LSST cadence for Supernovae federica b. bianco, NYU Science Collaborations Coordinator, LSST LSST Transient and Variable Stars Science Collaborations Co-Chair @fedhere fedhere and 

Large Synoptic Survey Telescope LSST 2022-2032 effective aperture of 6.7 m FoV 9.6 deg2 large etendue : collecting area x FoV 

federica bianco NYU LSST 2022-2032 Wide-Deep-Fast 2022-2032 effective aperture of 6.7 m FoV 9.6 deg2 large etendue : collecting area x FoV 75-95% total time 

federica bianco NYU LSST 2022-2032 Wide-Deep-Fast 2022-2032 effective aperture of 6.7 m FoV 9.6 deg2 large etendue : collecting area x FoV cover large swaths of sky 75-95% total time 

federica bianco NYU cover large swaths of sky to faint magnitudes LSST 2022-2032 Wide-Deep-Fast 2022-2032 effective aperture of 6.7 m FoV 9.6 deg2 large etendue : collecting area x FoV 75-95% total time 

federica bianco NYU cover large swaths of sky to faint magnitudes in a short amount of time LSST 2022-2032 Wide-Deep-Fast 2022-2032 effective aperture of 6.7 m FoV 9.6 deg2 large etendue : collecting area x FoV 75-95% total time 

federica bianco NYU cover large swaths of sky to faint magnitudes repeatedly at short intervals LSST 2022-2032 LSST 2022-2032 effective aperture of 6.7 m FoV 9.6 deg2 large etendue : collecting area x FoV 2022-2032 Wide-Deep-Fast 75-95% total time 

federica bianco NYU Dark energy and dark matter (via measurements of strong and weak lensing, large-scale structure, clusters of galaxies, and supernovae)
 Exploring the transient and variable universe
 Studying the structure of the Milky Way galaxy and its neighbors via resolved stellar populations
 An inventory of the Solar System, including Near Earth Asteroids and Potential Hazardous Objects, Main Belt Asteroids, and Kuiper Belt Objects Science Drivers 

federica bianco NYU Dark energy and dark matter (via measurements of strong and weak lensing, large-scale structure, clusters of galaxies, and supernovae)
 Exploring the transient and variable universe - supernovae
 Studying the structure of the Milky Way galaxy and its neighbors via resolved stellar populations - supernovae precursors
 An inventory of the Solar System, including Near Earth Asteroids and Potential Hazardous Objects, Main Belt Asteroids, and Kuiper Belt Objects moving objects Science Drivers all relevant to trasients + variable Universe! 

federica bianco NYU Color single-filter cadence density Regular/persistent WIDE DEEP Fast/Dense LSST constrained optimization problem and the SCs https://github.com/LSSTScienceCollaborations/ObservingStrategy https://arxiv.org/pdf/1708.04058.pdf 

federica bianco NYU LSST 2022-2032 LSST 2022-2032 2022-2032 LSST Cadence questions a SN scientist should ask: 

federica bianco NYU LSST 2022-2032 LSST 2022-2032 2022-2032 Can we study SNe from LSST Data Products? Can we classify She from LSST Prompt Release data? Can we follow-up the SN? (faint and many!) LSST Cadence questions a SN scientist should ask: 

federica bianco NYU LSST 2022-2032 LSST 2022-2032 2022-2032 Can we study SNe from LSST Data Products? Can we classify SNe from LSST Prompt Release data? Can we follow-up the SN? (faint and many!) LSST Cadence questions a SN scientist should ask: 

federica bianco NYU LSST 2022-2032 LSST 2022-2032 2022-2032 Can we study SNe from LSST Data Products? Can we classify SNe from LSST Prompt Release data? Can we follow-up the SN? (faint and many!) LSST Cadence questions a SN scientist should ask: 

federica bianco NYU LSST 2022-2032 LSST 2022-2032 2022-2032 Can we study SNe from LSST Data Products? Can we classify SNe from LSST Prompt Release data? Can we follow-up the SN? (faint and many!) 

federica bianco NYU LSST 2022-2032 LSST 2022-2032 2022-2032 Wide-Deep-Fast (75-95% total time) LSST Cadence 

federica bianco NYU LSST 2022-2032 LSST 2022-2032 2022-2032 Wide-Deep-Fast (75-95% total time) Minisurveys LSST Cadence 

federica bianco NYU LSST 2022-2032 LSST 2022-2032 2022-2032 Wide-Deep-Fast (75-95% total time) Minisurveys Deep Drilling Fields LSST Cadence 

federica bianco NYU LSST 2022-2032 LSST 2022-2032 2022-2032 Wide-Deep-Fast (85.1%) Baseline Cadence: Minion1016 

federica bianco NYU LSST 2022-2032 LSST 2022-2032 2022-2032 North Ecliptic Survey (6.5%) The NES is an extension to reach the Ecliptic at higher airmass than the WFD survey typically covers, no u Wide-Deep-Fast (85.1%) Baseline Cadence: Minion1016 

federica bianco NYU LSST 2022-2032 LSST 2022-2032 2022-2032 North Ecliptic Survey (6.5%) The NES is an extension to reach the Ecliptic at higher airmass than the WFD survey typically covers, no u South Celestial Pole (2.2%): higher airmass decl>−65 degrees. includes ugrizy, but takes fewer exposures/field than the WFD and does not collect in pairs. Wide-Deep-Fast (85.1%) Baseline Cadence: Minion1016 

federica bianco NYU LSST 2022-2032 LSST 2022-2032 2022-2032 Galactic Plane (1.7%): covers the region where LSST is expected to be highly confused by the density of stellar sources; fewer total exposures/ field and does not collect in pairs North Ecliptic Survey (6.5%) The NES is an extension to reach the Ecliptic at higher airmass than the WFD survey typically covers, no u South Celestial Pole (2.2%): higher airmass decl>−65 degrees. includes ugrizy, but takes fewer exposures/field than the WFD and does not collect in pairs. Wide-Deep-Fast (85.1%) Baseline Cadence: Minion1016 

federica bianco NYU LSST 2022-2032 LSST 2022-2032 2022-2032 Deep Drilling Fields DDF (4.5%) North Ecliptic Survey The NES is an extension to reach the Ecliptic at higher airmass than the WFD survey typically covers, no u Galactic Plane (1.7%): covers the region where LSST is expected to be highly confused by the density of stellar sources; fewer total exposures/ field and does not collect in pairs South Celestial Pole (2.2%): higher airmass decl>−65 degrees. includes ugrizy, but takes fewer exposures/field than the WFD and does not collect in pairs. Wide-Deep-Fast (85.1%) Baseline Cadence: Minion1016 

federica bianco NYU Baseline Cadence: Minion1016 Fraction of SNe Ia detected pre-peak at z=0.5 

federica bianco NYU Baseline Cadence: Minion1016 inter- and intra-night gap 

federica bianco NYU Median Inter-Night Gap (days) Median Intra-Night Gap (hours) any filter any filter Alternative Cadences: inter- and intra-night gap Chapter 

federica bianco NYU Median Inter-Night Gap (days) Median Intra-Night Gap (hours) Chapter single filter: r single filter: r Alternative Cadences: inter- and intra-night gap 

federica bianco NYU Median Inter-Night Gap (days) Median Intra-Night Gap (hours) Chapter single filter: r single filter: r Alternative Cadences: inter- and intra-night gap 

federica bianco NYU Can we classify SNe from LSST Prompt Release data? 

time... in seconds 

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federica bianco NYU Transients’ similarity measured as the Euclidean distance between the magnitude change of each transient’s pair represented in log scale black -> |∆Mag1 − ∆Mag2 | ∼ 8 white -> |∆Mag1 − ∆Mag2 | ∼ 0 Chapter Can we classify SNe from LSST Prompt Release data? 

federica bianco NYU Transients’ similarity measured as the Euclidean distance between the magnitude change of each transient’s pair represented in log scale black -> |∆Mag1 − ∆Mag2 | ∼ 8 white -> |∆Mag1 − ∆Mag2 | ∼ 0 Chapter inter-night minutes days intra-night 8 25 25 30 Can we classify SNe from LSST Prompt Release data? 

federica bianco NYU Renee Hlozek, Rick Kessler Can we classify SNe from LSST Prompt Release data? 

federica bianco NYU Brokers : Antares (Matheson, Narayan ++) UK broker (Smartt ++) Can we classify SNe from LSST Prompt Release data? 

federica bianco NYU Can we classify SNe from LSST Prompt Release data? goo.gl/orHKBn LSST TVSSC Task Force Characterize the functionality needed from a community-broker interface Brokers : Antares (Matheson, Narayan ++) UK broker (Smartt ++) 

federica bianco NYU Can we study SNe from LSST Data Products? 

federica bianco NYU Wide Deep Fast Can we study SNe from LSST Data Products? 

federica bianco NYU Deep Drilling Fields Can we study SNe from LSST Data Products? 

federica bianco NYU days to peak days to peak flux (units of peak flux) flux (units of peak flux) Bianco+ 11 SNLS 3 years: ~100 SNe 5 days color sampling we set a 20% upper limit to WD-RG progenitors Can we study SNe from LSST Data Products? Progenitor studies from light curves - SN Ia example 

federica bianco NYU LSST can do constrain a 5% SD progenitors contribution in <=3 months! • Create synthetic progenitor populations with a fraction of single degenerate progenitor systems 0.05 ≤ fSD ≤ 0.6 in 0.05 intervals and random lines of sight with respect to the binary’s geometry. • We need 1000 detections within 1 day in 2 filters at a SNR ≥ 7 Science-Driven Optimization of the LSST Observing Strategy The LSST Science Collaborations (Marshall+ 2017) 

federica bianco NYU LSST compared to other SN-relevant surveys 5-sigma depth per visit (mag) Single filter gap (days) area (sq deg) u g r i z Y 104 102 103 10 26 20 23 0 20 10 Dan Scolnic with Kessler, Biswas, Jha, Hložek, DESC SNWG Scolnic+ 2017 ApJ 852,1 Minion1016 

federica bianco NYU 5-sigma depth per visit (mag) Single filter gap (days) area (sq deg) u g r i z Y 104 102 103 10 26 20 23 0 20 10 Dan Scolnic with Kessler, Biswas, Jha, Hložek, DESC SNWG Scolnic+ 2017 ApJ 852,1 Minion1016 LSST compared to other SN-relevant surveys 

federica bianco NYU Rolling Cadences: inter- and intra-night gap 5-sigma depth per visit (mag) Single filter gap (days) area (sq deg) u g r i z Y 104 102 103 10 26 20 23 0 20 10 Dan Scolnic with Kessler, Biswas, Jha, Hložek, DESC SNWG Scolnic+ 2017 ApJ 852,1 Rolling cadence 

federica bianco NYU Deep Drilling Fields How many? What is the cadence? What coordinates (galactic vs extragalactic)? Current cadence current plan: 1 DDF/night, 5 filters, total~1hr few DDFs/night ~10-15min each nightly or every other night Sarah Jha, LSST-DESC 

federica bianco NYU Call for DDF and MiniSurvey White Paper proposals expected in Summer 2018 with a deadline in Fall 2018. Good News! You can still change all this! 

federica bianco NYU Good News! You can still change all this! especially if you are an LSST data rights holder… join an LSST Science Collaboration! Galaxies Michael Cooper (UC Irvine) Brant Robertson (University of California, Santa Cruz) Stars, Milky Way, and Local Volume John Bochanski (Rider University) John Gizis (University of Delaware) Nitya Jacob Kallivayalil(University of Virginia) Solar System Megan Schwamb (Gemini Observatory, Northern Operations Center) David Trilling (Northern Arizona University) 
 Dark Energy Eric Gawiser (Rutgers The State University of New Jersey) Phil Marshall (KIPAC) Active Galactic Nuclei Niel Brandt (Pennsylvania State University) Transients and variable stars Federica Bianco (New York University) Rachel Street (LCO) Strong Lensing Charles Keeton (Rutgers-The State University of New Jersey) Aprajita Verma (Oxford University) Informatics and Statistics Tom Loredo (Cornell University) Chad Schafer (Carnegie Mellon University) Currently there are 8 SCs ranging in size from ~1000 to ~50 members. 

federica bianco NYU Chile 69 UK: 60 Italy: 60 France:45 
 California: 174 Washington: 88 Pennsylvania: 63 SC over 100 members, membership across the world Good News! You can still change all this! especially if you are an LSST data rights holder… join an LSST Science Collaboration! 

federica bianco NYU Good News! You can still change all this! especially if you are an LSST data rights holder… join an LSST Science Collaboration! 

Somewhere, something incredible is waiting to be known. Carl Segan [email protected] - LSST SC coordinator, LSST TVSSC co-chair