2026
Time and location (unless otherwise noted):
One Tuesday each month (not conflicting with NSD Staff Meeting)
Building 50A Sessler (5132) - unless otherwise noted
11:00 am: Coffee, tea and cookies
11:30 am: Colloquium
Title: Low-Energy Searches of physics beyond the Standard Model
The Standard Model (SM) of particle physics is an extremely successful theory, which has passed a variety of stringent tests at both high- and low-energy. On the other hand, the SM is not the final theory of nature, as it cannot explain the origin of the matter-antimatter asymmetry in the Universe, it does not have a dark matter candidate and, in its minimal version, does not accommodate neutrino masses. A vibrant physics program is underway, searching for manifestations of physics beyond the SM (BSM) in different regimes, from high-energy collisions at the Large Hadron Collider to low-energy precision experiments at the intensity frontier. In this talk I will discuss the role that effective field theories (EFT) play in the multi-scale problem of identifying BSM physics at low energy. Focusing on the examples of neutrinoless double beta decay, precision beta decay and searches for electric dipole moments, I will discuss how EFTs allow for a very general organization of BSM operators, without reliance on specific models. I will then review how low-energy EFTs provide a bridge between the microscopic, quark-level dynamics and the many-body nuclear methods that are needed to calculate nuclear matrix elements.
I will finally discuss the progress towards the calculation of precise and accurate nuclear theory input, required for controlling the SM background and for extracting fundamental SM and BSM parameters from low-energy searches.
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Laura Fabbietti is a professor at TUM (Technical University of Munich) since 2008, she is a member of the ALICE collaboration at CERN and currently the project leader of the outer tracker for the future project ALICE3. She is PI of the excellence cluster ORIGINS II and deputy speaker of the SFB1258. Her research interests focus on the study of hadron interactions and connections to the physics of neutron stars and indirect dark matter searches.
Towards a complete QCD maps of hadrons interactions
A new technique has been developed in recent years at the LHC to study the residual strong interaction among hadrons: femtoscopy for interactions. This technique leverages on the high statistics pp collisions data recorded at the LHC and on the exquisite particle identification capabilities of the ALICE detector. In this talk we will discuss how femtoscopy for interactions allowed to extract for the first time the scattering parameters of hadron pairs containing (nearly)any combination of u, d, s and c quarks. How the technique was extended to three-hadron systems with the goal of testing the sensitity to nuclear three body forces and how the very same method recently allowed to directly observe the creation mechanism of antinuclei at the LHC.
Femtoscopy for interactions opened a new research field at the LHC and its future perspectives will be presented.
A universal fate for spin-orbit partners in the weak-binding regime?
A growing body of experimental data on effective single-particle energies in weakly bound systems has materialized over the past decade or so. The most illuminating of these data has been for the neutron-rich nuclei around N = 20 and 28, where both members of the 2p can be observed. Their separation, ΔSO, appears to decrease as the least bound of them approaches zero binding. This decrease is at odds with the well-established trends of measured spin-orbit splittings across the chart of nuclides for well-bound states as established by Mairle [Phys. Lett. B 304, 39 (1993)]. In comparing ΔSO from recent experimental data, mean-field descriptions, and the trends established by Mairle for both well-bound and weakly bound systems across the nuclear chart, a seemingly universal behavior emerges that could prove predictive. We focus on neutron spin-orbit partners in this work. Many of the regions explored have connections to other prominent topics in nuclear physics, such as r-process nucleosynthesis, where the ordering of single-particle energies near threshold in weakly bound systems plays a role in reaction rates. Throughout, I will highlight key results that have benefited from one of the principal methods used to extract such data: the solenoidal spectrometer technique, now used at ATLAS, ISOLDE, and FRIB. This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Nuclear Physics, under Contract Number DE-AC02-06CH11357.
Title: Advancing reaction theory to enable predictions and indirect measurements of cross sections for reactions on short-lived nuclei
Abstract: The past couple of decades have seen tremendous advances in nuclear structure and reaction theory. Innovative theory frameworks for describing the nuclear many-body system, increasingly powerful computers, and opportunities to confront theory predictions with data on unstable nuclei, have driven the field. An important goal is to move from phenomenological ingredients in reaction calculations to predictive theories based on microscopic frameworks. I will discuss ongoing efforts aimed at integrating microscopic descriptions of nuclear structure into reaction predictions for medium-mass and heavy nuclei. I will highlight areas where such efforts can improve nuclear reaction data for nuclear astrophysics and other applications and also enable indirect measurements of important reaction cross sections. I will present a selection of challenges that remain to be addressed.
* This work is performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. Support from the Laboratory Directed Research and Development Program at LLNL, Projects 19- ERD-017, 24-ERD-023, and 25-LW-063, is acknowledged.
Deep learning has shown promise in nuclear physics, with a large catalog of publications on the topic in recent years. Traditional deep learning models often require large amounts of labeled experimental data and significant time allocated to architecture development, training, and tuning the models. In this colloquium, I will present our recent work on developing a foundation model approach for nuclear physics time projection chambers (TPCs). Inspired not only by the success of self-supervised learning in natural language processing, but also work in point-cloud deep learning models, we explore various approaches to pretraining a TPC foundations model. Using data from the Active-Target Time Projection Chamber (AT-TPC) and the GADGET II detectors, we explore cross-experiment and cross-detector uses of our prototype foundation model. Specifically, we show that for downstream tasks of interest—such as counting reaction products in experiments—our pretrained model achieves an F1 score of 0.91 with only 250 labeled events. In contrast, a model trained from scratch requires well over 2,000 labeled events to reach similar performance. I will then discuss our vision and challenged for generalizing these models for more widespread use for the community.