Originally Posted by parejkoj

What do you mean, by "predate the use of fibers, so that is not an issue?" Whether the spectroscopy is performed by fibers, slits, grisms, prisms or gratings does not matter here. What matters is how the objects were chosen for inclusion in the catalog. Just picking as many objects as you can, from as many catalogs as you can, is great for creating a large database, but said catalog's uniformity isn't (uniform, that is)! As Nereid has pointed out,



which seems pretty clear to me. And what do they include as galaxies here? Defining a galaxy, spectroscopically, is almost as hairy as defining a quasar. As an example, I've attached a histogram of the redshifts of everything spectroscopically classified as "galaxy" in SDSS DR4 (this includes a good number of duplicate entries). The double-humped shape is due to LRGs and AGNs at higher redshift, since those galaxies are intrinsically brighter and, if I recall correctly, nearby clusters and blue galaxies at low redshift.

Again, this is well understood by those who put together these surveys, and most of the selection function happens before any assumptions about cosmology are made.

And, as in my previous example with the Bell 2004 paper, you can't just assume that a published paper has correctly identified selection effects, corrected for them and also has a uniform classification criterion. Even in the mainstream papers, sometimes folks get this wrong, because it tain't trivial atall.



First of all, the existence of your "7 evenly spaced peaks" is in doubt. Second of all, it is quite possible for a selection function to create several evenly spaced peaks: the SDSS quasar selection function does exactly this, in part because of the nature of the photometric filters that are used to select targets.



No, there are error bars. It is a physical measurement (number of galaxies with a given redshift), thus, by definition, it has an associated error--I've gotten burned on this before, myself! For something like this, one often assumes Poisson errors, as you have done above (square-root of the number of objects in the bin), but that includes some assumptions about the underlying distribution. Do you know what those assumptions are, and whether they apply to this sample?

Just so you are aware, these are not trivial questions, nor are they tangential to the point at hand. Determining whether something is a peak or not depends on what the function is that you are comparing it to. If the function itself is "peaky" (and selection functions often are)...



Uh-uh. No. Just drawing a line through the points says nothing about whether your claimed peaks are really there or not. You have to have something to quantitatively compare it to, not just a hand-drawn function.

Personally? Except for the first "peak" in the plot you have (and I have a couple of guesses where it comes from), the "peaks" you claim look like ordinary statistical variation, and/or bin size effects.