Data processing and Supernovae discoveries

All DES SN survey imaging data are de-trended and co-added using a standard photomet- ric reduction pipeline at the National Centre for Supercomputing Applications (NCSA)

via the DES Data Management system (DESDM;Mohr et al.,2012;Desai et al.,2012),

producing approximately 30 ‘search images’ per field (in all filters) over the duration of

the five-month DES season. We performed difference imaging (Kessler et al.,2015) on

each of these search images, using deeper template images for each field created from the co-addition of several epochs of data obtained during previous seasons observations (ie. in Y1 the templates were constructed from images taken in SV season). Before differen- tiating, the search and template images were convolved to the same point spread function (PSF).

Objects were selected from the difference images using SEXTRACTORv2.18.10 (Bertin

and Arnouts, 1996), and previously unknown transient candidates were identified and examined using visual inspection (or ‘human scanning’) by DES SN group members.

Moreover, using the publicly available SNANA light curve simulations (Kessler et al.,

2009b), ‘fake SNe Ia’ were created and injected into the raw images, in order to monitor survey efficiency as described in detail byKessler et al.(2015).

Fig.3.6shows the scanning web interface used during Y1 for visual scanning of SNe

candidates, in which 13” × 13” cut-out images called ‘postage-stamps’ were used. Each scanner had a certain load of objects (∼ 100) to scan per night, with the options available

Figure 3.6: The Y1 scanning web interface used for visual inspection of a SN candidate. The left-hand side panel shows a time series of five filter/epoch triplets, each composed of three 13”×13” cut-out images, called ‘postage-stamps’, representing the search image on the left, the template image on the centre and the difference image on the right. The upper right panel consists two tables with general information about the current scanning session (scanner name, status, total objects unscanned, number of objects scanned in this session) and basic information about the SNe candidate (candidate ID number, ra dec, field). The second table lists information about each one of the filter/epoch search entries (signal to noise ration, band, time of observation, magnitude). The bottom, right-hand panel hosts the scanning selection options and operation buttons, which are used by the scanner in order to enter the desired classification to the system.

CHAPTER 3. THE DARK ENERGY SURVEY - YEAR ONE 69 being: ‘SN (inside/outside host, hostless, indeterminate)’, ‘artefact’, ‘Apparent Motion’,

‘Nothing Seen’ and ‘ALERT’, as seen in the bottom right-hand side panel of Fig.3.6.

As a scanner myself, I helped scan for SNe candidates in SV and Y1 seasons and I have visually inspected ∼ 10000 and ∼ 18000 candidates respectively.

During Y1 of operations on any given scanning session contacted by a single scanner, the average outcome was a majority of artefacts and a handful of real and fakes SNe (see postage-stamps on Fig.3.7). On average, the ratio between the SNe and artefacts reached 85%, a relatively high percentage which forced certain updates within the data pipeline

described in Sec.5.3and the development of a machine learning algorithm.

An automated machine learning algorithm (AUTOSCAN; Goldstein et al., 2015) was

developed and implemented in order to improve the efficiency of selecting real tran-

sients. Postage-stamps examples classified by the AUTOSCAN algorithm are shown in

Fig.3.7, for a variety of signal-to-noise ratios. As shown byGoldstein et al.(2015), the AUTOSCAN algorithm improved the number of transient candidates eligible for human scanning by a factor of 13.4, while only 1% of ‘fake SNe Ia’ were lost, most of which were very faint events.

Up to this data processing stage, the pipeline is known as the ‘Search’ photometric pipeline, as it searches through the photometric data (DECam images) and produces a list of newly discovered astrophysical transients.

DES transient names are formatted following the convention DESYYFFaaaa, where YY are the last two digits of the year in which the observing season began (i.e. 13), FF is the two character field name (i.e. S2), and the final characters (all letters, maximum of four) provide a running candidate identification, unique within an observing season, as is traditional in SN astronomy (i.e. abc).

Finally, DES transients were photometrically classified using the Photometric SN IDentification software (PSNID;Sako et al.,2008,2011) to determine the likely SN type. As described bySako et al.(2011), PSNID uses the observed photometry, calculates the

reduced χ2 _{against a grid of SN Ia light-curve models and CC SNe templates, and iden-}

tifies the best-matching SN type and set of parameters with, and without host galaxy redshift as priors in the grid search. For the SN Ia models, there are five model param- eters: redshift (z), V-band host galaxy extinction (AV), time of maximum light (Tmax),

B-band decline rate 15 days after peak (∆mB

15;Phillips,1993) and distance modulus (µ; Eq.1.29).

PSNID is run on data derived from the ‘Forced’ photometric pipeline, which differs from the ‘Search’ described above. The ‘Forced’ photometric pipeline takes the coor- dinates of an object of interest and performs aperture photometry at the same position in every difference image available in the database, old and new. Thus creating a more complete sample of photometric data which can include upper limits as well for each

Figure 3.7: Postage-stamps, centered on legitimate (green boxes) and spurious (red boxes) objects, at a variety of signal-to-noise ratios: (a) SNR6 10, (b) 10 <SNR6 30, (c) 30 <SNR6 100. The postage-stamps are subclassed to illustrate both the visual di- versity of spurious objects and the homogeneity of authentic ones. Objects in the ‘Tran- sient’ columns are real astrophysical transients that subtracted cleanly. Objects in the ‘Fake SN’ columns are fake SNe Ia injected into transient search images to monitor sur- vey efficiency. The column labeled ‘CR/Bad Column’ shows detections of cosmic rays (rows b and c) and a bad column on the CCD detector (row a). The columns labeled ‘Bad Sub’ show non-varying astrophysical sources that did not subtract cleanly; this can result from poor astrometric solutions, shallow templates, or bad observing conditions. The numbers at the bottom of each cut-out indicate the score that each detection received from AUTOSCAN; a score of 1.0 indicates that the algorithm is perfectly confident that the detection is not an artefact, while a score of 0.0 indicates the opposite. Plot is taken fromGoldstein et al.(2015).

CHAPTER 3. THE DARK ENERGY SURVEY - YEAR ONE 71 transient, as it performs the aperture photometry regardless of the existence of the tran- sient.

These SNe candidates were stored in an online SQL database hosted by NCSA, along- side associated data products of the two pipelines (‘search’ and ‘force’), and then the SNe were prioritized for spectroscopic follow-up by the Spectroscopic Marshall (Dr. Chris D’Andrea in Y1), according to the goals and time allocations available at the time, as explained in detail in the following section.