SNuDDD

SNuDDD (Solar Neutrinos for Direct Detection Diffrentiable) is a JAX implementation of SNuDD.

SNuDD (Solar Neutrinos for Direct Detection) is a python package for accurate computations of solar neutrino scattering rates at direct detection (DD) experiments in the presence of non-standard neutrino interactions (NSI). SNuDD was developed and utilised for the NSI sensitivity estimates of the xenon-based DD experiments XENON, LUX-ZEPLIN and DARWIN in A direct detection view of the neutrino NSI landscape.

When using SNuDD, please cite:

D. W. P. Amaral, D. Cerdeno, A. Cheek and P. Foldenauer,
A direct detection view of the neutrino NSI landscape,
arXiv:2302.12846 [hep-ph].

Prerequisites

SNuDD does not have any external dependencies. It relies, however, on the python modules

• numpy: v>= 1.20.3
• matplotlib: v>=3.3.4
• scipy: v>=1.6.2
• pandas: v>=1.3.0
• setuptools: v>=52.0.0
• pymultinest: v>=2.11

Installation

You can obtain the sources directly from the github repository either by using git:

git clone https://github.com/dwpamaral/SNuDD.git

or by downloading them as a tarball:

wget https://github.com/dwpamaral/SNuDD/archive/master.tar.gz
tar -xzvf master.tar.gz
mv SNuDD-main SNuDD

SNuDD can be locally installed from within the directory containing the SNuDD repository by calling:

pip install -e SNuDD

Usage

Once installed, SNuDD can be included in your Python code via

import snudd

In lieu of a manual, we have created a set of example jupyter notebooks in the notebooks sub-directory that explain the basic functionality of SNuDD and we recommend going through them.

probability_density.ipynb:

This notebook goes through the basic steps of how to generate an GeneralNSI model object and compute the solar neutrino survival and disappearance probabilites via the ProbabilityCalculator. Next, the construction of the solar neutrino denisty matrix via the DensityMatrixCalculator from a GeneralNSI object is explained. Finally, the difference betwen the denisty matrix elements in the SM and an example NSI are illustrated.

CEVNS_rate.ipynb:

This notebook illustrates how to compute the theoretical CEvNS rate (i.e. without taking into account detector effects) in a direct detection experiment. First, a Nucleus object is created, which contains all the functions for calculating the CEvNS spectra. This Nucleus object is then passed a SM GeneralNSI instance to compute the solar neutrino CEvNS rate in the SM in Xenon by calling the Nucleus method spectrum. In a second step, the Nucleus object is updated with a GeneralNSI object containing non-zero NSI elements to illustrated the difference in the expected CEvNS rate.

EVES_rate.ipynb:

Analogously, this notebook illustrates how to compute the theoretical EvES rate (again without taking into account detector effects) in a direct detection experiment. After setting up the Xenon Nucleus object an Electron object is created, which contains all the functions for calculating the EvES spectra. This Electron object is then passed a SM GeneralNSI instance to compute the solar neutrino EvES rate in the SM in Xenon by calling the Electron method spectrum. In a second step, the Electron object is updated with a GeneralNSI object containing non-zero NSI elements to illustrated the difference in the expected EvES rate.

scan_Xnt_LZ_2022.ipynb:

Finally, this script illustrates how to perform a grid scan in the NSI paramter space using SNuDD. Therefore, a 2D sample grid of NSI values $\varepsilon$ and charged-plane angles $\varphi$ is created. Next, looping over this grid the corresponding number of events for the LZ 2022 and XENONnT 2022 exposures is computed, including detecto effects. Lastly, from the 2D grid of events a rough NSI exclusion plot is generated.

Reporting bugs

SNuDD is a work in progress and so far is in an alpha release stage. If you find any bugs, please report them by creating an Issue on the project GitHub page.