The AstroStat Slog

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’ »

Another deduced conclusion from reading preprints listed in arxiv/astro-ph is that astronomers tend to confuse classification and clustering and to mix up methodologies. They tend to think any algorithms from classification or clustering analysis serve their purpose since both analysis algorithms, no matter what, look like a black box. I mean a black box as in neural network, which is one of classification algorithms. Continue reading ‘Classification and Clustering’ »

A nice book by Christopher Bishop.
While I was reading abstracts and papers from astro-ph, I saw many applications of algorithms from pattern recognition and machine learning (PRML). The frequency will increase as large scale survey projects numerate, where recommending a good textbook or a reference in the field seems timely. Continue reading ‘[Book] pattern recognition and machine learning’ »

Physicists believe that the Gaussian law has been proved in mathematics while mathematicians think that it was experimentally established in physics — Henri Poincare

Continue reading ‘Why Gaussianity?’ »

10:00am local time, Sept. 10th, 2008
As the first light from Fermi or GLAST, LHC First Beam is also a big moment for particle physicists. Find more from http://lhc-first-beam.web.cern.ch/lhc-first-beam/Welcome.html. Continue reading ‘LHC First Beam’ »

(Inspired by vlk’s “keV vs keV”)

Beside the obvious benefit of confusing the public and colleagues in other fields, the apparent chaotic use of physical units like keV and Kevin has an addictive convenience beyond a simple matter of convention. Yes, I said “convenience”. Continue reading ‘A Confession from a former “keV” Junkie: 1. It’s a Plague.’ »

I didn’t realize this post was sitting for a month during which I almost neglected the slog. As if great books about probability and information theory for statisticians and engineers exist, I believe there are great statistical physics books for physicists. On the other hand, relatively less exist that introduce one subject to the other kind audience. In this regard, I thought the lecture note can be useful.

[arxiv:physics.data-an:0808.0012]
Lectures on Probability, Entropy, and Statistical Physics by Ariel Caticha
Abstract: Continue reading ‘A lecture note of great utility’ »

Like spherical cows, true blackbodies do not exist. Not because “black objects are dark, duh”, as I’ve heard many people mistakenly say — black here simply refers to the property of the object where no wavelength is preferentially absorbed or emitted, and all the energy input to it is converted into radiation. There are many famous astrophysical cases which are very good approximations to perfect blackbodies — the 2.73K microwave background radiation left over from the early Universe, for instance. Even the Sun is a good example. So it is often used to model the emission from various objects. Continue reading ‘Blackbody Radiation [Eqn]’ »

UChicago, my alma mater, is doing alright for itself in the spacecraft naming business.

First there was Edwin Hubble (S.B. 1910, Ph.D. 1917).
Then came Arthur Compton (the “MetLab”).
Followed by Subramanya Chandrasekhar (Morton D. Hull Distinguished Service Professor of Theoretical Astrophysics).

And now, Enrico Fermi.

I still remember my first class as a new grad student. As a cocky Physics graduate, I was quite sure I knew plenty of astronomy. Astro 301, class 1, and it took all of 20 minutes of talk about stellar magnitudes to put that notion to permanent rest. So, for the sake of our stats colleagues, here’s a brief primer on one of the basic building blocks of astronomy. Continue reading ‘Magnitude [Eqn]’ »

Differential Emission Measures (DEMs) are a summary of the temperature structure of the outer atmospheres (aka coronae) of stars, and are usually derived from a select subset of line fluxes. They are notoriously difficult to estimate. Very few algorithms even bother to calculate error envelopes on them. They are also subject to numerous systematic uncertainties which can play havoc with proper interpretation. But they are nevertheless extremely useful since they allow changes in coronal structures to be easily discerned, and observations with one instrument can be used to derive these DEMs and these can then be used to predict what is observable with some other instrument. Continue reading ‘Differential Emission Measure [Eqn]’ »

As mentioned before, background subtraction plays a big role in astrophysical analyses. For a variety of reasons, it is not a good idea to subtract out background counts from source counts, especially in the low-counts Poisson regime. What Bayesians recommend instead is to set up a model for the intensity of the source and the background and to infer these intensities given the data. Continue reading ‘Background Subtraction, the Sequel [Eqn]’ »

I have noticed that our statistician collaborators are often confused by our units. (Not a surprise; I, too, am constantly confused by our units.) One of the biggest culprits is the unit of energy, [keV], Continue reading ‘keV vs keV [Eqn]’ »

With the LHC coming on line anon, it is appropriate to highlight the Banff Challenge, which was designed as a way to figure out how to place bounds on the mass of the Higgs boson. The equations that were to be solved are quite general, and are in fact the first attempt that I know of where calibration data are directly and explicitly included in the analysis. Continue reading ‘The Banff Challenge [Eqn]’ »

A GLAST-related opportunity: A Summer Science Institute at SLAC on Cosmic Accelerators is scheduled for August 4-15 in anticipation of GLAST science, and the co-directors welcome participation by students, postdocs, and researchers (even those with no background in astrophysics). The registration deadline is July 31. Continue reading ‘SLAC Summer Institute’ »