1

SuperMacho & Supernovae:SuperMacho & Supernovae:Time Domain AstronomyTime Domain Astronomy

SuperMacho & Supernovae:SuperMacho & Supernovae:Time Domain AstronomyTime Domain Astronomy

Christopher StubbsChristopher Stubbs

DepartmeDepartment nt of Astronomyof Astronomy

Department of PhysicsDepartment of Physics

University of WashingtonUniversity of Washington

Christopher StubbsChristopher Stubbs

DepartmeDepartment nt of Astronomyof Astronomy

Department of PhysicsDepartment of Physics

University of WashingtonUniversity of Washington

2Mass image2Mass image

2

What is the Galactic Dark Matter?What is the Galactic Dark Matter?What is the Galactic Dark Matter?What is the Galactic Dark Matter?

Exotic Particles New Physics!

Exotic Particles New Physics!

Baryonic Matter

Baryonic Matter

Ordinary StarsOrdinary Stars

Gas,DustGas,Dust

MAssiveCompactHaloObjects Jupiters? Black Holes? Brown Dwarfs?

MAssiveCompactHaloObjects Jupiters? Black Holes? Brown Dwarfs?

Massive NeutrinosMassive Neutrinos

AxionsAxions

WeaklyInteractingMassiveParticles

WeaklyInteractingMassiveParticles

????

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Searching for MACHOsSearching for MACHOs

How do you look for something that can’t be seen?How do you look for something that can’t be seen?

Searching for MACHOsSearching for MACHOs

How do you look for something that can’t be seen?How do you look for something that can’t be seen?

Use the one thing that is known about Dark Matter:Use the one thing that is known about Dark Matter:

- - It Gravitates!It Gravitates!

Use the one thing that is known about Dark Matter:Use the one thing that is known about Dark Matter:

- - It Gravitates!It Gravitates!

StarTelescope

MACHO

Gravitational microlensing of a star

Gravitational lensing of galaxies by a foreground galaxy cluster

5

The PrincipleThe Principle

D2

D1

star

detectorb

ReGM

c

D D

D D

ub

u

u u

4

2

4

21 2

1 2

2

2

( )

Reand if , then

A

A u

tM

Mjupiter

( ) .

1 134

3 days

Macho, mass M

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Microlensing SurveysMicrolensing SurveysOf LMC, SMC, Bulge, Of LMC, SMC, Bulge, & M31:& M31:

Hundreds of events Hundreds of events seen to date, most seen to date, most towards Galactic centertowards Galactic center

MACHOMACHOEROSEROSOGLEOGLE

Stringent DM limitsStringent DM limits

Puzzles!Puzzles!

Microlensing SurveysMicrolensing SurveysOf LMC, SMC, Bulge, Of LMC, SMC, Bulge, & M31:& M31:

Hundreds of events Hundreds of events seen to date, most seen to date, most towards Galactic centertowards Galactic center

MACHOMACHOEROSEROSOGLEOGLE

Stringent DM limitsStringent DM limits

Puzzles!Puzzles!

LMC surveysLMC surveysLMC surveysLMC surveys

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2 Major Dark Matter Results from MACHO2 Major Dark Matter Results from MACHO

Lack of LMC events of less than 20 days duration rules out low mass MACHOs

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

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

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95% CL exclusion

Hal

o M

ass

(1011

sol

ar m

asse

s)

MACHO Mass, M

MACHO collab.

9

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

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WhereWhere are the are the lenses?lenses?

We need many more LMC microlensing events

Figure of Merit for a Microlensing SurveyFigure of Merit for a Microlensing Survey

Essentially, how many stars per unit time can be monitored to a given SNR:

2star

2 2

2

2

( )SNR ,

( )

( )Nt

sky

sky

D QE tso

D QE t

D QE FOV

site

apparatus

A Next Generation Microlensing Survey:A Next Generation Microlensing Survey:“SuperMacho“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

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GoalsGoals GoalsGoals • Primary objectivePrimary objective: Ascertain nature of excess lensing : Ascertain nature of excess lensing

population towards LMCpopulation towards LMC

• Secondary objectives:Secondary objectives:Variable stars (instability strip @ 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 objectivePrimary objective: Ascertain nature of excess lensing : Ascertain nature of excess lensing

population towards LMCpopulation towards LMC

• Secondary objectives:Secondary objectives:Variable stars (instability strip @ 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

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SuperMacho TeamSuperMacho TeamSuperMacho TeamSuperMacho Team

C. Stubbs, R. Covarrubias, B. Henderson C. Stubbs, R. Covarrubias, B. Henderson Univ. of WashingtonUniv. of Washington

A. BeckerA. Becker Lucent/Univ. of Washington Lucent/Univ. of Washington

D. WelchD. Welch McMaster UniversityMcMaster University

C. Smith, R. Hiriart, K. Olsen, A. RestC. Smith, R. Hiriart, K. Olsen, A. Rest CTIO/NOAOCTIO/NOAO

K. Cook, A. Drake, S. Keller, G. Proctor, S. Nikolaev K. Cook, A. Drake, S. Keller, G. Proctor, S. Nikolaev LLNLLLNL

A. Clochiatti A. Clochiatti Universidad CatholicaUniversidad Catholica

C. Stubbs, R. Covarrubias, B. Henderson C. Stubbs, R. Covarrubias, B. Henderson Univ. of WashingtonUniv. of Washington

A. BeckerA. Becker Lucent/Univ. of Washington Lucent/Univ. of Washington

D. WelchD. Welch McMaster UniversityMcMaster University

C. Smith, R. Hiriart, K. Olsen, A. RestC. Smith, R. Hiriart, K. Olsen, A. Rest CTIO/NOAOCTIO/NOAO

K. Cook, A. Drake, S. Keller, G. Proctor, S. Nikolaev K. Cook, A. Drake, S. Keller, G. Proctor, S. Nikolaev LLNLLLNL

A. Clochiatti A. Clochiatti Universidad CatholicaUniversidad Catholica

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ImplementationImplementationImplementationImplementation

CTIO 4m with Mosaic imager: CTIO 4m with Mosaic imager:

150 half nights over 5 yrs. 150 half nights over 5 yrs.

Custom broadband V+R filter Custom broadband V+R filter

(5200A to 7300A) (5200A to 7300A)

60 fields, 23 sq degrees in LMC 60 fields, 23 sq degrees in LMC

Exposure times maximize # of starsExposure times maximize # of stars

= 0.1 mag at 23= 0.1 mag at 23rdrd

Remote Observing from La Serena (and Remote Observing from La Serena (and

even Seattle…!) even Seattle…!)

CTIO 4m with Mosaic imager: CTIO 4m with Mosaic imager:

150 half nights over 5 yrs. 150 half nights over 5 yrs.

Custom broadband V+R filter Custom broadband V+R filter

(5200A to 7300A) (5200A to 7300A)

60 fields, 23 sq degrees in LMC 60 fields, 23 sq degrees in LMC

Exposure times maximize # of starsExposure times maximize # of stars

= 0.1 mag at 23= 0.1 mag at 23rdrd

Remote Observing from La Serena (and Remote Observing from La Serena (and

even Seattle…!) even Seattle…!)

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Model discrimination via spatial Model discrimination via spatial event distributionevent distribution

Model discrimination via spatial Model discrimination via spatial event distributionevent distribution

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Discrimination between self-lensing vs screen-Discrimination between self-lensing vs screen-lensing: spatially varying optical depthlensing: spatially varying optical depth

Discrimination between self-lensing vs screen-Discrimination between self-lensing vs screen-lensing: spatially varying optical depthlensing: spatially varying optical depth

• Zhao & Evans 2000: Models: Zhao & Evans 2000: Models: Bar unvirialized, misaligned, Bar unvirialized, misaligned, and offset from LMC diskand offset from LMC disk

• Optical depth depends onOptical depth depends on mass ratio disk/bar Misalignment

• Predicts asymmetry and Predicts asymmetry and concentration along barconcentration along bar

• Zhao & Evans 2000: Models: Zhao & Evans 2000: Models: Bar unvirialized, misaligned, Bar unvirialized, misaligned, and offset from LMC diskand offset from LMC disk

• Optical depth depends onOptical depth depends on mass ratio disk/bar Misalignment

• Predicts asymmetry and Predicts asymmetry and concentration along barconcentration along bar

Zhao & Evans 2000

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Microlensing event rate ratiosMicrolensing event rate ratiosMicrolensing event rate ratiosMicrolensing event rate ratios

Differential approach reduces sensitivity to LF, efficiencies, etc.

Differential approach reduces sensitivity to LF, efficiencies, etc.

• Red: screen-lensing

• Black: self-lensing, Zhao et al

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Data “Reduction” Data “Reduction” PipelinePipelineData “Reduction” Data “Reduction” PipelinePipeline• Project requires same-night detection of Project requires same-night detection of

microlensing from ~ 60 fields/nightmicrolensing from ~ 60 fields/night

• Variability must be Variability 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 Project requires same-night detection of microlensing from ~ 60 fields/nightmicrolensing from ~ 60 fields/night

• Variability must be Variability 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

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Why did I Why did I decide to decide to do do astronomy?astronomy?

Why did I Why did I decide to decide to do do astronomy?astronomy?

excessive.fits

big.fits

Really big.fits

too big.fits

Flats.fits

biases.fits

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Preliminary Steps - MSCpipePreliminary Steps - MSCpipePreliminary Steps - MSCpipePreliminary Steps - MSCpipe• Recognizes images, zeros and flatsRecognizes images, zeros and flats• Crosstalk correction with IRAF Crosstalk correction with IRAF

executables executables

• Mosaic image WCS registration with Mosaic image WCS registration with IRAF msccmatch, to UCAC1 catalog, IRAF msccmatch, to UCAC1 catalog, typical rms of 0.08 arcsectypical rms of 0.08 arcsec

• Chopping intoChopping into amplifiers, distribution amplifiers, distribution into flat or image directories as into flat or image directories as

appropriate.appropriate.

• Recognizes images, zeros and flatsRecognizes images, zeros and flats• Crosstalk correction with IRAF Crosstalk correction with IRAF

executables executables

• Mosaic image WCS registration with Mosaic image WCS registration with IRAF msccmatch, to UCAC1 catalog, IRAF msccmatch, to UCAC1 catalog, typical rms of 0.08 arcsectypical rms of 0.08 arcsec

• Chopping intoChopping into amplifiers, distribution amplifiers, distribution into flat or image directories as into flat or image directories as

appropriate.appropriate.

8K x 8K MOSAIC8K x 8K MOSAICImageImage

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(High-z Supernova Team)

Image Subtraction Image Subtraction

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Modular Image Subtraction PipelineModular Image Subtraction PipelineModular Image Subtraction PipelineModular 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

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Hardware for “real-time” reductionsHardware for “real-time” reductionsHardware for “real-time” reductionsHardware for “real-time” reductions

Dual networksDual networks1 Gb/sec compute link1 Gb/sec compute link100 Mb/s admin link100 Mb/s admin link

10 compute nodes10 compute nodes2 x 1.2 GHz CPUs2 x 1.2 GHz CPUs1 GB RAM each1 GB RAM each300 GB local IDE disk300 GB local IDE disk

1 TB SCSI RAID disk array1 TB SCSI RAID disk array

2 TB IDE RAID disk array2 TB IDE RAID disk array

Dual networksDual networks1 Gb/sec compute link1 Gb/sec compute link100 Mb/s admin link100 Mb/s admin link

10 compute nodes10 compute nodes2 x 1.2 GHz CPUs2 x 1.2 GHz CPUs1 GB RAM each1 GB RAM each300 GB local IDE disk300 GB local IDE disk

1 TB SCSI RAID disk array1 TB SCSI RAID disk array

2 TB IDE RAID disk array2 TB IDE RAID disk array

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Discrimination and ClassificationDiscrimination and ClassificationDiscrimination and ClassificationDiscrimination and ClassificationPhotometry on difference images:Photometry on difference images:

• fixed PSF DoPhotfixed PSF DoPhot• sigma image (requires significant sigma image (requires significant -flux)-flux)• sensitive to both positive and negative sensitive to both positive and negative -flux-flux

Detected objects are filtered:Detected objects are filtered:• PSF fit chi-squared (rejects CR’s, subtr. resid.’s) PSF fit chi-squared (rejects CR’s, subtr. resid.’s) • DoPhot object typeDoPhot object type• Away from edges or masked pixelsAway from edges or masked pixels

Photometry on difference images:Photometry on difference images:• fixed PSF DoPhotfixed PSF DoPhot• sigma image (requires significant sigma image (requires significant -flux)-flux)• sensitive to both positive and negative sensitive to both positive and negative -flux-flux

Detected objects are filtered:Detected objects are filtered:• PSF fit chi-squared (rejects CR’s, subtr. resid.’s) PSF fit chi-squared (rejects CR’s, subtr. resid.’s) • DoPhot object typeDoPhot object type• Away from edges or masked pixelsAway from edges or masked pixels

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Browser/ClassifierBrowser/ClassifierBrowser/ClassifierBrowser/Classifier

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SQL-compatible (Postgres) DatabaseSQL-compatible (Postgres) DatabaseSQL-compatible (Postgres) DatabaseSQL-compatible (Postgres) Database

““Raw”Raw”Sequence of observations, pointers to imagesSequence of observations, pointers to imagesDetections of sources in difference images, as “boxes”Detections of sources in difference images, as “boxes”Pipeline configuration and parametersPipeline configuration and parameters

““Derived”Derived”Aggregations of detections into astronomical sourcesAggregations of detections into astronomical sources

spatial coincidence or “clustering”spatial coincidence or “clustering”eventually, orbits?eventually, orbits?

Classification of sourcesClassification of sourcesasteroidsasteroidsSNeSNevariable starsvariable starsQSO/AGNsQSO/AGNs

““Raw”Raw”Sequence of observations, pointers to imagesSequence of observations, pointers to imagesDetections of sources in difference images, as “boxes”Detections of sources in difference images, as “boxes”Pipeline configuration and parametersPipeline configuration and parameters

““Derived”Derived”Aggregations of detections into astronomical sourcesAggregations of detections into astronomical sources

spatial coincidence or “clustering”spatial coincidence or “clustering”eventually, orbits?eventually, orbits?

Classification of sourcesClassification of sourcesasteroidsasteroidsSNeSNevariable starsvariable starsQSO/AGNsQSO/AGNs

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SuperMacho StatusSuperMacho StatusSuperMacho StatusSuperMacho StatusJust finished second season of observationsJust finished second season of observations

Great working partnership with NOAO staff and scientists- thanks!Great working partnership with NOAO staff and scientists- thanks!

Initial frame subtraction has been done on both years’ imagesInitial frame subtraction has been done on both years’ images

Detection efficiency tests on year 1 images look very favorableDetection efficiency tests on year 1 images look very favorable

Next tasks:Next tasks:Search for variable sources in only 1 of the 2 years, Search for variable sources in only 1 of the 2 years, Implement alert system for next season. Implement alert system for next season.

No proprietary data period- raw and flat-fielded images available!No proprietary data period- raw and flat-fielded images available!

Just finished second season of observationsJust finished second season of observations

Great working partnership with NOAO staff and scientists- thanks!Great working partnership with NOAO staff and scientists- thanks!

Initial frame subtraction has been done on both years’ imagesInitial frame subtraction has been done on both years’ images

Detection efficiency tests on year 1 images look very favorableDetection efficiency tests on year 1 images look very favorable

Next tasks:Next tasks:Search for variable sources in only 1 of the 2 years, Search for variable sources in only 1 of the 2 years, Implement alert system for next season. Implement alert system for next season.

No proprietary data period- raw and flat-fielded images available!No proprietary data period- raw and flat-fielded images available!

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Seeing in 2002 is considerably worse

2001

2002

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

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(Hubble Space Telescope, NASA)(Hubble Space Telescope, NASA)

Supernovae are powerful cosmological probes

Distances to ~6% from brightness

Redshifts from features in spectra

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Cosmic ArithmeticCosmic ArithmeticCosmic ArithmeticCosmic Arithmetic

General Relativity + isotropy and homogeneity require General Relativity + isotropy and homogeneity require that (in the relevant units)that (in the relevant units)

geometrygeometry + + mattermatter + + = 1 = 1

If the underlying geometry is flat, and if If the underlying geometry is flat, and if mm <1 then the <1 then the

cosmological constant term cosmological constant term must must be non-zero. be non-zero.

CMB measurements demonstrate the curvature is zero.CMB measurements demonstrate the curvature is zero.

Mass inventories fall short of Mass inventories fall short of matter matter =1 =1

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High-z Supernova Search Team

Microwave Background

Cluster Masses

m

““Best Fit” at Best Fit” at

massmass ~ 0.3 ~ 0.3

~ 0.7~ 0.7

Is the expansion Is the expansion reallyreally accelerating? What does this accelerating? What does this mean?mean?

Insufficient mass to halt the Insufficient mass to halt the expansionexpansion

Rate of expansion is Rate of expansion is increasing…increasing…

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A Repulsive ResultA Repulsive ResultA Repulsive ResultA Repulsive Result

• Expansion of Universe is Expansion of Universe is acceleratingaccelerating!(?)!(?)

• Implies something new – and rather repulsiveImplies something new – and rather repulsive

• Regions of empty space Regions of empty space repelrepel each other! each other!

“Cosmological constant”…

Einstein’s greatest blunder?

What’s going on in the vacuum?

• Expansion of Universe is Expansion of Universe is acceleratingaccelerating!(?)!(?)

• Implies something new – and rather repulsiveImplies something new – and rather repulsive

• Regions of empty space Regions of empty space repelrepel each other! each other!

“Cosmological constant”…

Einstein’s greatest blunder?

What’s going on in the vacuum?

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Potential sources of systematic errorPotential sources of systematic errorPotential sources of systematic errorPotential sources of systematic error

Extinction by “gray” dust?Careful multicolor measurements, esp. in IRExploit 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 SneCalibrate detection efficiencies

K-corrections, Galactic extinction, photometric zeropoints....

Extinction by “gray” dust?Careful multicolor measurements, esp. in IRExploit 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 SneCalibrate detection efficiencies

K-corrections, Galactic extinction, photometric zeropoints....

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Dark Energy’s Equation of StateDark Energy’s Equation of StateDark Energy’s Equation of StateDark Energy’s 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...

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Essence Survey Goal: wEssence Survey Goal: w

Monte Carlo of

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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 WashingtonRicardo 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/NOAOKevin 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 CfAThomas 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 HawaiiJohn L. Tonry --- Univ of Hawaii

ESSENCE Survey TeamESSENCE Survey Team

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

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ESSENCE survey implementationESSENCE survey implementationESSENCE survey implementationESSENCE survey implementation

• NOAO Survey on CTIO 4m, MOSAIC NOAO Survey on CTIO 4m, MOSAIC • Same frame subtraction pipeline as SuperMacho Same frame subtraction pipeline as SuperMacho

project, scheduled in “other” halves of SuperMacho project, scheduled in “other” halves of SuperMacho nightsnights

• ~ 200 supernovae with 0.1 < z < 0.8~ 200 supernovae with 0.1 < z < 0.8

• 3 band photometry: V,R,I (observer frame)3 band photometry: V,R,I (observer frame)

• 2 sets of fields, so 2 sets of fields, so t=4 days t=4 days

• Goal is to determine a distance modulus in each bin Goal is to determine a distance modulus in each bin

(of (of z = 0.1) to 2%z = 0.1) to 2%

• ~3% photometry at peak SN brightness~3% photometry at peak SN brightness

• NOAO Survey on CTIO 4m, MOSAIC NOAO Survey on CTIO 4m, MOSAIC • Same frame subtraction pipeline as SuperMacho Same frame subtraction pipeline as SuperMacho

project, scheduled in “other” halves of SuperMacho project, scheduled in “other” halves of SuperMacho nightsnights

• ~ 200 supernovae with 0.1 < z < 0.8~ 200 supernovae with 0.1 < z < 0.8

• 3 band photometry: V,R,I (observer frame)3 band photometry: V,R,I (observer frame)

• 2 sets of fields, so 2 sets of fields, so t=4 days t=4 days

• Goal is to determine a distance modulus in each bin Goal is to determine a distance modulus in each bin

(of (of z = 0.1) to 2%z = 0.1) to 2%

• ~3% photometry at peak SN brightness~3% photometry at peak SN brightness

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Like High-z Fall 2001 Like High-z Fall 2001 continuous searchcontinuous search

Like High-z Fall 2001 Like High-z Fall 2001 continuous searchcontinuous search

Consistent photometryConsistent photometry

EfficientEfficient

Still requires spectroscopyStill requires spectroscopy

Seeing matters!Seeing matters!

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ESSENCE survey SNe ESSENCE survey SNe (as of Jan 3 2003)(as of Jan 3 2003)

ESSENCE survey SNe ESSENCE survey SNe (as of Jan 3 2003)(as of Jan 3 2003)

ZZ Ia’s II’sIa’s II’s

0.10.1 1 01 0

0.20.2 2 32 3

0.30.3 5 35 3

0.40.4 4 04 0

0.50.5 4 04 0

0.60.6 2 02 0

Totals Totals 18 618 6

SN types and redshifts fromSN types and redshifts fromKeckKeckMagellanMagellanVLTVLTGeminiGemini

18 are SN type Ia’s18 are SN type Ia’s6 are SN type II6 are SN type II5 unsure5 unsure

Good multiband light curves Good multiband light curves from CTIO 4m on 14 type Ia’s from CTIO 4m on 14 type Ia’s

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Two ObservationsTwo ObservationsTwo ObservationsTwo Observations1.1. Time domain surveys cast a wide netTime 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 These “by-products” are at best underexploited!underexploited!

1.1. Time domain surveys cast a wide netTime 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 These “by-products” are at best underexploited!underexploited!

2.2. The time domain is a unifying thread across many The time domain is a unifying thread across many important science goals and opportunitiesimportant science goals and opportunities

We can do a better job!We can do a better job!

2.2. The time domain is a unifying thread across many The time domain is a unifying thread across many important science goals and opportunitiesimportant science goals and opportunities

We can do a better job!We can do a better job!

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Large Synoptic Survey TelescopeLarge Synoptic Survey TelescopeLarge Synoptic Survey TelescopeLarge Synoptic Survey TelescopeHighly ranked in Decadal Survey

Optimized for time domain

7 square degree field

6.5m effective aperture

24th 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

24th mag in 20 sec

> 5 TBytes/night

Real-time analysis

Simultaneous multiple science goals

47

LSST: Massively Parallel AstronomyLSST: Massively Parallel AstronomyLSST: Massively Parallel AstronomyLSST: 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

Computer Evolution is StaggeringComputer Evolution is StaggeringComputer Evolution is StaggeringComputer Evolution is Staggering~1990 (MACHO era)~1990 (MACHO era)

60 MHz CPUs60 MHz CPUs2 GB disks K$’s2 GB disks K$’sReal-time DoPhot analysis on 5 Gbytes/nightReal-time DoPhot analysis on 5 Gbytes/night

Today (SuperMacho/ESSENCE era)Today (SuperMacho/ESSENCE era)

arrays arrays of > 2 GHz CPUs are routine Scripting languagesof > 2 GHz CPUs are routine Scripting languages 250 Gbyte drives for $400 Algorithmic Advances250 Gbyte drives for $400 Algorithmic Advances

Real-time subtractions on 20 Gbytes/nightReal-time subtractions on 20 Gbytes/night““Commercial” databases seem up to the taskCommercial” databases seem up to the task

Tomorrow (LSST era)Tomorrow (LSST era)Real-time reduction of 15 Terabytes/nightReal-time reduction of 15 Terabytes/nightEntire image archive on spinning disk (1000s of Terabytes)Entire image archive on spinning disk (1000s of Terabytes)

~1990 (MACHO era)~1990 (MACHO era)60 MHz CPUs60 MHz CPUs2 GB disks K$’s2 GB disks K$’sReal-time DoPhot analysis on 5 Gbytes/nightReal-time DoPhot analysis on 5 Gbytes/night

Today (SuperMacho/ESSENCE era)Today (SuperMacho/ESSENCE era)

arrays arrays of > 2 GHz CPUs are routine Scripting languagesof > 2 GHz CPUs are routine Scripting languages 250 Gbyte drives for $400 Algorithmic Advances250 Gbyte drives for $400 Algorithmic Advances

Real-time subtractions on 20 Gbytes/nightReal-time subtractions on 20 Gbytes/night““Commercial” databases seem up to the taskCommercial” databases seem up to the task

Tomorrow (LSST era)Tomorrow (LSST era)Real-time reduction of 15 Terabytes/nightReal-time reduction of 15 Terabytes/nightEntire image archive on spinning disk (1000s of Terabytes)Entire image archive on spinning disk (1000s of Terabytes)

49

LSST ChallengesLSST ChallengesLSST ChallengesLSST 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

A staged approachA staged approachA staged approachA staged approach Today LSST design and tradeoff studies Today LSST design and tradeoff studies

LSST precursor projects: LSST precursor projects: Software and database prototypingSoftware and database prototyping

2 - 5 years Dedicated 1.5 – 2.5m wide-field facilities?2 - 5 years Dedicated 1.5 – 2.5m wide-field facilities?

10-15 years: Full LSST operations10-15 years: Full LSST operations

Today LSST design and tradeoff studies Today LSST design and tradeoff studies LSST precursor projects: LSST precursor projects:

Software and database prototypingSoftware and database prototyping

2 - 5 years Dedicated 1.5 – 2.5m wide-field facilities?2 - 5 years Dedicated 1.5 – 2.5m wide-field facilities?

10-15 years: Full LSST operations10-15 years: Full LSST operations

APO 2.5m post-SDSS?APO 2.5m post-SDSS? PanStarrs Array? PanStarrs Array?

51

The LSST OpportunityThe LSST OpportunityThe LSST OpportunityThe LSST OpportunityCurrent trend is towards fewer (albeit larger aperture ) telescopes with open Current trend is towards fewer (albeit larger aperture ) telescopes with open

access…access…

LSST goes in the other direction:LSST goes in the other direction:Multiple projects fed from a common image stream Multiple projects fed from a common image stream

No proprietary data periodNo proprietary data period

Exploits the 3 enabling technologies of our era:Exploits the 3 enabling technologies of our era:Large aperture telescopesLarge aperture telescopes

Silicon detector arraysSilicon detector arraysComputing and mass storage technologyComputing and mass storage technology

Highly Efficient multitasking systemHighly Efficient multitasking system

Current trend is towards fewer (albeit larger aperture ) telescopes with open Current trend is towards fewer (albeit larger aperture ) telescopes with open access…access…

LSST goes in the other direction:LSST goes in the other direction:Multiple projects fed from a common image stream Multiple projects fed from a common image stream

No proprietary data periodNo proprietary data period

Exploits the 3 enabling technologies of our era:Exploits the 3 enabling technologies of our era:Large aperture telescopesLarge aperture telescopes

Silicon detector arraysSilicon detector arraysComputing and mass storage technologyComputing and mass storage technology

Highly Efficient multitasking systemHighly Efficient multitasking system

52

Session 134. LSST Oral, Thursday, Session 134. LSST Oral, Thursday, January 9, 2003, 2:00-3:30pm, 6ABJanuary 9, 2003, 2:00-3:30pm, 6AB

The Large Synoptic Survey Telescope: Opening New Windows The Large Synoptic Survey Telescope: Opening New Windows J. A. Tyson (Bell Labs, Lucent Technologies), LSST CollaborationJ. A. Tyson (Bell Labs, Lucent Technologies), LSST Collaboration

Science Opportunities with the LSST: Science Opportunities with the LSST: From Near-Earth Asteroids to High-redshift large-scale structure From Near-Earth Asteroids to High-redshift large-scale structure M.A. Strauss (Princeton University)M.A. Strauss (Princeton University)

Asteroids: with SDSS towards LSST 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: Hunting for Near-Earth Asteroids Using LSST: Detection Methods and Observational Strategies Detection Methods and Observational Strategies E. Bowell (Lowell Observatory),E. Bowell (Lowell Observatory), A. W. Harris (Space Science Institute) A. W. Harris (Space Science Institute)

Managing the Data Flow from the LSST Managing the Data Flow from the LSST A. Connolly (University of Pittsburgh), LSST TeamA. Connolly (University of Pittsburgh), LSST Team

Petabyte Scale Data Mining: Dream or Reality? Petabyte Scale Data Mining: Dream or Reality? A.S. Szalay (JHU), J. Gray (Microsoft Research), J. Vandenberg (JHU)A.S. Szalay (JHU), J. Gray (Microsoft Research), J. Vandenberg (JHU)

53

SuperMacho and ESSENCE ImagesSuperMacho and ESSENCE ImagesSuperMacho and ESSENCE ImagesSuperMacho and ESSENCE Images

Raw frames:Raw frames:ftp://archive2.tuc.noao.edu/SM_SN/

NOAO Science Archive:NOAO Science Archive:http://archive.noao.edu/nsa/

Raw frames:Raw frames:ftp://archive2.tuc.noao.edu/SM_SN/

NOAO Science Archive:NOAO Science Archive:http://archive.noao.edu/nsa/

54

SN rates: reality vs. aspirationsSN rates: reality vs. aspirationsSN rates: reality vs. aspirationsSN rates: reality vs. aspirations

Goal is 200 type Ia light curves in 5 seasonsGoal is 200 type Ia light curves in 5 seasons

This implies 40/yr, we got 15. What’s up?This implies 40/yr, we got 15. What’s up?

1.1. First of 3 lunations was first epoch: templatesFirst of 3 lunations was first epoch: templatesExpect 1.5x as many in future ~ 22Expect 1.5x as many in future ~ 22

2.2. Seeing was usually worse than 1.5 arcseconds! Seeing was usually worse than 1.5 arcseconds!

3. Opportunity to use SDSS for detection out to z~0.3, 3. Opportunity to use SDSS for detection out to z~0.3, where 4m is inefficient where 4m is inefficient

55

Discrimination (on a 1K x 4K amp)Discrimination (on a 1K x 4K amp)Discrimination (on a 1K x 4K amp)Discrimination (on a 1K x 4K amp)130 Detections in R band difference image130 Detections in R band difference image 21 Detections in R band survive cuts21 Detections in R band survive cuts

DoPhot PSF chi-squaredDoPhot PSF chi-squared Npix >0Npix >0 MaskedMaskedDoPhot object typeDoPhot object type Npix<0Npix<0 SaturatedSaturated

808 Detections in I band difference image808 Detections in I band difference image119 Detections in I band survive cuts119 Detections in I band survive cuts

114 Detections in V band difference image114 Detections in V band difference image 30 Detections in V band survive cuts30 Detections in V band survive cuts

Spatial coincidence in 2 or more filters:Spatial coincidence in 2 or more filters: A single candidateA single candidate

56

Number of Monitored Stars, from Added Star testsNumber of Monitored Stars, from Added Star testsNumber of Monitored Stars, from Added Star testsNumber of Monitored Stars, from Added Star tests

1.2

12

0.12

Num

ber

o f l e

n se d

sta

rs a

t

= 1

.2 x

10-7 NNlensedlensed ~ ~ x N x Nobsobs

NNeventsevents ~ ~ T(exp) Nobs T(exp) Nobs tteventevent

We expect 8-12 We expect 8-12 ongoing LMC events ongoing LMC events per season per season