Publications

Order by: publication date number of citations

• Annual modulation of dark matter signals: Experimental results and new ideas

  Felix Kahlhoefer

  SciPost Phys. Proc. 12, 004 (2023) · published 3 July 2023 | Toggle abstract · pdf

  Direct detection experiments searching for the scattering of dark matter particles off nuclei expect an annual modulation in their event rate. In this presentation, I will review the theoretical predictions and the experimental status of the search for annual modulations, with a focus on ongoing and planned experiments using NaI detectors. In particular, I will discuss the interpretation of the DAMA signal and related model-building efforts.

• Dark matter bound states: A window into the early universe

  Juri Smirnov

  SciPost Phys. Proc. 12, 003 (2023) · published 3 July 2023 | Toggle abstract · pdf

  Despite significant experimental sensitivity to point-like, weakly interacting particles at the electroweak mass scale, dark matter has not been found yet. This could hint at a more complex dark sector with multiple states or composite dark particles, much like the complexity of the standard model. Currently, our experimental sensitivity to such scenarios is limited by a lack of theoretical understanding. The investigation of Dark Matter systems with unstable or stable bound states provides a playground that leads to the development of important tools needed for the understanding of dark sectors with complex phenomena and allows to experimentally test well-motivated DM scenarios.

  1 citation

• Directional dark matter searches

  Elisabetta Baracchini

  SciPost Phys. Proc. 12, 002 (2023) · published 3 July 2023 | Toggle abstract · pdf

  The importance of directly detect and experimentally probe the nature of Dark Matter (DM) is universally and incontrovertibly recognised as one of the most compelling tasks of today's fundamental physics. Directional DM searches aim at developing experimental techniques that can give access to the measurement of the incoming direction of the DM particle. This can provide a correlation with an astrophysical source that no background whatsoever can mimic and offers an unique key for a positive, unambiguous identification of a DM signal.

• Even lighter particle dark matter

  Alvaro E. Chavarria

  SciPost Phys. Proc. 12, 001 (2023) · published 3 July 2023 | Toggle abstract · pdf

  We report on recent progress in the search for dark matter particles with masses from 1 Mev to 1 Gev . Several dark matter candidates in this mass range are expected to generate measurable electronic-recoil signals in direct-detection experiments. We focus on dark matter particles scattering with electrons in semiconductor detectors since they have fundamentally the highest sensitivity due to their low ionization threshold. Charge-coupled device (CCD) silicon detectors are the leading technology, with significant progress expected in the coming years. We present the status of the CCD program and briefly report on other efforts.

  1 citation

• Quantum local random networks and the statistical robustness of quantum scars

  Federica Maria Surace, Marcello Dalmonte, Alessandro Silva

  SciPost Phys. 14, 174 (2023) · published 29 June 2023 | Toggle abstract · pdf

  We investigate the emergence of quantum scars in a general ensemble of random Hamiltonians (of which the PXP is a particular realization), that we refer to as quantum local random networks. We find a class of scars, that we call "statistical", and we identify specific signatures of the localized nature of these eigenstates by analyzing a combination of indicators of quantum ergodicity and properties related to the network structure of the model. Within this parallelism, we associate the emergence of statistical scars to the presence of "motifs" in the network, that reflects how these are associated to links with anomalously small connectivity. Most remarkably, statistical scars appear at well-defined values of energy, predicted solely on the base of network theory. We study the scaling of the number of statistical scars with system size: by continuously changing the connectivity of the system we find that there is a transition from a regime where the constraints are too weak for scars to exist for large systems to a regime where constraints are stronger and the number of statistical scars increases with system size. This allows to define the concept of "statistical robustness" of quantum scars.

  2 citations

• Many bounded versions of undecidable problems are NP-hard

  Andreas Klingler, Mirte van der Eyden, Sebastian Stengele, Tobias Reinhart, Gemma de las Cuevas

  SciPost Phys. 14, 173 (2023) · published 29 June 2023 | Toggle abstract · pdf

  Several physically inspired problems have been proven undecidable; examples are the spectral gap problem and the membership problem for quantum correlations. Most of these results rely on reductions from a handful of undecidable problems, such as the halting problem, the tiling problem, the Post correspondence problem or the matrix mortality problem. All these problems have a common property: they have an NP-hard bounded version. This work establishes a relation between undecidable unbounded problems and their bounded NP-hard versions. Specifically, we show that NP-hardness of a bounded version follows easily from the reduction of the unbounded problems. This leads to new and simpler proofs of the NP-hardness of bounded version of the Post correspondence problem, the matrix mortality problem, the positivity of matrix product operators, the reachability problem, the tiling problem, and the ground state energy problem. This work sheds light on the intractability of problems in theoretical physics and on the computational consequences of bounding a parameter.

• Charge fluctuation entropy of Hawking radiation: A replica-free way to find large entropy

  Alexey Milekhin, Amirhossein Tajdini

  SciPost Phys. 14, 172 (2023) · published 28 June 2023 | Toggle abstract · pdf

  We study the fluctuation entropy for two-dimensional matter systems with an internal symmetry coupled to Jackiw-Teitelboim (JT) gravity joined to a Minkowski region. The fluctuation entropy is the Shannon entropy associated to probabilities of finding particular charge for a region. We first consider a case where the matter has a global symmetry. We find that the fluctuation entropy of Hawking radiation shows an unbounded growth and exceeds the entanglement entropy in presence of islands. This indicates that the global symmetry is violated. We then discuss the fluctuation entropy for matter coupled to a two-dimensional gauge field. We find a lower bound on the gauge coupling $g_0$ in order to avoid a similar issue. Also, we point out a few puzzles related to the island prescription in presence of a gauge symmetry.

  9 citations

• Exploring unsupervised top tagging using Bayesian inference

  Ezequiel Alvarez, Manuel Szewc, Alejandro Szynkman, Santiago A. Tanco, Tatiana Tarutina

  SciPost Phys. Core 6, 046 (2023) · published 28 June 2023 | Toggle abstract · pdf

  Recognizing hadronically decaying top-quark jets in a sample of jets, or even its total fraction in the sample, is an important step in many LHC searches for Standard Model and Beyond Standard Model physics as well. Although there exists outstanding top-tagger algorithms, their construction and their expected performance rely on Montecarlo simulations, which may induce potential biases. For these reasons we develop two simple unsupervised top-tagger algorithms based on performing Bayesian inference on a mixture model. In one of them we use as the observed variable a new geometrically-based observable $\tilde{A}_{3}$, and in the other we consider the more traditional $\tau_{3}/\tau_{2}$ $N$-subjettiness ratio, which yields a better performance. As expected, we find that the unsupervised tagger performance is below existing supervised taggers, reaching expected Area Under Curve AUC $\sim 0.80-0.81$ and accuracies of about 69\% $-$ 75\% in a full range of sample purity. However, these performances are more robust to possible biases in the Montecarlo that their supervised counterparts. Our findings are a step towards exploring and considering simpler and unbiased taggers.

• Autoregressive neural Slater-Jastrow ansatz for variational Monte Carlo simulation

  Stephan Humeniuk, Yuan Wan, Lei Wang

  SciPost Phys. 14, 171 (2023) · published 27 June 2023 | Toggle abstract · pdf

  Direct sampling from a Slater determinant is combined with an autoregressive deep neural network as a Jastrow factor into a fully autoregressive Slater-Jastrow ansatz for variational quantum Monte Carlo, which allows for uncorrelated sampling. The elimination of the autocorrelation time leads to a stochastic algorithm with provable cubic scaling (with a potentially large prefactor), i.e. the number of operations for producing an uncorrelated sample and for calculating the local energy scales like $\mathcal{O}(N_s^3)$ with the number of orbitals $N_s$. The implementation is benchmarked on the two-dimensional $t-V$ model of spinless fermions on the square lattice.

  1 citation

• Do machines dream of atoms? Crippen's logP as a quantitative molecular benchmark for explainable AI heatmaps

  Maria H. Rasmussen, Diana S. Christensen, Jan H. Jensen

  SciPost Chem. 2, 002 (2023) · published 27 June 2023 | Toggle abstract · pdf

  While there is a great deal of interest in methods aimed at explaining machine learning predictions of chemical properties, it is difficult to quantitatively benchmark such methods, especially for regression tasks. We show that the Crippen logP model (J. Chem. Inf. Comput. Sci. 1999, 39, 868) provides an excellent benchmark for atomic attribution/heatmap approaches, especially if the ground truth heatmaps can be adjusted to reflect the molecular representation. The "atom attribution from finger prints"-method developed by Riniker and Landrum (J. Chem. Inf. Comput. Sci. 2013, 5, 43) gives atomic attribution heatmaps that are in reasonable agreement with the atomic contribution heatmaps of the Crippen logP model for most molecules, with average heatmap overlaps of up to 0.54. The agreement is increased significantly (to 0.75) when the atomic contributions are adjusted to match the fact that the molecular representation is fragment-based rather than atom-based (the finger print-adapted (FPA) ground truth vector). Most heatmaps and the corresponding FPA overlaps are relatively insensitive to the training set size and the results are close to converged for a training set size of 1000 molecules, although for molecules with low overlap some heatmaps change significantly. Using the "remove an atom" approach for graph convolutional neural networks (GCNNs) suggested by Matveieva and Polishchuk (J. Cheminform. 2021, 13, 41) we find an average heatmap overlap of 0.47 for the atomic contribution heatmaps of the Crippen logP model. Like the simpler attribution benchmarks for classification tasks that have come before it, this work sets the bar for regression tasks.

  3 citations