2022 Open House Poster Presentation Abstracts
Charles Ahn Group
Kidae Shin, 5th Year Graduate Student
kidae.shin@yale.edu
Molecular Beam Epitaxy of Rare-earth Doped Transition Metal Oxides
Rare earth ions (REIs) in solids are attractive for quantum information processing due to long coherence time and scalability. To identify suitable host crystals for Er3+ ions and demonstrate control of REI spins in thin films, we grow Er-doped anatase TiO2 using molecular beam epitaxy (MBE) and characterize them with x-ray diffraction, atomic force microscopy and optical measurements.
Yeongjae Shin, Associate Research Scientist
yeongjae.shin@yale.edu
Kinetically driven low-temperature growth of BaTiO3 thin films
The perovskite oxide BaTiO3 is a prototypical displacive ferroelectric material with high-performance ferroelectric/piezoelectric properties as well as a high dielectric constant and large electro-optic coefficient. The structural simplicity of BaTiO3 makes it possible to integrate it with other functional materials in a single crystalline form, such as with semiconducting Si, MgO, Ge, and superconducting curprates, providing an opportunity to electrically control functional properties of adjacent layers. The low-temperature synthesis of high-quality crystalline BaTiO3 thin films may allow the integration of ferroelectrics with other materials and devices requiring a limited thermal budget.
Here, we describe the synthesis of BaTiO3 thin films by molecular beam epitaxy at various temperatures. Using reflection high energy diffraction, we demonstrate the surface mobility of BaO and TiO2 adatoms high enough to promote ferroelectric crystal growth at low temperatures. With this growth condition, the minimum temperature for epitaxial BaTiO3 growth is observed to be around 310 C, which is lower than the previously reported BaTiO3 crystallization temperature. Piezoresponse force microscopy provides a clear demonstration of ferroelectric switching of BaTiO3 films for growth temperatures down to 310 °C.
Helen Caines Group
Evan Craft, 1st Year Graduate Student
evan.craft@yale.edu
Relativistic Heavy Ion Group
The Yale Relativistic Heavy Ion group studies nuclear matter at extreme temperatures and pressures. In such conditions, quarks and gluons become deconfined from protons, neutrons, and other hadrons in order to form a new state of matter called the Quark Gluon Plasma. Our group can create such a state by colliding heavy ions such as ionized Pb or Au. The QGP is exists for only fractions of a second after the collision, and as the plasma cools, the newly freed quarks and gluons recombine to form normal nuclear matter. We can therefore study the properties of the QGP itself by indirectly probing the particles produced. Our group is heavily involved in a number of experiments such as STAR at the Relativistic Heavy Ion Collider (RHIC) which is a part of Brookhaven National Lab (BNL) and also ALICE which is a part of the Large Hadron Collider (LHC) at CERN. And we are looking forward to the great discoveries which lie ahead.
Eduardo da Silva Neto Group
Kirsty Scott, 2nd Year Graduate Student
kirsty.scott@yale.edu
Spectroscopic Techniques to Investigate Quantum Materials
The da Silva Neto lab utilizes different spectroscopic imaging techniques, primarily STM, X-ray scattering, and ARPES, to study quantum materials, particularly unconventional superconductors. 1) Our in-house scanning tunneling microscope (STM) images a material’s topography and density of states by tunneling a current from a tip to sample. 2) Resonant X-ray scattering (RXS) makes use of the geometry of photons scattering of a sample to understand the material’s electronic structures. 3) Angle-Resolved Photoemission Spectroscopy (ARPES) probes the energy and momentum states of electrons in a material to study its band structure.
Unconventional superconductors do not conform to the standard Bardeen-Cooper-Schriefer (BCS) theory of superconductivity, which applies to simple metals. Since the origins of the superconducting (SC) phase are more elusive than in conventional superconductors, we investigate phases which interact with superconductivity to gain a better understanding. For instance, charge density waves (CDWs), or periodic accumulations of electronic charge, which break discrete translational symmetry of a periodic lattice, are known to compete with the SC phase. Current projects include characterizing the first known CDW in an iron-based superconductor, a discovery recently made by the group with STM, and studying ring-like CDW correlations in cuprates, discovered by our group and collaborators using RXS.
Steven Girvin and Shruti Puri Group
Shraddha Singh, 3rd Year Graduate Student
shraddha.singh@yale.edu
High Fidelity Magic State Injection for Biased-Noise Architecture
Magic state distillation is a resource intensive subroutine that consumes noisy input states to produce high-fidelity resource states that are used to perform logical operations in practical quantum-computing architectures. The resource cost of magic state distillation can be reduced by improving the fidelity of the raw input states. To this end, we propose an initialization protocol that offers a quadratic improvement in the error rate of the input magic states in architectures with biased noise. This is achieved by preparing an error-detecting code which detects the dominant errors that occur during state preparation. We obtain this advantage by exploiting the native gate operations of an underlying qubit architecture that experiences biases in its noise profile. We perform simulations to analyze the performance of our protocol with the XZZX surface code. Even at modest physical parameters with a two-qubit gate error rate of 0.7% and total probability of dominant errors in the gate O(10^3) larger compared to that of non-dominant errors, we find that our preparation scheme delivers magic states with logical error rate O(10^{-8}) after a single round of the standard 15-to-1 distillation protocol; two orders of magnitude lower than using conventional state preparation. Our approach therefore promises considerable savings in overheads with near-term technology.
Reina Maruyama Group
Sumita Ghosh, 6th Year Graduate Student
sumita.ghosh@yale.edu
A Search for Dark Photons using HAYSTAC
The 85% of matter in the universe undetectable with current technology can be made of a single type of dark matter particle or made up of an entire hidden sector of many particles. The dark photon has been theorized as a dark force mediator, but is also a dark matter candidate in its own right, allowing for both possibilities of dark matter’s composition.
Dark photons can be detected with the HAYSTAC experiment by probing for a power excess caused by the dark photon’s kinetic mixing with photons. HAYSTAC’s sensitivity to the dark photon depends on the dark photon’s direction of polarization, which is unknown but may change with the rotation of the earth. To address this, we use 1) a statistical analysis of the possible directions of polarization and 2) the overlap between subsequent tuning steps during the measurement of the haloscope.
David Moore Group
Benjamin Siegel, 3rd Year Graduate Student
b.siegel@yale.edu
Search for New Interactions Using Optically Levitated Microspheres
Levitated optomechanics can be used for a wide range of experiments involving sub-attonewton force sensors. Our group has cooled the center-of-mass motion of levitated silicon microspheres to sub 100 µK and reached force sensitivities of sub aN/√ Hz. Our focus is applying this to study beyond the standard model physics, including our search for millicharged particles bound in matter and for dark matter scattering impulse detection. Current projects include building an array of levitated microspheres, measuring recoils from single nuclear decays, studying Newton’s law at µm distances, and studying whispering gallery modes in levitated spheres.
Sierra Wilde, 2nd Year Graduate Student
sw979@yale.edu
nEXO: a Tonne Scale Neutrinoless Double Beta Decay Experiment
nEXO is a tonne scale liquid xenon (LXe) detector that aims to detect neutrinoless double beta decay in Xe-136. If this decay is observed, it would indicate that neutrinos are Majorana fermions and that a decay exists in which matter is produced without antimatter. nEXO’s detector will compose of 5 tonnes of LXe and will be able to detect both ionization electrons and scintillation photons from interactions that occur in the LXe. These signals will allow for the reconstruction of the energy and position of each event. The Yale Purity Monitor is an ongoing project that aims to determine how the purity of LXe affects the lifetime of electrons drifting through the chamber and to reach a 5-10 ms electron lifetime in order to accomplish nEXO’s energy resolution goal of <1% σ/Q_ββ.
Nir Navon Group
Grant Schumacher, 5th Year Graduate Student
grant.schumacher@yale.edu
Ultracold Fermi Gases
Strongly interacting quantum systems are ubiquitous in modern physics, but understanding these systems remains a challenge. Using ultracold gases, we can create effectively arbitrary short-range interactions by tuning the atomic scattering properties of the gas. This tunability allows us to simulate the universal physics of strongly interacting systems, relevant for everything from nuclei to neutron stars. This wide range of applications is reflected in the number of projects currently ongoing in the lab, including Joule expansion of unitary Fermi gases, dissipation in strongly driven systems, stability and dimer formation in 2- and 3-component systems, and collective excitations. These experiments can all be conducted on the same machine thanks to our use of programmable optics (Digital Micromirror Devices) that allow us to engineer arbitrary optical potentials in an ultracold gas.
Peter Zhou, 3rd Year Graduate Student
p.zhou@yale.edu
Optical tweezer array of ultracold strontium atoms
In the past few years, there has been considerable interest in using a “bottom-up” approach for quantum simulators and computers where cold neutral atoms are individually trapped using optical tweezers and then assembled atom-by-atom into programmable atomic arrays. This technique results in shorter experimental cycle times and the realization of highly-configurable array geometries. We present our progress towards building such an apparatus for strontium neutral atoms, which as an alkali-earth metal has several properties such as narrow-line transitions that are attractive for applications in quantum information and metrology.
Meg Urry Group
Chuan Tian, 6th Year Graduate student
chuan.tian@yale.edu
An ML Framework for Estimating Bayesian Posteriors of Galaxy Morphological Parameters
Galaxy morphology is connected to various fundamental properties of a galaxy and its environment. Thus, studying the morphology of large samples of galaxies can be a crucial clue to understanding galaxy formation and evolution. In the past few years, although machine learning has been increasingly used to determine the morphology of galaxies, most previous works have provided only broad morphological classifications without any attempt at the computation of full Bayesian posteriors.
We have developed Galaxy Morphology Posterior Estimation Network (GaMPEN), a machine learning framework that can estimate the Bayesian posteriors for a galaxy’s bulge-to-total light ratio, effective radius, and flux. To predict posteriors, GaMPEN uses the Monte Carlo Dropout technique and incorporates the full covariance matrix in its loss function. The latter also allows GaMPEN to incorporate structured relationships between the output parameters into its predictions. We have demonstrated that GaMPEN’s predicted posteriors are well-calibrated and accurate. GaMPEN also contains a Spatial Transformer Network (STN) that automatically crops input galaxy frames to an optimal size before determining their morphology. The STN trains along with the rest of the framework, with no additional supervision, and will be crucial in applying GaMPEN to new survey data with no radius measurements.
By training and testing GaMPEN on galaxies simulated to match z < 0.25 Hyper Suprime Cam Wide g-band data, we demonstrate that GaMPEN achieves reasonable typical errors. Testing shows that GaMPEN predictions become less precise for especially small or faint galaxies, where the algorithm correctly predicts correspondingly larger uncertainties. We demonstrate that by a qualitative transformation of the predicted values in regions where GaMPEN’s residuals are higher, we can achieve accuracies of >95%. GaMPEN is the first machine learning framework for determining joint posterior distributions of multiple morphological parameters and is also the first application of an STN in astronomy.
Wright Laboratory
Yale Wright Laboratory: Transforming Discovery
Wright Lab is advancing the frontiers of fundamental physics through a broad research program in nuclear, particle, and astrophysics that includes precision studies of neutrinos, searches for dark matter, investigations of the building blocks and interactions of matter, and observations of the early Universe. Discover Wright Lab and how its on-site facilities and worldwide collaborations enable Wright Lab researchers to explore the invisible Universe.