Search NASA⌕ Search

Engineering topics

Psihas, Fernanda

Publications and source records attributed to Psihas, Fernanda.

12 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Calibrating LArPix for TinyTPC

LArTPCs provide sensitivity to GeV signals, such as accelerator neutrinos and part of the supernova neutrino spectrum. TinyTPC is a LArTPC test stand for R&D of LAr doping to expand the reach of LArTPCs down to the 1-10 MeV range, which would substantially enhance the flagship analyses of experiments like DUNE, while enabling low energy analyses. We aim to dope LAr with Xe and pho- tosensitive dopants to expand the LArTPC range by converting hard-to-detect scintillation light to efficiently detected ionization charge. A critical element of the data analysis in TinyTPC is calibrating the readout. This poster will cover the calibration of TinyTPC, a pixelated liquid argon detector, where we find the distance a muon travels through each pixel. We then calculate the energy loss of muons traveling through the detector from cosmic data. We can reconstruct the path of the particles through the TPC using a density-based clustering algorithm designed to sort straight cosmic muon tracks from low energy radioactive decay curled paths and electronic noise

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

TinyTPC - A Test Stand for Photosensitive Dopants

Liquid argon time projection chambers (LArTPCs) are widely used to study accelerator neutrinos. Since LArTPCs are sensitive down to 10– 100 keV, neutrino experiments' physics programs could be extended to lower energies. However, in this scale, LArTPCs' energy resolution is significantly degraded because only charge is collected efficiently. One solution is the introduction of photosensitive dopants, which convert scintillation light to ionization yield. We have built a test stand (TinyTPC) with a LArPix pixelated anode plane and an active mass of 2.1 kg to study these charge enhancements. Our plan is to measure TinyTPC’s energy resolution with and without dopants using radioactive sources. This poster presentation will discuss TinyTPC's status and goals.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

R&D for LAr + Xe + photosensitive dopants

LArTPCs are the technology of choice for current and future neutrino experiments, including those expected to make eagerly awaited measurements of accelerator neutrino oscillations in the coming decade. This technology provides a large active volume and sensitivity to GeV signals like accelerator neutrinos all the way down to 10s of MeV, covering part of the supernova neutrino spectrum.Expanding the reach of LArTPCs to below the 10 MeV range would substantially enhance the flagship analyses of experiments like DUNE, while potentially enabling the physics of solar neutrinos, dark matter searches, and neutrinoless double beta decay searches.We outline the R&D pathway for photosensitive dopants, whose introduction into the LAr active medium, has the potential to substantially increase ionization yields of LAr detectors and enable the detection of low energy signals in large LArTPCs. This R&D program will demonstrate the feasibility and impacts of introducing doped LAr into current and future neutrino detectors at the kTon scale including the Xenon + photosensitive doping strategy.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

TinyTPC - A test stand for photosensitive dopants

LArTPCs highly doped with Xenon could be interesting platforms for probing MeV- and sub-MeV physics including neutrinoless-double beta decay. A main hurdle is the small fraction of scintillation photons that are collected, which significantly impacts LArTPCs' energy resolution. One solution is the use of photosensitive dopants, which convert light to charge. A team from Rutgers University and FNAL have built a test stand (TinyTPC) with a LArPix pixelated anode plane and an active mass of 2.1 kg to study these charge enhancements. Our plan is to measure the TinyTPC’s energy resolution with and without dopants for radioactive gamma sources, which is crucial for accurate calorimetric energy reconstruction of low-energy physics signals. The doping agent that will be used is isobutylene and the radiation sources are Co-60, Y-88, and Th-228.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Snowmass Early Career

The Snowmass 2021 strategic planning process provided an essential opportunity for the United States high energy physics and astroparticle (HEPA) community to come together and discuss upcoming physics goals and experiments. As this forward-looking perspective on the field often reaches far enough into the future to surpass the timescale of a single career, consideration of the next generation of physicists is crucial. The 2021 Snowmass Early Career (SEC) organization aimed to unite this group, with the purpose of both educating the newest generation of physicists while informing the senior generation of their interests and opinions. SEC is the latest in a series of the previously dubbed "Snowmass Young" organizations, from 2013 and 2001. This iteration has expanded on these efforts to significantly increase involvement and broaden the representation of the early career community in the process. Early career physicists are the future of the field. They will design, build, and operate next-generation experiments, and put in the work to usher in new discoveries. They are also disproportionately involved in work to improve the climate within HEPA. This document summarizes the work of SEC in consolidating a huge variety of physics perspectives and community opinions towards a bright, strategic future.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

A Review on Machine Learning for Neutrino Experiments

Neutrino experiments study the least understood of the Standard Model particles by observing their direct interactions with matter or searching for ultra-rare signals. The study of neutrinos typically requires overcoming large backgrounds, elusive signals, and small statistics. The introduction of state-of-the-art machine learning tools to solve analysis tasks has made major impacts to these challenges in neutrino experiments across the board. Machine learning algorithms have become an integral tool of neutrino physics, and their development is of great importance to the capabilities of next generation experiments. An understanding of the roadblocks, both human and computational, and the challenges that still exist in the application of these techniques is critical to their proper and beneficial utilization for physics applications. This review presents the current status of machine learning applications for neutrino physics in terms of the challenges and opportunities that are at the intersection between these two fields.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2018

Machine Learning in High Energy Physics Community White Paper

Machine learning is an important applied research area in particle physics, 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 in machine learning in particle physics with 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.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2018

Measurement of Long Baseline Neutrino Oscillations and Improvements from Deep Learning

NOvA is a long-baseline neutrino oscillation experiment which measures the oscillation of muon neutrinos from the NuMI beam at Fermilab after they travel through the Earth for 810 km. In this dissertation I describe the operations and monitoring of the detectors which make it possible to record over 98% of the delivered neutrino beam. I also present reconstruction and identification techniques using deep convolutional neural networks (CNNs), which are applicable to multiple analyses. Lastly, I detail the oscillation analyses in the $\nu_{\mu} \to \nu_{\mu}$ and $\nu_{\mu} \to\nu_e$ channels on a dataset of 8:85 x $10^{20}$ protons on target, with emphasis on the application of deep learning, energy reconstruction, and treatment of systematic uncertainties....

Psihas, Fernanda↗
DOE OSTI2017

Event Reconstruction in the NOvA Experiment

The NOvA experiment observes oscillations in two channels (electron-neutrino appearance and muon-neutrino disappearance) using a predominantly muon-neutrino NuMI beam. The Near Detector records multiple overlapping neutrino interactions in each event and the Far Detector has a large background of cosmic rays due to being located on the surface. The oscillation analyses rely on the accurate reconstruction of neutrino interactions in order to precisely measure the neutrino energy and identify the neutrino flavor and interaction mode. Similarly, measurements of neutrino cross sections using the Near Detector require accurate identification of the particle content of each interaction. A series of pattern recognition techniques have been developed to split event records into individual spatially and temporally separated interactions, to estimate the interaction vertex, and to isolate and classify individual particles within the event. This combination of methods to achieve full event reconstruction in the NOvA detectors has discussed.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

The Convolutional Visual Network for Identification and Reconstruction of NOvA Events

In 2016 the NOvA experiment released results for the observation of oscillations in the vμ and ve channels as well as ve cross section measurements using neutrinos from Fermilab’s NuMI beam. These and other measurements in progress rely on the accurate identification and reconstruction of the neutrino flavor and energy recorded by our detectors. This presentation describes the first application of convolutional neural network technology for event identification and reconstruction in particle detectors like NOvA. The Convolutional Visual Network (CVN) Algorithm was developed for identification, categorization, and reconstruction of NOvA events. It increased the selection efficiency of the ve appearance signal by 40% and studies show potential impact to the vμ disappearance analysis.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗