Theoretical Physics seminars

Taillte May investigates how unstable black-hole quasinormal-mode spectra affect observed gravitational-wave signals. Small changes in a gravitational potential can substantially shift the frequencies normally used to test astrophysical black holes. A simple time-domain model reveals a delay before those shifts appear, tied to the echo travel time between the perturbation and the main potential. In the perturbative regime, the frequency change after one echo agrees with the conventional frequency-domain prediction. Beyond that regime, the intermediate waveform cannot be represented by a single constant frequency shift.

Benoit Assi examines limits on reliable theoretical predictions for present and future particle colliders. Low-order simulations of QCD radiation carry large uncertainties, hadronization is commonly modeled rather than derived, and effective theories contain more operators than measurements can constrain. Information theory and machine learning can incorporate improved calculations into simulations, quantify uncertainty, choose informative observables and advance hadronization theory. Geometry of effective-theory field space combines infinite operator families into finite physical quantities, while information measures identify the combinations experiments can resolve. The talk develops these complementary approaches to extracting the available information from LHC and future-collider data.

Giada Santoro examines strong-field gravity and compact-object structure using GW250114, a gravitational-wave event with signal-to-noise ratio around eighty. The unusually strong signal supports precise tests of departures from general relativity and detailed measurements of black-hole ringdown. No tidal deformability is detected: the analysis places a ninety-percent upper bound of 34.8 on the effective tidal-deformability parameter. This is consistent with the vanishing tidal response predicted for Kerr black holes.

Seth Koren shows how global symmetry structure and field-space topology affect particle-physics predictions beyond the usual analysis of small field fluctuations. Different possible global forms of the Standard Model gauge group imply different model-independent predictions for fractionally charged particles. Collider searches for these particles could identify the gauge-group structure and exclude unification models. The talk then examines axion theories, using the DFSZ model to show how the global properties of scalar fields and gauge symmetries modify axion strings and can resolve the cosmological domain-wall problem.

Benjamin Safdi studies the axion mass constraints obtained when grand unification and string theory are considered together with the QCD axion. Unitarity reasoning and explicit string compactifications, including examples from the Kreuzer–Skarke type-IIB ensemble, favor masses between 10^-11 and 10^-8 electronvolts. The talk explains how these theoretical frameworks combine to restrict a candidate extension of the Standard Model.

Luca Delacretaz investigates a nonperturbative description of Fermi surfaces, whose many low-energy excitations, collective modes, entanglement and possible non-Fermi-liquid behavior are difficult to handle with conventional field theory. Bosonization describes their dynamics using a collective field in phase space, but quantizing that field has been a longstanding obstacle beyond one dimension. The talk presents an exact description through a particular large-N limit of a level-one U(N) Wess–Zumino–Witten model, with a hierarchy of irrelevant corrections. Matrix degrees of freedom capture noncommutative phase space, and solvable strong-coupling dynamics removes the apparent excess of collective-field modes without dividing the Fermi surface into patches.

Samuel Garratt presents rigorous upper limits on entanglement entropy for locally interacting quantum many-body states at fixed energy. Ground states usually exhibit an area law, unlike generic states whose entanglement scales with volume, and gapless systems can introduce corrections. The framework constrains ground-state entanglement for gapped and gapless systems in arbitrary spatial dimension, follows the transition toward volume-law behavior as energy increases, and bounds the computational resources needed to calculate response functions at zero temperature. These results connect spectral information with the cost of tensor-network calculations.

The affinity between statistical physics and machine learning has a long history. I will describe the main lines of this long-lasting friendship in the context of current theoretical challenges and open questions about deep learning. Theoretical physics often proceeds in terms of solvable synthetic models, I will describe the related line of work on solvable models of simple feed-forward neural networks. I will highlight a path forward to capture the subtle interplay between the structure of the data, the architecture of the network, and the optimization algorithms commonly used for learning.

How can black holes be established both as a consequence of gravitational theory and as objects in the sky? Roger Penrose examines the mathematical development of gravitational collapse and space-time singularities, explaining why black-hole formation does not depend on perfectly symmetric idealisations. Reinhard Genzel and Andrea Ghez trace the observational effort to establish a massive compact object at the centre of the Milky Way. Their accounts connect infrared observations, improvements in imaging through atmospheric distortion, and long-term measurements of stellar motions around Sagittarius A*. Together, the lectures present the theoretical and observational foundations of the 2020 physics prize: the robustness of black-hole formation in general relativity and the evidence for the Galactic Centre’s supermassive black hole.

Many of us came to biology from physics. There we have been trained on such classic examples as muon g-2, where experimental data and theoretical predictions agree to many significant digits. Now, working in biology, we routinely hear that it is messy, most details matter, and that the best hope for theory in biology is to be semi-qualitative, predict general trends, and to forgo the hope of ever making quantitative predictions with the precision that we are used to in physics. Colloquially, we should be satisfied even if data and models differ so much that plotting them on the same plot makes little sense. However, some of us won’t be satisfied by this. So can we take theory in biology seriously and predict experimental outcomes within (small) error bars? Certainly, we won’t be able to predict everything, but this is never required, even in traditional physics. But we should be able to choose some features of data that are nontrivial and interesting, and focus on them. We also should be able to find different classes of models --- maybe even null models --- that match biology better, and thus allow for a better agreement. It is even possible that large-dimensional datasets of modern high-throughput experiments, and the ensuing “more is different” statistical physics style models will make quantitative, precise theory easier. To explore the role of quantitative theory in biology, in this workshop, eight speakers will address some of the following general questions based on their specific work in different corners of biology: Which features of biological data are predictable? Which types of models are best suited to making quantitative predictions in different fields? Should theorists interested in quantitative predictions focus on different questions, not typically asked by biologists? Do large, multidimensional datasets make theories (and which theories?) more or less likely to succeed? This will be an unapologetically theoretical physics workshop — we won’t focus on a specific subfield of biology, but will explore these questions across the fields, hoping that the underlying theoretical frameworks will help us find the missing connections.