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1 SuperMacho & Supernovae: Time Domain Astronomy Christopher Stubbs Departme nt of Astronomy Department of Physics University of Washington Christopher.

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Presentation on theme: "1 SuperMacho & Supernovae: Time Domain Astronomy Christopher Stubbs Departme nt of Astronomy Department of Physics University of Washington Christopher."— Presentation transcript:

1 1 SuperMacho & Supernovae: Time Domain Astronomy Christopher Stubbs Departme nt of Astronomy Department of Physics University of Washington Christopher Stubbs Departme nt of Astronomy Department of Physics University of Washington 2Mass image

2 2 What is the Galactic Dark Matter? Exotic Particles New Physics! Exotic Particles New Physics! Baryonic Matter Baryonic Matter Ordinary Stars Gas, Dust Gas, Dust MAssive Compact Halo Objects Jupiters? Black Holes? Brown Dwarfs? MAssive Compact Halo Objects Jupiters? Black Holes? Brown Dwarfs? Massive Neutrinos Massive Neutrinos Axions Weakly Interacting Massive Particles Weakly Interacting Massive Particles ??

3 3 Searching for MACHOs How do you look for something that cant be seen? Searching for MACHOs How do you look for something that cant be seen? Use the one thing that is known about Dark Matter: - It Gravitates! - It Gravitates! Use the one thing that is known about Dark Matter: - It Gravitates! - It Gravitates! Star Telescope MACHO

4 Gravitational microlensing of a star Gravitational lensing of galaxies by a foreground galaxy cluster

5 5 The Principle D2D2 D1D1 star detector b Macho, mass M

6 6 Microlensing Surveys Of LMC, SMC, Bulge, & M31: Hundreds of events seen to date, most towards Galactic center MACHOEROSOGLE Stringent DM limits Puzzles! Microlensing Surveys Of LMC, SMC, Bulge, & M31: Hundreds of events seen to date, most towards Galactic center MACHOEROSOGLE Stringent DM limits Puzzles! LMC surveys

7 7 2 Major Dark Matter Results from MACHO Lack of LMC events of less than 20 days duration rules out low mass MACHOs Rate of detected events exceeds that expected from known stellar backgrounds, and corresponds to a MACHO fraction of between 8% and 50% of the standard halo

8 8 95% CL exclusion Halo Mass (10 11 solar masses) MACHO collab.

9 9

10 10 Assumes uniform priors in f and log(m) Best fit is f = 0.2, M=0.5 Note that f = 0 and 100% are both excluded! Even at f=0.2, this is more mass than all known MW components Assumes uniform priors in f and log(m) Best fit is f = 0.2, M=0.5 Note that f = 0 and 100% are both excluded! Even at f=0.2, this is more mass than all known MW components

11 11 Where are the lenses? We need many more LMC microlensing events

12 Figure of Merit for a Microlensing Survey Essentially, how many stars per unit time can be monitored to a given SNR: site apparatus

13 A Next Generation Microlensing Survey: SuperMacho A Next Generation Microlensing Survey: SuperMacho An approved 5 year NOAO survey project Goal is to determine the location of the lensing population(s) Sufficient statistics to test spatial and stellar density dependencies Exploit exotic events An approved 5 year NOAO survey project Goal is to determine the location of the lensing population(s) Sufficient statistics to test spatial and stellar density dependencies Exploit exotic events

14 14 GoalsGoals Primary objective: Ascertain nature of excess lensing population towards LMCPrimary objective: Ascertain nature of excess lensing population towards LMC Secondary objectives:Secondary objectives: Variable stars (instability main seq in LMC) Solar system objects at ecliptic pole High proper motion objects Supernovae behind LMC LMC proper motion w.r.t quasars Develop software for Large Synoptic Survey Telescope Primary objective: Ascertain nature of excess lensing population towards LMCPrimary objective: Ascertain nature of excess lensing population towards LMC Secondary objectives:Secondary objectives: Variable stars (instability main seq in LMC) Solar system objects at ecliptic pole High proper motion objects Supernovae behind LMC LMC proper motion w.r.t quasars Develop software for Large Synoptic Survey Telescope

15 15 SuperMacho Team C. Stubbs, R. Covarrubias, B. Henderson Univ. of Washington A. BeckerLucent/Univ. of Washington D. WelchMcMaster University C. Smith, R. Hiriart, K. Olsen, A. RestCTIO/NOAO K. Cook, A. Drake, S. Keller, G. Proctor, S. Nikolaev LLNL A. Clochiatti Universidad Catholica C. Stubbs, R. Covarrubias, B. Henderson Univ. of Washington A. BeckerLucent/Univ. of Washington D. WelchMcMaster University C. Smith, R. Hiriart, K. Olsen, A. RestCTIO/NOAO K. Cook, A. Drake, S. Keller, G. Proctor, S. Nikolaev LLNL A. Clochiatti Universidad Catholica

16 16ImplementationImplementation CTIO 4m with Mosaic imager: 150 half nights over 5 yrs. Custom broadband V+R filter (5200A to 7300A) (5200A to 7300A) 60 fields, 23 sq degrees in LMC Exposure times maximize # of stars = 0.1 mag at 23 rd = 0.1 mag at 23 rd Remote Observing from La Serena (and even Seattle…!) CTIO 4m with Mosaic imager: 150 half nights over 5 yrs. Custom broadband V+R filter (5200A to 7300A) (5200A to 7300A) 60 fields, 23 sq degrees in LMC Exposure times maximize # of stars = 0.1 mag at 23 rd = 0.1 mag at 23 rd Remote Observing from La Serena (and even Seattle…!)

17 17 Model discrimination via spatial event distribution

18 18 Discrimination between self-lensing vs screen- lensing: spatially varying optical depth Zhao & Evans 2000: Models: Bar unvirialized, misaligned, and offset from LMC diskZhao & Evans 2000: Models: Bar unvirialized, misaligned, and offset from LMC disk Optical depth depends onOptical depth depends on mass ratio disk/bar Misalignment Predicts asymmetry and concentration along barPredicts asymmetry and concentration along bar Zhao & Evans 2000: Models: Bar unvirialized, misaligned, and offset from LMC diskZhao & Evans 2000: Models: Bar unvirialized, misaligned, and offset from LMC disk Optical depth depends onOptical depth depends on mass ratio disk/bar Misalignment Predicts asymmetry and concentration along barPredicts asymmetry and concentration along bar Zhao & Evans 2000

19 19 Microlensing event rate ratios Differential approach reduces sensitivity to LF, efficiencies, etc. Red: screen-lensing Black: self-lensing, Zhao et al Red: screen-lensing Black: self-lensing, Zhao et al

20 20 Data Reduction Pipeline Project requires same-night detection of microlensing from ~ 60 fields/nightProject requires same-night detection of microlensing from ~ 60 fields/night Variability must beVariability must be 1)detected 2)classified and 3)distributed Data pipeline is treated as an LSST prototypeData pipeline is treated as an LSST prototype Present version is robust, flexible and efficient.Present version is robust, flexible and efficient. 2 ingredients: Mosaic images, amplifier images2 ingredients: Mosaic images, amplifier images Project requires same-night detection of microlensing from ~ 60 fields/nightProject requires same-night detection of microlensing from ~ 60 fields/night Variability must beVariability must be 1)detected 2)classified and 3)distributed Data pipeline is treated as an LSST prototypeData pipeline is treated as an LSST prototype Present version is robust, flexible and efficient.Present version is robust, flexible and efficient. 2 ingredients: Mosaic images, amplifier images2 ingredients: Mosaic images, amplifier images

21 21 Why did I decide to do astronomy? excessive.fits big.fits Really big.fits too big.fits Flats.fits biases.fits

22 22 Preliminary Steps - MSCpipe Recognizes images, zeros and flatsRecognizes images, zeros and flats Crosstalk correction with IRAF executablesCrosstalk correction with IRAF executables Mosaic image WCS registration with IRAF msccmatch, to UCAC1 catalog, typical rms of 0.08 arcsecMosaic image WCS registration with IRAF msccmatch, to UCAC1 catalog, typical rms of 0.08 arcsec Chopping into amplifiers, distribution into flat or image directories as appropriate.Chopping into amplifiers, distribution into flat or image directories as appropriate. Recognizes images, zeros and flatsRecognizes images, zeros and flats Crosstalk correction with IRAF executablesCrosstalk correction with IRAF executables Mosaic image WCS registration with IRAF msccmatch, to UCAC1 catalog, typical rms of 0.08 arcsecMosaic image WCS registration with IRAF msccmatch, to UCAC1 catalog, typical rms of 0.08 arcsec Chopping into amplifiers, distribution into flat or image directories as appropriate.Chopping into amplifiers, distribution into flat or image directories as appropriate. 8K x 8K MOSAIC Image

23 23 (High-z Supernova Team) Image Subtraction

24 24 Modular Image Subtraction Pipeline #stage actions prestage diffstageflag FINDNEWIMAGES findnewimages START 0 CREATECALFRAMES createcalframes FINDNEWIMAGES 0 FLATTEN flatten CREATECALFRAMES 0 QUICKDOPHOT quickdophot FLATTEN 0 MATCHTEMPLATES matchtemplates QUICKDOPHOT 1 DIFFIM remap,diffim MATCHTEMPLATES 1 DIFFDOPHOT diffdophot DIFFIM 1 PIXCHK pixchk DIFFDOPHOT 1 DIFFCLEANIM diffcleanim PIXCHK 1 DIFFCUT diffcut DIFFCLEANIM 1

25 25 Hardware for real-time reductions Dual networks 1 Gb/sec compute link 100 Mb/s admin link 10 compute nodes 2 x 1.2 GHz CPUs 1 GB RAM each 300 GB local IDE disk 1 TB SCSI RAID disk array 2 TB IDE RAID disk array Dual networks 1 Gb/sec compute link 100 Mb/s admin link 10 compute nodes 2 x 1.2 GHz CPUs 1 GB RAM each 300 GB local IDE disk 1 TB SCSI RAID disk array 2 TB IDE RAID disk array

26 26 Discrimination and Classification Photometry on difference images: fixed PSF DoPhot fixed PSF DoPhot sigma image (requires significant -flux) sigma image (requires significant -flux) sensitive to both positive and negative -flux sensitive to both positive and negative -flux Detected objects are filtered: PSF fit chi-squared (rejects CRs, subtr. resid.s) PSF fit chi-squared (rejects CRs, subtr. resid.s) DoPhot object type DoPhot object type Away from edges or masked pixels Away from edges or masked pixels Photometry on difference images: fixed PSF DoPhot fixed PSF DoPhot sigma image (requires significant -flux) sigma image (requires significant -flux) sensitive to both positive and negative -flux sensitive to both positive and negative -flux Detected objects are filtered: PSF fit chi-squared (rejects CRs, subtr. resid.s) PSF fit chi-squared (rejects CRs, subtr. resid.s) DoPhot object type DoPhot object type Away from edges or masked pixels Away from edges or masked pixels

27 27 Browser/ClassifierBrowser/Classifier

28 28 SQL-compatible (Postgres) Database Raw Sequence of observations, pointers to images Detections of sources in difference images, as boxes Pipeline configuration and parameters Derived Aggregations of detections into astronomical sources spatial coincidence or clustering eventually, orbits? Classification of sources asteroidsSNe variable stars QSO/AGNsRaw Sequence of observations, pointers to images Detections of sources in difference images, as boxes Pipeline configuration and parameters Derived Aggregations of detections into astronomical sources spatial coincidence or clustering eventually, orbits? Classification of sources asteroidsSNe variable stars QSO/AGNs

29 29 SuperMacho Status Just finished second season of observations Great working partnership with NOAO staff and scientists- thanks! Initial frame subtraction has been done on both years images Detection efficiency tests on year 1 images look very favorable Next tasks: Search for variable sources in only 1 of the 2 years, Implement alert system for next season. No proprietary data period- raw and flat-fielded images available! Just finished second season of observations Great working partnership with NOAO staff and scientists- thanks! Initial frame subtraction has been done on both years images Detection efficiency tests on year 1 images look very favorable Next tasks: Search for variable sources in only 1 of the 2 years, Implement alert system for next season. No proprietary data period- raw and flat-fielded images available!

30 30 Seeing in 2002 is considerably worse

31 31 SN 2002 B, a type Ia z=0.143

32 32 (Hubble Space Telescope, NASA) Supernovae are powerful cosmological probes Distances to ~6% from brightness Redshifts from features in spectra

33 33

34 34 Cosmic Arithmetic General Relativity + isotropy and homogeneity require that (in the relevant units) geometry + matter + = 1 geometry + matter + = 1 If the underlying geometry is flat, and if m <1 then the cosmological constant term must be non-zero. CMB measurements demonstrate the curvature is zero. Mass inventories fall short of matter =1

35 35 High-z Supernova Search Team Microwave Background Cluster Masses m Best Fit at mass ~ 0.3 mass ~ 0.3 ~ 0.7 ~ 0.7 Is the expansion really accelerating? What does this mean? Insufficient mass to halt the expansion Rate of expansion is increasing…

36 36 A Repulsive Result Expansion of Universe is accelerating!(?)Expansion of Universe is accelerating!(?) Implies something new – and rather repulsiveImplies something new – and rather repulsive Regions of empty space repel each other!Regions of empty space repel each other! Cosmological constant… Einsteins greatest blunder? Whats going on in the vacuum?

37 37 Potential sources of systematic error Extinction by gray dust? Careful multicolor measurements, esp. in IR Exploit different z-dependence, go to higher z Evolutionary Effects? Use stellar populations of different ages as a proxy Selection differences in nearby vs. distant samples? Increase the sample of well-monitored Sne Calibrate detection efficiencies K-corrections, Galactic extinction, photometric zeropoints....

38 38 Dark Energys Equation of State w = 0, matter P = w w = -1, w = 1/3,radiation (a) ~ a -3(1+w) So by carefully measuring a(z) can determine w... w = 0, matter P = w w = -1, w = 1/3,radiation (a) ~ a -3(1+w) So by carefully measuring a(z) can determine w...

39 39 Essence Survey Goal: w Monte Carlo of

40 40 Claudio Aguilera --- CTIO/NOAO Claudio Aguilera --- CTIO/NOAO Brian Barris --- Univ of Hawaii Brian Barris --- Univ of Hawaii Andy Becker --- Bell Labs/Univ. of Washington Andy Becker --- Bell Labs/Univ. of Washington Peter Challis --- Harvard-Smithsonian CfA Peter Challis --- Harvard-Smithsonian CfA Ryan Chornock --- Harvard-Smithsonian CfA Ryan Chornock --- Harvard-Smithsonian CfA Alejandro Clocchiatti --- Univ Catolica de Chile Alejandro Clocchiatti --- Univ Catolica de Chile Ricardo Covarrubias --- Univ of Washington Ricardo Covarrubias --- Univ of Washington Alex V. Filippenko --- Univ of Ca, Berkeley Alex V. Filippenko --- Univ of Ca, Berkeley Peter M. Garnavich --- Notre Dame University Peter M. Garnavich --- Notre Dame University Stephen Holland --- Notre Dame University Stephen Holland --- Notre Dame University Saurabh Jha --- Harvard-Smithsonian CfA Saurabh Jha --- Harvard-Smithsonian CfA Robert Kirshner --- Harvard-Smithsonian CfA Robert Kirshner --- Harvard-Smithsonian CfA Kevin Krisciunas --- CTIO/NOAO Kevin Krisciunas --- CTIO/NOAO Bruno Leibundgut --- European Southern Observatory Bruno Leibundgut --- European Southern Observatory Weidong D. Li --- Univ of California, Berkeley Weidong D. Li --- Univ of California, Berkeley Thomas Matheson --- Harvard-Smithsonian CfA Thomas Matheson --- Harvard-Smithsonian CfA Anthony Miceli --- Univ of Washington Anthony Miceli --- Univ of Washington Gajus Miknaitis --- Univ of Washington Gajus Miknaitis --- Univ of Washington Armin Rest --- Univ of Washington/CTIO Armin Rest --- Univ of Washington/CTIO Adam G. Riess --- Space Telescope Science Institute Adam G. Riess --- Space Telescope Science Institute Brian P. Schmidt --- Mt. Stromlo Siding Springs Observatories Brian P. Schmidt --- Mt. Stromlo Siding Springs Observatories Chris Smith --- CTIO/NOAO Chris Smith --- CTIO/NOAO Jesper Sollerman --- Stockholm Observatory Jesper Sollerman --- Stockholm Observatory Jason Spyromilio --- European Southern Observatory Jason Spyromilio --- European Southern Observatory Christopher Stubbs --- Univ of Washington Christopher Stubbs --- Univ of Washington Nicholas B. Suntzeff --- CTIO/NOAO Nicholas B. Suntzeff --- CTIO/NOAO John L. Tonry --- Univ of Hawaii John L. Tonry --- Univ of Hawaii ESSENCE Survey Team

41 41 A joint analysis, including results from Supernovae, Supernovae, CMB, and CMB, and large scale structure large scale structure should allow us to determine equation of state parameter to 10%.

42 42 ESSENCE survey implementation NOAO Survey on CTIO 4m, MOSAICNOAO Survey on CTIO 4m, MOSAIC Same frame subtraction pipeline as SuperMacho project, scheduled in other halves of SuperMacho nightsSame frame subtraction pipeline as SuperMacho project, scheduled in other halves of SuperMacho nights ~ 200 supernovae with 0.1 < z < 0.8~ 200 supernovae with 0.1 < z < band photometry: V,R,I (observer frame)3 band photometry: V,R,I (observer frame) 2 sets of fields, so t=4 days2 sets of fields, so t=4 days Goal is to determine a distance modulus in each bin (of z = 0.1) to 2%Goal is to determine a distance modulus in each bin (of z = 0.1) to 2% ~3% photometry at peak SN brightness ~3% photometry at peak SN brightness NOAO Survey on CTIO 4m, MOSAICNOAO Survey on CTIO 4m, MOSAIC Same frame subtraction pipeline as SuperMacho project, scheduled in other halves of SuperMacho nightsSame frame subtraction pipeline as SuperMacho project, scheduled in other halves of SuperMacho nights ~ 200 supernovae with 0.1 < z < 0.8~ 200 supernovae with 0.1 < z < band photometry: V,R,I (observer frame)3 band photometry: V,R,I (observer frame) 2 sets of fields, so t=4 days2 sets of fields, so t=4 days Goal is to determine a distance modulus in each bin (of z = 0.1) to 2%Goal is to determine a distance modulus in each bin (of z = 0.1) to 2% ~3% photometry at peak SN brightness ~3% photometry at peak SN brightness

43 43 Like High-z Fall 2001 continuous search Consistent photometry Efficient Still requires spectroscopy Seeing matters!

44 44 ESSENCE survey SNe (as of Jan ) Z Ias IIs Totals Totals 18 6 SN types and redshifts from KeckMagellanVLTGemini 18 are SN type Ias 6 are SN type II 5 unsure Good multiband light curves from CTIO 4m on 14 type Ias

45 45 Two Observations 1.Time domain surveys cast a wide net Microlensing surveys find variables, supernovae… planets even. Microlensing surveys find variables, supernovae… planets even. Weak lensing surveys find KBOs, supernovae… Weak lensing surveys find KBOs, supernovae… Supernova searches find asteroids, variable stars… Supernova searches find asteroids, variable stars… NEO/KBO searches find RR Lyrae, supernovae… NEO/KBO searches find RR Lyrae, supernovae… These by-products are at best underexploited! 1.Time domain surveys cast a wide net Microlensing surveys find variables, supernovae… planets even. Microlensing surveys find variables, supernovae… planets even. Weak lensing surveys find KBOs, supernovae… Weak lensing surveys find KBOs, supernovae… Supernova searches find asteroids, variable stars… Supernova searches find asteroids, variable stars… NEO/KBO searches find RR Lyrae, supernovae… NEO/KBO searches find RR Lyrae, supernovae… These by-products are at best underexploited! 2.The time domain is a unifying thread across many important science goals and opportunities We can do a better job! 2.The time domain is a unifying thread across many important science goals and opportunities We can do a better job!

46 46 Large Synoptic Survey Telescope Highly ranked in Decadal Survey Optimized for time domain 7 square degree field 6.5m effective aperture 24 th mag in 20 sec > 5 TBytes/night Real-time analysis Simultaneous multiple science goals Highly ranked in Decadal Survey Optimized for time domain 7 square degree field 6.5m effective aperture 24 th mag in 20 sec > 5 TBytes/night Real-time analysis Simultaneous multiple science goals

47 47 LSST: Massively Parallel Astronomy Multiband source catalog as shakedown project: early impact Near Earth Objects Trans-Neptunian Objects Time-resolved stellar photometry, parallaxes & proper motions in MW RR Lyrae throughout the entire local group Gravitational microlensing across the entire sky Gamma Ray Bursts (both with and without gamma rays!) Weak lensing maps across wide fields, with photometric redshifts Lensed QSO microlensing and time delays Line-of-sight mass structures via dispersion of Ia distance moduli …Plus substantial potential for discovery! Multiband source catalog as shakedown project: early impact Near Earth Objects Trans-Neptunian Objects Time-resolved stellar photometry, parallaxes & proper motions in MW RR Lyrae throughout the entire local group Gravitational microlensing across the entire sky Gamma Ray Bursts (both with and without gamma rays!) Weak lensing maps across wide fields, with photometric redshifts Lensed QSO microlensing and time delays Line-of-sight mass structures via dispersion of Ia distance moduli …Plus substantial potential for discovery!

48 48 Computer Evolution is Staggering ~1990 (MACHO era) 60 MHz CPUs 2 GB disks K$s Real-time DoPhot analysis on 5 Gbytes/night Today (SuperMacho/ESSENCE era) arrays of > 2 GHz CPUs are routine Scripting languages 250 Gbyte drives for $400 Algorithmic Advances 250 Gbyte drives for $400 Algorithmic Advances Real-time subtractions on 20 Gbytes/night Commercial databases seem up to the task Tomorrow (LSST era) Real-time reduction of 15 Terabytes/night Entire image archive on spinning disk (1000s of Terabytes) ~1990 (MACHO era) 60 MHz CPUs 2 GB disks K$s Real-time DoPhot analysis on 5 Gbytes/night Today (SuperMacho/ESSENCE era) arrays of > 2 GHz CPUs are routine Scripting languages 250 Gbyte drives for $400 Algorithmic Advances 250 Gbyte drives for $400 Algorithmic Advances Real-time subtractions on 20 Gbytes/night Commercial databases seem up to the task Tomorrow (LSST era) Real-time reduction of 15 Terabytes/night Entire image archive on spinning disk (1000s of Terabytes)

49 49 LSST Challenges Large effective aperture wide field telescope(s)Large effective aperture wide field telescope(s) Monster focal plane(s)Monster focal plane(s) Real-time analysis pipeline and alert distributionReal-time analysis pipeline and alert distribution Variability Classification (85% SN, 15% AGN…?)Variability Classification (85% SN, 15% AGN…?) On-the-fly detection efficiencies, for ratesOn-the-fly detection efficiencies, for rates Aggregating detections into objectsAggregating detections into objects Database representation and indexing structuresDatabase representation and indexing structures Optimal co-adding of imagesOptimal co-adding of images Joint science optimization (bands, cadence: SWG)Joint science optimization (bands, cadence: SWG) Large effective aperture wide field telescope(s)Large effective aperture wide field telescope(s) Monster focal plane(s)Monster focal plane(s) Real-time analysis pipeline and alert distributionReal-time analysis pipeline and alert distribution Variability Classification (85% SN, 15% AGN…?)Variability Classification (85% SN, 15% AGN…?) On-the-fly detection efficiencies, for ratesOn-the-fly detection efficiencies, for rates Aggregating detections into objectsAggregating detections into objects Database representation and indexing structuresDatabase representation and indexing structures Optimal co-adding of imagesOptimal co-adding of images Joint science optimization (bands, cadence: SWG)Joint science optimization (bands, cadence: SWG)

50 50 A staged approach Today LSST design and tradeoff studies Today LSST design and tradeoff studies LSST precursor projects: Software and database prototyping years Dedicated 1.5 – 2.5m wide-field facilities? years Dedicated 1.5 – 2.5m wide-field facilities? years: Full LSST operations years: Full LSST operations Today LSST design and tradeoff studies Today LSST design and tradeoff studies LSST precursor projects: Software and database prototyping years Dedicated 1.5 – 2.5m wide-field facilities? years Dedicated 1.5 – 2.5m wide-field facilities? years: Full LSST operations years: Full LSST operations APO 2.5m post-SDSS? PanStarrs Array?

51 51 The LSST Opportunity Current trend is towards fewer (albeit larger aperture ) telescopes with open access… LSST goes in the other direction: Multiple projects fed from a common image stream No proprietary data period Exploits the 3 enabling technologies of our era: Large aperture telescopes Silicon detector arrays Computing and mass storage technology Highly Efficient multitasking system Current trend is towards fewer (albeit larger aperture ) telescopes with open access… LSST goes in the other direction: Multiple projects fed from a common image stream No proprietary data period Exploits the 3 enabling technologies of our era: Large aperture telescopes Silicon detector arrays Computing and mass storage technology Highly Efficient multitasking system

52 52 Session 134. LSST Oral, Thursday, January 9, 2003, 2:00-3:30pm, 6AB The Large Synoptic Survey Telescope: Opening New Windows J. A. Tyson (Bell Labs, Lucent Technologies), LSST Collaboration J. A. Tyson (Bell Labs, Lucent Technologies), LSST Collaboration Science Opportunities with the LSST: From Near-Earth Asteroids to High-redshift large-scale structure M.A. Strauss (Princeton University) Asteroids: with SDSS towards LSST Z. Ivezic, R.H. Lupton, M. Juric (Princeton University) Z. Ivezic, R.H. Lupton, M. Juric (Princeton University) Hunting for Near-Earth Asteroids Using LSST: Detection Methods and Observational Strategies E. Bowell (Lowell Observatory), A. W. Harris (Space Science Institute) Managing the Data Flow from the LSST A. Connolly (University of Pittsburgh), LSST Team Petabyte Scale Data Mining: Dream or Reality? A.S. Szalay (JHU), J. Gray (Microsoft Research), J. Vandenberg (JHU)

53 53 SuperMacho and ESSENCE Images Raw frames: ftp://archive2.tuc.noao.edu/SM_SN/ NOAO Science Archive: Raw frames: ftp://archive2.tuc.noao.edu/SM_SN/ NOAO Science Archive:

54 54 SN rates: reality vs. aspirations Goal is 200 type Ia light curves in 5 seasons This implies 40/yr, we got 15. Whats up? 1.First of 3 lunations was first epoch: templates Expect 1.5x as many in future ~ 22 2.Seeing was usually worse than 1.5 arcseconds! 3. Opportunity to use SDSS for detection out to z~0.3, where 4m is inefficient

55 55 Discrimination (on a 1K x 4K amp) 130 Detections in R band difference image 21 Detections in R band survive cuts 21 Detections in R band survive cuts DoPhot PSF chi-squaredNpix >0Masked DoPhot object typeNpix<0Saturated 808 Detections in I band difference image 119 Detections in I band survive cuts 114 Detections in V band difference image 30 Detections in V band survive cuts 30 Detections in V band survive cuts Spatial coincidence in 2 or more filters: A single candidate A single candidate

56 56 Number of Monitored Stars, from Added Star tests Number of lensed stars at = 1.2 x N lensed ~ x N obs N events ~ T(exp) Nobs N events ~ T(exp) Nobs t event t event We expect 8-12 ongoing LMC events per season


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