White dwarfs counts metric?

[keatonb]

I'd like to see a metric that gives the number of white dwarfs detected in both coadded and single visit exposures. @lundmb, I believe you mentioned to me in Bremerton that you had done this already when working on your StarCounts metric. Would you be able to upload that? I'd love to see the output.

If it doesn't exist I'll write it myself (eventually).

[willclarkson]

Thanks Keaton - we are interested in this too for the Milky Way chapter (see e.g. chapter 4.3)...

@lundmb https://github.com/lundmb can you cc @willclarkson and @akvivas when you have uploaded please?

Best

Will

Dr. Will Clarkson Assistant Professor of Physics and Astronomy University of Michigan-Dearborn

On Thu, Feb 11, 2016 at 4:01 PM, Keaton Bell notifications@github.com wrote:

I'd like to see a metric that gives the number of white dwarfs detected in both coadded and single visit exposures. @lundmb https://github.com/lundmb, I believe you mentioned to me in Bremerton that you had done this already when working on your StarCounts metric. Would you be able to upload that? I'd love to see the output.

If it doesn't exist I'll write it myself (eventually).

— Reply to this email directly or view it on GitHub https://github.com/LSST-nonproject/sims_maf_contrib/issues/28.

[rhiannonlynne]

I believe the starCounts metric is already in maf_contrib? https://github.com/LSST-nonproject/sims_maf_contrib/tree/master/mafContrib/StarCounts/StarCounts (although I confess, I haven't gone through the code to see if it's complete and addresses the question you're asking).

Lynne

On Thu, Feb 11, 2016 at 1:12 PM Will Clarkson notifications@github.com wrote:

Thanks Keaton - we are interested in this too for the Milky Way chapter (see e.g. chapter 4.3)...

@lundmb https://github.com/lundmb can you cc @willclarkson and @akvivas when you have uploaded please?

Best

Will

Dr. Will Clarkson Assistant Professor of Physics and Astronomy University of Michigan-Dearborn

On Thu, Feb 11, 2016 at 4:01 PM, Keaton Bell notifications@github.com wrote:

I'd like to see a metric that gives the number of white dwarfs detected in both coadded and single visit exposures. @lundmb https://github.com/lundmb, I believe you mentioned to me in Bremerton that you had done this already when working on your StarCounts metric. Would you be able to upload that? I'd love to see the output.

If it doesn't exist I'll write it myself (eventually).

— Reply to this email directly or view it on GitHub https://github.com/LSST-nonproject/sims_maf_contrib/issues/28.

— Reply to this email directly or view it on GitHub https://github.com/LSST-nonproject/sims_maf_contrib/issues/28#issuecomment-183061426 .

[keatonb]

@rhiannonlynne, I took a look at that code and, from what I could tell, that metric seems to assume main sequence model magnitudes. It would be easy to modify to use white dwarf models in place of MS stars, and I'll go ahead and do that if @lundmb hasn't already.

[lundmb]

The code currently only addresses main sequence stars; I'm not familiar enough with white dwarf frequency to know how to incorporate that, so please feel free to add in a way to handle white dwarfs with that.

On Thu, Feb 11, 2016 at 3:37 PM, Keaton Bell notifications@github.com wrote:

@rhiannonlynne https://github.com/rhiannonlynne, I took a look at that code and, from what I could tell, that metric seems to assume main sequence model magnitudes. It would be easy to modify to use white dwarf models in place of MS stars, and I'll go ahead and do that if @lundmb https://github.com/lundmb hasn't already.

— Reply to this email directly or view it on GitHub https://github.com/LSST-nonproject/sims_maf_contrib/issues/28#issuecomment-183068182 .

[keatonb]

Okay @lundmb, I'll take a stab at it. Not in the very near future, but eventually.

[rhiannonlynne]

@yoachim has added WD templates and density maps to core maf (although I believe this may still be in review?).

[keatonb]

Excellent! Thanks @yoachim ! Please let me know what MAF version has these templates/maps. Can I update to this version through conda?

[yoachim]

It's in review and should be merged to master (and then conda-packaged) pretty soon. Probably 1-2 week timescale? If you're antsy to try it out, you can clone the sims_maf and sims_utils repos and checkout the branch feature/SIM-1851-star-color-templates on both of them.

I updated this notebook to also show the WD density maps: https://github.com/LSST-nonproject/sims_maf_contrib/blob/master/science/static/StellarDensityMap.ipynb

On the stellar colors I added 'WD_25000_80', 'HeWD_11000_80', 'WD_3050_80' to the standard list of OBAFGKM. So you can do things like check astrometric precision for specific white dwarf colors.

[keatonb]

Hey @yoachim , it looks like there might have been a mixup in the WD parameters that I suggested would be useful: I'd like to study He (DB) WDs near 25,000 K (where they pulsate), and H (DA) WDs near 11,000 where they pulsate.

I'm working on getting the versions on github to work on my machine. Hopefully I can get some analysis done in time to include in the Observing Strategy White Paper.

[yoachim]

Hi @keatonb, I figured there would be mixups so I wrote things in a way that would be easy to drop in more. Here's the full list of SEDs I have. Let me know which ones are your favorites and I'll put those in as pre-computed color options.

Here's what the filenames mean: The models in this directory were provided by Pierre Bergeron via private communication. They were parsed using the script extendBergeron.py. bergeron_models_needed.sav is a list of the models we needed to provide for our SED library.

see here for citation

http://www.astro.umontreal.ca/~bergeron/CoolingModels/

The models in this directory are named something like

bergeron_6000_65.dat_6100

The first number represents the lower of two Teffs used to interpolate the model spectrum (i.e. in the example above, we have a model with Teff=6100 arrived at by interpolating between a model with Teff=6000 and a model with Teff=6250). The midell number is the log of the surface gravity (the example above has log(g)=6.5). The final number is the actual Teff of the model.

Helium white dwarf models are named

bergeronHe######.dat####

bergeron_10000_75.dat_10000.gz bergeron_10000_75.dat_10100.gz bergeron_10000_75.dat_10200.gz bergeron_10000_75.dat_10300.gz bergeron_10000_75.dat_10400.gz bergeron_10000_80.dat_10100.gz bergeron_10000_80.dat_10200.gz bergeron_10000_80.dat_10300.gz bergeron_10000_80.dat_10400.gz bergeron_10000_80.dat_10500.gz bergeron_10000_85.dat_10000.gz bergeron_10000_85.dat_10100.gz bergeron_10000_85.dat_10200.gz bergeron_10000_85.dat_10300.gz bergeron_10000_85.dat_10400.gz bergeron_10500_75.dat_10500.gz bergeron_10500_75.dat_10600.gz bergeron_10500_75.dat_10700.gz bergeron_10500_75.dat_10800.gz bergeron_10500_75.dat_10900.gz bergeron_10500_80.dat_10600.gz bergeron_10500_80.dat_10700.gz bergeron_10500_80.dat_10800.gz bergeron_10500_80.dat_10900.gz bergeron_10500_85.dat_10500.gz bergeron_10500_85.dat_10600.gz bergeron_10500_85.dat_10700.gz bergeron_10500_85.dat_10800.gz bergeron_10500_85.dat_10900.gz bergeron_10500_85.dat_11000.gz bergeron_11000_75.dat_11000.gz bergeron_11000_75.dat_11100.gz bergeron_11000_75.dat_11200.gz bergeron_11000_75.dat_11300.gz bergeron_11000_75.dat_11400.gz bergeron_11000_80.dat_11000.gz bergeron_11000_80.dat_11100.gz bergeron_11000_80.dat_11200.gz bergeron_11000_80.dat_11300.gz bergeron_11000_80.dat_11400.gz bergeron_11000_80.dat_11500.gz bergeron_11000_85.dat_11100.gz bergeron_11000_85.dat_11200.gz bergeron_11000_85.dat_11300.gz bergeron_11000_85.dat_11400.gz bergeron_11000_85.dat_11500.gz bergeron_11500_75.dat_11500.gz bergeron_11500_75.dat_11600.gz bergeron_11500_75.dat_11700.gz bergeron_11500_75.dat_11800.gz bergeron_11500_75.dat_11900.gz bergeron_11500_75.dat_12000.gz bergeron_11500_80.dat_11600.gz bergeron_11500_80.dat_11700.gz bergeron_11500_80.dat_11800.gz bergeron_11500_80.dat_11900.gz bergeron_11500_85.dat_11600.gz bergeron_11500_85.dat_11700.gz bergeron_11500_85.dat_11800.gz bergeron_11500_85.dat_11900.gz bergeron_11500_85.dat_12000.gz bergeron_12000_75.dat_12100.gz bergeron_12000_75.dat_12200.gz bergeron_12000_75.dat_12300.gz bergeron_12000_75.dat_12400.gz bergeron_12000_75.dat_12500.gz bergeron_12000_80.dat_12000.gz bergeron_12000_80.dat_12100.gz bergeron_12000_80.dat_12200.gz bergeron_12000_80.dat_12300.gz bergeron_12000_80.dat_12400.gz bergeron_12000_85.dat_12100.gz bergeron_12000_85.dat_12200.gz bergeron_12000_85.dat_12300.gz bergeron_12000_85.dat_12400.gz bergeron_12000_85.dat_12500.gz bergeron_12500_75.dat_12600.gz bergeron_12500_75.dat_12700.gz bergeron_12500_75.dat_12800.gz bergeron_12500_75.dat_12900.gz bergeron_12500_75.dat_13000.gz bergeron_12500_80.dat_12500.gz bergeron_12500_80.dat_12600.gz bergeron_12500_80.dat_12700.gz bergeron_12500_80.dat_12800.gz bergeron_12500_80.dat_12900.gz bergeron_12500_80.dat_13000.gz bergeron_12500_85.dat_12600.gz bergeron_12500_85.dat_12700.gz bergeron_12500_85.dat_12800.gz bergeron_12500_85.dat_12900.gz bergeron_12500_85.dat_13000.gz bergeron_13000_75.dat_13100.gz bergeron_13000_75.dat_13200.gz bergeron_13000_75.dat_13300.gz bergeron_13000_75.dat_13400.gz bergeron_13000_75.dat_13500.gz bergeron_13000_80.dat_13100.gz bergeron_13000_80.dat_13200.gz bergeron_13000_80.dat_13300.gz bergeron_13000_80.dat_13400.gz bergeron_13000_80.dat_13500.gz bergeron_13000_85.dat_13100.gz bergeron_13000_85.dat_13200.gz bergeron_13000_85.dat_13300.gz bergeron_13000_85.dat_13400.gz bergeron_13500_75.dat_13600.gz bergeron_13500_75.dat_13700.gz bergeron_13500_75.dat_13800.gz bergeron_13500_75.dat_13900.gz bergeron_13500_75.dat_14000.gz bergeron_13500_80.dat_13600.gz bergeron_13500_80.dat_13700.gz bergeron_13500_80.dat_13800.gz bergeron_13500_80.dat_13900.gz bergeron_13500_80.dat_14000.gz bergeron_13500_85.dat_13500.gz bergeron_13500_85.dat_13600.gz bergeron_13500_85.dat_13700.gz bergeron_13500_85.dat_13800.gz bergeron_13500_85.dat_13900.gz bergeron_13500_85.dat_14000.gz bergeron_14000_75.dat_14100.gz bergeron_14000_75.dat_14200.gz bergeron_14000_75.dat_14300.gz bergeron_14000_75.dat_14400.gz bergeron_14000_75.dat_14500.gz bergeron_14000_80.dat_14100.gz bergeron_14000_80.dat_14200.gz bergeron_14000_80.dat_14300.gz bergeron_14000_80.dat_14400.gz bergeron_14000_85.dat_14100.gz bergeron_14000_85.dat_14200.gz bergeron_14000_85.dat_14300.gz bergeron_14000_85.dat_14400.gz bergeron_14000_85.dat_14500.gz bergeron_14500_75.dat_14600.gz bergeron_14500_75.dat_14700.gz bergeron_14500_75.dat_14800.gz bergeron_14500_75.dat_14900.gz bergeron_14500_80.dat_14500.gz bergeron_14500_80.dat_14600.gz bergeron_14500_80.dat_14700.gz bergeron_14500_80.dat_14800.gz bergeron_14500_80.dat_14900.gz bergeron_14500_80.dat_15000.gz bergeron_14500_85.dat_14600.gz bergeron_14500_85.dat_14700.gz bergeron_14500_85.dat_14800.gz bergeron_14500_85.dat_14900.gz bergeron_14500_85.dat_15000.gz bergeron_15000_75.dat_15000.gz bergeron_15000_75.dat_15100.gz bergeron_15000_75.dat_15200.gz bergeron_15000_75.dat_15300.gz bergeron_15000_75.dat_15400.gz bergeron_15000_75.dat_15500.gz bergeron_15000_80.dat_15100.gz bergeron_15000_80.dat_15200.gz bergeron_15000_80.dat_15300.gz bergeron_15000_80.dat_15400.gz bergeron_15000_80.dat_15500.gz bergeron_15000_85.dat_15100.gz bergeron_15000_85.dat_15200.gz bergeron_15000_85.dat_15300.gz bergeron_15000_85.dat_15400.gz bergeron_15000_85.dat_15500.gz bergeron_15500_75.dat_15600.gz bergeron_15500_75.dat_15700.gz bergeron_15500_75.dat_15800.gz bergeron_15500_75.dat_15900.gz bergeron_15500_75.dat_16000.gz bergeron_15500_80.dat_15600.gz bergeron_15500_80.dat_15700.gz bergeron_15500_80.dat_15800.gz bergeron_15500_80.dat_15900.gz bergeron_15500_80.dat_16000.gz bergeron_15500_85.dat_15600.gz bergeron_15500_85.dat_15700.gz bergeron_15500_85.dat_15800.gz bergeron_15500_85.dat_15900.gz bergeron_15500_85.dat_16000.gz bergeron_16000_75.dat_16100.gz bergeron_16000_75.dat_16200.gz bergeron_16000_75.dat_16300.gz bergeron_16000_75.dat_16400.gz bergeron_16000_75.dat_16500.gz bergeron_16000_80.dat_16100.gz bergeron_16000_80.dat_16200.gz bergeron_16000_80.dat_16300.gz bergeron_16000_80.dat_16400.gz bergeron_16000_80.dat_16500.gz bergeron_16000_85.dat_16100.gz bergeron_16000_85.dat_16200.gz bergeron_16000_85.dat_16300.gz bergeron_16000_85.dat_16400.gz bergeron_16500_75.dat_16600.gz bergeron_16500_75.dat_16700.gz bergeron_16500_75.dat_16800.gz bergeron_16500_75.dat_16900.gz bergeron_16500_80.dat_16600.gz bergeron_16500_80.dat_16700.gz bergeron_16500_80.dat_16800.gz bergeron_16500_80.dat_16900.gz bergeron_16500_85.dat_16500.gz bergeron_16500_85.dat_16600.gz bergeron_16500_85.dat_16700.gz bergeron_16500_85.dat_16800.gz bergeron_16500_85.dat_16900.gz bergeron_17000_70.dat_17600.gz bergeron_17000_70.dat_18200.gz bergeron_17000_70.dat_18800.gz bergeron_17000_70.dat_19400.gz bergeron_17000_75.dat_17000.gz bergeron_17000_75.dat_17600.gz bergeron_17000_75.dat_18200.gz bergeron_17000_75.dat_18800.gz bergeron_17000_75.dat_19400.gz bergeron_17000_75.dat_20000.gz bergeron_17000_80.dat_17000.gz bergeron_17000_80.dat_17600.gz bergeron_17000_80.dat_18200.gz bergeron_17000_80.dat_18800.gz bergeron_17000_80.dat_19400.gz 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bergeron_He_9500_70.dat_9500.gz bergeron_He_9500_70.dat_9600.gz bergeron_He_9500_70.dat_9700.gz bergeron_He_9500_70.dat_9800.gz bergeron_He_9500_70.dat_9900.gz bergeron_He_9500_75.dat_10000.gz bergeron_He_9500_75.dat_9600.gz bergeron_He_9500_75.dat_9700.gz bergeron_He_9500_75.dat_9800.gz bergeron_He_9500_75.dat_9900.gz bergeron_He_9500_80.dat_9600.gz bergeron_He_9500_80.dat_9700.gz bergeron_He_9500_80.dat_9800.gz bergeron_He_9500_80.dat_9900.gz bergeron_He_9500_85.dat_10000.gz bergeron_He_9500_85.dat_9600.gz bergeron_He_9500_85.dat_9700.gz bergeron_He_9500_85.dat_9800.gz bergeron_He_9500_85.dat_9900.gz bergeron_He_9500_90.dat_10000.gz bergeron_He_9500_90.dat_9500.gz bergeron_He_9500_90.dat_9600.gz bergeron_He_9500_90.dat_9700.gz bergeron_He_9500_90.dat_9800.gz bergeron_He_9500_90.dat_9900.gz

[keatonb]

Looking at the list of what is available, I think the three most useful for coming up with figures of merit are: 'bergeron_He_24000_80.dat_25200.gz' 'bergeron_10500_85.dat_11000.gz' 'bergeron_2750_85.dat_3000.gz'

However, I am not entirely sure how to interpret the output of the white dwarf map. The given units are stars per square arcsec. With MS stars, this makes sense because you can categorize all the stars into O, B, A, etc. But with these white dwarfs of given Teff and logg, we can't really define numbers without binning things up. So what exactly is going on here? Are you binning the white dwarfs up into logg and Teff bins according to the file naming convention above. Or are you imposing one of these SEDs onto all WDs in the sample when the map is accessed?

The ideal thing would be to return a density map for a user-defined logg and Teff range, H or He atmosphere, and desired magnitude range. If the actual interpolated colors could be fetched for these stars, that would be amazing, but I think that might be more intensive than practical. A simpler thing would be very useful for the white paper would be if one of these pre-selected templates could be applied to all stars in the given range. For example, it would be great to ask how many WD stars are in the range 10600 < Teff < 12500, 7 < logg < 9 in some magnitude range (to the coadded or single-visit depth), where every star in this range is treated as having the SED of 'bergeron_10500_85.dat_11000.gz'.

If I gain an understanding of what the current map is returning, this interpretation may be able to be made after the fact rather than within MAF. For example, if the maps are currently applying the requested SED to all WD stars, one could multiply the resulting density by the fraction of all WDs that would be expected in the parameter range of interest. Though this should be determined from the same set of models that the density maps were derived with for consistency.

And my final way to be a pain: I'm having trouble getting the relevant branches of sims_maf and sims_utils to run on my local machine. I have lsst-sims-maf conda installed and I'm not sure how to direct my import statement to the versions cloned from github instead of the conda installed bit. Do I need to go in and replace things where the conda installation lives? If you know an easy solution to this, I'd appreciate the assistance.

Thanks! -Keaton

[rhiannonlynne]

@keatonb - @yoachim should correct me if I'm wrong, but I'm guessing that the map is made from our CatSim database. So basically, in the CatSim database, we have a model of the galaxy based on GalFast, that has white dwarfs included - each white dwarf has its own appropriate SED for the logg/Teff it was assigned in the galaxy model. Thus each WD in CatSim has colors in ugrizy that are appropriate for its particular SED (and with added dust extinction). At this point, Peter just counts the number of WDs which are visible per square degree, brighter than the given magnitude - a luminosity function at each RA/Dec point.

Thus, it's not quite either of the situations that you described above. We could try to remake the map, so that there are separate maps for WDs of a particular range of logg/Teff (i.e. replace the single map with multiple maps). Or maybe Peter has some better ideas.

As for your other point: you can indeed mix and match github + conda installations of sims_maf. With a working conda installation, your usual way to get 'sims_maf' activate in your environment would be:

source ~/anaconda/bin/eups-setups.csh (or your equivalent) setup sims_maf

If you want to use a github repo instead, simply clone the repo (let's say you put it into ~/lsstRepos/sims_maf) and then (in a clean shell):

source ~/anaconda/bin/eups-setups.csh (or your equivalent) cd ~/lsstRepos/sims_maf eups declare sims_maf git -r . eups declare sims_maf git -t $USER

(you have to do these in separate steps right now due to an eups bug .. but that should be fixed soon) You only have to do this once, and then eups will remember this version of sims_maf.

Then to use your github repo version, instead of the conda version, do the following at each login:

source ~/anaconda/bin/eups-setups.csh (or your equivalent) setup sims_maf -t $USER

You would do a similar thing for the sims_maps and sims_utils packages.

[yoachim]

@keatonb , @rhiannonlynne is correct, the stellar density map is for all WD stars. The point of making the density map is to cut down on the expense of querying the catsim catalog a bunch of times. For the purposes of comparing different simulated surveys, you usually don't need super high fidelity to figure out which one is better. For example, you could just say the density of He_24000_80 is 5% the total WD density. Sure, that's not "right", but for comparing the relative performance of runs it'll do fine.

[keatonb]

Thank you @rhiannonlynne ! The code is working for me now. I did have to change the filenames in sims_maps from things like "starDensity_i_wdstars_nside_64.npz" to "starDensity_i_wdstarsnside_64.npz" (deleting one underscore).

@yoachim , okay I think I understand what the WD stellar map returns now. I guess I'm still confused where the different Bergeron SEDs come into play.

I think for the metrics I want to develop I can assess, as you suggest, the fractional number of white dwarfs of different types out of the whole population. Some of this may be quite dependent of the characteristics of the WDs in CatSim, so I'll try to explore CatSim some to inform my interpretations.

[willclarkson]

Hi @keatonb - the white dwarf metric looks really promising! (cc @akvivas)

In the Galaxy chapter we mention white dwarf recovery in the solar neighborhood (Figure of Merit 4.2 in Table 4.3), but this discussion is currently mostly placeholder material. It sounds from this discussion like your white dwarf metric goes a long way to realizing such a figure of merit.

Are you able to seed the survey with arbitrary white dwarf distributions over the CatSim output?

Thanks!

Will

Dr. Will Clarkson Assistant Professor of Physics and Astronomy University of Michigan-Dearborn

On Thu, Apr 21, 2016 at 3:40 PM, Keaton Bell notifications@github.com wrote:

Thank you @rhiannonlynne https://github.com/rhiannonlynne ! The code is working for me now. I did have to change the filenames in sims_maps from things like "starDensity_i_wdstars_nside_64.npz" to "starDensity_i_wdstarsnside_64.npz" (deleting one underscore).

@yoachim https://github.com/yoachim , okay I think I understand what the WD stellar map returns now. I guess I'm still confused where the different Bergeron SEDs come into play.

I think for the metrics I want to develop I can assess, as you suggest, the fractional number of white dwarfs of different types out of the whole population. Some of this may be quite dependent of the characteristics of the WDs in CatSim, so I'll try to explore CatSim some to inform my interpretations.

— You are receiving this because you were mentioned. Reply to this email directly or view it on GitHub https://github.com/LSST-nonproject/sims_maf_contrib/issues/28#issuecomment-213084044

[keatonb]

@willclarkson - My primary motivation here is to assess how many pulsating WD variables we will be able to study in the time domain as this pertains my PhD thesis. But I am interested in advocating all things WD, so will try to contribute to the Galaxy chapter where I can. What can be done by the draft due date will, however, be limited. My opinion of the best way to contribute to this, is to assess the distance to which LSST will probe the coolest objects in the WD luminosity function, constraining what populations we will be able to date through WD cosmochronology. I planned to do that through a photometric detectability threshold standpoint, but of course limits on the parallax and proper motion precision will determine how many WDs we have distances and population memberships for to turn catalog magnitude into luminosity. As I am trying to do with pulsating WDs to count the number of pulsators out to magnitude limit on variability, so too can we count WDs (total number and the number near luminosity function turnover) out to some threshold magnitude on determining reliable distances or population memberships. It seems most of the parallax and proper motion work is in place already. I can try to tie the results of those metrics to the WD counts to create an overall figure of merit.

@yoachim - could you share the code you used to generate the WD density maps? The easiest way forward for my science would be to generate just a few more (~3) specific maps of different types of WD up front so that CatSim doesn't have to be queried repeatedly in later analyses. About how long does querying for each map take? I already have the necessary access to fatboy.

[yoachim]

Here's the code to generate stellar density maps (it's in MAF already): https://github.com/lsst/sims_maf/blob/master/python/lsst/sims/maf/maps/createStarDensitymap.py

I think it's a few hours per filter if I recall.

[keatonb]

Thanks for pointing me in the right direction, @yoachim !

[willclarkson]

Hi @keatonbell - this sounds really good, and appropriate for the impending deadline. It sounds like the material you are producing should indeed be provocative to the reader.

We are still attempting to get another "recovery fraction of population X" into the Milky Way chapter by the late-April deadline, but I doubt it will be as sophisticated as the multiple-criterion Figure of Merit you discuss here.

Let's talk more after the late-April rush about how the two sets of metrics/Figures of merit (between chapters 4 and 5) will feed off each other.

Best,

Will

Dr. Will Clarkson Assistant Professor of Physics and Astronomy University of Michigan-Dearborn

On Thu, Apr 21, 2016 at 6:09 PM, Keaton Bell notifications@github.com wrote:

@willclarkson https://github.com/willclarkson - My primary motivation here is to assess how many pulsating WD variables we will be able to study in the time domain as this pertains my PhD thesis. But I am interested in advocating all things WD, so will try to contribute to the Galaxy chapter where I can. What can be done by the draft due date will, however, be limited. My opinion of the best way to contribute to this, is to assess the distance to which LSST will probe the coolest objects in the WD luminosity function, constraining what populations we will be able to date through WD cosmochronology. I planned to do that through a photometric detectability threshold standpoint, but of course limits on the parallax and proper motion precision will determine how many WDs we have distances and population memberships for to turn catalog magnitude into luminosity. As I am trying to do with pulsating WDs to count the number of pulsators out to magni tude limit on variability, so too can we count WDs (total number and the number near luminosity function turnover) out to some threshold magnitude on determining reliable distances or population memberships. It seems most of the parallax and proper motion work is in place already. I can try to tie the results of those metrics to the WD counts to create an overall figure of merit.

@yoachim https://github.com/yoachim - could you share the code you used to generate the WD density maps? The easiest way forward for my science would be to generate just a few more (~3) specific maps of different types of WD up front so that CatSim doesn't have to be queried repeatedly in later analyses. About how long does querying for each map take? I already have the necessary access to fatboy.

— You are receiving this because you were mentioned. Reply to this email directly or view it on GitHub https://github.com/LSST-nonproject/sims_maf_contrib/issues/28#issuecomment-213132440

[keatonb]

@yoachim - was able to achieve what I wanted by adding SQL constraints on the .query_columns() call.

For ZZ Ceti pulsators, "(sedfilename NOT LIKE '%He%') AND (t BETWEEN 10600 AND 12500)" gives H atmosphere white dwarfs within the ZZ Ceti instability strip.

Thanks again.