22 research outputs found

    Supernova Model Discrimination with Hyper-Kamiokande

    Get PDF
    Supernovae are among the most magnificent events in the observable universe. They produce many of the chemical elements necessary for life to exist and their remnants—neutron stars and black holes—are interesting astrophysical objects in their own right. However, despite millennia of observations and almost a century of astrophysical study, the explosion mechanism of supernovae is not yet well understood. Hyper-Kamiokande is a next-generation neutrino detector that will be able to observe the neutrino flux from the next galactic supernova in unprecedented detail. In this thesis, I investigate how well such an observation would allow us to reconstruct the explosion mechanism. I develop a high-precision supernova event generator and use a detailed detector simulation and event reconstruction to explore Hyper-Kamiokande’s response to five supernova models simulated by different groups around the world. I show that 300 neutrino events in Hyper-Kamiokande—corresponding to a supernova at a distance of at least 60 kpc—are sufficient to distinguish between these models with high accuracy. These findings indicate that, once the next galactic supernova happens, Hyper-Kamiokande will be able to determine details of the supernova explosion mechanism

    Simulating fast time variations in the supernova neutrino flux in Hyper-Kamiokande

    Get PDF
    Hyper-Kamiokande is a proposed next-generation water Cherenkov detector. If a galactic supernova happens, it will deliver a high event rate (O(105)\mathcal{O}(10^5) neutrino events in total) as well as event-by-event energy information. Recent supernova simulations exhibit the Standing Accretion Shock Instability (SASI) which causes oscillations in the number flux and mean energy of neutrinos. The amplitude of these oscillations is energy-dependent, so the energy information available in Hyper-Kamiokande could be used to improve the detection prospects of these SASI oscillations. To determine whether this can be achieved in the presence of detector effects like backgrounds and finite energy uncertainty, we have started work on a detailed simulation of Hyper-Kamiokande's response to a supernova neutrino burst

    Physics Potential of a Few Kiloton Scale Neutrino Detector at a Deep Underground Lab in Korea

    Full text link
    The demand for underground labs for neutrino and rare event search experiments has been increasing over the last few decades. Yemilab, constructed in October 2022, is the first deep (∼\sim1~km) underground lab dedicated to science in Korea, where a large cylindrical cavern (D: 20~m, H: 20~m) was excavated in addition to the main caverns and halls. The large cavern could be utilized for a low background neutrino experiment by a liquid scintillator-based detector (LSC) where a 2.26 kiloton LS target would be filled. It's timely to have such a large but ultra-pure LS detector after the shutdown of the Borexino experiment so that solar neutrinos can be measured much more precisely. Interesting BSM physics searches can be also pursued with this detector when it's combined with an electron linac, a proton cyclotron (IsoDAR source), or a radioactive source. This article discusses the concept of a candidate detector and the physics potential of a large liquid scintillator detector.Comment: 63 pages, 36 figures, 8 table

    SNEWS2/snewpy: v1.2

    No full text
    What's Changed <ul> <li>Rewrote <code>simulate()</code> and <code>collate()</code> functions in <code>snewpy.snowglobes</code> to be significantly faster (ca. 6×, depending on the workflow)</li> <li>Added <code>detector_effects=False</code> argument to <code>snewpy.snowglobes.collate()</code> to use a simplified rate computation that does not require GLoBES</li> <li>Fixed issue where <code>Fornax_2021</code> model could not be used with SNOwGLoBES</li> <li>Added sample script that writes SNOwGLoBES output to ROOT files</li> <li>Started work to support pre-supernova models (not ready for general usage yet)</li> <li>Various minor bugfixes, performance, documentation and other improvements</li> </ul> Compatibility and Deprecations <ul> <li>This version of SNEWPY supports Python 3.7–3.10.</li> <li>To prepare for pre-SN model support, the existing <code>SupernovaModel</code> subclasses were moved from <code>snewpy.models</code> to <code>snewpy.models.ccsn</code>. (In this version, importing them from their previous location will still work, but generate a warning.)</li> <li><code>snewpy.snowglobes.simulate()</code>:<ul> <li>The <code>verbose</code> parameter is deprecated in favor of the <a href="https://docs.python.org/3/library/logging.html"><code>logging</code></a> module.</li> </ul> </li> <li><code>snewpy.snowglobes.collate()</code><ul> <li>The <code>verbose</code> parameter is deprecated in favor of the <a href="https://docs.python.org/3/library/logging.html"><code>logging</code></a> module.</li> <li>The <code>detector_input</code> parameter is deprecated. SNEWPY will now automatically use all detectors included in the tarball generated by <code>simulate()</code>.</li> <li>The <code>remove_generated_files</code> parameter is deprecated.</li> </ul> </li> </ul> New Contributors <ul> <li>@svalder made their first contribution in <a href="https://github.com/SNEWS2/snewpy/pull/168">https://github.com/SNEWS2/snewpy/pull/168</a></li> <li>@soso128 made their first contribution in <a href="https://github.com/SNEWS2/snewpy/pull/171">https://github.com/SNEWS2/snewpy/pull/171</a></li> </ul> <p><strong>Full Changelog</strong>: <a href="https://github.com/SNEWS2/snewpy/compare/v1.1...v1.2">https://github.com/SNEWS2/snewpy/compare/v1.1...v1.2</a></p&gt

    SNEWS2/snewpy: v1.1b2

    No full text
    <p><a href="https://doi.org/10.5281/zenodo.4498940"></a> (TODO: This DOI always points to the latest version. Replace it with the DOI for this specific release!)</p> <ul> <li>add documentation (<a href="https://snewpy.rtfd.io">https://snewpy.rtfd.io</a>) and automated unit tests</li> <li>merge all SNOwGLoBES handling code into a single <code>snewpy.snowglobes</code> module</li> <li>API changes in <code>snewpy.snowglobes</code></li> </ul&gt

    SNEWS2/snewpy: v1.1b1

    No full text
    <ul> <li>Added multiple new models (Zha_2021, Fornax_2019, Fornax_2021, Tamborra_2014, Tamborra_2014, Walk_2018, Walk_2019)</li> <li>SNEWPY is now <a href="https://pypi.org/project/snewpy/">available on PyPI</a></li> <li>User can select which models to download</li> <li>Various minor bugfixes and improvements</li> </ul> <p>To install: <code>pip install snewpy</code></p> <p>To download models: <code>python -c 'import snewpy; snewpy.get_models()'</code></p&gt

    SNEWS2/snewpy: v1.1b3

    No full text
    <p><a href="https://doi.org/10.5281/zenodo.4498940"></a> (TODO: This DOI always points to the latest version. Replace it with the DOI for this specific release!)</p> <ul> <li>minor API simplification</li> </ul> <p>This version was submitted to the <a href="https://joss.theoj.org/">Journal of Open Source Software</a>.</p&gt
    corecore