Modeling the Sun’s Energy Output
Ana Cristina Cadavid
Stephen Walton
Department of Physics and Astronomy
California State University, Northridge

Goal 1: to expose students to a research experience using a large data set
Goal 2: to guide students to rediscover the discrepancy between the models and the satellite measurements of solar irradiance for Cycle 23

• Students will learn basics about the
Sun
• Students will learn about solar variability, measurements and models
• Students will learn MATLAB and
Excel
• Students will learn how to obtain solar data through the Internet
• Students will learn how to model data via linear regression analysis
• Students will learn how to conduct a research project working in groups
• Students will prepare a scientific report and presentation

• After the first meeting, students are organized in groups of 3-5 members
• A field trip to the San Fernando
Observatory and three guest speakers during Weeks 1 and 2
• Weeks 1 and 2
The course is structured as a mixture of lecture and computational laboratory experiences
• Week 3
Major project

Lectures
• Solar Spectral Analysis
• Radiative Transfer
• Solar magnetism and dynamo theory
• Features: sunspots, faculae
• Total Solar Irradiance (TSI)
• Observations: satellite and ground based
• Models for TSI

Laboratory Experiences
• Learning MATLAB: Image handling, graphics
• Learning Excel: linear regression capabilities
• Internet access to solar databases
• Friday of 2 nd week: Mock experience of big project

Assignments
Both in class and overnight
• Numerical:
Basics of MATLAB and Excel
Download data
Analysis of magnetograms (mask)
Preparation of irradiance data
Linear fit
• Analytical:
Radiative transfer
Dynamo

Major Project
• TSI Modeling: One mandatory and several optional components
• Students worked in groups of
3 —5
• Groups discovered discrepancy in models when comparing cycle 22 (1986 —
1996) with cycle 23 (1996 — present)
• Presentations made on last day of week
• Written report on last day

• The TSI is modeled as the sum of a basal level, a sunspot contribution, and a facular contribution
• The balance between them determines short (weeks) and long
(decade) variability
• Models for cycle 22 (1986—1996) match data extremely well (R=0.9 or more)
• The same models do not match cycle 23 (1996 to present)

• Total Solar Irradiance measured from different satellites
• Data put on the same scale by cross-calibration: the
'composite' record
• Daily digital images in continuum showing sunspots in the photosphere
• Ca II line core images showing faculae in the chromosphere
• An optional third UV component

1. Which of the topics in the class did you find most interesting?
• Solar structure, dynamo (5)
• Magnetic fields, sunspots, faculae
(4)
• Solar irradiance (4)
• MATLAB, Excel linear regression, computer applications (4)
• Data analysis, image processing model fitting (4)
• Project (3)
• Solar atmosphere and radiative transfer
• Linear regression and linear algebra
• Field Trip

2. Which of the topics in the class did you find least interesting?
• Solar atmospheres and radiative transfer. Related first assignment (6)
• MATLAB exercises
• Phenomenology of sunspots
• Data discussion
• Dynamo

3 . Which topic(s) would you like to have spent more time on?
• Final project (4)
• Radiative transfer (to make it clear) (4)
• Dynamo (3)
• Basic solar physics, Coronal
Mass Ejections (3)
• Solar irradiance (2)
• Working with MATLAB (2)
• Practice analyzing data
• Linear regression
• Fractal patterns on the Sun

4 . Which topic(s) would you like to have spent less time on?
• Introduction to solar atmosphere and radiative transfer (5)
• Exercises with software,
MATLAB problems (4)
• Sunspot and faculae phenomenology
• Dynamo

5. What was your opinion of the final project?
• Interesting (7)
• Teaches how to work with real data (7)
• Puts concepts together (6)
• Excellent, the best part (5)
• Very educational. Learned a lot
(3)
• Enjoyed presenting the final project results (2)
• Enjoyed working in groups
• Fun
• All
• Still vague even at the end
• More stressful than it should be

6. What advice would you give a future student taking this class?
• Ask lots of questions, take notes, pay attention in class, record the course
(6)
• Be patient. In the beginning there is confusion but things come together
(5)
• Work hard (4)
• Enjoy it (4)
• Read about the Sun ahead of time (4)
• Have previous knowledge of statistics
• Have previous knowledge of MATLAB and Excel
• Use your group. Explain to each other
• Did not learn as much as the effort invested

The Evaluation Forms used at California State
University Northridge were used to get the student evaluation of faculty. The scores range from not at all descriptive (1), to very descriptive (5), and a non-applicable option
(N/A). There were 24 responses.
W=Walton C=Cadavid
1. The professor demonstrated knowledge of the field well.
4.9

0.3 (W) 4.9

0.3 (C)
2. The professor organized the course well.
4.9

0.2 (W) 4.9

0.3 (C)
3. The professor prepared well for class sessions.
5 (W) 4.9

0.3 (C)
4. The professor communicated the knowledge effectively.
4.6

0.7 (W) 4.8

0.4 (C)

5. The professor encouraged questions and discussion by students.
4.9

0.2 (W) 5 (C)
6. Exams and other requirements are adequate for fair evaluation of student achievement
4.5

0.8 (W) 4.7

0.5 (C)
7. The professor was helpful in case difficulties were encountered in he course
4.9

0.2 (W) 4.9

0.2 (C)
8. The professor was punctual in meeting and conducting classes.
5 (W) 5 (C)
9. The professor was available to me during office hours.
5 (W) 5 (C)
10. The overall performance of the professor in the course was excellent
4.9

0.2 (W) 5 (C)

• Students received tutorial on
Fourier and Wavelet analysis of time series:
Theory and numerical analysis using MATLAB
(Cadavid, Shubin, Walton)
• Speaker series on topics in
Astronomy

Feb 13 Matt Penn (CSUN)
Solar Coronal Magnetic Fields
Feb 20 Mike Jura (UCLA)
Infrared Observations of pre-supernova stars
Mar 6 Phil Goode (BBSO/NJIT)
Solar Physics at Big Bear Solar Observatory
Mar 13 Kim Griest (UCSD)
Dark Matter in the Universe
Mar 20 James Colbert (UCLA)
Searching the High Redshift Universe
Mar 27 Angelle Tanner (UCLA)
Journey to the Center of the Galaxy
Apr 3 David B. Cline (UCLA)
Supernova Type II Dynamics and the Neutrino
Mass
Apr 17 Shoko Sakai
Measuring the Expansion Rate of the
Universe using the Hubble Space Telescope
Apr 24 Peter Gallagher (BBSO/OVSA)
Solar Radio Astronomy
May 8 Albert Lazzarini (LIGO)
Gravitational Waves

• Present a global overview the first day
• Simplify the radiative transfer presentation
• Tie the radiative transfer more closely to the TSI problem
• Different backgrounds of students a challenge: perhaps
‘student leaders’ the first week?
• Additional project options