Dr. Taran Driver, PULSE institute and SLAC National Accelerator Laboratory and Linac Coherent Light Source

Probing Correlated Multi-Electron Dynamics on the Attosecond Timescale

The interaction of light with matter is a fundamental process for probing and engineering the quantum properties of a molecule or material. This interaction is mediated by electrons, and understanding the many-body dynamics of electronic systems in the first moments following light-driven excitation is a frontier challenge. The characteristic timescale for this electron motion is set by the splitting of the relevant energy levels, which results in motion on the attosecond (10-18 s) timescale. It is now possible to generate pulses of light lasting on the order of one hundred attoseconds, both on the tabletop in the laboratory and at large free-electron laser facilities. I will present recent work using attosecond x-ray pulses to probe the ultrafast dynamics of multi-electron systems. We measured the photoemission delay in the core-level ionization of a molecule. This is the delay between the arrival of a photon and the emission of an electron in photoionization, which was long considered an instantaneous process. In fact, this delay reveals strong modulations due to electron correlation. We also time-resolved the response of an aromatic molecule, para-aminophenol, to impulsive photoionization. By accessing the dynamics within the first femtosecond following the removal of an electron, we observed the interplay between the sub-femtosecond decay of shake-up states and coherent charge density oscillation on the few-femtosecond timescale. I will also touch on future directions which will harness these new methods to develop ultrafast probes of electron motion and exotic light-engineered states in quantum materials.

Prof. Lina Necib, Department of Physics, MIT

Mapping out the Dark Matter in the Milky Way

In this talk, I will explore the interfacing of simulations, observations, and machine learning techniques to construct a detailed map of Dark Matter in the Milky Way, focusing on the Galactic Center/Halo and dwarf galaxies. For the Galactic Halo, I will present a recent work that reveals a decline in the stellar circular velocity, inducing tensions with established estimates of the Milky Way’s mass and Dark Matter content. I will discuss how the underestimated systematic errors in such a common methodology necessitates a revised approach that combines theory, observations, and machine learning. In dwarf galaxies, I will present a novel Graph Neural Network methodology that facilitates the accurate extraction of Dark Matter density profiles, validated against realistic simulations. I will conclude with a discussion on the future trajectory of astroparticle physics, emphasizing the need for the integration of astrophysical probes with experimental Dark Matter research, potentially leading to a better understanding of the nature of Dark Matter.

Prof. Philip Mannheim, University of Connecticut

The Accelerating Universe

I will describe some of the background that led to the award of the Nobel prize to Dr. Adam Riess, who will be our 2024 Katzenstein speaker on November 15.

Prof. Matthew Guthrie, University of Connecticut;

A New Era for Connecticut’s Oldest Planetarium: Historic Roots to Modern Revival

The UConn Planetarium, built in 1954, has long been a central resource for astronomy education and outreach at the University of Connecticut. In this talk, I will present an overview of the planetarium’s historical roots at UConn, its significance in the community, and the extensive renovations we have completed to bring this important facility back to life. After years of disuse, the planetarium has been fully upgraded with modern technology and will officially reopen on November 1st, immediately following this colloquium.

Our efforts to restore the planetarium are guided by the legacy of Dr. Cynthia Peterson, UConn’s first woman physics faculty member and a pioneer in science education. The planetarium now officially bears her name as the Cynthia Wyeth Peterson Memorial Planetarium, in honor of her decades of dedication to astronomy outreach. Following my presentation, Nora Berrah and Celeste Peterson will speak about Dr. Peterson’s life and achievements - how her contributions to UConn and the wider scientific community continue to resonate today.

Prof. Philip Kim, Harvard University

Searching for Anyon in Quantum Materials

The search for anyons, quasiparticles with fractional charge and exotic exchange statistics, has inspired decades of condensed matter research. Moreover, it has been predicted that exchange braiding of these particles, especially non-abelian anyons, can produce topologically protected logic operations that can serve as building blocks for fault-tolerant quantum computing. In this talk, I will discuss the progress of research on two quantum materials platforms to realize these exotic particles. In the first example, we will discuss anyons arising in fractional quantum Hall (FQH) effects, using quantum Hall interferometers for direct observation of the anyon braiding phase around a confined cavity. In the second example, we will discuss our recent experimental efforts to realize non-abelian anyons in proximitized topological insulator surfaces by controlled manipulation of magnetic vortices containing non-abelian anyons.

Prof. Jun Ye, University of Colorado and JILA

Title and abstract TBA

Prof. Mingda Li, Nuclear Science and Engineering, MIT

Exploring Potential Roles of Machine Learning in Quantum Materials Research

In recent years, machine learning has achieved great success in chemistry and materials science, but quantum materials face unique challenges. These include the scarcity of data (volume challenge), high dimensionality and computational costs (complexity challenge), elusive experimental signatures (experimental challenge), and unreliable ground truth (validation challenge).

In this Physics Colloquium, we present our recent efforts to support the study of quantum materials with machine learning. For scenarios with high data volumes, such as density-functional-theory (DFT) level studies with weak correlation, machine learning can predict lower-dimensional properties. We introduce a convolutional neural network classifier predicting band topology class based on X-ray absorption (XAS) signals [1]. This approach can also be applied to experimental data, demonstrated by an autoencoder-based protocol to study the magnetic proximity effect with polarized neutron reflectometry, improving fitting resolution [2].

For lower data volumes due to higher computational costs, incorporating symmetry into neural networks can reduce data volume needs. Using the O(3) Euclidean neural network, we predict phonon density-of-states [3], dielectric functions [4], and quantum weight [5] directly from crystal structures. Machine learning without data can also be performed by using differential equations as constraints [5].

For high output dimensions and low input data volumes, such as phonon dispersion relations, we introduce additional approaches like virtual nodes in a graph neural network [6], showing improved efficiency compared to machine-learning potential without losing accuracy.

To address unreliable ground truth, we use machine learning to distinguish Majorana zero modes in scanning tunneling spectroscopy for topological quantum computation [7]. For cases like quantum spin liquids, where experimental signatures are unclear and computational costs are high, we generate materials with potential geometrical frustration. Our latest work, SCIGEN, produces eight million materials belonging to Archimedean lattices, with over 50% passing DFT stability checks after pre-screening [8].

Despite progress, applying machine learning to quantum materials is still in its infancy. We reflect on the out-of-distribution problem, aiming to generate genuine surprises and new features rather than merely recognizing patterns. Additionally, we must address accuracy limitations in many machine learning approaches, especially with complex quantum systems and phase diagram studies.

[1] “Machine learning spectral indicators of topology,” Advanced Materials 34, 202204113 (2022).

[2] “Elucidating proximity magnetism through polarized neutron reflectometry and machine learning,” Applied Physics Review 9, 011421 (2022).

[3] “Direct prediction of phonon density of states with Euclidean neural networks,” Advanced Science 8, 2004214 (2021).

[4] “Ensemble-Embedding Graph Neural Network for Direct Prediction of Optical Spectra from Crystal Structure,” arXiv:2406.16654.

[5] “Panoramic mapping of phonon transport from ultrafast electron diffraction and machine learning,” Advanced Materials 35, 2206997 (2023).

[6] “Virtual Node Graph Neural Network for Full Phonon Prediction,” Nature Computational Science 4, 522 (2024).

[7] “Machine Learning Detection of Majorana Zero Modes from Zero Bias Peak Measurements,” Matter 7, 2507 (2024).

[8] “Structural Constraint Integration in Generative Model for Discovery of Quantum Material Candidates,” arXiv:2407.04557.

Prof.. Reina Maruyama,Yale University

What is dark matter?

Astrophysical observations give overwhelming evidence for the existence of dark matter. Several theoretical particles have been proposed as dark matter candidates, including weakly interacting massive particles (WIMPs), axions, and, more recently, their much lighter counterparts. However, there has yet to be a definitive detection of dark matter. For years, one group, the DAMA collaboration, has asserted that they observe a dark matter-induced annual modulation signal in their NaI(Tl)-based detectors. Their observations are inconsistent with those from other direct detection dark matter experiments under most assumptions of dark matter. In this talk, I will describe how I came to work on this topic and the debate’s current status, the worldwide experimental effort to test this extraordinary claim, and our progress toward resolving the current stalemate in the field.

Note: The pre-colloquium reception will be 3-4pm in the Gant Light Court

Prof. Cassandra Paul (San Jose State University)

Title and abstract (TBD)

Contact: Prof. Erin Scanlon

Remote talk (details forthcoming)

TITLE:

Quantum acoustics and the physics of the strange metals

ABSTRACT:

Quantum acoustics is the analog of quantum optics, with phonons playing the role of photons. The classical fields (electromagnetic, acoustic) are reached by virtue of coherent states in both cases. Quantum acoustics leads to two time dependent, interacting wave fields, one lattice, one quantum. The electron diffuses at a Planckian rate, independent of electron-lattice coupling and temperature, and the calculated resistivity is linear in temperature. Mott-Ioffe-Regel and Drude peak mysteries are also resolved. A rather different carrier transport scenario emerges for the strange metals.

Dr. Nick Hutzler, California Institute of Technology

UConn Physics Colloquium

Title and abstract: TBD

Contact: Profs. Tom Blum and Dan McCarron

Dr. James Cryan, SLAC National Lab (UConn Physics Colloquium)

Ultrafast dynamics using X-ray attosecond pulses.

Contact: Prof. Nora Berrah