# Neutrino Interferometry - `nu_waves` Python library
## 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?
```bash
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
```python
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()
```
Raw data
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"description": "# Neutrino Interferometry - `nu_waves` Python library\n\n## What is it?\n\n**Neutrino Interferometry**, or `nu_waves`, is a simple Python library\nthat calculate flavor oscillation of neutrinos.\nYou can input your own parameters and get the oscillation probabilities.\n\n\n## How to install?\n\n```bash\npip install nu-waves\n```\n\n\n## Features\n\n- Embedded GPU acceleration (MPS, CUDA)\n- Oscillation framework with `N` neutrinos\n- Vacuum oscillations\n- Custom smearing function (L and E)\n- Constant matter MSW\n- Multi-layer matter MSW\n- Earth model (PREM) with `cosz`\n- Adiabatic transitions\n\n## Some nice pictures\n\n\n\n\n\n\n\n\n## Examples\n\n### 2 flavors oscillation in vacuum\n\n```python\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom nu_waves.models.mixing import Mixing\nfrom nu_waves.models.spectrum import Spectrum\nfrom nu_waves.propagation.oscillator import Oscillator\nimport nu_waves.utils.flavors as flavors\n\n# sterile test\nosc_amplitude = 0.1 # sin^2(2\\theta)\nangles = {(1, 2): np.arcsin(np.sqrt(osc_amplitude)) / 2}\npmns = Mixing(n_neutrinos=2, mixing_angles=angles)\nU_pmns = pmns.build_mixing_matrix()\nprint(np.round(U_pmns, 3))\n\n# 1 eV^2\nspec = Spectrum(n_neutrinos=2, m_lightest=0.)\nspec._generate_dm2_matrix({(2, 1): 1})\nspec.summary()\nm2_diag = np.diag(spec.get_m2())\n\n# oscillator object that calculates the oscillation probability\nosc = Oscillator(mixing_matrix=U_pmns, m2_list=spec.get_m2())\n\n# get the oscillation probabilities\nE_fixed = 3E-3\nL_min, L_max = 1e-3, 20e-3\nL_list = np.linspace(L_min, L_max, 200)\nprint(L_list)\nP = osc.probability(\n L_km=L_list, E_GeV=E_fixed,\n flavor_emit=flavors.electron,\n flavor_det=flavors.electron, # muon could be sterile\n antineutrino=True\n)\n\n# draw it\nplt.figure(figsize=(6.5, 4.0))\n\nplt.plot(L_list * 1000, P, label=r\"$P_{e e}$ disappearance\", lw=2)\nplt.plot(L_list * 1000, [1] * len(L_list), \"--\", label=\"Total probability\", lw=1.5)\n\nplt.xlabel(r\"$L_\\nu$ [m]\")\nplt.ylabel(r\"Probability\")\nplt.title(f\"eV$^2$ sterile with $E_\\\\nu$ = {E_fixed * 1000} MeV\")\n# plt.xlim(L_min, L_max)\nplt.ylim(0, 1.05)\nplt.legend()\nplt.tight_layout()\nplt.show()\n```\n\n\n",
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