Exploratory look at the lightcurves

[1] "Created: Wed Sep  3 16:19:24 2014"

Load in the data and helper functions:

load("lcdb.rda")
source("funcs.R")
library(ggplot2,quietly=TRUE)

Create Figure 1. Dataset is small so we create it in code:

eglco <- structure(list(mag = c(16.086072, 16.069502, 16.077044, 16.041818,
16.293768, 19.422619, 19.393279, 18.047673, 18.923392, 18.583543
), magerr = c(0.0697555975990846, 0.0696261830512544, 0.069760157493194,
0.0693726807224113, 0.0730024749645618, 0.222866958691984, 0.198915754273044,
0.121596853985125, 0.176581268240101, 0.151394544333253), mjd = c(55323.1398805091,
55323.1444230904, 55323.149035845, 55323.1536520487, 55323.158246852,
55544.3732165624, 55679.1372606134, 55984.0921198726, 55984.0971821761,
55984.1080515971)), .Names = c("mag", "magerr", "mjd"), row.names = c(2L,
3L, 4L, 5L, 6L, 7L, 8L, 10L, 11L, 12L), class = "data.frame")
eglcu <- structure(list(mag = c(20.218671, 20.215383), magerr = c(0.257789646738158,
0.256095571363926), mjd = c(55130.3877009605, 55867.3710062266
)), .Names = c("mag", "magerr", "mjd"), row.names = c(1L, 9L), class = "data.frame")

We construct the plot:

p <- ggplot(eglco, aes(x=mjd, y=mag), ylim=c(15.5, 20.5)) +  xlab("Mean Julian Date") + ylab("Magnitude")
p <- p + scale_y_reverse() + geom_errorbar(aes(ymin=mag-magerr, ymax=mag+magerr), color=gray(0.75)) + geom_point()
p <- p + geom_point(data = eglcu, aes(y=mag), pch=4)
p

An index for the curves is found in lcdbi. We reproduce Table 1:

head(lcdbi)

                       id type nobs
1 CSS071216:110407-045134  agn  237
2 CSS080314:141858+144617  agn  141
3 CSS080403:170550+265903  agn   23
4 CSS081031:013628+311031  agn   40
5 CSS081220:034643+142027  agn   32
6 CSS090202:105407+010236  agn   49

table(lcdbi$type)

   agn blazar     cv downes  flare     nv  rrlyr     sn 
   140    124    461    376     66   1971    292    536 

The curves are stored as a list in lcdb. We look at the 12th member of the list which is a short curve:

lcdb[[12]]

     ra    dec   mjd   mag magerr measgrp
3 22.46 -6.874 54355 20.80 0.4743       0
5 22.46 -6.874 55060 19.04 0.1922       1
9 22.46 -6.874 55060 19.58 0.2513       1
6 22.46 -6.874 55060 19.58 0.2511       1
4 22.46 -6.874 55090 19.61 0.2577       2
1 22.46 -6.874 55090 19.45 0.2358       2
8 22.46 -6.874 55098 20.00 0.3210       3
2 22.46 -6.873 55857 19.84 0.2466       4
7 22.46 -6.873 55857 19.68 0.2230       4

The last column, measgrp, is an indicator of cluster of measurements occuring within a 30 minute timespan as explained in the paper.

We reproduce the four example cases as seen in the paper:

lcobj <- "CSS071216:110407-045134"
lcs <- lcdb[[which(lcobj == lcdbi$id)]]
p1 <- lcplot(lcs, title="AGN")
p1

lcobj <- "CSS110405:141104+011153"
lcs <- lcdb[[which(lcobj == lcdbi$id)]]
p2 <- lcplot(lcs, title="Supernova")
p2

lcobj <- "CSS111103:230309+400608"
lcs <- lcdb[[which(lcobj == lcdbi$id)]]
p3 <- lcplot(lcs, title="Flare")
p3

lcobj <- "3019048007678"
lcs <- lcdb[[which(lcobj == lcdbi$id)]]
p4 <- lcplot(lcs, title = "Non Transient")
p4

Here is random sampling of four curves from each type:

types <- levels(lcdbi$type)
for(i in seq(along=types)){
    ilc <- sample(lcdbi$id[lcdbi$type == types[i]],size=4)
    for(j in 1:4){
    lcs <- lcdb[[which(ilc[j] == lcdbi$id)]]
    p <- lcplot(lcs, title = paste(types[i],ilc[j]))
    print(p)
    }
}