arXiv subjects

Ariel Schwartzman

Publications and source records attributed to Ariel Schwartzman.

At least 19 recordsLinked to original sources

arXiv2026

Geometric Signatures of Conceptual Reorganization: A Counterfactual Embedding Framework for Detecting Scientific Revolutions

We introduce document embedding geometry as a quantitative observable of conceptual reorganization and develop a counterfactual ablation framework for measuring how individual concepts influence the organization of scientific knowledge, providing a quantitative framework for detecting scientific revolutions. The observable is defined by the geometric perturbation induced when removing documents associated with a candidate concept from the embedding space before and after its historical emergence. Statistical validation is performed using five historical case studies spanning physics, mathematics, and machine learning: special relativity, Gödel's incompleteness theorems, the Higgs mechanism, deep learning, and the attention mechanism underlying transformer architectures. Across the historical case studies, the framework identifies measurable geometric signatures associated with conceptual reorganization, while the validation studies expose important limitations arising from document assignment and sparse historical data. These results establish embedding geometry as a medium for quantifying conceptual reorganization, providing a new approach for studying how scientific fields restructure over time.

cs.DL↗

arXiv2026

Towards 6D Tracking: A Study Of Using Fast-Timing For Measuring Track Position, Time, And Angles

Next-generation particle tracking detectors will incorporate precision timing capabilities with resolutions approaching tens of picoseconds. Using Technology Computer-Aided Design (TCAD) simulations of Low-Gain Avalanche Diode (LGAD) detectors, we demonstrate that tilted particle tracks traversing multiple pixels exhibit systematic timing variations of hundreds of picoseconds between adjacent pixels. We derive an analytical linear model relating inter-pixel timing differences to incident track angles, enabling single-layer angular reconstruction with a precision of a few degrees for tracks which traverse at least three pixels. Stochastic energy loss fluctuations (Landau fluctuations) impose a fundamental limit on both angular resolution and reconstruction efficiency. Comparison with neural network approaches demonstrates that the linear model achieves near-optimal angular resolution, indicating that the physics of charge collection geometry, rather than algorithmic sophistication, dominates the achievable angular resolution.

physics.ins-det↗

arXiv2026

Higgs Physics with the XFEL Compton $\boldsymbol{γγ}$ Collider Concept at $\boldsymbol{\sqrt{s}=125}$ GeV

We investigate single Higgs production in $\sqrt{s}=125$ GeV $γγ$ collisions at the X-Ray Free Electron (XFEL) Compton Collider (XCC) concept and present an analysis targeting the major hadronic, semi-leptonic, and fully leptonic final states of the Higgs boson, including $H\to s\overline{s}$. In addition to studying Higgs production at a novel collider concept, our approach couples a novel set transformer-based deep learning framework that acts on particle-flow object point clouds with a genetic algorithm optimizer for signal-background discrimination, yielding significantly higher sensitivity than traditional methods. Our results demonstrate that an XFEL $γγ$ collider can probe the Higgs sector with extremely high precision and enable new physics opportunities, complementary to proposed $e^+e^-$ machines.

hep-ph↗

arXiv2026

Probing the Higgs Self-Coupling with an XFEL Compton $γγ$ Collider at $\sqrt{s} = 380$ GeV

We present a study probing the Higgs self-coupling with the X-ray free-electron laser Compton $γγ$ Collider (XCC) concept. The analysis is performed considering the $γγ\to HH \to bb\overline{bb}$ channel, and results are then extrapolated to obtain a projection on the Higgs self-coupling sensitivity that ranges between 7% and 12%. An ensemble of boosted decision trees is trained to discriminate between signal and backgrounds, paired with a genetic algorithm optimizer to combine the final classifier outputs. This study suggests that an X-ray FEL-based $γγ$ collider is a powerful tool to probe the mechanism of electroweak symmetry breaking, complementary to $e^+e^-$ Higgs factories and future high-energy hadron colliders.

hep-ph↗

arXiv2026

PanopTag: Simultaneously Tagging All Jets in a Particle Collision Event

Jet tagging, identifying the origin of jets produced in particle collisions, is a critical classification task in high-energy physics. Despite the revolutionary impact of deep learning on jet tagging over the past decade, the paradigm has remained unchanged. In particular, jets are classified independently, one at a time. This single-jet approach ignores correlations, overlaps, and wider event context between jets. We introduce PanopTag, a new paradigm for jet tagging that departs from traditional single-jet tagging approaches. Rather than classifying jets independently, PanopTag simultaneously tags all jets by employing an encoder-decoder architecture that uses jet kinematics as queries to cross-attend to particle flow object embeddings. We evaluate PanopTag on heavy-flavor $(b/c)$-tagging and demonstrate remarkable performance improvements over state-of-the-art single-jet baselines that are only accessible by exploiting event-level features and correlations between jets.

hep-ph↗

arXiv2025

Design Initiative for a 10 TeV pCM Wakefield Collider

This document outlines a community-driven Design Study for a 10 TeV pCM Wakefield Accelerator Collider. The 2020 ESPP Report emphasized the need for Advanced Accelerator R\&D, and the 2023 P5 Report calls for the ``delivery of an end-to-end design concept, including cost scales, with self-consistent parameters throughout." This Design Study leverages recent experimental and theoretical progress resulting from a global R\&D program in order to deliver a unified, 10 TeV Wakefield Collider concept. Wakefield Accelerators provide ultra-high accelerating gradients which enables an upgrade path that will extend the reach of Linear Colliders beyond the electroweak scale. Here, we describe the organization of the Design Study including timeline and deliverables, and we detail the requirements and challenges on the path to a 10 TeV Wakefield Collider.

physics.acc-ph↗

arXiv2022

Novel Light Field Imaging Device with Enhanced Light Collection for Cold Atom Clouds

We present a light field imaging system that captures multiple views of an object with a single shot. The system is designed to maximize the total light collection by accepting a larger solid angle of light than a conventional lens with equivalent depth of field. This is achieved by populating a plane of virtual objects using mirrors and fully utilizing the available field of view and depth of field. Simulation results demonstrate that this design is capable of single-shot tomography of objects of size $\mathcal{O}$(1 mm$^3$), reconstructing the 3-dimensional (3D) distribution and features not accessible from any single view angle in isolation. In particular, for atom clouds used in atom interferometry experiments, the system can reconstruct 3D fringe patterns with size $\mathcal{O}$(100 $μ$m). We also demonstrate this system with a 3D-printed prototype. The prototype is used to take images of $\mathcal{O}$(1 mm$^{3}$) sized objects, and 3D reconstruction algorithms running on a single-shot image successfully reconstruct $\mathcal{O}$(100 $μ$m) internal features. The prototype also shows that the system can be built with 3D printing technology and hence can be deployed quickly and cost-effectively in experiments with needs for enhanced light collection or 3D reconstruction. Imaging of cold atom clouds in atom interferometry is a key application of this new type of imaging device where enhanced light collection, high depth of field, and 3D tomographic reconstruction can provide new handles to characterize the atom clouds.

physics.ins-det↗

arXiv2022

Snowmass2021 Cosmic Frontier White Paper: Future Gravitational-Wave Detector Facilities

The next generation of gravitational-wave observatories can explore a wide range of fundamental physics phenomena throughout the history of the universe. These phenomena include access to the universe's binary black hole population throughout cosmic time, to the universe's expansion history independent of the cosmic distance ladders, to stochastic gravitational-waves from early-universe phase transitions, to warped space-time in the strong-field and high-velocity limit, to the equation of state of nuclear matter at neutron star and post-merger densities, and to dark matter candidates through their interaction in extreme astrophysical environments or their interaction with the detector itself. We present the gravitational-wave detector concepts than can drive the future of gravitational-wave astrophysics. We summarize the status of the necessary technology, and the research needed to be able to build these observatories in the 2030s.

gr-qc↗

arXiv2022

4-Dimensional Trackers

4-dimensional (4D) trackers with ultra fast timing (10-30 ps) and very fine spatial resolution (O(few $μ$m)) represent a new avenue in the development of silicon trackers, enabling new physics capabilities beyond the reach of the existing tracking detectors. This paper reviews the impact of integrating 4D tracking capabilities on several physics benchmarks both in potential upgrades of the HL-LHC experiments and in several detectors at future colliders, and summarizes the currently available sensor technologies as well as electronics, along with their limitations and directions for R$\&$D.

physics.ins-det↗

arXiv2019

Parametrizing the Detector Response with Neural Networks

In high energy physics, characterizing the response of a detector to radiation is one of the most important and basic experimental tasks. In many cases, this task is accomplished by parameterizing summary statistics of the full detector response probability density. The parameterized detector response can then be used for calibration as well as for directly improving physics analysis sensitivity. This paper discusses how to parameterize summary statistics of the detector response using neural networks. In particular, neural networks are powerful tools for incorporating multidimensional data and the loss function used during training determines which summary statistic is learned. One common summary statistic that has not been combined with deep learning (as far as the authors are aware) is the mode. A neural network-based approach to mode learning is proposed and empirically demonstrated in the context of high energy jet calibrations. Altogether, the neural network-based toolkit for detector response parameterization can enhance the utility of data collected at high energy physics experiments and beyond.

physics.data-an↗

arXiv2019

Machine Learning in High Energy Physics Community White Paper

Machine learning has been applied to several problems in particle physics research, beginning with applications to high-level physics analysis in the 1990s and 2000s, followed by an explosion of applications in particle and event identification and reconstruction in the 2010s. In this document we discuss promising future research and development areas for machine learning in particle physics. We detail a roadmap for their implementation, software and hardware resource requirements, collaborative initiatives with the data science community, academia and industry, and training the particle physics community in data science. The main objective of the document is to connect and motivate these areas of research and development with the physics drivers of the High-Luminosity Large Hadron Collider and future neutrino experiments and identify the resource needs for their implementation. Additionally we identify areas where collaboration with external communities will be of great benefit.

physics.comp-ph↗

arXiv2017

Weakly Supervised Classification in High Energy Physics

As machine learning algorithms become increasingly sophisticated to exploit subtle features of the data, they often become more dependent on simulations. This paper presents a new approach called weakly supervised classification in which class proportions are the only input into the machine learning algorithm. Using one of the most challenging binary classification tasks in high energy physics - quark versus gluon tagging - we show that weakly supervised classification can match the performance of fully supervised algorithms. Furthermore, by design, the new algorithm is insensitive to any mis-modeling of discriminating features in the data by the simulation. Weakly supervised classification is a general procedure that can be applied to a wide variety of learning problems to boost performance and robustness when detailed simulations are not reliable or not available.

hep-ph↗

arXiv2017

Jet-Images -- Deep Learning Edition

Building on the notion of a particle physics detector as a camera and the collimated streams of high energy particles, or jets, it measures as an image, we investigate the potential of machine learning techniques based on deep learning architectures to identify highly boosted W bosons. Modern deep learning algorithms trained on jet images can out-perform standard physically-motivated feature driven approaches to jet tagging. We develop techniques for visualizing how these features are learned by the network and what additional information is used to improve performance. This interplay between physically-motivated feature driven tools and supervised learning algorithms is general and can be used to significantly increase the sensitivity to discover new particles and new forces, and gain a deeper understanding of the physics within jets.

hep-ph↗

arXiv2015

Jet energy calibration at the LHC

Jets are one of the most prominent physics signatures of high energy proton proton (p-p) collisions at the Large Hadron Collider (LHC). They are key physics objects for precision measurements and searches for new phenomena. This review provides an overview of the reconstruction and calibration of jets at the LHC during its first Run. ATLAS and CMS developed different approaches for the reconstruction of jets, but use similar methods for the energy calibration. ATLAS reconstructs jets utilizing input signals from their calorimeters and use charged particle tracks to refine their energy measurement and suppress the effects of multiple p-p interactions (pileup). CMS, instead, combines calorimeter and tracking information to build jets from particle flow objects. Jets are calibrated using Monte Carlo (MC) simulations and a residual in situ calibration derived from collision data is applied to correct for the differences in jet response between data and Monte Carlo. Large samples of dijet, Z+jets, and photon+jet events at the LHC allowed the calibration of jets with high precision, leading to very small systematic uncertainties. Both ATLAS and CMS achieved a jet energy calibration uncertainty of about 1% in the central detector region and for jets with transverse momentum pT>100 GeV. At low jet pT, the jet energy calibration uncertainty is less than 4%, with dominant contributions from pileup, differences in energy scale between quark and gluon jets, and jet flavor composition.

hep-ex↗

arXiv2015

Jet-Images: Computer Vision Inspired Techniques for Jet Tagging

We introduce a novel approach to jet tagging and classification through the use of techniques inspired by computer vision. Drawing parallels to the problem of facial recognition in images, we define a jet-image using calorimeter towers as the elements of the image and establish jet-image preprocessing methods. For the jet-image processing step, we develop a discriminant for classifying the jet-images derived using Fisher discriminant analysis. The effectiveness of the technique is shown within the context of identifying boosted hadronic W boson decays with respect to a background of quark- and gluon- initiated jets. Using Monte Carlo simulation, we demonstrate that the performance of this technique introduces additional discriminating power over other substructure approaches, and gives significant insight into the internal structure of jets.

hep-ph↗

arXiv2015

Fuzzy Jets

Collimated streams of particles produced in high energy physics experiments are organized using clustering algorithms to form jets. To construct jets, the experimental collaborations based at the Large Hadron Collider (LHC) primarily use agglomerative hierarchical clustering schemes known as sequential recombination. We propose a new class of algorithms for clustering jets that use infrared and collinear safe mixture models. These new algorithms, known as fuzzy jets, are clustered using maximum likelihood techniques and can dynamically determine various properties of jets like their size. We show that the fuzzy jet size adds additional information to conventional jet tagging variables. Furthermore, we study the impact of pileup and show that with some slight modifications to the algorithm, fuzzy jets can be stable up to high pileup interaction multiplicities.

hep-ph↗

arXiv2014

Jets from Jets: Re-clustering as a tool for large radius jet reconstruction and grooming at the LHC

Jets with a large radius $R\gtrsim 1$ and grooming algorithms are widely used to fully capture the decay products of boosted heavy particles at the Large Hadron Collider (LHC). Unlike most discriminating variables used in such studies, the jet radius is usually not optimized for specific physics scenarios. This is because every jet configuration must be calibrated, insitu, to account for detector response and other experimental effects. One solution to enhance the availability of large-$R$ jet configurations used by the LHC experiments is {\it jet re-clustering}. Jet re-clustering introduces an intermediate scale $r<R$ at which jets are calibrated and used as the inputs to reconstruct large radius jets. In this paper we systematically study and propose new jet re-clustering configurations and show that re-clustered large radius jets have essentially the same jet mass performance as large radius groomed jets. Jet re-clustering has the benefit that no additional large-R calibration is necessary, allowing the re-clustered large radius parameter to be optimized in the context of specific precision measurements or searches for new physics.

hep-ph↗

arXiv2014

Spatial Mass

In analogy to the transverse mass constructed from two objects, we define the spatial mass constructed from the input objects 3-vector momenta. This observable is insensitive to jet mass scale and resolution uncertainties when constructed from small-radius hadronic jets. Thus it improves the effective resolution on multijet masses for searches and measurements in hadronic final states. To illustrate the efficacy of the spatial mass, we consider a top quark mass measurement at the Large Hadron Collider (LHC) in the 3-jet final state. The reduction in uncertainty comes with a negligible cost in sensitivity.

hep-ph↗