You don’t know me, but if you’ve written an astrophysics code useful for producing published results, I’d like to know you, or at least know of your code. I’m Alice Allen, primary editor of the Astrophysics Source Code Library (ASCL). The ASCL is a free online reference library of (wait for it…. ) …yes! Astrophysics codes! Founded in 1999 by Robert Nemiroff and John F. Wallin, the ASCL has been dusted off, moved to a new site, and over the past year, greatly expanded. In fact, we (those of us working on the ASCL; we have an Advisory Committee and everything!) believe the ASCL is the largest collection of astrophysics codes anywhere. And the ASCL is still growing, with codes added every month.

Why a library of codes? Aside from the importance of ensuring reproducibility of one’s work (that scientific method stuff, you know), increasing falsifiability, demonstrating the integrity of a method or methods used to produce results, and increasing communication between researchers and those who study their research, working on it keeps me in at night (always a good thing). The ASCL also provides a way for astrophysicists to find codes that may be useful to their projects. We hope to provide a way for astrophysicist coders to get a little more recognition for their work, too, and are looking for a way to make the ASCL citable.

More and more astronomers are writing, running, and using massive computer simulations. The complexity of these simulations (often 3D space + time) means that visualization can be challenging. Over at Astropython, they have posted about the - yt - toolkit that allows you to analyze and visualize the results of adaptive mesh refinement simulation codes (AMR). AMR codes are used to simulate the formation of cosmological large scale structure, the collapse of gas clouds to form stars, fluid dynamics involving shocks, jets, etc. The yt toolkit is written in python and works with several AMR codes including Enzo, Orion, FLASH, and preliminary support for RAMSES, ART, Chombo, CASTRO and MAESTRO. Data from one of these AMR codes can be sliced, diced, profiled, binned, and volume-rendered.

yt is described in Turk et al. 2010 and can be downloaded for free. The documentation includes a nice summary and orientation. I have updated our Python wiki page to provide links.

Have you used yt and what was your experience like? What are the similar visualization packages for smooth-particle hydrodynamics (SPH) codes?

Wiki: Python, Simulations

Luisa Rebull is a Research Scientist at the Spitzer Science Center at the Infrared Processing and Analysis Center (IPAC) at Caltech. She is the Archive Scientist for Spitzer, and has been working on the development of the Spitzer Heritage Achive.

There are two important recent developments in the Spitzer world: (1) the handover to IRSA of the Archive, and (2) the recent release of version 2.0 of the Spitzer Heritage Archive.

The IRSA handover

The Spitzer project has reached a major milestone in the completion of the Spitzer cryogenic mission. Since the end of the cryogenic mission in May 2009, the Spitzer Science Center (SSC) has been conducting the final data processing

Wiki Upgrade

Shows a main-sequence isochrone and several evolutionary tracks for stars of a specific mass. Click on image to see references at Wikipedia.

Some of you may have noticed that the wiki appearance has changed. This is because we have upgraded the wiki to the latest and greatest version of TikiWiki (6.2). Some of the functionality, especially wiki editing, should be greatly improved. We are still in the process of importing all of our stylistic customizations so the look and feel will improve with time.

We are also aware that the site has been generally slow and sometimes the wiki is inaccessible. Our hosting service is throttling our performance due to excess CPU usage. We are still in the process of tracking down why this is happening.

New Wiki Page on Stellar Evolution and Atmosphere Models

In the meantime, I have put together a collection of links and papers useful for modeling stars or stellar populations on a new Data Modeling page. It is thorough, but not yet complete, so please add your favorite evolution and atmosphere models and codes. Listing and linking to these models is the easy part. A more difficult proposition is deciding when to use different sets of models (mass range, age range, metallicity range, etc.). Unfortunately, there doesn’t seem to be one code for stellar evolution or atmospheres that is applicable over an entire mass range of stars (0.08 Msun – 150 Msun) even at a fixed age (I work with young populations). What are your favorite models and when do you use them? Are there libraries of synthetic spectra for atmospheres that I am missing (especially extending beyond the optical wavelengths)?

[title revised Jan 19]

Since Jane’s questions have proved to be very popular and useful posts, we’ve decided to make “Ask AstroBetter” a regular feature.

Continuing on the theme of spectral analysis, Eilat asks,

Is there a straightforward way to take a table of wavelength, flux, error spanning the same range as SDSS spectra and put it into SDSS spectrum format that is then digestible by SDSS-specific software. If anyone knows how to do this, I imagine it would be useful beyond my own specific needs, since there are lots of codes developed for analyzing SDSS spectra that best recognize that particular file format.

Looking forward to your responses in the comments!

Got a nagging question? Drop us a line at tips@astrobetter.com. Depending on how many questions we get, we may be motivated to get a proper discussion board started.

For many astronomers, statistics is an integral part of our analysis procedure; yet, we typically get very little formal training in this area. Recently, several astro-statistics papers have been posted on astro-ph that specifically address some of the unique attributes of astronomical data (small numbers of data points, frequent systematic errors or outliers, non-linear models, heteroskedastic error bars and limits, etc.). I would like to round up these and other similar astro-stats papers and post them as a collection on the wiki. Do you know of additional astro-stats papers to include in this collection?

• Error Estimation in Astronomy: a Guide, Andrae’ (arXiv:1009.2755)
  • critical look at a common practice of rescaling error bars to get reduced chi-squared=1.
  • very good description of the different methods for defining confidence intervals
• Dos and Don’ts of Reduced Chi-Squared, Andrae’, Schulze-Hartung, & Melchior (arXiv:1012.3754)
• Data Analysis Recipes: Fitting a Model to Data, Hogg, Bovy, & Lang (arXiv:1008.4686)
  • describes the basics of both frequentist and bayesian approaches

Note: There are some excellent books for purchase on astro-statistics; but here I am collecting papers that are available via astro-ph or in journals that most institutions have access to.

Rethinking the scientific method | The New Yorker (subscription required, unfortunately)
Extremely disturbing article about well-intentioned scientists’ experiments and conclusions gone awry. Most upsetting is the lack of reproducibility in many important studies and the likelihood that those works don’t get published.

The disturbing implication of his study is that a lot of extraordinary scientific data is nothing but noise. This suggests that the decline effect is actually a decline of illusion. Many scientific theories continue to be considered true even after failing numerous experimental tests. The decline effect is troubling because it reminds us how difficult it is to prove anything.

The article is about medicine, but I wonder how big of a problem this is in Astronomy. I posed this question to my Buzz followers, and Gus Muench pointed out this article on the distances the LMC. The author describes an unsettling cluster of distance around the HST Key Project (HSTKP) value.

Indeed, these measures cluster too tightly around the HSTKP value, with 68% of the measures being within 0.5-sigma of 18.50 mag. A Kolmogorov-Smirnov test proves that this concentration deviates from the expected Gaussian distribution at a >3-sigma probability level. This concentration is a symptom of a worrisome problem. Interpretations considered include correlations between papers, widespread over-estimation of error bars, and band-wagon effects. The purpose of this paper is to alert workers in the field that this is a serious problem that should be addressed.

Do you know of other examples of band-wagon effects that we should be aware (and wary) of? Are there astronomical questions that are more susceptible to these types of biases than others?

This is a guest post—featuring our first screencast!—from Niall Deacon who studies brown dwarfs and white dwarfs in the Pan-STARRS survey at the University of Hawai`i. Niall also blogs about astronomy at weareallinthegutter.

If you are drowning in search windows from different data archives and wasting time writing code to plot graphs of the simplest datasets then maybe it’s time to look at Topcat, a nifty little Java application to make life easier.

There’s a huge amount of astronomical data on the web and Topcat can give you easy access to a big chunk of it. Take for example large surveys like 2MASS and SDSS, these can be queried from the cone search (if you want all the objects in a particular area) or the multiple cone search (for extracting photometry for a list of objects). Want to check your candidates against the literature? Just search for any data table in Vizier and cross reference with it. There is also a spectroscopic database to query against. Want to quickly eyeball a couple of candidates? The activation actions can be used to bring up finders from 2MASS, SuperCOSMOS or SDSS.

For anyone who uses python and ds9 to visualize their FITS files, I think pyds9 is now a must-have. It is officially written and developed through SAOImage ds9 so it will be supported for the long haul. Here are the primary links to get going:

• TARball for installation: Source
• Documentation for installation and use: Docs

The documentation is pretty basic so I put together a very small tutorial of a way I might typically use it. Most of the controls work through the XPA interface, which seems very flexible, if a little tricky to figure out exactly how to make it work for specific use-cases. If you have samples of how you use pyds9 or ds9 xpa, post them in the comments or on this wiki page.

from scipy import stats
import numpy as np
import ds9
 
# Make a 2D gaussian image that is stored in a 2D numpy array
x = np.arange(-3, 3, 0.1)
xx, yy = np.meshgrid(x, x)
gauss2d = stats.norm.pdf(xx) * stats.norm.pdf(yy)
 
# Now open ds9 (this assumes no ds9 instance is yet running)
d = ds9.ds9()
 
# Load up our 2D gaussian
d.set_np2arr(gauss2d)
 
# Zoom to fit
d.set('zoom to fit')
 
# Change the colormap and scaling
d.set('cmap bb')
d.set('scale log')
 
# Add a label
d.set('regions command {text 30 20 #text="Fun with pyds9" font="times 18 bold"}')
 
# Now you can play in ds9 to your heart's content.
# Check back to see what the current color scale is.
print d.get('scale')
 
# Finally, save your completed image (including regions or labels)
d.set('saveimage png my_pyds9_img.png')

Interpolation

Interpolation: Filter profiles may be reported as transmission vs. wavelength data points. To resample the filter profiles to a different (regularly spaced) wavelength grid, I used scipy's interpolation package.

Interpolation is a used for many astronomical applications. Interpolation is required to combine sub-pixel dithered images or spectroscopy, sample grids of stellar evolution or stellar atmosphere models, calculate extinction from observed extinction curves, and many many more applications. The scipy.interpolate package in python has some nice built-in interpolation functions and I have gathered a few links describing the capabilities (in addition to the documentation). I would recommend using splrep/splev over interp1d for speed.

• Scipy cookbook on interpolation
• A slideshow outlining the interpolation capabilities in Scipy
• A thread explaining terms in spline interpolation/smoothing

from scipy import integrate
def myfunc(x, a, b):
return (x**b) + a
 
# These are the arguments that will be passed as a and b to myfunc()
args = (1.0, -2.0)
 
# Integrate myfunc() from 0.5 to 1.5
results = integrate.quad(myfunc, 0.5, 1.5, args)
print 'Integral = ', results[0], ' with error = ', results[1]