Archive for the ‘CHASC’ Category.

[AAS-HEAD 2011] Time Series in High Energy Astrophysics

We organized a Special Session on Time Series in High Energy Astrophysics: Techniques Applicable to Multi-Dimensional Analysis on Sep 7, 2011, at the AAS-HEAD conference at Newport, RI. The talks presented at the session are archived at http://hea-www.harvard.edu/AstroStat/#head2011

A tremendous amount of information is contained within the temporal variations of various measurable quantities, such as the energy distributions of the incident photons, the overall intensity of the source, and the spatial coherence of the variations. While the detection and interpretation of periodic variations is well studied, the same cannot be said for non-periodic behavior in a multi-dimensional domain. Methods to deal with such problems are still primitive, and any attempts at sophisticated analyses are carried out on a case-by-case basis. Some of the issues we seek to focus on are methods to deal with are:
* Stochastic variability
* Chaotic Quasi-periodic variability
* Irregular data gaps/unevenly sampled data
* Multi-dimensional analysis
* Transient classification

Our goal is to present some basic questions that require sophisticated temporal analysis in order for progress to be made. We plan to bring together astronomers and statisticians who are working in many different subfields so that an exchange of ideas can occur to motivate the development of sophisticated and generally applicable algorithms to astronomical time series data. We will review the problems and issues with current methodology from an algorithmic and statistical perspective and then look for improvements or for new methods and techniques.

mini-Workshop on Computational AstroStatistics [announcement]

mini-Workshop on Computational Astro-statistics: Challenges and Methods for Massive Astronomical Data
Aug 24-25, 2010
Phillips Auditorium, CfA,
60 Garden St., Cambridge, MA 02138

URL: http://hea-www.harvard.edu/AstroStat/CAS2010
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[MADS] multiscale modeling

A few scientists in our group work on estimating the intensities of gamma ray observations from sky surveys. This work distinguishes from typical image processing which mostly concerns the point estimation of intensity at each pixel location and the size of overall white noise type error. Often times you will notice from image processing that the orthogonality between errors and sources, and the white noise assumptions. These assumptions are typical features in image processing utilities and modules. On the other hand, CHASC scientists relate more general and broad statistical inference problems in estimating the intensity map, like intensity uncertainties at each point and the scientifically informative display of the intensity map with uncertainty according to the Poisson count model and constraints from physics and the instrument, where the field, multiscale modeling is associated. Continue reading ‘[MADS] multiscale modeling’ »

survey and design of experiments

People of experience would say very differently and wisely against what I’m going to discuss now. This post only combines two small cross sections of each branch of two trees, astronomy and statistics. Continue reading ‘survey and design of experiments’ »

The LRT is worthless for …

One of the speakers from the google talk series exemplified model based clustering and mentioned the likelihood ratio test (LRT) for defining the number of clusters. Since I’ve seen the examples of ill-mannerly practiced LRTs from astronomical journals, like testing two clusters vs three, or a higher number of components, I could not resist indicating that the LRT is improperly used from his illustration. As a reply, the citation regarding the LRT was different from his plot and the test was carried out to test one component vs. two, which closely observes the regularity conditions. I was relieved not to find another example of the ill-used LRT. Continue reading ‘The LRT is worthless for …’ »

[ArXiv] Astronomy Job Market in US

It’s a report about the job market in US.

[astro-ph:0712.2820] The Production Rate and Employment of Ph.D. Astronomers T.S. Metcalfe

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Arrogant?

I once talked about the relationship between astronomers and statisticians in the slog posting Data Doctors. To astronomers, statisticians are assistants. Statisticians are just helping astronomical data analysis with statistically limited eyes. Less frequently statistical improvements and modification occurred in the astronomical society through collaborations with statisticians compared to other fields.
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[ArXiv] NGC 6397 Deep ACS Imaging, Aug. 29, 2007

From arxiv/astro-ph:0708.4030v1
Deep ACS Imaging in the Globular Cluster NGC 6397: The Cluster Color Magnitude Diagram and Luminosity Function by H.B. Richer et.al

This paper presented an observational study of a globular cluster, named NGC 6397, enhanced and more informative compared to previous observations in a sense that 1) a truncation in the white dwarf cooling sequence occurs at 28 magnitude, 2) the cluster main sequence seems to terminate approximately at the hydrogen-burning limit predicted by two independent stellar evolution models, and 3) luminosity functions (LFs) or mass functions (MFs) are well defined. Nothing statistical, but the idea of defining color magnitude diagrams (CMDs) and LFs described in the paper, will assist developing suitable statistics on CMD and LF fitting problems in addition to the improved measurements (ACS imaging) of stars in NGC 6397.
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Quote of the Week, Aug 31, 2007

Once again, the middle of a recent (Aug 30-31, 2007) argument within CHASC, on why physicists and astronomers view “3 sigma” results with suspicion and expect (roughly) > 5 sigma; while statisticians and biologists typically assume 95% is OK:

David van Dyk (representing statistics culture):

Can’t you look at it again? Collect more data?

Vinay Kashyap (representing astronomy and physics culture):

…I can confidently answer this question: no, alas, we usually cannot look at it again!!

Ah. Hmm. To rephrase [the question]: if you have a “7.5 sigma” feature, with a day-long [imaging Markov Chain Monte Carlo] run you can only show that it is “>3sigma”, but is it possible, even with that day-long run, to tell that the feature is really at 7.5sigma — is that the question? Well that would be nice, but I don’t understand how observing again will help?

David van Dyk :

No one believes any realistic test is properly calibrated that far into the tail. Using 5-sigma is really just a high bar, but the precise calibration will never be done. (This is a reason not to sweet the computation TOO much.)

Most other scientific areas set the bar lower (2 or 3 sigma) BUT don’t really believe the results unless they are replicated.

My assertion is that I find replicated results more convincing than extreme p-values. And the controversial part: Astronomers should aim for replication rather than worry about 5-sigma.

[ArXiv] Numerical CMD analysis, Aug. 28th, 2007

From arxiv/astro-ph:0708.3758v1
Numerical Color-Magnitude Diagram Analysis of SDSS Data and Application to the New Milky Way Satellites by J. T. A. de Jong et. al.

The authors applied MATCH (Dolphin, 2002[1] -note that the year is corrected) to M13, M15, M92, NGC2419, NGC6229, and Pal14 (well known globular clusters), and BooI, BooII, CvnI, CVnII, Com, Her, LeoIV, LeoT, Segu1, UMaI, UMaII and Wil1 (newly discovered Milky Way satellites) from Sloan Digital Sky Survey (SDSS) to fit Color Magnitude diagrams (CMDs) of these stellar clusters and find the properties of these satellites.
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  1. Numerical methods of star formation history measurement and applications to seven dwarf spheroidals,Dolphin (2002), MNRAS, 332, p. 91[]

“They let you in now?”

Much to everybody’s surprise, they let some astronomers into the recently concluded Joint Statistical Meeting at Salt Lake City, UT. There were two three astrostat sessions: [#45 on Probing the Universe with Nonparametric Methods,] #367 on Bayesian Applications in Astronomy and Physics (chaired by David van Dyk), and #411 on Image Analysis in Solar- and Astro-physics (chaired by Yaming Yu and Thomas Lee). Both [of the latter] sessions were dominated by presentations from CHASC collaborators.

[ArXiv] Geneva-Copenhagen Survey, July 13, 2007

From arxiv/astro-ph:0707.1891v1
The Geneva-Copenhagen Survey of the Solar neighborhood II. New uvby calibrations and rediscussion of stellar ages, the G dwarf problem, age-metalicity diagram, and heating mechanisms of the disk by Holmberg, Nordstrom, and Andersen

Researchers, including scientists from CHASC, working on color magnitude diagrams to infer ages, metalicities, temperatures, and other physical quantities of stars and stellar clusters may find this paper useful.
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[ArXiv] Solar Cycle, June 18, 2007

From arxiv/astro-ph, arXiv:0706.2590v1 Extreme Value Theory and the Solar Cycle by Ramos, A. This paper might drag a large attention from CHASC members.
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[ArXiv] A Lecture Note, June 17, 2007

From arxiv/astro-ph:0706.1988,
Lectures on Astronomy, Astrophysics, and Cosmology looks helpful to statisticians who like to know astronomy, astrophysics, and cosmology. The lecture note starts from introducing fundamentals of astronomy, UNITS!!!, and its history. It also explains astronomical measures such as distances and their units, luminosity, and temperature; HR diagram (astronomers’ summary diagram); stellar evolution; and relevant topics in cosmology. At least, a third of the article will be useful to grasp a rough idea of astronomy as a scientific subject beyond colorful pictures. Statisticians who are keen to cosmology are recommended to read beyond.

This is not a high energy lecture note; therefore, statisticians interested in high energy are encouraged to visit Astro Jargon for Statisticians and CHASC.

An excerpt from “A Conversation with Leo Breiman”

Leo Breiman (1928-2005) was one of the most dominant statisticians from the 20th century. He was well known for his textbook in probability theory as well as his contributions to the machine learning, such as CART (Classification and Regression Tree), bagging (bootstrap aggregation), and Random Forest. He was the founding father of statistical machine learning. His works can be found from http://www.stat.berkeley.edu/~breiman/

An excerpt from “A Conversation with Leo Breiman,” from Statistical Science, by Richard Olshen (2001), 16(2), pp. 184–198, casts a second thought on the direction of statistical researches:
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