Massive Spectroscopic Followup of Transients from the Multi-Epoch Nearby Cluster Survey Gregory O'Brien, David J. Sand (Texas Tech University), Melissa Graham (UC Berkeley)

SNID SNID was used as a cross correlating program, comparing different known spectrographs to unknown samples. SNID takes unknown spectra and attempts to filter out noise and renormalize. Next SNID transforms the spectra into Fouler-space, giving a continuos function. Know templates are stretched by different redshifts and a correlation analysis is performed between filtered samples and red-shifted templates. The correlations are plotted verses their respective redshifts where the highest peak is used to determine the correct red shift. This is done for every template, and convolved with overlap to give an r-lap value . (See ṨData reduction.) The highest r-lap is indicative of SNID’s best guess to the object species. and redshift

Introduction ! Spectroscopy takes sample light and attempts to divides the light into its individual constituent parts. Every day light is composed of a mixture of different wavelengths. When light is refracted or reflected across a diffraction gradient, wavelengths can constructively interfere or refract at different angles. The separation between the light is proportionate to the wavelength of the light. Measuring the position and flux helps give chemical signature to the source of light. The Multi Epoch Nearby Cluster Survey (MENeaCS) was designed to measure Type Ia supernova (SN Ia) rate in 57 galaxies clusters at 0.05 < z < 0.15 with X-ray emissions objects in NASA/IPAC Extragalactic Database (NED). The survey also picked up soft supernovae in the clusters and 540 unknown transient objets. Light curves and spectrographs were taken for these objects. Light curves were used eliminate red-sequence objects. Unknown transient object spectrographs were analyzed with SNID and RVSAO. Redshifts determined were carefully cross checked by hand with known emission and absorption lines redshifted.

Abstract The Multi-Epoch Nearby Cluster Survey (MENeaCS) monitored 60 low redshift (0.05<z<0.15), X-ray luminous galaxy clusters for two years, with the primary science goal of measuring cluster type Ia supernova rate, and constraining the type Ia delay time distribution. A fraction of our spectroscopic follow-up of supernova candidates was done with Hectospec, a 300-fiber, ~1deg2 spectrograph on the MMT. We utilized spare fibers, typically ~65 per visit, to classify unknown transient sources, without observational preconditions or selection criteria. A total set of 540 spectra allow for an unbiased look at the transient sky population, which we will present basic demographics for. We will also discuss the implications for spectroscopic follow-up in the era of the Large Synoptic Survey Telescope.

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Data Reduction and Analysis Transient spectrographs were analyzed with two pieces of software, Supernova Identification (SNID) software (Blondin & Tonry 2007) and Radial Velocity in IRAF (RVSAO). SNID cross correlating objects with now spectral templates. This can be difficult combining the effects of background noise and redshift and reddening. Assuming redshift stretch is given by !!SNID finds the appropriate as such that the cross correlation is maximized, with overlap !Where * is the cross correlation product, s(n) is the sample spectra and t(n) is our transformation axis factor (Blondin & Tonry 2007). Normalizing the spectra by a χ2 distribution, the symmetric difference is !!Differentiating the above we find the minimum to be σs αmin = σ c(δ) where σs,n is given by !!Blondin & Tonry 2007) Spectra are renormalized to where the mean is zero. Continuum removal helps reduce discontinuities in the spectra especial at the ends. The correlation height noise ratio is define as !!!Where h is the hight of a given peak.(Blondin & Tonry 2007). Perfect correlations have a normalized h=1 and σa=0. R is not a complete representer of well fitting. Overlap in log-space, where ln(λ) represents the ranges of sample and template matches (λ0 to λ1) with !!From this a data quality factor (dfq) is assigned each fit; r-lap=r *lap. One difficulties of using SNID was the template selection of many type Ia SNe and few AGN or Gal templates, giving skewed results favoring SNe and low z values.

c(n) = s(n)* t(n)

Χ2 (α ,δ ) = [αt(n −δ )− s(n)]2n=0

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∑ ⇒α 2Nσ t2 − 2αNσ sσ tc(δ )+ Nσ s

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s(n),t(n)2n=0

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r = h2σ a

lap = ln(λ1λ0)

λ = λo(1+ zs )

ResultsPopulation

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QSO SNe M-Dwarf

QSOs, m-dwarfs,galaxies, and SNe were all found in our sample. Each spectra were compared with know emission limes. Emission lines with constant redshift help identify species.

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Spectroscopy MENeaCS survey ran 2 weeks out of every month on the CFHT. 1 night per month was allocated to spectroscopic follow up of the brightest SNe candidates. Fibers not used for SNe targets were aligned with dimmer nearby discovered transients objects. Follow up preference was given to closer objects, brighter objects. Hostless transients were given the highest priority (Sand et al. 2011). Objects were cross referenced with NED Spectroscopy was done with 1 of 3 instruments Gemini North and the Gemini Multi Object Spectrograph (GMOS; Hook et al. 2004), the MMT and its BCS (Schmidt et al. 1989), and the MMT with Hectospec. GMOS collections used R400 680nm centered grating with a GG455 blocking filter.

References !!

Blondin S. and Tonry J.L., Determining the Type, Redshift, and Age of a Supernova Spectrum, The Astrophysical Journal, 666:1024-1047, 2007 September 10 !Fabricant, Daniel, et al., Hectospec, the MMT's 300 Optical Fiber-Fed Spectrograph, The Publications of the Astronomical Society of the Pacific, Volume 117, Issue 838, pp. 1411-1434. 12/2005 !Lidman C. et al, An Efficient Approach to Obtaining Large Numbers of Distant Supernova Host Galaxy Redshifts, arXiv:1205.1306 [astro-ph.CO] !Michael J. Kurtz and Douglas J. Mink, "RVSAO 2.0: Digital Redshifts and Radial Velocities" 1998, PASP, v. 110, pp. 934-977. !Tonry, J.L. and Davis, M., A Survey of Galaxy Redshifts I. Data Reduction, Astron. J., 84,1511 1979

Hectospec ! The multi-object spectrograph has a pair of six-axis robots positioning 300 optical fiber probes at the f/5 focus of the converted MMT. Hectospec consists of three major parts: (1) the fiber positioning unit that is mounted on the telescope, (2) a large stationary spectrograph mounted on a 1.8x3.7 m Invar-surfaced optical bench and (3) a 26 m-long bundle of optical fibers connecting the fiber probes to the spectrograph.  !  ! Fiber robots position 300 fibers in about 300 seconds to an accuracy of ~25 μm.  Each fiber has a core diameter of 250 μm, subtending 1.5" of the sky.  Adjacent fibers can be spaced as closely as 20," but positioning constraints are complicated by tubes extending from the fiber probe head.!  ! A  270 line mm-1 grating blazed at ~5000 and a 600 line mm-1 grating blazed at ~6000 are used. The detector array consists of two butted EEV CCDs, each with 2048 by 4608 pixels. CCDs have a readout noise of about 2.8 e− rms, and operate at a gain of 1e− ADU−1.

Sample Our sample consisted of objects discovered near our supernova candidates. MMT follow-ups helped reject slow moving objects and complete SNe light curves. Subtractions were determined using IRAF ”defined”. Sources were rejected if a flux was not detected simultaneously in r’ and g’. During the spectroscopical following up the supernova, fibers not being used were dedicated to exam these unknown transient objects.

Future Works Information gather form the spectra help further refine the best follow-up method for newly discovered LLST objects. One specific example is false positives of supernovae from m-dwarfs and eliminating such objects form SNE surveys. Our initial data sample involved transients and red-sequence objects. Work is currently being done also classifying the red-sequence spectra form the survey, help giving cluster mass estaminets and a complete population demographic.