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Below, we discuss the characteristics and origin of the tables i= n the LSST database. For a detailed list of the available tables and = columns, see this page.
Several database tables exist containing different types of stellar obje= cts (Main Sequence stars, RR Lyrae, etc.). These tables can be access= ed through the classes provided in sims_catUtils/python/lsst/sims/catUtils/= baseCataogModels/StarModels.py. Not all of these catalogs are accurat= ely distributed across the sky. Some of them are just `proof of conce= pt' tables. Below, we list the available tables of stars, along with = the python class which accesses them, and their extent across the sky. = ; Those which cover the `whole' sky (0-360 degrees in RA; -90 to 35 degrees= in Dec) may be taken to be distributed according to some physically accura= te model. Those which are not distributed over the whole sky should n= ot be treated as physically accurate.
Main Sequence stars
White Dwarfs
Eclipsing binaries
RR Lyrae
dwarf galaxy stars
Cepheid variables
`easter eggs'
The galaxy simulation is based on dark matter haloes from the Millennium= simulation \citep{springel05} (with an assumed standard $\Lambda$-CDM cosm= ology) and a semi-analytic baryon model grafted upon the Millennium results= as described in \citet{springel05} and \citet{delucia}. This semi-analytic= model features radiative cooling, star formation, and the dynamics of blac= k holes, supernovae, and AGNs. It includes explicitly following dark matter= haloes, even afteraccretion onto larger systems, in order to follow the dy= namics of satellite galaxies for an extended period of time as well as `rad= io mode' feedback of AGNs. The model was adjusted to mimic the luminosity, = color, and morphology distributions of low redshift galaxies \citep{delucia= }. LSST cosmological catalogs were generated from the \citet{delucia} data = by constructing a lightcone, covering redshifts 0$<$z$<$6, from 58 50= 0h$^{-1}$Mpc simulation snapshots. This lightcone covers a 4.5x4.5 degree f= ootprint on the sky and samples halo masses over the range $2.5\times10^9$ = to $10^{12}$ $M_\odot$.
Dynamically tiling this footprint across the sky enables the simulation = of the full LSST footprint while keeping the underlying data volume small (= but at the expense of introducing periodicity in the large scale structure)= . For all sources, a spectral energy distribution (SED), is fit to the gala= xy colors using Bruzual and Charlot spectral synthesis models \citep{bruzua= l}. The \citet{delucia} catalog includes BVRIK magnitudes and dust values f= or the disk and bulge components of each galaxy as well as radii, redshift,= coordinates, stellar age, masses and metallicities. These parameters are u= sed in constraining the assignment of SEDs to each disk and bulge component= . Fits are undertaken independently for the bulge and disk and include incl= ination dependent reddening. Morphologies are modeled using two Sersi{\'c} = profiles and a single point source (for the AGN) with bulge-to-disk ratios = and disk scale lengths from \citet{delucia}. Half-light radii for bulges ar= e estimated using the empirical absolute-magnitude vs half-light radius rel= ation given by \citet{gonzalez09}. AGNs are derived using the \citet{bongio= rno12} luminosity function. The B-band absolute magnitudes are converted to= bolometric luminosities using Eqn. 2 in \citet{hopkins07}. Empirical relat= ions derived from the SDSS enable computation of the colors and stellar mas= s of the AGNs host galaxy from its luminosity. These parameters are used, t= ogether with the redshift values from the AGN catalog, to match each AGN to= a galaxy in the galaxy catalog. In general, the AGNs match to galaxies hav= ing higher stellar masses, approximately $10^{9}$ to $10^{11}$ $M_{\odot}$ = which is comparable to recent analysis of host galaxies done by \citet{xue1= 1}. The AGN SED is taken from the mean AGN spectrum of \citet{vandenberk}. = Comparisons between the redshift and number-magnitude distributions of the = simulated catalogs with those derived from deep imaging and spectroscopic s= urveys showed that the De Lucia models under-predict the density of sources= at faint magnitudes and high redshifts. To correct for these effects, sour= ces are ``cloned'' in magnitude and redshift space until their densities re= flect the average observed properties (see \S \ref{sec:galaxycounts}).
Galaxies are stored in a single table that is $4.5^{\circ}$ on a side an= d comprises 17,428,284 galaxies. The schema for these galaxies is described= in the Database Schema page. Through a database stored p= rocedure we provide a virtual replication of this table; tiling it across t= he full sky (see Figure~\ref{fig:galcoverage}). All queries outside of the = footprint of the primary table are transformed, based on the bounding box o= f the tiles that they intersect, to lie within the primary table. Positions= for sources that are returned from the database query are then transformed= to their appropriate positions on the sky using the bounding boxes of the = input tiles.
\begin{figure}[h]
\centering \includegraphics[width=3D0.5\textwidth]{validation_figur= es/basicDemo.png} \caption{The galaxy catalog is replicated (virtually= ) across the LSST footprint using a series of tiles. These tiles correspond= to the footprint of the galaxy table in the database. Queries are transfor= med using the tile bounding box such that they map to the galaxy table. Whe= n positions are returned through this query they are mapped back to the app= ropriate sky coordinates using the tile bounding boxes.} \label{fig:ga= lcoverage}
\end{figure}
The left panel of Figure \ref{fig:gcounts} shows a comparison of the cum= ulative galaxy counts in CatSim to a compilation of observations provided b= y Metcalfe et al. (see {\tt http://ls.st/fus}). This comparison is undertak= en in the $i$ band to minimize the effects of dust extinction which are som= ewhat uncertain in the Metcalfe compilations. A single transform of I$_{kc}= $ =3D i$_{AB}$ - 0.6 has been applied to the Metcalfe magnitudes to take th= em from the Kron-Cousins photometric system to the SDSS AB photometric syst= em \citep{ellis07}.
The right panel of Figure \ref{fig:gratio} shows the ratio of the cumula= tive counts taken from the simulations to a polynomial fit to the cumulativ= e counts derived from the Metcalfe data. The error bars are estimated from = the published uncertainties on the Metcalfe galaxy number counts. The requi= rement on galaxy number densities is that they agree within $\pm10\%$ of th= e observed counts (to a coadded i-band depth of 26.8). This requirement is = set due to the variance in the counts of galaxies at faint magnitudes (due = to the small areal coverage of galaxy surveys at these depths). For magnitu= des $20.25<i<25.75$ the simulated galaxy catalog meets the LSST requi= rements. For brighter magnitudes the simulated catalogs over-predict the ga= laxy counts by up to 25\%. This discrepancy is to be expected as the volume= sampled by the simulated galaxies (covering $4.5^o \times 4.5^o$) is small= relative to the observations and the cosmic variance in the simulated data= will be large. For magnitudes fainter than $i>25.75$ the galaxy counts = fail to meet the number density requirement; deviating by up to 13\% from t= he observed counts. % We have %taken their compilations from: {\tt % http:/= /star-www.dur.ac.uk/~nm/pubhtml/counts/idata.txt} accessed on %06/01/2013. = \begin{figure}[h] \centering \includegraphics[width=3D0.45\textwidth]{valid= ation_figures/Ngals-i.png} \hfil \includegraphics[width=3D0.45\textwidth]{v= alidation_figures/CumulativeFraction_i.png} \caption{A comparison of the Me= tcalfe galaxy number counts (symbols) to those derived from the simulated c= atalog \label{fig:gcounts}. The left panel shows the differential counts an= d the right panel the ratio of the cumulative counts. Error bars are from d= erived from those published by the individual surveys. \label{fig:gratio} T= he vertical dashed line represents the 5$\sigma$ magnitude limit for galaxi= es.} \end{figure}
Stars are represented as point sources and are drawn from the Galfast&nb= sp;model of \citet{galfast}. Galfas= t generates stars according to density laws derived from fitting SDSS data = to a model of a thick and thin disk, and a halo \citep{juric}. Using an inp= ut luminosity function measured from SDSS for each class of star (e.g.\ mai= n sequence, white dwarf, blue horizontal branch, etc.), Galfast samples sta= rs in space and magnitude from a 4-dimensional probability density function= $\rho$(x,y,z,M). After this stage, using Fe/H and kinematics models from \= citet{ivezic08} and \citet{bond09} (also derived from SDSS data), each star= is assigned a metallicity, proper motion, and parallax. Spectral energy di= stributions are fit to the predicted colors using the models of \citet{kuru= czCD} for main sequence stars and giants, \citet{bergeron95} for white dwar= fs, and a combination of spectral models and SDSS spectra for M, L, and T d= warfs \citep[e.g.][]{cushing05,bochanski07,burrows06,pettersen89,kowalski10= }. For Galactic reddening, a value of E(B-V) is assigned to each star using= the three-dimensional Galactic model of \citet{amores05}. For consistency = with extragalactic observations the dust model in the Milky Way is re-norma= lized to match the \citet{schlegel98} dust maps at a fiducial distance of 1= 00 kpc. Once the extinction and SED are assigned, observed magnitudes are c= alculated in the SDSS and LSST photometric systems using fiducial system th= roughput curves. Binary stars are included in the luminosity functions from= which the stellar colors are sampled but are assumed to be unresolved and = non-variable (except for a selection of eclipsing binaries described later)= .
Stellar populations included within the current implementation of the mo= del are:
Approximately 10= \% of the stellar sources are variable at a level detectable by LSST= . Variability is modeled for sources within the base catalogs by defining a= light curve, its amplitude, a period, and a phase. For queries that contai= n time constraints the magnitude of the source is adjusted based on the pro= perties of the light curve (the current implementation only allows for mono= chromatic variations in the fluxes). Variables modeled range from cataclysm= ic variables, flaring M-dwarfs, and micro-lensing events. For transient sou= rces, the period of the light curve is set to $>10$ years such that the sources will= not repeat within the period of the LSST observations.
For all of these sources, the generation of magnitudes and colors, and t=
he application of time dependent astrometric corrections (e.g. precession, =
parallax, proper motion) are calculated using Python subclasses of the Inst=
anceCatalog object. \subsubsection{Variable Sources} The fra=
mework is able to support several types of variability: periodic, stochasti=
c, and repeating. The variability models used in the database include: {itemize} \item M-dwarf flares -- full sky \item AGN/QSOs -- full sky \item R=
Rly -- full sky \item Cepheids -- exemplar in=
dividuals \item Eclipsing binaries -- exempla=
r individuals \item Am CVn -- exemplar indivi=
duals \item Micro lensing -- exemplar individ=
uals \end{itemi=
ze} Each type of variability is described by =
either a parametric model or an interpolated lookup table (see \S\ref{sec:determine} for a description of these models). To date only =
mono-chromatic variability has been implemented. % (see Figure \ref{fig:lcs} for example lig=
htcurves). %Variable sources are implemented through the InstanceCatalog API <=
span class=3D"hl slc" style=3D"color: rgb(131,129,131);">%\citep{XXX}. This=
API takes the name of the variability model and the %parameters associated with that=
model (both of which are stored in the %database) and modifies the brightness of a s=
ource based on the time of %observation.
We consider five representative fields at varying Galactic latitudes and=
at a Galactic longitude of $l=3D90$. We compare the number counts of main sequence sta=
rs as a function of $i$-band limiting magnitude for the Galfast model \citep{juric} (using the composite dust model of \citet{amores05} normalized to \citet{schlegel98}) to the
The Solar System model is a realization of the \c=
itet{grav11} model. All major groups of Solar System bodies are represented includi=
ng: main belt asteroids, near earth objects, trojans of the major planets, =
trans-neptunian objects, and comets. There are approximately 11 million objects in the =
Solar System catalog with the vast majority (about 9 million) being main belt asteroids=
. Populations are complete down to apparent magnitudes of V=3D24.5. Each object is as=
signed a carbonaceous or stony composition spectrum derived from extending =
the reflectance spectra from \citet{demeo} by linear extrapo=
lation from 4500$\AA$ to 3000$\AA$ and =
then multiplying by a Kurucz solar spectrum. The choice of a C or S type sp=
ectra for an object is assigned based upon a simple relation to the size of=
its orbit that approximately matches SDSS asteroid observations. Each obje=
ct's brightness during a specific observation is calculated from its locati=
on, phase, $H_V$ and g values. $H_V$ is the object's absolute magnitude and=
corresponds to the brightness if it were observed at 1 AU from the Sun and at zero pha=
se angle. The $H_V$ distribution is modeled independently for each source p=
opulation (NEO, TNO, main belt, etc.) as described in 3 of \citet{gra=
v11}. The g value relates the change in brigh=
tness of an object with the change in phase and is set at 0.15 for all objects across a=
ll bands, which is a typical value for asteroid phase curves. A more accura=
te modeling of the asteroid phase curves would require more realistic rotat=
ion and composition models which may be included in future work. The locati=
on of the Earth at the time of a particular observation is incorporated thr=
ough the orbital ephemeris software oorb (\citet{granvik};{\tt http://ls.st=
/rd4}) that calculates a V band apparent magn=
itude which is then used with the object's assigned C or S type SED to deri=
ve the corresponding LSST band observations.
Solar System sources are the most complicated table in the database. The=
typical way to characterize a Solar System object is to store its 6 orbital elements a=
nd propagate the orbit of the source to the time of the observation. Propag=
ation of all orbits would require a numerical integration over 11 million sources (for=
each query). To accomplish for all sources within the Solar System table f=
or each query would be computationally prohibitive (requiring 222,000s to propagate one year into=
the future). We, therefore, pre-cache the positions of asteroids within th=
e database and interpolate their positions based on the time of the observa=
tion. Ephemerides are calculated for all Solar System sources within the da=
tabase for a ten year period. The time between ephemerides is variable and =
depends on the asteroid population (i.e.\ it is set by the velocity of the =
asteroid and the complexity of its orbital track). For main belt asteroids =
the positions are stored every two days together with the Chebyshev polynom=
ial coefficients required to interpolate between these positions. Figure~\ref{fig:asteroid=
} shows that, using a cubic interpolation, as=
teroid positions are returned with an rms accuracy of $<1$ mas (sufficient to meet r=
equirement {\it=
Catalogs: Requirements 5}). These cached positions are i=
ndexed using a HTM to speed the spatial lookup. This results in a query for=
20,000 asteroids (i.e.\=
larger than a full focal plane) requiring 30 ms to complete. \b=
egin{figure}[=
span>h] \centering \includegraphics[<=
/span>width=3D0.65<=
/span>\textwidth]{validation_figures/ErrorHistogramsLinear.pdf} \caption{Th=
e distribution of maximum errors introduced to the orbital positions of ast=
eroids due to the adopted interpolation scheme. These distributions are giv=
en as a function of asteroid populations. All populations meet the requirem=
ent that the interpolation be accurate to an rms of 1 mas.} \label{fig:a=
steroid} \end{figure}