arXiv subjects

Jeffrey Lazar

Publications and source records attributed to Jeffrey Lazar.

15 recordsLinked to original sources

arXiv2026

SPORE: An Event-Level Sampling Pipeline for Multi-Telescope Neutrino Astronomy

We present an open-source Python package for simulating neutrino events from astrophysical point and extended sources using tabulated instrument response functions (IRFs). The package encodes three detector response components - effective area, point spread function, and energy resolution - in a selection-agnostic HDF5 format, which can represent a neutrino telescope whose response is supplied in that form, whether from a public release or a private study. Sampling algorithms cover point sources (inverse-CDF with Poisson or fixed-count modes), extended sky distributions (hierarchical inverse-CDF sampling, including full RA- and declination-dependent flux maps), and multi-detector joint analyses. We validate the framework via a round-trip consistency test using the publicly available IceCube 10-year tracks data release: the released IRFs are ingested into the package and used to generate a synthetic event set, whose declination distribution reproduces the observed one to 10-15% across the northern sky. The reconstructed-energy distribution agrees to within about a third over the bulk of the sample but exceeds the data by up to a factor of three below 600 GeV, a discrepancy we trace to the coarse true-energy binning of the public smearing matrix rather than to the sampling: an independent forward fold of the same IRFs reproduces it. We further compare against the IceCube HESE 7.5-year public data release: the sampled deposited-energy spectrum tracks the published best-fit expectation, and the observed data fall within the goodness-of-fit distribution built from 1,000 sampled pseudo-experiments, though on its well-fitting side, as expected for an expectation that was itself fit to those data.

astro-ph.IM↗

arXiv2026

Improving Neutrino Point Source Sensitivity with Source-Informed Event Selection

Neutrino telescopes employ multi-level reconstruction chains, where computationally expensive high-quality reconstructions are applied only to events that survive initial quality cuts based on fast, coarse directional estimates. Currently, event selection between reconstruction levels is source-agnostic, giving no priority to events from directions of known neutrino source candidates. We propose a simple modification to inter-level event selection: preferentially retain events whose early-level reconstruction places them within an angular tolerance of pre-specified candidate source directions from established multi-messenger catalogs, while continuing to subsample remaining events at the baseline rate. Using a realistic two-level detector model with energy-dependent angular resolution, we show that this source-informed selection can improve median point source sensitivity by factors of $\sim 2$--$3$ compared to uniform subsampling, with the improvement depending on the baseline selection efficiency, angular tolerance, and correlation between reconstruction qualities at different levels. For catalogs of $\mathcal{O}(100)$ sources, the additional computational overhead is modest ($\sim 7$--$14\%$). This approach offers a path to substantially enhance the discovery potential of current and future neutrino telescopes without requiring new detector capabilities.

hep-ex↗

arXiv2026

NuBench: An Open Benchmark for Deep Learning-Based Event Reconstruction in Neutrino Telescopes

Neutrino telescopes are large-scale detectors designed to observe Cherenkov radiation produced from neutrino interactions in water or ice. They exist to identify extraterrestrial neutrino sources and to probe fundamental questions pertaining to the elusive neutrino itself. A central challenge common across neutrino telescopes is to solve a series of inverse problems known as event reconstruction, which seeks to resolve properties of the incident neutrino, based on the detected Cherenkov light. In recent times, significant efforts have been made in adapting advances from deep learning research to event reconstruction, as such techniques provide several benefits over traditional methods. While a large degree of similarity in reconstruction needs and low-level data exists, cross-experimental collaboration has been hindered by a lack of diverse open-source datasets for comparing methods. We present NuBench, an open benchmark for deep learning-based event reconstruction in neutrino telescopes. NuBench comprises seven large-scale simulated datasets containing nearly 130 million charged- and neutral-current muon-neutrino interactions spanning 10 GeV to 100 TeV, generated across six detector geometries inspired by existing and proposed experiments. These datasets provide pulse- and event-level information suitable for developing and comparing machine-learning reconstruction methods in both water and ice environments. Using NuBench, we evaluate four reconstruction algorithms - ParticleNeT and DynEdge, both actively used within the KM3NeT and IceCube collaborations, respectively, along with GRIT and DeepIce - on up to five core tasks: energy and direction reconstruction, topology classification, interaction vertex prediction, and inelasticity estimation.

hep-ex↗

arXiv2026

Machine Learning on Heterogeneous, Edge, and Quantum Hardware for Particle Physics (ML-HEQUPP)

The next generation of particle physics experiments will face a new era of challenges in data acquisition, due to unprecedented data rates and volumes along with extreme environments and operational constraints. Harnessing this data for scientific discovery demands real-time inference and decision-making, intelligent data reduction, and efficient processing architectures beyond current capabilities. Crucial to the success of this experimental paradigm are several emerging technologies, such as artificial intelligence and machine learning (AI/ML), silicon microelectronics, and the advent of quantum algorithms and processing. Their intersection includes areas of research such as low-power and low-latency devices for edge computing, heterogeneous accelerator systems, reconfigurable hardware, novel codesign and synthesis strategies, readout for cryogenic or high-radiation environments, and analog computing. This white paper presents a community-driven vision to identify and prioritize research and development opportunities in hardware-based ML systems and corresponding physics applications, contributing towards a successful transition to the new data frontier of fundamental science.

physics.ins-det↗

arXiv2025

Prospective Sensitivity to Solar Dark Matter using the IceCube Upgrade

While astrophysical observations imply that 85% of the matter content is unaccounted for, the nature of this dark matter (DM) component remains unknown. Weakly Interacting Massive Particles (WIMPs) - DM particles that interact at or below the weak interaction scale - could naturally explain this missing matter. These interactions with the Standard Model (SM) allow them to be gravitationally captured in celestial bodies like the Sun. Trapped DM in the solar core could subsequently annihilate, producing stable SM particles, of which only neutrinos can escape the Sun's dense interior. Therefore, an excess of neutrinos originating from the direction of the Sun would serve as evidence of DM. The IceCube Upgrade, a dense infill of the IceCube Neutrino Observatory, will lower the energy threshold and improve sensitivity in the range from 1 to 500 GeV, thereby enhancing IceCube's ability to detect GeV-scale DM. In this contribution, I present projections of the IceCube Upgrade's sensitivity to the DM-proton scattering cross section for DM masses between 3 GeV and 500 GeV. These sensitivities position IceCube as the most sensitive indirect detection experiment for DM in the mass range from 3 GeV to 10 TeV.

astro-ph.HE↗

arXiv2025

Limits on WIMP-Scattering Cross Sections using Solar Neutrinos with Ten Years of IceCube Data

Although dark matter (DM) comprises 84\% of the matter content of the Universe, its nature remains unknown. One broad class of particle DM motivated by extensions of the Standard Model (SM) is weakly interacting massive particles (WIMPs). Generically, WIMPs will scatter off nuclei in large celestial bodies such as the Sun, thus becoming gravitationally bound. Subsequently, WIMPs can annihilate to stable SM particles, ultimately releasing most of their energy as high-energy neutrinos which escape from the Sun. Thus, an excess of neutrinos from the Sun's direction would be evidence for WIMPs. The IceCube Neutrino Observatory is well-suited to such searches since it is sensitive to WIMPs with masses in the region preferred by supersymmetric extensions of the SM. I will present the results of IceCube's most recent solar WIMP search, which includes all neutrino flavors, covers the WIMP mass range from 20 GeV to 10 TeV, and has world-leading sensitivity over this entire range for most channels considered.

astro-ph.HE↗

arXiv2025

Simulation and Performance Studies for the Tau Air-Shower Mountain-Based Observatory

While IceCube's detection of astrophysical neutrinos at energies up to a few PeV has opened a new window to our Universe, much remains to be discovered regarding these neutrinos' origin and nature. In particular, the difficulty of differentiating electron- and tau-neutrino charged-current (CC) events limits our ability to measure precisely the flavor ratio of this flux. The Tau Air-Shower Mountain-Based Observatory (TAMBO) is a next-generation neutrino observatory capable of producing a high-purity sample of tau-neutrino CC events in the energy range from 1 PeV--100 PeV, i.e. just above the IceCube measurements. An array of water Cherenkov tanks and plastic scintillators deployed in the Colca Canyon will observe the air-shower produced when a tau lepton, produced in a tau-neutrino CC interaction, emerges from the opposite face and decays in the air. In this contribution, I will present the performance studies for TAMBO -- including the expected rates, effective areas, and discrimination potential -- as well as the simulation on which these studies are based.

astro-ph.HE↗

arXiv2024

New Pathways in Neutrino Physics via Quantum-Encoded Data Analysis

Ever-increasing amount of data is produced by particle detectors in their quest to unveil the laws of Nature. The large data rate requires the use of specialized triggers that promptly reduce the data rate to a manageable level; however, in doing so, unexpected new phenomena may escape detection. Additionally, the large data rate is increasingly difficult to analyze effectively, which has led to a recent revolution on machine learning techniques. Here, we present a methodology based on recent quantum compression techniques that has the capacity to store exponentially more amount of information than classically available methods. To demonstrate this, we encode the full neutrino telescope event information using parity observables in an IBM quantum processor using 8 qubits. Then we show that we can recover the information stored on the quantum computer with a fidelity of 84%. Finally, we illustrate the use of our protocol by performing a classification task that separates electron-neutrino events to muon-neutrinos events in a neutrino telescope. This new capability would eventually allow us to solve the street light effect in particle physics, where we only record signatures of particles with which we are familiar.

hep-ex↗

arXiv2023

Trigger-Level Event Reconstruction for Neutrino Telescopes Using Sparse Submanifold Convolutional Neural Networks

Convolutional neural networks (CNNs) have seen extensive applications in scientific data analysis, including in neutrino telescopes. However, the data from these experiments present numerous challenges to CNNs, such as non-regular geometry, sparsity, and high dimensionality. Consequently, CNNs are highly inefficient on neutrino telescope data, and require significant pre-processing that results in information loss. We propose sparse submanifold convolutions (SSCNNs) as a solution to these issues and show that the SSCNN event reconstruction performance is comparable to or better than traditional and machine learning algorithms. Additionally, our SSCNN runs approximately 16 times faster than a traditional CNN on a GPU. As a result of this speedup, it is expected to be capable of handling the trigger-level event rate of IceCube-scale neutrino telescopes. These networks could be used to improve the first estimation of the neutrino energy and direction to seed more advanced reconstructions, or to provide this information to an alert-sending system to quickly follow-up interesting events.

hep-ex↗

arXiv2023

Prometheus: An Open-Source Neutrino Telescope Simulation

Neutrino telescopes are gigaton-scale neutrino detectors comprised of individual light-detection units. Though constructed from simple building blocks, they have opened a new window to the Universe and are able to probe center-of-mass energies that are comparable to those of collider experiments. \prometheus{} is a new, open-source simulation tailored for this kind of detector. Our package, which is written in a combination of \texttt{C++} and \texttt{Python} provides a balance of ease of use and performance and allows the user to simulate a neutrino telescope with arbitrary geometry deployed in ice or water. \prometheus{} simulates the neutrino interactions in the volume surrounding the detector, computes the light yield of the hadronic shower and the out-going lepton, propagates the photons in the medium, and records their arrival times and position in user-defined regions. Finally, \prometheus{} events are serialized into a \texttt{parquet} file, which is a compact and interoperational file format that allows prompt access to the events for further analysis.

hep-ex↗

arXiv2022

Quantum computing for data analysis in high energy physics

Some of the biggest achievements of the modern era of particle physics, such as the discovery of the Higgs boson, have been made possible by the tremendous effort in building and operating large-scale experiments like the Large Hadron Collider or the Tevatron. In these facilities, the ultimate theory to describe matter at the most fundamental level is constantly probed and verified. These experiments often produce large amounts of data that require storing, processing, and analysis techniques that often push the limits of traditional information processing schemes. Thus, the High-Energy Physics (HEP) field has benefited from advancements in information processing and the development of algorithms and tools for large datasets. More recently, quantum computing applications have been investigated in an effort to understand how the community can benefit from the advantages of quantum information science. In this manuscript, we provide an overview of the state-of-the-art applications of quantum computing to data analysis in HEP, discuss the challenges and opportunities in integrating these novel analysis techniques into a day-to-day analysis workflow, and whether there is potential for a quantum advantage.

physics.data-an↗

arXiv2021

TauRunner: A Public Python Program to Propagate Neutral and Charged Leptons

In the past decade IceCube's observations have revealed a flux of astrophysical neutrinos extending to $10^{7}~\rm{GeV}$. The forthcoming generation of neutrino observatories promises to grant further insight into the high-energy neutrino sky, with sensitivity reaching energies up to $10^{12}~\rm{GeV}$. At such high energies, a new set of effects becomes relevant, which was not accounted for in the last generation of neutrino propagation software. Thus, it is important to develop new simulations which efficiently and accurately model lepton behavior at this scale. We present TauRunner a PYTHON-based package that propagates neutral and charged leptons. TauRunner supports propagation between $10~\rm{GeV}$ and $10^{12}~\rm{GeV}$. The package accounts for all relevant secondary neutrinos produced in charged-current tau neutrino interactions. Additionally, tau energy losses of taus produced in neutrino interactions is taken into account, and treated stochastically. Finally, TauRunner is broadly adaptable to divers experimental setups, allowing for user-specified trajectories and propagation media, neutrino cross sections, and initial spectra.

hep-ph↗

arXiv2021

Recent Progress in Solar Atmospheric Neutrino Searches with IceCube

Cosmic-rays interacting with nucleons in the solar atmosphere produce a cascade of particles that give rise to a flux of high-energy neutrinos and gamma-rays. Fermi has observed this gamma-ray flux; however, the associated neutrino flux has escaped observation. In this contribution, we put forward two strategies to detect these neutrinos, which, if seen, would push forward our understanding of the solar atmosphere and provide a new testing ground of neutrino properties. First, we will extend the previous analysis, which used high-energy through-going muon events collected in the years of maximum solar activity and yielded only flux upper limits, to include data taken during the solar minimum from 2018 to 2020. Extending the analysis to the solar minimum is important as the gamma-ray data collected during past solar cycles indicates a possible enhancement in the high-energy neutrino flux. Second, we will incorporate sub-TeV events and include contributions from all neutrino flavors. These will improve our analysis sensitivity since the solar atmospheric spectrum is soft and, due to oscillation, contains significant contributions of all neutrino flavors. As we will present in this contribution, these complementary strategies yield a significant improvement in sensitivity, making substantial progress towards observing this flux.

astro-ph.HE↗

arXiv2020

$χ$aro$ν$: a tool for neutrino flux generation from WIMPs

Indirect searches for signatures of corpuscular dark matter have been performed using all cosmic messengers: gamma rays, cosmic rays, and neutrinos. The search for dark matter from neutrinos is of particular importance since they are the only courier that can reach detectors from dark matter processes in dense environments, such as the core of the Sun or Earth, or from the edge of the observable Universe. In this work, we introduce $χ$aro$ν$, a software package that, in the spirit of its mythological Greek namesake $χ\acuteαρων$, bridges the dark sector and Standard Model by predicting neutrino fluxes from different celestial dark matter agglomerations. The flux at the point of production is either computed internally by $χ$aro$ν$ or is taken from user supplied tables. $χ$aro$ν$ then propagates this flux through vacuum or dense media and returns the expected neutrino yield at an observer's location. In developing $χ$aro$ν$, we have revisited and updated the production of neutrinos in dense media, updated the propagation of high-energy neutrinos, and studied the sources of uncertainty in neutrino transport. This package is coupled to a new calculation that includes electroweak corrections resulting in the most up-to-date and complete repository of neutrino fluxes from dark matter decay and annihilation over the energy range of 1 GeV to 10 PeV coming from the Earth, the Sun, and the Galactic halo.

hep-ph↗

arXiv2019

Search for Dark Matter Annihilation to Neutrinos from the Sun

Weakly interacting massive particles (WIMPs) can be gravitationally captured by the Sun and trapped in its core. The annihilation of those WIMPs into Standard Model particles produces a spectrum of neutrinos whose energy distribution is related to the dark matter mass. In this work, we present the theoretical framework for relating an observed neutrino flux to the WIMP-nucleon cross section and summarize a previous solar WIMP search carried out by IceCube. We then outline an ongoing updated solar WIMP search, focusing on improvements over the previous search.

astro-ph.HE↗