In-Class Assignment 10

In-Class Assignment 10#

Stellar Energy Sources II#

Learning Objectives#

  • identify the dominant burning sources in stars during H-burning in a massive star

  • explore He-burning in a massive star

  • visualizing main-sequence stellar models using tulips

To produce plots in the optional exercises we will use mesaplot and Tulips - A Tool for Understanding the Lives, Interiors, and Physics of Stars and the Paper here. Install each by performing the following commands to install them using pip:

mesaPlot

Note

mesaplot is not available on PyPI, so it can be installed using pip as:

pip install git+https://github.com/rjfarmer/mesaplot

Tulips Installation

Note

py_mesa_reader is not available on PyPI, so it can be installed using pip as:

pip install astro-tulips

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

a. H-Burning In a Massive Star#

Individually/ with the person next to you:#

Download, or reuse from last class:

Using the \(20M_\odot\) model MESA history data (20m_he_dep_history.data):

  1. Plot the log luminosity (\(L_{\rm{nuc}}\)) for the pp and cno burning categories as a function of the model_number (x-axis) to determine which dominates in each model.

Set your xlim to (370,600) to focus on the main-sequence

  1. To help guide follow the temporal evolution, also plot the center_h1 as a function of model number for both models on the same plot and label.

With your larger group, try to answer the following:#

  1. Conclude the dominant burning source on the main-sequence and state if there are any crossover points in a few words.

  2. Why might the 20M model not have any crossover points like that of the 3\(M_{\odot}\) model?

  3. Based on your answer to 1., which step in the burning cycle limits or acts as a bottleneck in nuclear burning on the main-sequence in this model?

  4. Discuss what it might mean for a nuclear reaction to serve as a bottleneck in the main-sequence and give two examples of stellar or galactic properties that it would affect?

# load 20m data
#twenty_m_ms_history = pd.read_csv('###',sep=r'\s+',header=4)
#twenty_m_ms_model_number = twenty_m_ms_history['##'] # model number
#twenty_m_ms_pp = twenty_m_ms_history['##'] # L_nuc due to pp chains
#twenty_m_ms_cno = twenty_m_ms_history['##'] # L_nuc due to cno chains
#twenty_m_ms_h1 = twenty_m_ms_history['##'] # h1 mass fraction

## 1-2 result here
#plt.title('20$M_\odot$ MESA Model During H-burning')

#plt.plot(twenty_m_ms_model_number,
#         twenty_m_ms_pp,label=r'$20M_\odot - pp-chains$')

#plt.plot(##,label=r'$20M_\odot - CNO-chains$')

#plt.plot(##,label=r'$20M_\odot - ^{1}\rm{H}$')

#plt.ylim(
#plt.xlim(

#plt.legend()
#plt.xlabel(r'$\rm{Model \ Number}$')
#plt.ylabel(r'$\rm{log}~L_{\rm{nuc}} \ (\rm{erg\ \rm{s}^{-1}})$')

Summarize Your Group Responses#

Your thoughtful responses to 3-6 here.

b. He-Burning In a Massive Star#

Individually / with the person next to you:#

Using the \(20M_\odot\) model MESA history data (20m_he_dep_history.data):

  1. Plot the log luminosity (\(L_{\rm{nuc}}\)) for the tri_alpha (\(3\alpha\)) and c_alpha (\({^{12}\rm{C}}(\alpha,\gamma){^{16}\rm{O}}\)) burning categories as a function of the model_number (x-axis) to determine which dominates during core He-burning.

Set your xlim to (700,) to focus on the He-burning

  1. To help guide follow the temporal evolution, also plot the center_he4,center_c12, and center_o16 mass fractions as a function of model numbe on the same plot and label.

Hint: set ylim to (0,5.2)

With your larger group, try to answer the following:#

  1. Which He-burning category initiates first and why?

  2. Later in the evolution, the (\({^{12}\rm{C}}(\alpha,\gamma){^{16}\rm{O}}\)) takes over as the dominant He-burning reaction, give at least two reasons why this occurs.

  3. In lower mass stars that produce, C/O white dwarfs, the final C/O ratio of the WD is very important. If you wanted to produce a CO WD with a large CO ratio, which He-burning reaction rate would you enhance and why? What about a small CO ratio?

# load 20m additional data
#twenty_m_ms_tri_alpha = twenty_m_ms_history['###'] # L_nuc due to 3alpha chains
#twenty_m_ms_c_alpha = twenty_m_ms_history['###'] # L_nuc due to c_alpha chains
#twenty_m_ms_he4 = twenty_m_ms_history['center_he4'] # center he4 mass fraction
# center c12 mass fraction
# center o16 mass fraction

## 1-3 result here
#plt.title('20$M_\odot$ MESA Model During He-burning')

#plt.plot(twenty_m_ms_model_number,
#         twenty_m_ms_tri_alpha,label=r'$20M_\odot - 3\alpha$')

#plt.plot(##,label=r'$20M_\odot - {^{12}\rm{C}(\alpha,\gamma)^{16}\rm{O}}$')

#plt.plot(twenty_m_ms_model_number,
#         twenty_m_ms_he4,label=r'$20M_\odot - ^{4}\rm{He}$')

#plt.plot(##,label=r'$20M_\odot - ^{12}\rm{C}$')

#plt.plot(##,label=r'$20M_\odot - ^{16}\rm{O}$')

#plt.ylim(
#plt.xlim(

#plt.legend()
#plt.xlabel(r'$\rm{Model \ Number}$')
#plt.ylabel(r'$\rm{log}~L_{\rm{nuc}} \ (\rm{erg\ \rm{s}^{-1}})$')

Summarize Your Group Responses#

Your thoughtful responses to 3-5 here.

c. (Optional!) Visualizing the Main-Sequence using Tulips#

Individually / with the person next to you:#

Import tulips and mesaPlot here if you did not import above and then:

Download this \(1M_{\odot}\) model evolved from MS to this H-shell burning phase:

  1. Create a folder in your data directory or anywhere really, named tulips and copy your 1m_thermohaline_history.data into the directory and rename it as history.data.

  2. Then set your directory path for tulips. It will look in this directory for a file called history.data.

  3. Create your data object for analysis using mesaPlot:

m1 = mp.MESA()

  1. Load the history data using this object:

m1.loadHistory(f=`YOUR_PATH_NAME`)

  1. Now, using tulips to produce an energy_and_mixing plot using your history data. It will create a series of these plots based on your defined step size N.

With your larger group, try to answer the following:#

  1. Lastly, open and view the plot, can you identify where the model is convective or radiative on the main-sequence?

  2. (Bonus), Can you see when are where the model experiences thermohaline/double diffusive mixing?

# only uncomment if you were able to successfully install
#import tulips

# note the capital `P`
#import mesaPlot as mp

## 1- 4 here
#SINGLE_M1_DIR = "./data/tulips"

#m1 = mp.MESA()
#m1.loadHistory(f=SINGLE_M1_DIR)
#N = 1

# compute and plot the viz
#tulips.energy_and_mixing(m1, time_ind=(0,-1, N), fps=10, fig_size=(8,6),
#                         show_total_mass=True,show_hrd_ticks_and_labels=True, show_mix=True, show_mix_legend=True, 
#                         output_fname="energy_and_mixing")
#plt.show()

# open and watch the visualization here
#from IPython.display import Video

#Video("energy_and_mixing.mp4", embed=True, width=700, height=600)

Summarize Your Group Responses#

Your thoughtful response to 6 (and 7 possibly) here.

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