Publications

Order by: publication date number of citations

• The mass of the $\pi^-$

  M. Daum, D. Gotta

  SciPost Phys. Proc. 5, 010 (2021) · published 6 September 2021 | Toggle abstract · pdf

  The most precise values of the mass of the negatively charged pion have been determined from several measurements of X-ray wavelengths for transitions in pionic atoms at PSI. The Particle Data Group gives the average $m_{\pi^-}= (139.570 61 \pm 0.000 24) MeV/c^2$.

  5 citations

• Scattering Amplitudes and BCFW in $\mathcal{N}=2^*$ Theory

  Md. Abhishek, Subramanya Hegde, Dileep P. Jatkar, Arnab Priya Saha

  SciPost Phys. 13, 008 (2022) · published 25 July 2022 | Toggle abstract · pdf

  We use massive spinor helicity formalism to study scattering amplitudes in $\mathcal{N}=2^*$ super-Yang-Mills theory in four dimensions. We compute the amplitudes at an arbitrary point in the Coulomb branch of this theory. We compute amplitudes using projection from $\mathcal{N}=4$ theory and write three point amplitudes in a convenient form using special kinematics. We then compute four point amplitudes by carrying out massive BCFW shifts of the amplitudes. We find some of the shifted amplitudes have a pole at $z=\infty$. Taking the residue at $z=\infty$ into account ensures little group covariance of the final result.

  5 citations

• Sub-GeV dark matter models and direct detection

  Tongyan Lin

  SciPost Phys. Lect. Notes 43 (2022) · published 18 March 2022 | Toggle abstract · pdf

  Lecture notes for Les Houches Summer School 2021: Dark Matter. These lectures give a brief introduction to sub-GeV dark matter models, and then reviews theory and approaches to direct detection of sub-GeV dark matter.

  5 citations

• Learning knot invariants across dimensions

  Jessica Craven, Mark Hughes, Vishnu Jejjala, Arjun Kar

  SciPost Phys. 14, 021 (2023) · published 21 February 2023 | Toggle abstract · pdf

  We use deep neural networks to machine learn correlations between knot invariants in various dimensions. The three-dimensional invariant of interest is the Jones polynomial $J(q)$, and the four-dimensional invariants are the Khovanov polynomial $\text{Kh}(q,t)$, smooth slice genus $g$, and Rasmussen's $s$-invariant. We find that a two-layer feed-forward neural network can predict $s$ from $\text{Kh}(q,-q^{-4})$ with greater than $99\%$ accuracy. A theoretical explanation for this performance exists in knot theory via the now disproven knight move conjecture, which is obeyed by all knots in our dataset. More surprisingly, we find similar performance for the prediction of $s$ from $\text{Kh}(q,-q^{-2})$, which suggests a novel relationship between the Khovanov and Lee homology theories of a knot. The network predicts $g$ from $\text{Kh}(q,t)$ with similarly high accuracy, and we discuss the extent to which the machine is learning $s$ as opposed to $g$, since there is a general inequality $|s| \leq 2g$. The Jones polynomial, as a three-dimensional invariant, is not obviously related to $s$ or $g$, but the network achieves greater than $95\%$ accuracy in predicting either from $J(q)$. Moreover, similar accuracy can be achieved by evaluating $J(q)$ at roots of unity. This suggests a relationship with $SU(2)$ Chern—Simons theory, and we review the gauge theory construction of Khovanov homology which may be relevant for explaining the network's performance.

  5 citations

• Charge quantization and detector resolution

  Roman-Pascal Riwar

  SciPost Phys. 10, 093 (2021) · published 29 April 2021 | Toggle abstract · pdf

  Charge quantization, or the absence thereof, is a central theme in quantum circuit theory, with dramatic consequences for the predicted circuit dynamics. Very recently, the question of whether or not charge should actually be described as quantized has enjoyed renewed widespread interest, with however seemingly contradictory propositions. Here, we intend to reconcile these different approaches, by arguing that ultimately, charge quantization is not an intrinsic system property, but instead depends on the spatial resolution of the charge detector. We show that the latter can be directly probed by unique geometric signatures in the correlations of the supercurrent. We illustrate these findings at the example of Josephson junction arrays in the superinductor regime, where the transported charge appears to be continuous. Finally, we comment on potential consequences of charge quantization beyond superconducting circuits.

  5 citations

• Dirac fermions and topological phases in magnetic topological insulator films

  Kai-Zhi Bai, Bo Fu, Shun-Qing Shen

  SciPost Phys. 17, 146 (2024) · published 27 November 2024 | Toggle abstract · pdf

  We develop a Dirac fermion theory for topological phases in magnetic topological insulator films. The theory is based on exact solutions of the energies and the wave functions for an effective model of the three-dimensional topological insulator (TI) film. It is found that the TI film consists of a pair of massless or massive Dirac fermions for the surface states, and a series of massive Dirac fermions for the bulk states. The massive Dirac fermion always carries zero or integer quantum Hall conductance when the valence band is fully occupied while the massless Dirac fermion carries a one-half quantum Hall conductance when the chemical potential is located around the Dirac point for a finite range. The magnetic exchange interaction in the magnetic layers in the film can be used to manipulate either the masses or chirality of the Dirac fermions and gives rise to distinct topological phases, which cover the known topological insulating phases, such as the quantum anomalous Hall effect, quantum spin Hall effect and axion effect, and also the novel topological metallic phases, such as the half-quantized Hall effect, half quantum mirror Hall effect, and metallic quantum anomalous Hall effect.

  5 citations

• Detecting nematic order in STM/STS data with artificial intelligence

  Jeremy B. Goetz, Yi Zhang, Michael J. Lawler

  SciPost Phys. 8, 087 (2020) · published 15 June 2020 | Toggle abstract · pdf

  Detecting the subtle yet phase defining features in Scanning Tunneling Microscopy and Spectroscopy data remains an important challenge in quantum materials. We meet the challenge of detecting nematic order from local density of states data with supervised machine learning and artificial neural networks for the difficult scenario without sharp features such as visible lattice Bragg peaks or Friedel oscillation signatures in the Fourier transform spectrum. We train the artificial neural networks to classify simulated data of isotropic and anisotropic two-dimensional metals in the presence of disorder. The supervised machine learning succeeds only with at least one hidden layer in the ANN architecture, demonstrating it is a higher level of complexity than nematic order detected from Bragg peaks which requires just two neurons. We apply the finalized ANN to experimental STM data on CaFe2As2, and it predicts nematic symmetry breaking with 99% confidence (probability 0.99), in agreement with previous analysis. Our results suggest ANNs could be a useful tool for the detection of nematic order in STM data and a variety of other forms of symmetry breaking.

  5 citations

• CDMFT+HFD: An extension of dynamical mean field theory for nonlocal interactions applied to the single band extended Hubbard model

  Sarbajaya Kundu, David Sénéchal

  SciPost Phys. Core 7, 033 (2024) · published 3 June 2024 | Toggle abstract · pdf

  We examine the phase diagram of the extended Hubbard model on a square lattice, for both attractive and repulsive nearest-neighbor interactions, using CDMFT+HFD, a combination of Cluster Dynamical Mean Field theory (CDMFT) and a Hartree-Fock mean-field decoupling of the inter-cluster extended interaction. For attractive non-local interactions, this model exhibits a region of phase separation near half-filling, in the vicinity of which we find islands of d-wave superconductivity, decaying rapidly as a function of doping, with disconnected regions of extended s-wave order at smaller (higher) electron densities. On the other hand, when the extended interaction is repulsive, a Mott insulating state at half-filling is destabilized by hole doping, in the strong-coupling limit, in favor of d-wave superconductivity. At the particle-hole invariant chemical potential, we find a first-order phase transition from antiferromagnetism (AF) to d-wave superconductivity as a function of the attractive nearest-neighbor interaction, along with a deviation of the density from the half-filled limit. A repulsive extended interaction instead favors charge-density wave (CDW) order at half-filling.

  5 citations

• Moral lines of credit: Forging race projects, citizenship, and nation on online U.S. spousal reunification forums

  Gina Marie Longo

  Mig. Pol. 1, 005 (2022) · published 24 October 2022 | Toggle abstract · pdf

  This study investigates how U.S. citizens petitioning for “green cards” on behalf of foreign national spouses uphold the U.S. racial project as they navigate the spousal reunification process. It also explores the role of online communities as crucial “brokers” and mediators between citizen, noncitizen, and the state. This work troubles the dichotomy between immigration officers/couples while giving primacy to the citizen-spouse’s voices. Using content analysis of an online forum where petitioners exchange advice with similar others, I show the citizen’s complicity with the racialized hierarchical order of the American nation. Ultimately, family migration policies and regulations are exercises in state-building, and nation-building, and citizens partake in it while trying to secure their own family, disciplining themselves to align with the state’s ideal of what a proper future nation should look like.

  5 citations

• Annealed averages in spin and matrix models

  Laura Foini, Jorge Kurchan

  SciPost Phys. 12, 080 (2022) · published 3 March 2022 | Toggle abstract · pdf

  A disordered system is denominated 'annealed' when the interactions themselves may evolve and adjust their values to lower the free energy. The opposite ('quenched') situation when disorder is fixed, is the one relevant for physical spin-glasses, and has received vastly more attention. Other problems however are more natural in the annealed situation: in this work we discuss examples where annealed averages are interesting, in the context of matrix models. We first discuss how in practice, when system and disorder adapt together, annealed systems develop `planted' solutions spontaneously, as the ones found in the study of inference problems. In the second part, we study the probability distribution of elements of a matrix derived from a rotationally invariant (not necessarily Gaussian) ensemble, a problem that maps into the annealed average of a spin glass model.

  5 citations