detail, however, is the need to search for environmentally friendly alternatives to gases with high global-warming potential, which are often needed in resistive gaseous detectors at present to achieve stable and sustained operation (CERN Courier July/August 2021 p20).

Abbrescia, Peskov and Fonte’s book will be useful to graduates specialising in high-energy physics, astronomy, astrophysics, medical physics and radiation measurements in general for undergraduate students and teachers.

What if scientists ruled the world?

A chemistry professor invents a novel way to produce chemical compounds, albeit with a small chance of toxicity. A paper is published. A quick chat with a science communicator leads to a hasty press release. But when the media picks up on it, the story is twisted.

“What if scientists ruled the world?” — a somewhat sensational but thought-provoking title for a play — is an interactive theatre production by the Australian Academy of Science in partnership with Falling Walls Engage. Staged on 8 May at the Shine Dome in Canberra, Australia, a hybrid performance explored the ramifications of an ill-considered press release, and provided a welcome opportunity for scientists to reflect on how best to communicate their research. The dynamic exchange of ideas between science experts and laypeople in the audience highlighted the power of words, and how they are used to inform, persuade, deceive or confuse.

After setting the scene, director Ali Clinch invited people participating remotely on Zoom and via a YouTube livestream to guide the actors’ actions, helping to advance and reframe the storyline with their ideas, questions and comments. Looking at the same story from different points of view invited the audience to think about the different stakeholders and their responsibility in communicating science. In the first part of the performance, for example, the science communicator talks excitedly about her job with students, but later has to face a crisis that the busy professor is unable or unwilling to deal with. At a critical point in the story, when a town-hall meeting is held to debate the future of a company that employs most of the people in the town, but which probably produced the same toxic chemical, everybody felt part of the performance. The audience could even take the place of an actor, or act in a new role.

The play highlighted the pleasures and tribulations of work at the interface between research and public engagement

The play highlighted the pleasures and tribulations of work at the interface between research and public engagement during euphoric discoveries and crisis moments alike, and has parallels both with the confusion encountered during the early stages of the COVID-19 pandemic and misguided early fears that the LHC could generate a black hole. In an age of fake news, sensationalism and misinformation, the performance adeptly highlighted the complexities and vested interests inherent in science communication today.

A relational take on quantum mechanics

It is often said that “nobody understands quantum mechanics” – a phrase usually attributed to Richard Feynman. This statement may, however, be misleading to the uninitiated. There is certainly a high level of understanding of quantum mechanics. The point, moreover, is that there is more than one way to understand the theory, and each of these ways requires us to make some disturbing concessions.

Carlo Rovelli’s Helgoland is therefore a welcome popular book – a well-written and easy-to-follow exploration of quantum mechanics and its interpretation. Rovelli is a theorist working mainly on quantum gravity and foundational aspects of physics. He is also a very successful popular author, distinguished by his erudition and his ability to illuminate the bigger picture. His latest book is no exception.

Helgoland is a barren German island of the North Sea where Heisenberg co-invented quantum mechanics in 1925 while on vacation. The extraordinary sequence of events between 1925 and 1926, when Heisenberg, Jordan, Born, Pauli, Dirac and Schrödinger formulated quantum mechanics, is the topic of the opening chapter of the book.

Rovelli only devotes a short chapter to discuss interpretations in general. This is certainly understandable, since the author’s main target is to discuss his own brainchild: relational quantum mechanics. This approach, however, does not do justice to popular ideas among experts, such as the many-worlds interpretation. The reader may be surprised not to find anything about the Copenhagen (or, more appropriately, Bohr’s) interpretation. This is for very good reason, however, since it is not generally considered to be a coherent interpretation. Having mostly historical significance, it has served as inspiration to approaches that keep the spirit of Bohr’s ideas, like consistent histories (not mentioned in the book at all), or Rovelli’s relational quantum mechanics.

Relational quantum mechanics was introduced by Rovelli in an original technical article in 1996 (Int. J. Theor. Phys. 35 1637). Helgoland presents a simplified version of these ideas, explained in more detail in Rovelli’s article, and in a way suitable for a more general audience. The original article, however, can serve as very nice complementary reading for those with some physics background. Relational quantum mechanics claims to be compatible with several of Bohr’s ideas. In some ways it goes back to the original ideas of Heisenberg by formulating the theory without a reference to a wavefunction. The properties of a system are defined only when the system interacts with another system. There is no distinction between observer and observed system. Rovelli meticulously embeds these ideas in a more general historical and philosophical context, which he presents in a captivating manner. He even speculates whether this way of thinking can help us understand topics that, in his opinion, are unrelated to quantum mechanics, such as consciousness.

Helgoland’s potential audience is very diverse and manages to transcend the fact that it is written for the general public. Professionals from both the sciences and the humanities will certainly learn something, especially if they are not acquainted with the nuances of the interpretations of modern physics. The book, however, as is explicitly stated by Rovelli, takes a partisan stance, aiming to promote relational quantum mechanics. As such, it may give a somewhat skewed view of the topic. In that respect, it would be a good idea to read it alongside books with different perspectives, such as Sean Carroll’s Something Deeply Hidden (2019) and Adam Becker’s What is Real? (2018).

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