Archive for the ‘Stars’ Category.
This question came to the CfA Public Affairs office, and I am sharing it with y’all because I think the solution is instructive.
A student had to figure out the name of a stellar object as part of an assignment. He was given the following information about it:
- apparent [V] magnitude = 5.76
- B-V = 0.02
- E(B-V) = 0.00
- parallax = 0.0478 arcsec
- radial velocity = -18 km/s
- redshift = 0 km/s
He looked in all the stellar databases but was unable to locate it, so he asked the CfA for help.
Just to help you out, here are a couple of places where you can find comprehensive online catalogs:
See if you can find it!
Continue reading ‘An Instructive Challenge’ »
The Solar Dynamics Observatory, which promises a flood of data on the Sun, was launched today from Cape Kennedy.
Among billion objects in our Galaxy, outside the Earth, our Sun drags most attention from astronomers. These astronomers go by solar physicists, who enjoy the most abundant data including 400 year long sunspot counts. Their joy is not only originated from the fascinating, active, and unpredictable characteristics of the Sun but also attributed to its influence on our daily lives. Related to the latter, sometimes studying the conditions on the Sun is called space weather forecast. Continue reading ‘space weather’ »
I cannot remember when I first met Chernoff face but it hooked me up instantly. I always hoped for confronting multivariate data from astronomy applicable to this charming EDA method. Then, somewhat such eager faded, without realizing what’s happening. Tragically, this was mainly due to my absent mind. Continue reading ‘[MADS] Chernoff face’ »
Personally, it was a highly anticipated symposium at CfA because I was fascinated about the female computers’ (or astronomers’) contributions that occurred here about a century ago even though at that time women were not considered as scientists but mere assistants for tedious jobs. Continue reading ‘after “Thanks to Henrietta Leavitt”’ »
The CfA is celebrating the 100th anniversary of the discovery of the Cepheid period-luminosity relation on Nov 6, 2008. See http://www.cfa.harvard.edu/events/2008/leavitt/ for details.
[Update 10/03] For a nice introduction to the story of Henrietta Swan Leavitt, listen to this Perimeter Institute talk by George Johnson: http://pirsa.org/06050003/
[Update 11/06] The full program is now available. The symposium begins at Noon today.
Our hometown rag (the Boston Globe) runs an occasional series of photo collections that highlight news stories called The Big Picture. This week, they take a look at the Sun: http://www.boston.com/bigpicture/2008/10/the_sun.html
The pictures come from space and ground observatories, from SoHO, TRACE, Hinode, STEREO, etc. Goes without saying, the images are stunning, and some are even animated. The real kicker is that images such as these are being acquired by the hundreds, every hour upon the hour, 24/7/365.25 . It is like sipping from a firehose. Nobody can sit there and look at them all, so who knows what we are missing out on. Can statistics help? Can we automate a statistically robust “interestingness” criterion to filter the data stream that humans can then follow up on?
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]’ »
Spectral lines are a ubiquitous feature of astronomical data. This week, we explore the special case of optically thin emission from low-density and high-temperature plasma, and consider the component factors that determine the line intensity. Continue reading ‘Line Emission [EotW]’ »
Astronomy is ruled by Gaussian distribution with a Poisson distribution duchy. From time to time, ranks are awarded to other distributions without their own territories to be governed independently. Among these distributions, Pareto deserves a high rank. There is a preprint of this week on the Pareto distribution: Continue reading ‘[ArXiv] Pareto Distribution’ »
One of the big problems that has come up in recent years is in how to represent the uncertainty in certain estimates. Astronomers usually present errors as +-stddev on the quantities of interest, but that presupposes that the errors are uncorrelated. But suppose you are estimating a multi-dimensional set of parameters that may have large correlations amongst themselves? One such case is that of Differential Emission Measures (DEM), where the “quantity of emission” from a plasma (loosely, how much stuff there is available to emit — it is the product of the volume and the densities of electrons and H) is estimated for different temperatures. See the plots at the PoA DEM tutorial for examples of how we are currently trying to visualize the error bars. Another example is the correlated systematic uncertainties in effective areas (Drake et al., 2005, Chandra Cal Workshop). This is not dissimilar to the problem of determining the significance of a “feature” in an image (Connors, A. & van Dyk, D.A., 2007, SCMA IV). Continue reading ‘Dance of the Errors’ »
Avalanches are a common process, occuring anywhere that a system can store stress temporarily without “snapping”. It can happen on sand dunes and solar flares as easily as on the snow bound Alps.
Melatos, Peralta, & Wyithe (arXiv:0710.1021) have a nice summary of avalanche processes in the context of pulsar glitches. Their primary purpose is to show that the glitches are indeed consistent with an avalanche, and along the way they give a highly readable description of what an avalanche is and what it entails. Briefly, avalanches result in event parameters that are distributed in scale invariant fashion (read: power laws) with exponential waiting time distributions (i.e., Poisson).
Hence the title of this post: the “Avalanche distribution” (indulge me! I’m using stats notation to bury complications!) can be thought to have two parameters, both describing the indices of power-law distributions that control the event sizes, a, and the event durations, b, and where the event separations are distributed as an exponential decay. Is there a canned statistical distribution that describes all this already? (In our work modeling stellar flares, we assumed that b=0 and found that
a>2 a<-2, which has all sorts of nice consequences for coronal heating processes.)
[arXiv:0709.3093v1] Short Timescale Coronal Variability in Capella (Kashyap & Posson-Brown)
We recently submitted that paper to AJ, and rather ironically, I did the analysis during the same time frame as this discussion was going on, about how astronomers cannot rely on repeating observations. Ironic because the result reported there hinges on the existence of small, but persistent signal that is found in repeated observations of the same source. Doubly ironic in fact, in that just as we were backing and forthing about cultural differences I seemed to have gone and done something completely contrary to my heritage! Continue reading ‘Betraying your heritage’ »
[arXiv:0709.2358] Cleaning the USNO-B Catalog through automatic detection of optical artifacts, by Barron et al.
Statistically speaking, “false sources” are generally in the domain of
Type II Type I errors, defined by the probability of detecting a signal where there is none. But what if there is a clear signal, but it is not real? Continue reading ‘Spurious Sources’ »