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Kate Scholberg

Publications and source records attributed to Kate Scholberg.

At least 19 recordsLinked to original sources
arXiv2026

Investigating the Neutrino Mass Ordering Problem via Ternary Plots

We explore what may be deduced about the neutrino mass ordering problem from the observation of core-collapse supernova burst neutrinos in modern terrestrial detectors. We employ ternary plots in a novel way to visualize the time evolution of the flavor composition of various supernova neutrino flux models from the SNEWPY software package. Through our analysis of several models using a simplified unfolding process, we have explored potential robust discriminants between the normal and inverted mass orderings. We find that the normal and inverted mass orderings tend to occupy different regions in ternary space across different models.

hep-ph↗
arXiv2024

Data-driven core collapse supernova multilateration with first neutrino events

A Galactic core-collapse supernova (CCSN) is likely to be observed in neutrino detectors around the world minutes to hours before the electromagnetic radiation arrives. The SNEWS2.0 network of neutrino and dark matter detectors aims to use the relative arrival times of the neutrinos at the different experiments to point back to the supernova so as to facilitate follow-up observation. One of the simplest methods to estimate the CCSN direction is to use the first neutrino events detected through the inverse beta decay (IBD) process, $\overline{\nu}_e p\rightarrow e^+n$. We will consider neutrino detectors sensitive to IBD interactions with low backgrounds. The difference in signal arrival times between a large and a small detector will be biased, however, with the first event at the smaller detector, on average, arriving later than that at the larger detector. This bias can be mitigated by using these first events in a data-driven approach without recourse to simulations or models. The resulting method requires, at minimum, only the times of the first events at most detectors, along with a longer time series of events from one larger detector to act as a reference lightcurve. In this article, we demonstrate this method and its uncertainty estimate using pairs of detectors of different sizes and with different supernova distances. Finally, we use this method to calculate probability skymaps using four detectors currently in operation (Super-Kamiokande, JUNO, LVD, and SNO+) and show that the calculated probabilities yield appropriate confidence intervals for all supernova directions. The area of the 68\% confidence interval varies by distance and direction, but is expected to be a few thousand square degrees. The resulting skymaps should be useful for the multi-messenger community as a rapid, initial pointing to follow up on the SNEWS2.0 Galactic CCSN neutrino alert.

astro-ph.HE↗
arXiv2024

Mineral Detection of Neutrinos and Dark Matter 2024. Proceedings

The second "Mineral Detection of Neutrinos and Dark Matter" (MDvDM'24) meeting was held January 8-11, 2024 in Arlington, VA, USA, hosted by Virginia Tech's Center for Neutrino Physics. This document collects contributions from this workshop, providing an overview of activities in the field. MDvDM'24 was the second topical workshop dedicated to the emerging field of mineral detection of neutrinos and dark matter, following a meeting hosted by IFPU in Trieste, Italy in October 2022. Mineral detectors have been proposed for a wide variety of applications, including searching for dark matter, measuring various fluxes of astrophysical neutrinos over gigayear timescales, monitoring nuclear reactors, and nuclear disarmament protocols; both as paleo-detectors using natural minerals that could have recorded the traces of nuclear recoils for timescales as long as a billion years and as detectors recording nuclear recoil events on laboratory timescales using natural or artificial minerals. Contributions to this proceedings discuss the vast physics potential, the progress in experimental studies, and the numerous challenges lying ahead on the path towards mineral detection. These include a better understanding of the formation and annealing of recoil defects in crystals; identifying the best classes of minerals and, for paleo-detectors, understanding their geology; modeling and control of the relevant backgrounds; developing, combining, and scaling up imaging and data analysis techniques; and many others. During the last years, MDvDM has grown rapidly and gained attention. Small-scale experimental efforts focused on establishing various microscopic readout techniques are underway at institutions in North America, Europe and Asia. We are looking ahead to an exciting future full of challenges to overcome, surprises to be encountered, and discoveries lying ahead of us.

astro-ph.CO↗
arXiv2023

Report of the 2021 U.S. Community Study on the Future of Particle Physics (Snowmass 2021) Summary Chapter

The 2021-22 High-Energy Physics Community Planning Exercise (a.k.a. ``Snowmass 2021'') was organized by the Division of Particles and Fields of the American Physical Society. Snowmass 2021 was a scientific study that provided an opportunity for the entire U.S. particle physics community, along with its international partners, to identify the most important scientific questions in High Energy Physics for the following decade, with an eye to the decade after that, and the experiments, facilities, infrastructure, and R&D needed to pursue them. This Snowmass summary report synthesizes the lessons learned and the main conclusions of the Community Planning Exercise as a whole and presents a community-informed synopsis of U.S. particle physics at the beginning of 2023. This document, along with the Snowmass reports from the various subfields, will provide input to the 2023 Particle Physics Project Prioritization Panel (P5) subpanel of the U.S. High-Energy Physics Advisory Panel (HEPAP), and will help to guide and inform the activity of the U.S. particle physics community during the next decade and beyond.

hep-ex↗
arXiv2023

Applications of Deep Learning to physics workflows

Modern large-scale physics experiments create datasets with sizes and streaming rates that can exceed those from industry leaders such as Google Cloud and Netflix. Fully processing these datasets requires both sufficient compute power and efficient workflows. Recent advances in Machine Learning (ML) and Artificial Intelligence (AI) can either improve or replace existing domain-specific algorithms to increase workflow efficiency. Not only can these algorithms improve the physics performance of current algorithms, but they can often be executed more quickly, especially when run on coprocessors such as GPUs or FPGAs. In the winter of 2023, MIT hosted the Accelerating Physics with ML at MIT workshop, which brought together researchers from gravitational-wave physics, multi-messenger astrophysics, and particle physics to discuss and share current efforts to integrate ML tools into their workflows. The following white paper highlights examples of algorithms and computing frameworks discussed during this workshop and summarizes the expected computing needs for the immediate future of the involved fields.

hep-ex↗
arXiv2023

Mineral Detection of Neutrinos and Dark Matter. A Whitepaper

Minerals are solid state nuclear track detectors - nuclear recoils in a mineral leave latent damage to the crystal structure. Depending on the mineral and its temperature, the damage features are retained in the material from minutes (in low-melting point materials such as salts at a few hundred degrees C) to timescales much larger than the 4.5 Gyr-age of the Solar System (in refractory materials at room temperature). The damage features from the $O(50)$ MeV fission fragments left by spontaneous fission of $^{238}$U and other heavy unstable isotopes have long been used for fission track dating of geological samples. Laboratory studies have demonstrated the readout of defects caused by nuclear recoils with energies as small as $O(1)$ keV. This whitepaper discusses a wide range of possible applications of minerals as detectors for $E_R \gtrsim O(1)$ keV nuclear recoils: Using natural minerals, one could use the damage features accumulated over $O(10)$ Myr$-O(1)$ Gyr to measure astrophysical neutrino fluxes (from the Sun, supernovae, or cosmic rays interacting with the atmosphere) as well as search for Dark Matter. Using signals accumulated over months to few-years timescales in laboratory-manufactured minerals, one could measure reactor neutrinos or use them as Dark Matter detectors, potentially with directional sensitivity. Research groups in Europe, Asia, and America have started developing microscopy techniques to read out the $O(1) - O(100)$ nm damage features in crystals left by $O(0.1) - O(100)$ keV nuclear recoils. We report on the status and plans of these programs. The research program towards the realization of such detectors is highly interdisciplinary, combining geoscience, material science, applied and fundamental physics with techniques from quantum information and Artificial Intelligence.

astro-ph.IM↗
arXiv2022

Snowmass Neutrino Frontier Report

This report summarizes the current status of neutrino physics and the broad and exciting future prospects identified for the Neutrino Frontier as part of the 2021 Snowmass Process.

hep-ex↗
arXiv2022

A Facility for Low-Radioactivity Underground Argon

The DarkSide-50 experiment demonstrated the ability to extract and purify argon from deep underground sources and showed that the concentration of $^{39}$Ar in that argon was greatly reduced from the level found in argon derived from the atmosphere. That discovery broadened the physics reach of argon-based detector and created a demand for low-radioactivity underground argon (UAr) in high-energy physics, nuclear physics, and in environmental and allied sciences. The Global Argon Dark Matter Collaboration (GADMC) is preparing to produce UAr for DarkSide-20k, but a general UAr supply for the community does not exist. With the proper resources, those plants could be operated as a facility to supply UAr for most of the experiments after the DarkSide 20k production. However, if the current source becomes unavailable, or UAr masses greater than what is available from the current source is needed, then a new source must be found. To find a new source will require understanding the production of the radioactive argon isotopes underground in a gas field, and the ability to measure $^{37}$Ar, $^{39}$Ar, and $^{42}$Ar to ultra-low levels. The operation of a facility creates a need for ancillary systems to monitor for $^{37}$Ar, $^{39}$Ar, or $^{42}$Ar infiltration either directly or indirectly, which can also be used to vet the $^{37}$Ar, $^{39}$Ar, and $^{42}$Ar levels in a new UAr source, but requires the ability to separate UAr from the matrix well gas. Finding methods to work with industry to find gas streams enriched in UAr, or to commercialize a UAr facility, are highly desirable.

physics.ins-det↗
arXiv2021

Status and Perspectives of Neutrino Physics

This review demonstrates the unique role of the neutrino by discussing in detail the physics of and with neutrinos. We deal with neutrino sources, neutrino oscillations, absolute masses, interactions, the possible existence of sterile neutrinos, and theoretical implications. In addition, synergies of neutrino physics with other research fields are found, and requirements to continue successful neutrino physics in the future, in terms of technological developments and adequate infrastructures, are stressed.

hep-ph↗
arXiv2021

Multimessenger Analysis Strategy for Core-Collapse Supernova Search: Gravitational Waves and Low-energy Neutrinos

Core-collapse supernovae are fascinating astrophysical objects for multimessenger studies. Gravitational waves (GWs) are expected to play a role in the supernova explosion mechanism, but their modelling is also challenging due to the stochastic nature of the dynamics and the vast possible progenitors, and moreover, the GW detection from these objects is still elusive with the already advanced detectors. Low-energy neutrinos will be emitted enormously during the core-collapse explosion and can help for the gravitational wave counterpart search. In this work we develop a multi-messenger strategy to search for such astrophysical objects by exploiting a global network of both low-energy neutrino and gravitational wave detectors. First, we discuss how to improve the detection potential of the neutrino sub-network by exploiting the temporal behaviour of a neutrino burst from a core-collapse supernova. We show that with the proposed approach neutrino detectors can gain at least $10\%$ of detection efficiency at the distance where their efficiency drops. Then, we combine the information provided by GW and neutrino in a multimessenger strategy. In particular, we obtain an increase of the probability to detect the GW signal from a CCSN at $60$ kpc from zero when using only GW analysis to $\sim 33\%$ with our combined GW-$ν$ approach. Keywords: multimessenger, supernova, core-collapse, low-energy neutrino, gravitational wave.

astro-ph.HE↗
arXiv2021

Core-Collapse Supernova Search Strategy: GravitationalWaves and Low-Energy Neutrinos

Core-collapse supernovae are expected to produce multimessenger signals. Low-energy neutrinos and gravitational waves are important to study the explosion mechanism of these events. The simulations and detections of gravitational waves from these events are still challenging due to broad range of expected progenitors as well as their stochasticity. In this work, we present a possible method to combine low-energy neutrinos and gravitational waves to search for core-collapse supernovae, which is based on \cite{Halim2021} (arXiv:2107.02050). We discuss how to exploit the time profile of the neutrino signals in order to improve the efficiency search. Moreover, we describe the combination of neutrino data from several detectors. The goal is to produce a strategy for combining the neutrinos and gravitational waves as a multimessenger search.

astro-ph.HE↗
arXiv2021

Applications and Techniques for Fast Machine Learning in Science

In this community review report, we discuss applications and techniques for fast machine learning (ML) in science -- the concept of integrating power ML methods into the real-time experimental data processing loop to accelerate scientific discovery. The material for the report builds on two workshops held by the Fast ML for Science community and covers three main areas: applications for fast ML across a number of scientific domains; techniques for training and implementing performant and resource-efficient ML algorithms; and computing architectures, platforms, and technologies for deploying these algorithms. We also present overlapping challenges across the multiple scientific domains where common solutions can be found. This community report is intended to give plenty of examples and inspiration for scientific discovery through integrated and accelerated ML solutions. This is followed by a high-level overview and organization of technical advances, including an abundance of pointers to source material, which can enable these breakthroughs.

cs.LG↗
arXiv2021

SNEWPY: A Data Pipeline from Supernova Simulations to Neutrino Signals

Current neutrino detectors will observe hundreds to thousands of neutrinos from a Galactic supernovae, and future detectors will increase this yield by an order of magnitude or more. With such a data set comes the potential for a huge increase in our understanding of the explosions of massive stars, nuclear physics under extreme conditions, and the properties of the neutrino. However, there is currently a large gap between supernova simulations and the corresponding signals in neutrino detectors, which will make any comparison between theory and observation very difficult. SNEWPY is an open-source software package which bridges this gap. The SNEWPY code can interface with supernova simulation data to generate from the model either a time series of neutrino spectral fluences at Earth, or the total time-integrated spectral fluence. Data from several hundred simulations of core-collapse, thermonuclear, and pair-instability supernovae is included in the package. This output may then be used by an event generator such as sntools or an event rate calculator such as SNOwGLoBES. Additional routines in the SNEWPY package automate the processing of the generated data through the SNOwGLoBES software and collate its output into the observable channels of each detector. In this paper we describe the contents of the package, the physics behind SNEWPY, the organization of the code, and provide examples of how to make use of its capabilities.

astro-ph.IM↗
arXiv2019

The Low-Radioactivity Underground Argon Workshop: A workshop synopsis

In response to the growing need for low-radioactivity argon, community experts and interested parties came together for a 2-day workshop to discuss the worldwide low-radioactivity argon needs and the challenges associated with its production and characterization. Several topics were covered: experimental needs and requirements for low-radioactivity argon, the sources of low-radioactivity argon and its production, how long-lived argon radionuclides are created in nature, measuring argon radionuclides, and other applicable topics. The Low-Radioactivity Underground Argon (LRUA) workshop took place on March 19-20, 2018 at Pacific Northwest National Laboratory in Richland Washington, USA. This paper is a synopsis of the workshop with the associated abstracts from the talks.

physics.ins-det↗
arXiv2018

Observation of Coherent Elastic Neutrino-Nucleus Scattering by COHERENT

The COHERENT collaboration measured coherent elastic neutrino-nucleus scattering (CEvNS) for the first time at the Spallation Neutron Source at Oak Ridge National Laboratory, using a CsI[Na] detector. Here we discuss the nature of the CEvNS process, physics motivations, and experimental considerations for measuring CEvNS. We describe the CsI[Na] measurement, along with status and future of COHERENT.

hep-ex↗
arXiv2017

Supernova Signatures of Neutrino Mass Ordering

A suite of detectors around the world is poised to measure the flavor-energy-time evolution of the ten-second burst of neutrinos from a core-collapse supernova occurring in the Milky Way or nearby. Next-generation detectors to be built in the next decade will have enhanced flavor sensitivity and statistics. Not only will the observation of this burst allow us to peer inside the dense matter of the extreme event and learn about the collapse processes and the birth of the remnant, but the neutrinos will bring information about neutrino properties themselves. This review surveys some of the physical signatures that the currently-unknown neutrino mass pattern will imprint on the observed neutrino events at Earth, emphasizing the most robust and least model-dependent signatures of mass ordering.

hep-ex↗
arXiv2017

Neutrinos from type Ia supernovae: The gravitationally confined detonation scenario

Despite their use as cosmological distance indicators and their importance in the chemical evolution of galaxies, the unequivocal identification of the progenitor systems and explosion mechanism of normal type Ia supernova (SN Ia) remains elusive. The leading hypothesis is that such a supernova is a thermonuclear explosion of a carbon-oxygen white dwarf, but the exact explosion mechanism is still a matter of debate. Observation of a galactic SN Ia would be of immense value in answering the many open questions related to these events. One potentially useful source of information about the explosion mechanism and progenitor is the neutrino signal. In this paper we compute the expected neutrino signal from a gravitationally confined detonation (GCD) explosion scenario for a SN~Ia and show how the flux at Earth contains features in time and energy unique to this scenario. We then calculate the expected event rates in the Super-K, Hyper-K, JUNO, DUNE, and IceCube detectors and find both Hyper-K and IceCube would see a few events for a GCD supernova at 1 kpc or closer, while Super-K, JUNO, and DUNE would see a events if the supernova were closer than ${\sim}0.3$ kpc. The distance and detector criteria needed to resolve the time and spectral features arising from the explosion mechanism, neutrino production, and neutrino oscillation processes are also discussed. The neutrino signal from the GCD is then compared with the signal from a deflagration-to-detonation transition (DDT) explosion model computed previously. We find the overall event rate is the most discriminating feature between the two scenarios followed by the event rate time structure. Using the event rate in the Hyper-K detector alone, the DDT can be distinguished from the GCD at 2$σ$ if the distance to the supernova is less than $2.3\;{\rm kpc}$ for a normal mass ordering and $3.6\;{\rm kpc}$ for an inverted ordering.

astro-ph.HE↗