Neutrino Interferometry - nu-waves

What is it?

Neutrino Interferometry, or nu_waves, is a simple Python library that calculate flavor oscillation of neutrinos. You can input your own parameters and get the oscillation probabilities.

How to install?

pip install nu-waves

Features

Embedded GPU acceleration (MPS, CUDA)
Oscillation framework with N neutrinos
Vacuum oscillations
Custom smearing function (L and E)
Constant matter MSW
Multi-layer matter MSW
Earth model (PREM) with cosz
Adiabatic transitions

Some nice pictures

Examples

2 flavors oscillation in vacuum

import numpy as np
import matplotlib.pyplot as plt
from nu_waves.models.mixing import Mixing
from nu_waves.models.spectrum import Spectrum
from nu_waves.propagation.oscillator import Oscillator
import nu_waves.utils.flavors as flavors

# sterile test
osc_amplitude = 0.1  # sin^2(2\theta)
angles = {(1, 2): np.arcsin(np.sqrt(osc_amplitude)) / 2}
pmns = Mixing(n_neutrinos=2, mixing_angles=angles)
U_pmns = pmns.build_mixing_matrix()
print(np.round(U_pmns, 3))

# 1 eV^2
spec = Spectrum(n_neutrinos=2, m_lightest=0.)
spec._generate_dm2_matrix({(2, 1): 1})
spec.summary()
m2_diag = np.diag(spec.get_m2())

# oscillator object that calculates the oscillation probability
osc = Oscillator(mixing_matrix=U_pmns, m2_list=spec.get_m2())

# get the oscillation probabilities
E_fixed = 3E-3
L_min, L_max = 1e-3, 20e-3
L_list = np.linspace(L_min, L_max, 200)
print(L_list)
P = osc.probability(
    L_km=L_list, E_GeV=E_fixed,
    flavor_emit=flavors.electron,
    flavor_det=flavors.electron,  # muon could be sterile
    antineutrino=True
)

# draw it
plt.figure(figsize=(6.5, 4.0))

plt.plot(L_list * 1000, P, label=r"$P_{e e}$ disappearance", lw=2)
plt.plot(L_list * 1000, [1] * len(L_list), "--", label="Total probability", lw=1.5)

plt.xlabel(r"$L_\nu$ [m]")
plt.ylabel(r"Probability")
plt.title(f"eV$^2$ sterile with $E_\\nu$ = {E_fixed * 1000} MeV")
# plt.xlim(L_min, L_max)
plt.ylim(0, 1.05)
plt.legend()
plt.tight_layout()
plt.show()

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

nu_waves-1.2.5.tar.gz (36.6 kB view details)

Uploaded Jul 27, 2026 Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

The dropdown lists show the available interpreters, ABIs, and platforms. Enable javascript to be able to filter the list of wheel files.

nu_waves-1.2.5-py3-none-any.whl (45.0 kB view details)

Uploaded Jul 27, 2026 Python 3

File details

Details for the file nu_waves-1.2.5.tar.gz.

File metadata

Download URL: nu_waves-1.2.5.tar.gz
Upload date: Jul 27, 2026
Size: 36.6 kB
Tags: Source
Uploaded using Trusted Publishing? Yes
Uploaded via: twine/6.1.0 CPython/3.13.14

File hashes

Hashes for nu_waves-1.2.5.tar.gz
Algorithm	Hash digest
SHA256	`aae57aa73ee9bf1935ef4b61bef650a20ed09bad3d2881995d107ce12f9363e0`
MD5	`e5e507722772e9e3e0e38a373fb35442`
BLAKE2b-256	`d39d4126cca7f769e60ad727bd677cc2a18c3f04b71df56178ecf0f741781b69`

See more details on using hashes here.

Provenance

The following attestation bundles were made for nu_waves-1.2.5.tar.gz:

Publisher: publish.yml on nadrino/nu-waves

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Statement:
- Statement type: https://in-toto.io/Statement/v1
- Predicate type: https://docs.pypi.org/attestations/publish/v1
- Subject name: nu_waves-1.2.5.tar.gz
- Subject digest: aae57aa73ee9bf1935ef4b61bef650a20ed09bad3d2881995d107ce12f9363e0
- Sigstore transparency entry: 2257630091
- Sigstore integration time: Jul 27, 2026
Source repository:
- Permalink: nadrino/nu-waves@200696125c69af98b0514dc66b391de272fa97d8
- Branch / Tag: refs/tags/v1.2.5
- Owner: https://github.com/nadrino
- Access: public
Publication detail:
- Token Issuer: https://token.actions.githubusercontent.com
- Runner Environment: github-hosted
- Publication workflow: publish.yml@200696125c69af98b0514dc66b391de272fa97d8
- Trigger Event: push