Download presentation

Presentation is loading. Please wait.

Published byGarret Stanwood Modified over 3 years ago

1
A piece of humble pie This map shows the state of the southern California desert about 10,000 years ago. The presence of these lakes is confirmed by oral histories, packrat nests, water-line marks on mountain-sides, fish traps, lakeside campsites and other evidence. Is this much climate change really due to variations in the Earths orbit? Do we really think we understand climate change?

2
R.K. Ulrich 1 · D. Parker 1 · L. Bertello 1 · J. Boyden 1 1 Department of Physics and Astronomy, University of California, Los Angeles 90095 email: darylgparker@gmail.com email: ulrich@astro.ucla.edu email: bertello@astro.ucla.edu email: boyden@astro.ucla.edu

3
How AutoClass Works AutoClass works on a set of observations. Each observation has attributes which are values of observed parameters. Each observation is referred to as an instance. In our case the instance is a single pixel. The attributes are the absolute value of the magnetic field and a line intensity ratio Ir. The line intensity ratio is:

4
Sample Images Intensity ratioAbsolute magnetic field

5
How AutoClass Works (cont.) AutoClass takes all observed instances and uses posterior Bayesian statistics to determine a set of classes to which all observations can be assigned. The output of an AutoClass search is a number of classes. Each class is described by probability distribution functions for the values of the attributes. For our application we get central values and gaussian widths for Ir and |B|.

6
The PDFs for an 18-class classification. AutoClass finds that 18 classes describe a set of observations consisting of 12 image pairs. Each image pair consists of an absolute field magnetogram and in intensity ratiogram. The image pairs were selected as one per year for the period 1996 to 2008.

7
Application of AutoClass to MWO Data There are J classes denoted by index j. Each pixel i is assigned a probability that it belongs to class j. We remember which image the pixels come from and denote that image by index n. The sum over all pixels on image n of the probabilities each belongs to class j gives us an index A jn which is effectively the fractional area of solar image n covered by class j.

8
Properties of the Indicies The indicies obey: The TSI is reproduced by: The s j represent the TSI the sun would have if entirely covered by class j. A deviation in s j is usefully defined as: with

9
Properties of the classes

10
Modeling the TSI, part 1

11
Modeling the TSI part 2

12
Modeling the TSI part 3

13
Simulation of a TSI image

14
Comparison between an SBI image and a TSI simulation

15
Quiet Groups Q0 and Q1

16
Groups Network, Plage 0, Plage 1

17
Time trends of the groups

18
Log of the fractional areas

19
Final Scatter Diagram The final cross correlation reaches 0.97. The Virgo data has been detrended for the drift from the previous time of solar minimum to the present condition. The comparison has also been smoothed with a three-point wide Gaussian. Points at the beginning of the series are brown while those at the end are green.

Similar presentations

Presentation is loading. Please wait....

OK

Naive Bayes Classifier

Naive Bayes Classifier

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google