Study of hadron interactions in a lead-emulsion target

Topological and kinematical characteristics of hadron interactions have been studied using a lead-emulsion target exposed to 2, 4 and 10 GeV/c hadron beams. A total length of 60 m $\pi^-$ tracks was followed using a high speed automated emulsion scanning system. A total of 318 hadron interaction vertices and their secondary charged particle tracks were reconstructed. Measurement results of interaction lengths, charged particle multiplicity, emission angles and momenta of secondary charged particles are compared with a Monte Carlo simulation and appear to be consistent. Nuclear fragments emitted from interaction vertices were also detected by a newly developed emulsion scanning system with wide-angle acceptance. Their emission angle distributions are in good agreement with the simulated distributions. Probabilities of an event being associated with at least one fragment track are found to be greater than 50% for beam momentum $P > 4$ GeV/c and are well reproduced by the simulation. These experimental results validate estimation of the background due to hadron interactions in the sample of $\tau$ decay candidates in the OPERA $\nu_{\mu} \to \nu_{\tau}$ oscillation experiment.Comment: 14 pages, 11 figure

Stability analysis of gravity-driven infiltrating flow

[1] Stability analysis of gravity-driven unsaturated flow is examined for the general case of Darcian flow with a generalized nonequilibrium capillary pressure-saturation relation. With this nonequilibrium relation the governing equation is referred to as the nonequilibrium Richards equation (NERE). For the special case where the nonequilibrium vanishes, the NERE reduces to the Richards equation (RE), the conventional governing equation for describing unsaturated flow. A generalized linear stability analysis of the RE shows that this equation is unconditionally stable and therefore not able to produce gravity-driven unstable flows for infinitesimal perturbations to the flow field. A much stronger result of unconditional stability for the RE is derived using a nonlinear stability analysis applicable to the general case of heterogeneous porous media. For the general case of the NERE model, results of a linear stability analysis show that the NERE model is conditionally stable, with lower-frequency perturbations being unstable. A result of this analysis is that the nonmonotonicity of the pressure and saturation profile is a requisite condition for flow instability

muography with nuclear emulsions stromboli and other projects

The muon radiography is a novel imaging technique to probe the volcanoes interior, using the capability of high energy cosmic ray muons to penetrate large thicknesses of rock. In this way it is possible to derive a 2D density map along the muon trajectory of volcanic edifices and deduce information on the variations in the rock density distribution, like those expected from dense lava conduits, or low density magma supply paths. This method is applicable also to study geological objects as glaciers, faults, oil underground reservoirs, engineering constructions, where a density contrast is present. Nuclear emulsions are well suited to be employed in this context for their excellent angular resolution; they are compact and robust detectors, able to work in harsh environments without need of power supply. On the other side, a long exposure time is required for a reasonable detector surface (~10 m 2 ) in order to collect a sufficient statistics of muons, and a quasi-real time analysis of the emulsion data is rather difficult due to the scanning time needed by the optical microscopes. Such drawback is on the way to be overcome thanks to a recent R&D program on ultra-fast scanning systems. Muon radiography technique, even if limited to the summit part of the volcano edifice, represents an important tool of investigation, at higher spatial resolution, complementary to the conventional geophysics techniques. The first successful result in this field was obtained by a Japanese group that observed in 2007 the conduit structure of Mt. Asama. Since 2010, other interesting volcanoes have been probed with the same method: Stromboli in 2011, Mt. Teide in 2012 and La Palma in 2014. Here we discuss the muon imaging technique reporting the nuclear emulsion detector design exposed at Stromboli and results of the data analysis

SNEWS2/snewpy: v1.2.1

<p><a href="https://doi.org/10.5281/zenodo.4498940"></a></p> What's Changed <ul> <li>Fixed crash when trying to initialise some <code>SupernovaModel</code>s with NumPy 1.23 or above</li> <li>Fixed issue where some <code>SupernovaModel</code> subclasses would not distinguish between NU_X and NU_X_BAR. (This only affects users who had custom model files. Model files included with SNEWPY are not affected.)</li> <li>Correct equation of state for <code>Warren_2020</code> model</li> </ul> New Contributors <ul> <li>@joesmolsky made their first contribution in <a href="https://github.com/SNEWS2/snewpy/pull/187">https://github.com/SNEWS2/snewpy/pull/187</a></li> </ul> <p><strong>Full Changelog</strong>: <a href="https://github.com/SNEWS2/snewpy/compare/v1.2...v1.2.1">https://github.com/SNEWS2/snewpy/compare/v1.2...v1.2.1</a></p&gt

SNEWS2/snewpy: v1.3

What's Changed <ul> <li><strong>Remove dependency on GLoBES.</strong> SNEWPY now includes code to calculate event rates directly, making it much easier to install and improving performance of <code>snewpy.snowglobes.simulate()</code> when using multiple time bins. SNOwGLoBES still needs to be downloaded separately, but it no longer needs to be compiled.</li> <li>Added simplified interface to initialise models from physics parameters (e.g. progenitor mass, metallicity)<ul> <li>Added <code>param</code> property and <code>get_param_combinations()</code> function to each model class to explore available progenitors.</li> <li>The first time a specific progenitor is initialised, SNEWPY automatically downloads the required input files to the <a href="https://docs.astropy.org/en/stable/api/astropy.config.get_cache_dir.html?highlight=get_cache_dir">AstroPy cache directory</a>, so users no longer need to manage files manually.</li> </ul> </li> <li>Added <code>get_flux()</code> function to <code>SupernovaModel</code> subclasses in <code>snewpy.models</code></li> <li>Improved <code>get_initial_spectra(t, E)</code> and <code>get_transformed_spectra(t, E)</code> functions: all <code>SupernovaModel</code> subclasses in <code>snewpy.models</code> now support arrays of times as the argument <code>t</code></li> <li>Fixed issue when using the <code>ar40kt_he</code> and <code>wc100kt30prct_he</code> detector configurations with <code>snewpy.snowglobes.simulate()</code></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 or higher.</li> <li>Initialising a supernova model in <code>snewpy.models.ccsn</code> from a file name is deprecated in favour of initialising from physics parameters. For details on parameters available for each model class, please see the <code>param</code> property and <code>get_param_combinations()</code> function or <a href="https://snewpy.readthedocs.io/en/v1.3/models.html#module-snewpy.models.ccsn">read the documentation</a>. (Under the hood, there are now separate classes in <code>snewpy.models.loaders</code> that load models from a local file; however, these are not guaranteed to be stable and may change at any time without warning.)</li> </ul> <p><strong>Full Changelog</strong>: <a href="https://github.com/SNEWS2/snewpy/compare/v1.2.1...v1.3">https://github.com/SNEWS2/snewpy/compare/v1.2.1...v1.3</a></p&gt

SNEWS2/snewpy: v1.4.1

What's Changed Reverts name of an argument to MixingParameters to restore backwards compatibility Updates a few Jupyter notebooks to support new simulations added in SNEWPY v1.4 Full Changelog: https://github.com/SNEWS2/snewpy/compare/v1.4...v1.4.

SNEWS2/snewpy: v1.4

What's Changed Improved SNOwGLoBES integration. Data files for detectors are included when installing SNEWPY; SNOwGLoBES no longer needs to be downloaded separately. Users who want to use custom data files can still specify a SNOwGLoBES path as before. SNEWPY now requires SNOwGLoBES v1.3. Added several observer directions and progenitor masses for the Tamborra_2014, Walk_2018 and Walk_2019 models. Significant performance improvements for snewpy.snowglobes thanks to a new low-level interface for neutrino flux and event rate calculations. (Note: This low-level interface is currently not stable and should not be used directly.) Added a SNEWPY logo Various minor bugfixes, performance, documentation and other improvements Compatibility and Deprecations This version of SNEWPY supports Python 3.8 or higher. New Contributors @jakob2508 made their first contribution in https://github.com/SNEWS2/snewpy/pull/266 Full Changelog: https://github.com/SNEWS2/snewpy/compare/v1.3...v1.

SNEWPY: A Data Pipeline from Supernova Simulations to Neutrino Signals

International audienceCurrent 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