arXiv subjects

Yonatan Kahn

Publications and source records attributed to Yonatan Kahn.

At least 19 recordsLinked to original sources

arXiv2026

First-principles upper bounds on dark matter-electron scattering rates from condensed matter sum rules

A wide variety of condensed matter systems are used or proposed as detectors to search for dark matter-electron scattering. In general, the scattering rate depends on detailed knowledge of the electronic properties of these systems. However, when dark matter couples to electron density, the dark matter-electron scattering rate can be related to the electron energy loss function, whose integrals are bounded by first-principles sum rules that rely on only a few macroscopic target properties. In this paper, we use these first-principles sum rules to derive upper bounds on the dark matter-electron scattering rate depending on only a few material properties: the plasma frequency $\omega_\text{p}$, the target mass density $\rho_T$, and the static (longitudinal) dielectric function at finite momentum transfer, $\varepsilon(q, 0)$. The bulk material properties $\omega_\text{p}$ and $\rho_T$ vary only over a limited range across a wide variety of materials, and to a good approximation, the generic large-$q$ dependence of $\varepsilon(q, 0)$ can be understood from a simple scaling law depending only on $\omega_\text{p}$ which we verify with analytic and numerical examples. Thus, our upper bounds are largely material-agnostic, and place a fundamental limit on the sensitivity of any dark matter-electron direct detection experiment probing the coupling to electron density.

hep-ph↗

arXiv2026

Statistical Properties of Training & Generalization

Deep learning has managed to evade numerous intuitions from classical statistics to achieve unprecedented performance on a number of real-world tasks. In this article, we investigate the key features and surprises of deep learning from a physics-informed perspective, taking care to point out and justify where possible the many choices inherent in constructing a deep learning model. In particular, we review the phenomenon of neural scaling laws and discuss their interplay with the constraints and inductive biases which may be present when applying machine learning to problems in physics.

stat.ML↗

arXiv2026

Generative models on phase space

Deep generative models such as diffusion and flow matching are powerful machine learning tools capable of learning and sampling from high-dimensional distributions. They are particularly useful when the training data appears to be concentrated on a submanifold of the data embedding space. For high-energy physics data, consisting of collections of relativistic energy-momentum 4-vectors, this submanifold can enforce extremely strong physically-motivated priors, such as energy and momentum conservation. If these constraints are learned only approximately, rather than exactly, this can inhibit the interpretability and reliability of such generative models. To remedy this deficiency, we introduce generative models which are, by construction, confined at every step of their sampling trajectory to the manifold of massless N-particle Lorentz-invariant phase space in the center-of-momentum frame. In the case of diffusion models, the "pure noise" forward process endpoint corresponds to the uniform distribution on phase space, which provides a clear starting point from which to identify how correlations among the particles emerge during the reverse (de-noising) process. We demonstrate that our models are able to learn both few-particle and many-particle distributions with various singularity structures, paving the way for future interpretability studies using generative models trained on simulated jet data.

hep-ph↗

arXiv2026

Statistics of Daily Modulation in Dark Matter Direct Detection Experiments

The time-dependent modulation of the event rate in dark matter direct detection experiments, arising from the motion of the Earth with respect to the Galactic rest frame, is a distinctive signature whose observation is crucial for claiming a discovery of dark matter. While annual modulation has been well studied for decades, daily modulation due to the Earth's rotation has attracted increased attention recently due to the identification of anisotropic solid-state detector materials that yield a direction-dependent scattering rate without sacrificing the overall rate. We perform a statistical analysis of daily modulation in dark matter scattering experiments, with the goal of maximizing the statistical significance of a modulating signal in the presence of an unknown background rate, which may be either flat (non-modulating), or modulating over a 24-hour period with a known or unknown phase. In the background-dominated regime, we find that the discovery significance scales as $f_\text{RMS} \sqrt{T}$, where $T$ is the total exposure time and $f_\text{RMS}$ is the root-mean-square modulation amplitude; in particular, the significance continues to improve with exposure rather than saturating due to systematic uncertainties in the background rate. Using anisotropic trans-stilbene detectors for sub-GeV dark matter as a benchmark example, we provide prescriptions for optimizing the significance for a given total detector mass and location. In an example analysis using three detectors, optimizing the detector orientations can reduce the required exposure by a factor of $\sim 5$ for a desired discovery or exclusion significance, even after profiling over an unknown modulating background phase.

hep-ph↗

arXiv2025

Stringent Constraints on Gravitational Wave Signatures of Dark Electromagnetism in Neutron Star Binaries

Gravitational wave interferometers have studied compact object mergers and solidified our understanding of strong gravity. Their increasing precision raises the possibility of detecting new physics, especially in a neutron star binary system that may contain hidden-sector particles. In particular, a new vector force between binary constituents, giving rise to dark electromagnetic phenomena, could measurably alter the inspiral waveforms and thus be constrained by gravitational wave observations. In this work, we critically examine the mechanisms for neutron stars to acquire enough hidden-sector particles with requisite couplings to furnish a detectable signature from dark electromagnetism. We demonstrate that the repulsive nature of vector forces imposes stringent constraints on any putative particle physics model or astrophysical environment which could give rise to such gravitational signatures. We argue that absent an extreme fine-tuning of parameters, such signatures are well out of reach of any current or near-future gravitational wave observatory.

hep-ph↗

arXiv2025

Effects of Lighter-than-QCD Axions on Neutron Star Tidal Deformability

Finite density corrections to the lighter-than-QCD axion can invert the effective axion potential, sourcing a non-trivial axion field inside dense objects. We perform the first numerical study of the complete dynamics of the lighter-than-QCD axion in a neutron star in 1+1 general relativity, extending the region of analysis to low-mass axions with kilometer-scale Compton wavelengths. We calculate gravitational effects of the axion field on the neutron star and show that for a broad range of axion masses and decay constants, neutron star properties, such as the mass, radius, and compactness, are affected at the order-1 level. This result indicates that approximate universal tidal deformability-compactness relation for neutron stars is non-trivially broken and can serve as a probe of lighter-than-QCD axions, independent of the unknown nuclear equation of state. We comment on the potential for axion studies with future gravitational-wave observations of neutron stars and applications of this work to other new physics signatures.

hep-ph↗

arXiv2025

Quantum Calculations of the Cavity Shift in Electron Magnetic Moment Measurements

The measurement of the anomalous electron magnetic moment $g-2$ through quantum transitions of a single trapped electron is the most stringent test of quantum field theory. These experiments are now so precise that they must account for the effects of the cavity containing the electron. Classical calculations of this "cavity shift" must subtract the electron's divergent self-field, and thus require knowledge of the exact Green's function for the cavity's electromagnetic field. We perform the first fully quantum calculation of the cavity shift in a closed cavity, which instead involves subtracting linearly divergent cavity mode sums and integrals. Using contour integration methods, we find perfect agreement with existing classical results for both spherical and cylindrical cavities, justifying their current use. Moreover, our mode-based results can be naturally generalized to account for systematic effects, necessary to push future measurements to the next order of magnitude in precision.

hep-ph↗

arXiv2025

Probing the Critical Point (CritPt) of AI Reasoning: a Frontier Physics Research Benchmark

While large language models (LLMs) with reasoning capabilities are progressing rapidly on high-school math competitions and coding, can they reason effectively through complex, open-ended challenges found in frontier physics research? And crucially, what kinds of reasoning tasks do physicists want LLMs to assist with? To address these questions, we present the CritPt (Complex Research using Integrated Thinking - Physics Test, pronounced "critical point"), the first benchmark designed to test LLMs on unpublished, research-level reasoning tasks that broadly covers modern physics research areas, including condensed matter, quantum physics, atomic, molecular & optical physics, astrophysics, high energy physics, mathematical physics, statistical physics, nuclear physics, nonlinear dynamics, fluid dynamics and biophysics. CritPt consists of 71 composite research challenges designed to simulate full-scale research projects at the entry level, which are also decomposed to 190 simpler checkpoint tasks for more fine-grained insights. All problems are newly created by 50+ active physics researchers based on their own research. Every problem is hand-curated to admit a guess-resistant and machine-verifiable answer and is evaluated by an automated grading pipeline heavily customized for advanced physics-specific output formats. We find that while current state-of-the-art LLMs show early promise on isolated checkpoints, they remain far from being able to reliably solve full research-scale challenges: the best average accuracy among base models is only 5.7%, achieved by GPT-5 (high), moderately rising to around 10% when equipped with coding tools. Through the realistic yet standardized evaluation offered by CritPt, we highlight a large disconnect between current model capabilities and realistic physics research demands, offering a foundation to guide the development of scientifically grounded AI tools.

cs.AI↗

arXiv2025

Contrastive Normalizing Flows for Uncertainty-Aware Parameter Estimation

Estimating physical parameters from data is a crucial application of machine learning (ML) in the physical sciences. However, systematic uncertainties, such as detector miscalibration, induce data distribution distortions that can erode statistical precision. In both high-energy physics (HEP) and broader ML contexts, achieving uncertainty-aware parameter estimation under these domain shifts remains an open problem. In this work, we address this challenge of uncertainty-aware parameter estimation for a broad set of tasks critical for HEP. We introduce a novel approach based on Contrastive Normalizing Flows (CNFs), which achieves top performance on the HiggsML Uncertainty Challenge dataset. Building on the insight that a binary classifier can approximate the model parameter likelihood ratio, we address the practical limitations of expressivity and the high cost of simulating high-dimensional parameter grids by embedding data and parameters in a learned CNF mapping. This mapping yields a tunable contrastive distribution that enables robust classification under shifted data distributions. Through a combination of theoretical analysis and empirical evaluations, we demonstrate that CNFs, when coupled with a classifier and established frequentist techniques, provide principled parameter estimation and uncertainty quantification through classification that is robust to data distribution distortions.

physics.data-an↗

arXiv2025

High-Frequency Gravitational Wave Search with ABRACADABRA-10\,cm

High-frequency gravitational waves (HFGWs), above 10 kHz, promise a clean probe of new physics, largely free of the astrophysical backgrounds that complicate lower-frequency searches. Axion detectors, which search for axion dark matter via its coupling to electrodynamics in a strong magnetic field, should also be sensitive to HFGWs. We present the first dedicated search for HFGWs, using a modified ABRA-10cm axion detector, ABRA-GW, that runs simultaneously with a conventional axion search. ABRA-GW opens the 10 kHz-5 MHz band to HFGW searches and performs the first transient search by an axion experiment, targeting primordial black hole (PBH) mergers. Axion sensitivity is unaffected by the added gravitational-wave channel, and HFGW sensitivity matches theoretical expectations. This work demonstrates the broad physics reach of axion detectors and represents a first step toward a potential HFGW discovery.

hep-ex↗

arXiv2025

Uncertainty Quantification From Scaling Laws in Deep Neural Networks

Quantifying the uncertainty from machine learning analyses is critical to their use in the physical sciences. In this work we focus on uncertainty inherited from the initialization distribution of neural networks. We compute the mean $\mu_{\mathcal{L}}$ and variance $\sigma_{\mathcal{L}}^2$ of the test loss $\mathcal{L}$ for an ensemble of multi-layer perceptrons (MLPs) with neural tangent kernel (NTK) initialization in the infinite-width limit, and compare empirically to the results from finite-width networks for three example tasks: MNIST classification, CIFAR classification and calorimeter energy regression. We observe scaling laws as a function of training set size $N_\mathcal{D}$ for both $\mu_{\mathcal{L}}$ and $\sigma_{\mathcal{L}}$, but find that the coefficient of variation $\epsilon_{\mathcal{L}} \equiv \sigma_{\mathcal{L}}/\mu_{\mathcal{L}}$ becomes independent of $N_\mathcal{D}$ at both infinite and finite width for sufficiently large $N_\mathcal{D}$. This implies that the coefficient of variation of a finite-width network may be approximated by its infinite-width value, and may in principle be calculable using finite-width perturbation theory.

cs.LG↗

arXiv2025

Constraining dark-sector effects using gravitational waves from compact binary inspirals

Gravitational wave observations are a powerful tool to constrain fundamental physics. This work considers dark matter that carries charge under a dark abelian massive vector field. If such dark matter is bound inside coalescing neutron stars, the presence of the new force will modify the total energy of the binary, and the emission of dark radiation modes will impact the rate of inspiral. At leading order, the dark matter corrections introduce a Yukawa term to the potential energy and a dipole radiation mode. We calculate modifications to the binary inspiral waveform to first post-Newtonian order, incorporate these corrections into a state-of-the-art waveform model for neutron star binaries, and use this model in a Bayesian parameter estimation analysis on real data collected by current, second-generation ground-based detectors. Through this study, we place the first robust constraints on the Yukawa interaction strength and dipole emission parameter. We find that the strongest constraints are obtained from analysis of the GW170817 event, for which the Yukawa interaction strength is constrained to be less than $O(10^{-2})$ and the dipole emission parameter is less than $O(10^{-4})$ at 90% credibility.

gr-qc↗

arXiv2024

New Technologies for Axion and Dark Photon Searches

The search for dark matter and physics beyond the Standard Model has grown to encompass a highly interdisciplinary approach. In this review, we survey recent searches for light, weakly-coupled particles - axions and dark photons - over the past decade, focusing on new experimental results and the incorporation of technologies and techniques from fields as diverse as quantum science, microwave engineering, precision magnetometry, and condensed matter physics. We also review theoretical progress which has been useful in identifying new experimental directions, and identify the areas of most rapid experimental progress and the technological advances required to continue exploring the parameter space for axions and dark photons.

hep-ph↗

arXiv2024

Exotic Particles at the DUNE Near Detector from Charged Pion Scattering

Fixed-target proton-beam experiments produce a multitude of charged pions that rescatter in the beam dump. These charged pion scattering events can be an additional irreducible source of exotic particles which couple to photons or hadrons. We analyze the sensitivity of the DUNE Near Detector complex to millicharged particles (MCPs) and heavy axion-like particles (ALPs) with low-energy couplings to gluons. Using the framework of chiral perturbation theory, we demonstrate regimes of parameter space where the charged pion production channel dominates over previously-considered production mechanisms for both MCPs and ALPs, thereby improving the sensitivity of DUNE to these new particles compared to previous studies.

hep-ph↗

arXiv2024

Measuring the molecular Migdal effect with neutron scattering on diatomic gases

The Migdal effect is a key inelastic signal channel which could be used to detect low-mass dark matter, but it has never been observed experimentally using Standard Model probes. Here we propose a conceptual design for an experiment which could detect the Migdal effect in diatomic molecules through low-energy neutron scattering, and we provide the requirements on the beam spectrum and reducible backgrounds such that a detection may be achieved. The enhancement of the Migdal rate through non-adiabatic couplings, which are absent in isolated atoms, combined with the distinctive photon energies of electronic transitions in CO, suggest that a positive detection of the molecular Migdal effect may be possible with modest beam times at existing neutron facilities.

hep-ex↗

arXiv2024

Constraining Dark Matter-Proton Scattering from Molecular Cloud Ionization

Optically dense clouds in the interstellar medium composed predominantly of molecular hydrogen, known as molecular clouds, are sensitive to energy injection in the form of photon absorption, cosmic-ray scattering, and dark matter (DM) scattering. The ionization rates in dense molecular clouds are heavily constrained by observations of abundances of various molecular tracers. Recent studies have set constraints on the DM-electron scattering cross section using measurements of ionization rates in dense molecular clouds. Here we calculate the analogous bounds on the DM-proton cross section using the molecular Migdal effect, recently adapted from the neutron scattering literature to the DM context. These bounds may be the strongest limits on a strongly-coupled DM subfraction, and represent the first application of the Migdal effect to astrophysical systems.

hep-ph↗

arXiv2023

Scaling Laws in Jet Classification

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.

hep-ph↗

arXiv2023

Searching for High Frequency Gravitational Waves with Phonons

The gravitational wave (GW) spectrum at frequencies above a kHz is a largely unexplored frontier. We show that detectors with sensitivity to single-phonon excitations in crystal targets can search for GWs with frequencies, $\mathrm{THz} \lesssim f \lesssim 100 \, \mathrm{THz}$, corresponding to the range of optical phonon energies, $\mathrm{meV} \lesssim \omega \lesssim 100 \, \mathrm{meV}$. Such detectors are already being built to search for light dark matter (DM), and therefore sensitivity to high-frequency GWs will be achieved as a byproduct. We begin by deriving the absorption rate of a general GW signal into single phonons. We then focus on carefully defining the detector sensitivity to monochromatic and chirp signals, and compute the detector sensitivity for many proposed light DM detection targets. The detector sensitivity is then compared to the signal strength of candidate high-frequency GW sources, e.g., superradiant annihilation and black hole inspiral, as well as other recent detector proposals in the $\mathrm{MHz} \lesssim f \lesssim 100 \, \mathrm{THz}$ frequency range. With a judicious choice of target materials, a collection of detectors could optimistically achieve sensitivities to monochromatic signals with $h_0 \sim 10^{-23} - 10^{-25}$ over $\mathrm{THz} \lesssim f \lesssim 100 \, \mathrm{THz}$.

hep-ph↗