arXiv subjects

Jessica N. Howard

Publications and source records attributed to Jessica N. Howard.

17 recordsLinked to original sources

arXiv2026

Machine Learning Does It and Does It Better: Unearthing Primordial Dark-Matter Velocities from the Matter Power Spectrum

One effective way of learning about the production and properties of dark matter in the early universe is by extracting information about the primordial dark-matter phase-space distribution from the matter power spectrum. Several years ago a simple empirical formula was introduced which successfully reproduces most of the salient features of the primordial dark-matter phase-space distribution from the matter power spectrum -- even in situations in which this distribution is non-thermal, multi-modal, or exhibits other complicated features. Continuing this line of research, we investigate the extent to which machine-learning techniques can improve upon this analytic approach. Interestingly, we find that a one-dimensional convolutional neural network not only succeeds in reconstructing the dark-matter phase-space distribution with greater accuracy, but can also be applied to a broader range of matter power spectra.

astro-ph.CO↗

arXiv2026

AI and the Research-Education Environment of Physics

In the current era of AI transforming the research-education environment of physics, variety of issues and concerns arise. The KITP program "Generative AI for High and Low Energy Physics'' offered a discussion session on this, and here presented is a summary of the opinions provided in the discussion. The material is formulated such that it can serve as a starting point for further discussions in readers' research community/institution/group.

physics.ed-ph↗

arXiv2026

Optimal Transport for $e/π^0$ Particle Classification in LArTPC Neutrino Experiments

The efficient classification of electromagnetic activity from $π^0$ and electrons remains an open problem in the reconstruction of neutrino interactions in Liquid Argon Time Projection Chamber (LArTPC) detectors. We address this problem using the mathematical framework of Optimal Transport (OT), which has been successfully employed for event classification in other HEP contexts and is ideally suited to the high-resolution calorimetry of LArTPCs. Using a publicly available simulated dataset from the MicroBooNE collaboration, we show that OT methods achieve state-of-the-art reconstruction performance in $e/π^0$ classification. The success of this first application indicates the broader promise of OT methods for LArTPC-based neutrino experiments.

hep-ex↗

arXiv2025

Wilsonian Renormalization of Neural Network Gaussian Processes

Separating relevant and irrelevant information is key to any modeling process or scientific inquiry. Theoretical physics offers a powerful tool for achieving this in the form of the renormalization group (RG). Here we demonstrate a practical approach to performing Wilsonian RG in the context of Gaussian Process (GP) Regression. We systematically integrate out the unlearnable modes of the GP kernel, thereby obtaining an RG flow of the GP in which the data sets the IR scale. In simple cases, this results in a universal flow of the ridge parameter, which becomes input-dependent in the richer scenario in which non-Gaussianities are included. In addition to being analytically tractable, this approach goes beyond structural analogies between RG and neural networks by providing a natural connection between RG flow and learnable vs. unlearnable modes. Studying such flows may improve our understanding of feature learning in deep neural networks, and enable us to identify potential universality classes in these models.

cs.LG↗

arXiv2025

Bayesian RG Flow in Neural Network Field Theories

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' $\rightarrow$ `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' $\rightarrow$ `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.

hep-th↗

arXiv2025

Rising Stargirls: Benefits of a Creative Arts-Based Approach to Astronomy Education for Middle-School Girls from Underrepresented Groups

Women from historically marginalized groups in the sciences continue to be severely underrepresented in the fields of physics and astronomy. Young girls identifying with these groups often lose interest in science, technology, engineering, and math (STEM) fields well before college. Middle school (grades 6-8) emerges as a pivotal phase for nurturing science identities among girls. The educational program Rising Stargirls offers creative arts-based astronomy workshops for middle-school girls, with the aim of cultivating their science identities. We retrospectively analyze participants' responses to four key assessment items through which their engagement in science and their science identities before and after the workshops are assessed. Our findings overwhelmingly indicate that girls exhibit heightened engagement in science and enhanced science identities after engaging in the Rising Stargirls program. These outcomes underscore the merits of fostering creativity and integrating the arts into science education.

physics.ed-ph↗

arXiv2024

Loops in supergroups

We study the theory of a scalar in the fundamental representation of the internal supergroup $SU(N|M)$. Remarkably, for $M=N+1$ its tree-level mass does not receive quantum corrections at one loop from either self-coupling or interactions with gauge bosons and fermions. This property comes at the price of introducing both degrees of freedom with wrong statistics and with wrong sign kinetic terms. We detail a method to break $SU(N|M)$ down to its bosonic subgroup through a Higgs-like mechanism, allowing for the partial decoupling of the dangerous modes, and study the associated vacuum structure up to one loop.

hep-ph↗

arXiv2024

Supergroup Symmetries and the Hierarchy Problem

We show that the mass of a scalar field transforming in the fundamental of an $SU(N|N+1)$ supergroup internal symmetry is protected against one-loop corrections from quartic, gauge, and yukawa interactions. Although the negative-norm states responsible for this protection are likely fatal to a unitary interpretation of the theory, such ultraviolet insensitivity may open a new avenue to understanding the lightness of the Higgs.

hep-ph↗

arXiv2024

Exploring Optimal Transport for Event-Level Anomaly Detection at the Large Hadron Collider

Anomaly detection is a promising, model-agnostic strategy to find physics beyond the Standard Model. State-of-the-art machine learning methods offer impressive performance on anomaly detection tasks, but interpretability, resource, and memory concerns motivate considering a wide range of alternatives. We explore using the 2-Wasserstein distance from optimal transport theory, both as an anomaly score and as input to interpretable machine learning methods, for event-level anomaly detection at the Large Hadron Collider. The choice of ground space plays a key role in optimizing performance. We comment on the feasibility of implementing these methods in the L1 trigger system.

hep-ph↗

arXiv2022

Machine Learning and LHC Event Generation

First-principle simulations are at the heart of the high-energy physics research program. They link the vast data output of multi-purpose detectors with fundamental theory predictions and interpretation. This review illustrates a wide range of applications of modern machine learning to event generation and simulation-based inference, including conceptional developments driven by the specific requirements of particle physics. New ideas and tools developed at the interface of particle physics and machine learning will improve the speed and precision of forward simulations, handle the complexity of collision data, and enhance inference as an inverse simulation problem.

hep-ph↗

arXiv2022

Learning to Simulate High Energy Particle Collisions from Unlabeled Data

In many scientific fields which rely on statistical inference, simulations are often used to map from theoretical models to experimental data, allowing scientists to test model predictions against experimental results. Experimental data is often reconstructed from indirect measurements causing the aggregate transformation from theoretical models to experimental data to be poorly-described analytically. Instead, numerical simulations are used at great computational cost. We introduce Optimal-Transport-based Unfolding and Simulation (OTUS), a fast simulator based on unsupervised machine-learning that is capable of predicting experimental data from theoretical models. Without the aid of current simulation information, OTUS trains a probabilistic autoencoder to transform directly between theoretical models and experimental data. Identifying the probabilistic autoencoder's latent space with the space of theoretical models causes the decoder network to become a fast, predictive simulator with the potential to replace current, computationally-costly simulators. Here, we provide proof-of-principle results on two particle physics examples, $Z$-boson and top-quark decays, but stress that OTUS can be widely applied to other fields.

hep-ph↗

arXiv2021

Dark Matter Freeze-out during $\mathrm{SU}(2)_{\rm L}$ Confinement

We explore the possibility that dark matter is a pair of vector-like fermionic $\mathrm{SU}(2)_{\rm L}$ doublets and propose a novel mechanism of dark matter production that proceeds through the confinement of the weak sector of the Standard Model. This confinement phase causes the Standard Model doublets and dark matter to confine into pions. The dark pions freeze-out before the weak sector deconfines and generate a relic abundance of dark matter. We solve the Boltzmann equations for this scenario to determine the scale of confinement and constituent dark matter mass required to produce the observed relic density. We determine which regions of this parameter space evade direct detection, collider bounds, and successfully produce the observed relic density of dark matter. For a TeV scale pair of vector-like fermionic $\mathrm{SU}(2)_{\rm L}$ doublets, we find the weak confinement scale to be $\sim 700$ TeV.

hep-ph↗

arXiv2021

How to GAN Higher Jet Resolution

QCD-jets at the LHC are described by simple physics principles. We show how super-resolution generative networks can learn the underlying structures and use them to improve the resolution of jet images. We test this approach on massless QCD-jets and on fat top-jets and find that the network reproduces their main features even without training on pure samples. In addition, we show how a slim network architecture can be constructed once we have control of the full network performance.

hep-ph↗

arXiv2020

Learning to Identify Electrons

We investigate whether state-of-the-art classification features commonly used to distinguish electrons from jet backgrounds in collider experiments are overlooking valuable information. A deep convolutional neural network analysis of electromagnetic and hadronic calorimeter deposits is compared to the performance of typical features, revealing a $\approx 5\%$ gap which indicates that these lower-level data do contain untapped classification power. To reveal the nature of this unused information, we use a recently developed technique to map the deep network into a space of physically interpretable observables. We identify two simple calorimeter observables which are not typically used for electron identification, but which mimic the decisions of the convolutional network and nearly close the performance gap.

physics.data-an↗

arXiv2020

QCD Baryogenesis

We explore a simple model which naturally explains the observed baryon asymmetry of the Universe. In this model the strong coupling is promoted to a dynamical quantity, which evolves through the vacuum expectation value of a singlet scalar field that mixes with the Higgs field. In the resulting cosmic history, QCD confinement and electroweak symmetry breaking initially occur simultaneously close to the weak scale. The early confinement triggers the axion to roll toward its minimum, which creates a chemical potential between baryons and antibaryons through the interactions of the $η'$ meson, resulting in spontaneous baryogenesis. The electroweak sphalerons are sharply switched off after confinement and the baryon asymmetry is frozen in. Subsequently, evolution of the Higgs vacuum expectation value (which is modified in the confined phase) triggers a relaxation to a Standard Model-like vacuum. We identify viable regions of parameter space, and describe various experimental probes, including current and future collider constraints, and gravitational wave phenomenology.

hep-ph↗

arXiv2018

The $τ$ Magnetic Dipole Moment at Future Lepton Colliders

The magnetic moment of the $τ$ lepton is an interesting quantity that is potentially sensitive to physics beyond the Standard Model. Electroweak gauge invariance implies that a heavy new physics contribution to it takes the form of an operator which involves the Higgs boson, implying that rare Higgs decays are able to probe the same physics as $a_τ$. We examine the prospects for rare Higgs decays at future high energy lepton (electron or muon) colliders, and find that such a project collecting a few ab$^{-1}$ would be able to advance our understanding of this physics by roughly a factor of 10 compared to the expected reach of the high luminosity LHC.

hep-ph↗

arXiv2018

Invariant Tensors in Gauge Theories

Invariant tensors play an important role in gauge theories, for example, in dualities of N=1 gauge theories. However, for theories with fields in representations larger than the fundamental, the full set of invariant tensors is often difficult to construct. We present a new approach to the construction of these tensors, and use it to find the complete set of invariant tensors of a theory of SO(3) with fields in the symmetric tensor representation.

hep-th↗