Gutermuth color selection

Introduction

Spitzer is so sensitive that with just a few seconds of integration, you can easily see background galaxies. Many of those background galaxies are forming stars, so they have similar colors to individual young stars. Because Spitzer's spatial resolution is not very high, some of the things that Spitzer sees as point sources are actually knots of ISM. We need a good automatic way of weeding out the obvious contaminants to create a short list of sources that might actually be young stars.

No color cuts can perform this task flawlessly, though many have been discussed in the literature in the context of Spitzer observations (e.g., Allen et al. 2004, Padgett et al. 2008b, Rebull et al. 2007, Harvey et al. 2007, Gutermuth et al. 2008, 2009, Rebull et al. 2010, 2011).

Right after Spitzer started, there were some simple color cuts implemented to try and find these objects. There are some examples on the Color-Magnitude and Color-Color plots page.

By 2008-2009, the community had started to recognize that this was actually a more complex problem, requiring cuts in more than one space at a time. The basic approach of Gutermuth et al. (see Gutermuth et al. 2009, Appendix A is the easiest way to impose a series of cuts to get a first guess at a list of young stars. Here we now discuss these cuts in more detail.

Example of this concept using more familiar things

Sometimes, it is hard to understand what is going on during this process when you think about abstract things like distant stars or infrared colors. Let's try this with more familiar objects.

Find me the blue peanut M&Ms here:

How are you going to do this? You can’t touch (or taste) them; they're too far away. Could you use their (apparent) size? But what if your camera isn’t high enough spatial resolution?

And what if one of the peanut ones has a small peanut, or is edge-on (to be compared with a face-on plain one)?

You could use their colors. Look at these various views of this same pile of M&Ms (below). Notice how the different colors of M&Ms look different in the different color filters. Remember that your camera really sees the blurry view.

Now, maybe, you have a way of picking out the blue ones. How are you going to find the peanut ones? Remember, object size is not enough (and NASA won’t give you enough money to build a better/bigger camera). You need to find a "peanut wavelength" that can see inside the object. Here is a simulated peanut wavelength image:

For each object in the field of view, you have an estimated brightness in each of the four colors (blue, red, green, and "peanut"). You can compare the relative brightnesses in each combination. How many combinations are there? (I get six...)

• Red and green
• Red and blue
• Red and peanut
• Green and blue
• Green and peanut
• Blue and peanut

Now... For each combination, can compare colors for each object (e.g., make a plot). For each object, note properties of that object in that comparison. Then, have a big table, one entry per object, with properties of each object in each combination. You can then make an educated guess, based on properties in all available bands, which are the blue peanut M&Ms.

This is what we need to do using Spitzer colors for the young star candidates. We measure the properties of each object in all available bands. We make plots and compared them in many different color-color spaces. (You can do this manually, or let the computer do the comparison.) We make educated guesses as to which objects were likely YSOs or contaminants in each space, keeping track of which objects were which and which had which properties. (This bookkeeping should all be done within the computer.)

The Gutermuth method, broken down

See Gutermuth et al. 2009, Appendix A for the absolutely complete discussion, justification, etc. Technically, we should deredden the photometry for some of these steps, but we don’t have enough information to to that, and it should not matter too much for the stars we are working with.