Source code for mbtrack2.utilities.spectrum
# -*- coding: utf-8 -*-
"""
Module where bunch and beam spectrums and profile are defined.
"""
import numpy as np
from scipy.special import jv, spherical_jn
[docs]def spectral_density(frequency, sigma, m=1, k=0, mode="Hermite"):
"""
Compute the spectral density of different modes for various values of the
head-tail mode number, based on Table 1 p238 of [1].
Parameters
----------
frequency : list or numpy array
sample points of the spectral density in [Hz]
sigma : float
RMS bunch length in [s]
m : int, optional
head-tail (or azimutal/synchrotron) mode number
k : int, optional
radial mode number (such that |q|=m+2k, where |q| is the head-tail mode number)
mode: str, optional
type of the mode taken into account for the computation:
-"Hermite" modes for Gaussian bunches (typical for electrons)
-"Chebyshev" for airbag bunches
-"Legendre" for parabolic bunches (typical for protons)
-"Sacherer" or "Sinusoidal" simplifying approximation of Legendre modes from [3]
Returns
-------
numpy array
References
----------
[1] : Handbook of accelerator physics and engineering, 3rd printing.
[2] : Ng, K. Y. (2005). Physics of intensity dependent beam instabilities. WORLD SCIENTIFIC. https://doi.org/10.1142/5835
[3] : Sacherer, F. J. (1972). Methods for computing bunched beam instabilities. CERN Tech. rep. CERN/SI-BR/72-5 https://cds.cern.ch/record/2291670?ln=en
"""
if mode == "Hermite":
return 1 / (np.math.factorial(m) *
2**m) * (2 * np.pi * frequency * sigma)**(
2 * m) * np.exp(-(2 * np.pi * frequency * sigma)**2)
elif mode == "Chebyshev":
tau_l = 4 * sigma
return (jv(m, 2 * np.pi * frequency * tau_l))**2
elif mode == "Legendre":
tau_l = 4 * sigma
return (spherical_jn(m, np.abs(2 * np.pi * frequency * tau_l)))**2
elif mode == "Sacherer" or mode == "Sinusoidal":
y = 4 * 2 * np.pi * frequency * sigma / np.pi
return (2 * (m+1) / np.pi * 1 / np.abs(y**2 - (m + 1)**2) *
np.sqrt(1 + (-1)**m * np.cos(np.pi * y)))**2
else:
raise NotImplementedError("Not implemanted yet.")
[docs]def gaussian_bunch_spectrum(frequency, sigma):
"""
Compute a Gaussian bunch spectrum [1].
Parameters
----------
frequency : array
sample points of the beam spectrum in [Hz].
sigma : float
RMS bunch length in [s].
Returns
-------
bunch_spectrum : array
Bunch spectrum sampled at points given in frequency.
References
----------
[1] : Gamelin, A. (2018). Collective effects in a transient microbunching
regime and ion cloud mitigation in ThomX. p86, Eq. 4.19
"""
return np.exp(-1 / 2 * (2 * np.pi * frequency)**2 * sigma**2)
[docs]def gaussian_bunch(time, sigma):
"""
Compute a Gaussian bunch profile.
Parameters
----------
time : array
sample points of the bunch profile in [s].
sigma : float
RMS bunch length in [s].
Returns
-------
bunch_profile : array
Bunch profile in [s**-1] sampled at points given in time.
"""
return np.exp(-1 / 2 * (time**2 / sigma**2)) / (sigma * np.sqrt(2 * np.pi))
[docs]def beam_spectrum(frequency,
M,
bunch_spacing,
sigma=None,
bunch_spectrum=None):
"""
Compute the beam spectrum assuming constant spacing between bunches [1].
Parameters
----------
frequency : list or numpy array
sample points of the beam spectrum in [Hz].
M : int
Number of bunches.
bunch_spacing : float
Time between two bunches in [s].
sigma : float, optional
If bunch_spectrum is None then a Gaussian bunch with sigma RMS bunch
length in [s] is assumed.
bunch_spectrum : array, optional
Bunch spectrum sampled at points given in frequency.
Returns
-------
beam_spectrum : array
References
----------
[1] Rumolo, G - Beam Instabilities - CAS - CERN Accelerator School:
Advanced Accelerator Physics Course - 2014, Eq. 9
"""
if bunch_spectrum is None:
bunch_spectrum = gaussian_bunch_spectrum(frequency, sigma)
beam_spectrum = (bunch_spectrum *
np.exp(1j * np.pi * frequency * bunch_spacing * (M-1)) *
np.sin(M * np.pi * frequency * bunch_spacing) /
np.sin(np.pi * frequency * bunch_spacing))
return beam_spectrum