Publications

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Scaling laws in jet classification

Joshua Batson, Yonatan Kahn

SciPost Phys. Core 8, 034 (2025) · published 28 March 2025 | · pdf

We demonstrate the emergence of scaling laws in the benchmark top versus QCD jet classification problem in collider physics. Six distinct physically-motivated classifiers exhibit power-law scaling of the binary cross-entropy test loss as a function of training set size, with distinct power law indices. This result highlights the importance of comparing classifiers as a function of dataset size rather than for a fixed training set, as the optimal classifier may change considerably as the dataset is scaled up. We speculate on the interpretation of our results in terms of previous models of scaling laws observed in natural language and image datasets.
Engineering unique localization transition with coupled Hatano-Nelson chains

Ritaban Samanta, Aditi Chakrabarty, Sanjoy Datta

SciPost Phys. Core 8, 033 (2025) · published 27 March 2025 | · pdf

The paradigmatic Hatano-Nelson (HN) Hamiltonian induces a delocalization-localization (DL) transition in a one-dimensional (1D) lattice with random disorder, in striking contrast to its Hermitian counterpart. The DL transition also persists in the presence of a quasiperiodic potential separating completely delocalized and localized eigenstates. In this study, we reveal that coupling two 1D quasiperiodic Hatano-Nelson (QHN) lattices signiﬁcantly alters the nature of the DL transition and identify two critical points, $V_{c1} < V_{c2}$, when the nearest neighbors of the two 1D QHN lattices are cross-coupled with strong hopping amplitudes under periodic boundary conditions (PBC). Complete delocalization occurs below $V_{c1}$ and the states are completely localized above $V_{c2}$, while two mobility edges symmetrically emerge about Re[E] = 0 between $V_{c1}$ and $V_{c2}$. Notably, under speciﬁc asymmetric cross-hopping amplitudes, $V_{c1}$ approaches zero, resulting in localized states even for an inﬁnitesimally weak potential. Remarkably, we also ﬁnd that the mobility edges precisely divide the delocalized and localized states in equal proportions. We demonstrate a possible implementation of these findings in a coupled waveguided array which can be exploited to control and manipulate the light localization depending upon the hopping amplitude in the two QHN chains.
Two-loop gradient-flow renormalization of scalar QCD

Janosh Borgulat, Nils Felten, Robert V. Harlander, Jonas T. Kohnen

SciPost Phys. Core 8, 032 (2025) · published 21 March 2025 | · pdf

The gradient-flow formalism is applied to a non-Abelian gauge theory with scalar and fermionic particles, dubbed "scalar QCD". It is shown that the flowed scalar quark requires a field renormalization, albeit only beyond the one-loop level. A pseudo-physical, flow-time dependent renormalization scheme is defined, and the corresponding renormalization constant is evaluated at the two-loop level. We also calculate the gluon action density as well as the condensates of the scalar and the fermionic quark at three-loop level in this theory. The results validate the consistency of the gradient-flow formalism in this theory and provide a further step towards applying the gradient-flow formalism to the full Standard Model.
Phase transitions in quantum dot-Majorana zero mode coupling systems

Yue Mao, Qing-Feng Sun

SciPost Phys. Core 8, 031 (2025) · published 11 March 2025 | · pdf

The magnetic doublet ground state (GS) of a quantum dot (QD) could be changed to a spin-singlet GS by coupling to a superconductor. In analogy, here we study the GS phase transitions in QD-Majorana zero mode (MZM) coupling systems: GS behaves phase transition versus intra-dot energy level and QD-MZM coupling strength. The phase diagrams of GS are obtained, for cases with and without Zeeman term. Along with the phase transition, we also study the change of spin feature and density of states. The properties of the phase transition are understood via a mean-field picture. Our study not only serves as an analogue to QD-superconductor phase transitions, but also gives alternative explanations on MZM-relevant experiments.
Recipes for the digital quantum simulation of lattice spin systems

Guido Burkard

SciPost Phys. Core 8, 030 (2025) · published 10 March 2025 | · pdf

We describe methods to construct digital quantum simulation algorithms for quantum spin systems on a regular lattice with local interactions. In addition to tools such as the Trotter-Suzuki expansion and graph coloring, we also discuss the efficiency gained by parallel execution of an extensive number of commuting terms. We provide resource estimates and quantum circuit elements for the most important cases and classes of spin systems. As resource estimates we indicate the total number of gates $N$ and simulation time $T$, expressed in terms of the number $n$ of spin 1/2 lattice sites (qubits), target accuracy $\epsilon$, and simulated time $t$. We provide circuit constructions that realize the simulation time $T^{(1)}\propto nt^2/\epsilon$ and $T^{(2q)}\propto t^{1+\eta}n^\eta/\epsilon^\eta$ for arbitrarily small $\eta = 1/2q$ for the first-order and higher-order Trotter expansions. We also discuss the potential impact of scaled gates, which have not yet been fully explored.
Identifying quantum phase transitions with minimal prior knowledge by unsupervised learning

Mohamad Ali Marashli, Ho Lai Henry Lam, Hamam Mokayed, Fredrik Sandin, Marcus Liwicki, Ho-Kin Tang, Wing Chi Yu

SciPost Phys. Core 8, 029 (2025) · published 6 March 2025 | · pdf

In this work, we proposed a novel approach for identifying quantum phase transitions in one-dimensional quantum many-body systems using AutoEncoder (AE), an unsupervised machine learning technique, with minimal prior knowledge. The training of the AEs is done with reduced density matrix (RDM) data obtained by Exact Diagonalization (ED) across the entire range of the driving parameter and thus no prior knowledge of the phase diagram is required. With this method, we successfully detect the phase transitions in a wide range of models with multiple phase transitions of different types, including the topological and the Berezinskii-Kosterlitz-Thouless transitions by tracking the changes in the reconstruction loss of the AE. The learned representation of the AE is used to characterize the physical phenomena underlying different quantum phases. Our methodology demonstrates a new approach to studying quantum phase transitions with minimal knowledge, small amount of needed data, and produces compressed representations of the quantum states.
Characterizing far from equilibrium states of the one-dimensional nonlinear Schrödinger equation

Abhik Kumar Saha, Romain Dubessy

SciPost Phys. Core 8, 028 (2025) · published 6 March 2025 | · pdf

We use the mathematical toolbox of the inverse scattering transform to study quantita- tively the number of solitons in far from equilibrium one-dimensional systems described by the defocusing nonlinear Schrödinger equation. We present a simple method to iden- tify the discrete eigenvalues in the Lax spectrum and provide a extensive benchmark of its efficiency. Our method can be applied in principle to all physical systems described by the defocusing nonlinear Schrödinger equation and allows to identify the solitons velocity distribution in numerical simulations and possibly experiments.
Bayesian RG flow in neural network field theories

Jessica N. Howard, Marc S. Klinger, Anindita Maiti, Alexander G. Stapleton

SciPost Phys. Core 8, 027 (2025) · published 5 March 2025 | · pdf

The Neural Network Field Theory correspondence (NNFT) is a mapping from neural network (NN) architectures into the space of statistical field theories (SFTs). The Bayesian renormalization group (BRG) is an information-theoretic coarse graining scheme that generalizes the principles of the exact renormalization group (ERG) to arbitrarily parameterized probability distributions, including those of NNs. In BRG, coarse graining is performed in parameter space with respect to an information-theoretic distinguishability scale set by the Fisher information metric. In this paper, we unify NNFT and BRG to form a powerful new framework for exploring the space of NNs and SFTs, which we coin BRG-NNFT. With BRG-NNFT, NN training dynamics can be interpreted as inducing a flow in the space of SFTs from the information-theoretic 'IR' $→$ 'UV'. Conversely, applying an information-shell coarse graining to the trained network's parameters induces a flow in the space of SFTs from the information-theoretic 'UV' $→$ 'IR'. When the information-theoretic cutoff scale coincides with a standard momentum scale, BRG is equivalent to ERG. We demonstrate the BRG-NNFT correspondence on two analytically tractable examples. First, we construct BRG flows for trained, infinite-width NNs, of arbitrary depth, with generic activation functions. As a special case, we then restrict to architectures with a single infinitely-wide layer, scalar outputs, and generalized cos-net activations. In this case, we show that BRG coarse-graining corresponds exactly to the momentum-shell ERG flow of a free scalar SFT. Our analytic results are corroborated by a numerical experiment in which an ensemble of asymptotically wide NNs are trained and subsequently renormalized using an information-shell BRG scheme.
Jet diffusion versus JetGPT – Modern networks for the LHC

Anja Butter, Nathan Huetsch, Sofia Palacios Schweitzer, Tilman Plehn, Peter Sorrenson, Jonas Spinner

SciPost Phys. Core 8, 026 (2025) · published 3 March 2025 | · pdf

We introduce two diffusion models and an autoregressive transformer for LHC physics simulations. Bayesian versions allow us to control the networks and capture training uncertainties. After illustrating their different density estimation methods for simple toy models, we discuss their advantages for Z plus jets event generation. While diffusion networks excel through their precision, the transformer scales best with the phase space dimensionality. Given the different training and evaluation speed, we expect LHC physics to benefit from dedicated use cases for normalizing flows, diffusion models, and autoregressive transformers.

1 citation
Mixmasters in Wonderland: Chaotic dynamics from Carroll limits of gravity

Gerben Oling, Juan F. Pedraza

SciPost Phys. Core 8, 025 (2025) · published 27 February 2025 | · pdf

We demonstrate that the Carroll limit of general relativity coupled to matter captures the chaotic mixmaster dynamics of near-singularity limits. Zooming in on the behavior of general relativity close to spacelike singularities reveals rich and solvable ultra-local Belinski-Khalatnikov-Lifshitz (BKL) dynamics, which we show to be captured by a Carroll limit. Specifically, building on recent work on geometric Carroll expansions of general relativity, we establish that leading-order Carroll gravity, with suitable matter coupling, accurately describes well-known cosmological billiards behavior. Since the Carroll limit implements the ultra-local limit off shell, this opens up the door to a wide range of possible tractable applications, including spatially inhomogeneous setups and the emergence of spikes at late times. This further suggests that Carroll gravity, along with its subleading corrections, could serve as a valuable tool for studying deep infrared physics in AdS/CFT.

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Publications

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