Vera C. Rubin Observatory:
Ushering a New Era of Time Domain Astronomy
Federica Bianco
University of Delaware
Department of Physics and Astronomy
Biden School of Public Policy and Administration
Data Science Institute
Rubin Legacy Survey of Space and Time
Science Collaboration Coordinator
Transients & Variable Stars SC CoChair
federica bianco - fbianco@udel.edu
@fedhere
- Introduction: what will LSST measure?
- Variable vs. non-variable stars
- What can you do with non-variable stars?
- LSST vs. Gaia
- What can you do with variable stars?
federica bianco - fbianco@udel.edu
@fedhere
At this level of precision,everything is variable, everything is blended, everything is moving.
| u,g,r,i,z,y | |
|---|---|
| Photometric precision Photometric accuracy Astrometric precision Astrometric accuracy # visits Single image 5σ depths 10-year stack 5σ depth |
5 mmag 10 mmag 10 mas 50 mas 56, 80, 184, 184, 160, 160 23.9, 25.0, 24.7, 24.0, 23.3, 22.1 26.1, 27.4, 27.5, 26.8, 26.1, 24.9 |
the Rubin LSST
Science Collaborations
Rubin LSST Science Collaborations
federica bianco - fbianco@udel.edu
@fedhere
8 SCs - 6 continents - 2000 people - 25 countries
LSST in the time-domain
federica bianco fbianco@udel.edu
@fedhere
Rubin LSST Science Collaborations
Wide Fast Deep
8oo img over 18,000 sq deg
pairs of observations in <1hour to track asteroid (and get transient colors)
80% of the sky time
Many surveys in one
Minisurveys
Minisurveys
Deep Drilling Fields
Targets of Opportunity
}
20%
federica bianco fbianco@udel.edu
@fedhere
Rubin LSST survey design
Bianco, Jones, Ivezič et al, 2021
distributions of time gaps in 76 OpSims
Rubin LSST survey design
Rubin data and time domain data products
Time-domain data products
federica bianco - fbianco@udel.edu
@fedhere
federica bianco - fbianco@udel.edu
world public
data right holders
public after 2 years
Time-domain data products
world public
Time-domain data products
world public
Time-domain data products
A change of perspective
from rare to statistical samples
from dense time-limited or color-limited to sparse multiband
Magnitude -> Flare energy
Star displacement -> color -> flare temperature
What can we learn from 1 data point?
Because LSST will have exquisite image quality we may be able to measure color from atmospheric diffraction
from dense time-limited or color-limited to sparse multiband
Magnitude -> Flare energy
Star displacement -> color -> flare temperature
What can we learn from 1 data point?
Because LSST will have exquisite image quality we may be able to measure color from atmospheric diffraction
from dense time-limited or color-limited to sparse multiband high accuracy
Riley Clarke, Davenport, Gizis, Bianco, in prep
What can we learn from 1 data point?
Because LSST will have exquisite image quality we may be able to measure color from atmospheric diffraction
5,000K flare on dM
30,000K flare on dM
Magnitude -> Flare energy
Star displacement -> color -> flare temperature
from dense time-limited or color-limited to sparse multiband high accuracy
Riley Clarke, Davenport, Gizis, Bianco, in prep
What can we learn from 1 data point?
Because LSST will have exquisite image quality we may be able to measure color from atmospheric diffraction
5,000K flare on dM
30,000K flare on dM
Magnitude -> Flare energy
Star displacement -> color -> flare temperature
Rubin Observatory LSST
Stars and Pies in the sky:
what is possible with Rubin LSST?
The unprecedented sensitivity, spatial coverage and observing cadence of LSST will allow for the first time a statistical approach for the discovery and monitoring of "rare" phenomena
Detailed studies of variable star populations; 2% or better accurate multicolor light curves will be available for a sample of at least 50 million variable stars, enabling studies of cataclysmic variables, eclipsing binary systems, and rare types of variables.
some examples
Rubin Observatory LSST
federica bianco - fbianco@udel.edu
@fedhere
Dark Energy Science Collaboration
(DESC)
+
Transients and Variable
Science Collaboration
(TVS SC)
DDF
WFD
Stellar Accretion Processes: Uniquely, LSSTwill provide both the long-baseline timeseries data necessary to quantify outburst cycles and object occurrence rates but also the rapid alerts necessary to enable outburst phases to be identified while inprogress, triggering more detailed follow-up studies (relies on alerts)
Sara Bonito chair of the Non-Degenerate Eruptive Variables TVS subgroup
TVS Roadmap - in preparation
Studies enabled by statistical samples:
- Observe changes of state of any MSP, LMXBs and tMSP, known and/or newly discovered: the multi-color photometry of the LSST main survey will enable an alert in real time aboutany change of state of known systems (whether MSP, LMXB or tMSP)
- only∼250 Galactic and∼70 extragalactic SySt are known - increasing the sample helps SN Ia progenitor studies
- the accretion process and its intrinsic variability
- the spatial distribution of stellar rotation in young clusters,
- stellar magnetic activity, and
- accretion and inner disk geometries.
Stellar Eruptions: The Giant Eruption of Eta Carinae reached m=1.4, while in an obscured Little Eruption the system reached magnitude 6.2.
- With the single-band limit of LSST at magnitude 24-25 a Giant or a Little Eruption would be visible in the LMC (distance modulus = 18.5), and
- a Giant Eruptionin can be detected in the Local Group (distance modulus 24.4).
TVS Roadmap - in preparation
Historical lightcurve ot Eta Carinae (1800s) and "supernova impostors" Smith & Frew 2011
from rare to statistical samples
Stellar Eruptions:
- 1000 observations over a decade would support the study of long term variation in Eruptions and constraind eruption mechanisms (binarity, winds, magnetism, etc)
- For stellar eruptions in our Galaxy the depth and image quality of Rubin LSST will enable the search of progenitors and progenitor variations on a statistical scale
TVS Roadmap - in preparation
Historical lightcurve ot Eta Carinae (1800s) and "supernova impostors" Smith & Frew 2011
from rare to statistical samples
YOLO + attention mechanism
precision 80% at 70% recall with a training set of 19 light echo examples!
NSF Award #2108841
Detecting and studying light echoes in the era of Rubin and Artificial Intelligence
P.I. Bianco
Li et al. almost submitted
AILE: the first AI-based platform for the detection and study of Light Echoes
from rare to statistical samples
Micro- and meso-lensing for stellar physics
- detect microlensing events where both the lens and source lie in the Magellanic Clouds, and explore stellar and stellar remnant populations in another galaxy.
- LSST will investigate the mass distribution offaint objects in the local neighborhood, such as low mass dwarfs, stellar remnants, andfree-floating planets.
TVS Roadmap - in preparation
Rachel Street co-chair of TVS
Somayeh Khakpash, chair of microlensingsubgroup
Micro- and meso-lensing for stellar physics
- detect microlensing events where both the lens and source lie in the Magellanic Clouds, and explore stellar and stellar remnant populations in another galaxy.
- LSST will investigate the mass distribution offaint objects in the local neighborhood, such as low mass dwarfs, stellar remnants, andfree-floating planets.
Survey coordination
Rubin + Roman
TVS Roadmap - in preparation
Rachel Street co-chair of TVS
Somayeh Khakpash, chair of microlensingsubgroup
Micro- and meso-lensing for stellar physics
- detect microlensing events where both the lens and source lie in the Magellanic Clouds, and explore stellar and stellar remnant populations in another galaxy.
- LSST will investigate the mass distribution offaint objects in the local neighborhood, such as low mass dwarfs, stellar remnants, andfree-floating planets.
Survey coordination
Rubin + Roman
TVS Roadmap - in preparation
Rachel Street co-chair of TVS
Somayeh Khakpash, chair of microlensingsubgroup
ZZ Cetis: 1-3% r.m.s. pulsation
(~1000 ZZ Cetis detected if pulsation 3% in r)
Pulsation stars
- The dependence of pulsational power on a star’s position in 5-color parameter space, as well as the relative pulsation amplitudes in different passbands, informs the least understood aspects of pulsation theory: mode selection and amplitude limiting mechanisms.
- The different colors provided by LSST will enable the temperature determinations. When combined with distances, it will be possible to obtain luminosities. Thus LSST will provide the opportunity to generatea pulsational H-R diagram
-
Stellar Envelop Tomography of long-period variables to probe probing layers of different atmospheric depths (Alvarez 2001)... in 6 filters. But these methodologies largely rely on NIR filters, can it be done with z and Y?
-
with DDF pulsations oh ~hour can be constraieds: Red Giant pulsators
K. Humbleton
TVS Roadmap - in preparation
ZZ Cetis: 1-3% r.m.s. pulsation
(~1000 ZZ Cetis detected if pulsation 3% in r)
Pulsation stars
- The dependence of pulsational power on a star’s position in 5-color parameter space, as well as the relative pulsation amplitudes in different passbands, informs the least understood aspects of pulsation theory: mode selection and amplitude limiting mechanisms.
- The different colors provided by LSST will enable the temperature determinations. When combined with distances, it will be possible to obtain luminosities. Thus LSST will provide the opportunity to generatea pulsational H-R diagram
-
Stellar Envelop Tomography of long-period variables to probe probing layers of different atmospheric depths (Alvarez 2001)... in 6 filters. But these methodologies largely rely on NIR filters, can it be done with z and Y?
-
with DDF pulsations oh ~hour can be constraieds: Red Giant pulsators
K. Humbleton
TVS Roadmap - in preparation
Pulsation stars
- The dependence of pulsational power on a star’s position in 5-color parameter space, as well as the relative pulsation amplitudes in different passbands, informs the least understood aspects of pulsation theory: mode selection and amplitude limiting mechanisms.
- The different colors provided by LSST will enable the temperature determinations. When combined with distances, it will be possible to obtain luminosities. Thus LSST will provide the opportunity to generatea pulsational H-R diagram
-
Stellar Envelop Tomography of long-period variables to probe probing layers of different atmospheric depths (Alvarez 2001)... in 6 filters. But these methodologies largely rely on NIR filters, can it be done with z and Y?
-
with DDF pulsations oh ~hour can be constraieds: Red Giant pulsators
K. Humbleton
Jørgen Christensen-Dalsgaard for the book Asterosesimology (2010) by Conny Aerts
TVS Roadmap - in preparation
What is needed?
- Handling large data samples tools including visualization and computational resources (Rubin Science Platform also see TVS Software Workshop )
- Tools to deal with multi-filter light curves, e.g., developing parametric models; methods for correctly treating sparsely sampled data.
- Algorithms for photometric classification, especially at early times (in the making)
- Software to enable automatic triggering of follow-up resources (in the making)
- Additional follow-up resources (building up)
- Algorithmic development for crowded field phometry (DIA based is ok, so mostly for static science - underway See Massimo Dall'Ora and TVS task force
Mini and Micro surveys to create training data
To connect sparse photometric LSST lightcurves with excellently characterized stellar properties in the TDA
The result will be a unique dataset of∼1 million regularly spaced stellar lightcurves. The lightcurves will gives a particularly comprehensive collection of late-type stellar flaring, but also short-period binary systems and cataclysmic variables, possibly young stellar objects and ultra-short period exoplanets, and unknown anomalous behaviors.
A single night continuous survey on 1 field during commissioning
A DDF synchronized with Roman
A Carina micro-survey
Astronomy’s Discovery Chain
Discovery Engine
10M alerts/night
Community Brokers
target observation managers
the astronomy discovery chain
federica bianco - fbianco@udel.edu
@fedhere
thank you!
University of Delaware
Department of Physics and Astronomy
Biden School of Public Policy and Administration
Data Science Institute
@fedhere
federica bianco
fbianco@udel.edu
thank you!
University of Delaware
Department of Physics and Astronomy
Biden School of Public Policy and Administration
Data Science Institute
@fedhere
federica bianco
fbianco@udel.edu
Rubin Observatory LSST
Rubin and LEOsats
Rubin Observatory LSST
MMA and LEOsats
Iridium satellite number 35 lit up the predawn sky west of Boston at 5 a.m. EST on February 1, 1998, Sky & Telescope
Satellite flares
@fedhere
federica bianco fbianco@udel.edu
Time domain Rubin LSST science
can be mitigated:
- orientation of satellite,
- directing flares away from observer
- knowing coordinates to associate them to alerts
if not mitigate there would be bogus alerts and images ruined by saturating flares
Science Collaborations
Science Collaborations
@fedhere
federica bianco fbianco@udel.edu
modification to planned schedule carry long lasting consequences on the program
ToO enabled for GW need to scan ~100 sq deg footprint upon trigger - will be compromised
Time domain : the LSST observing strategy
Science Collaborations
@fedhere
federica bianco fbianco@udel.edu
Static Sky: correlated noise and cross talk
Cosmology probes are systematic dominated
mitigation: simulation of the non-linear crosstalk to measure the effect on precision cosmology and effectiveness of removal algorithms
(image credit: Canada-France Hawaii Telescope)
Xiaolong Li et al. almost submitted
AILE: the first AI-based platform for the detection and study of Light Echoes
NSF Award #2108841
Detecting and studying light echoes in the era of Rubin and Artificial Intelligence
P.I. Bianco
Pessimal AI problem:
- small training data
- inaccurate labels
- imbalance classes
- diverse morphology
- low SNR
AILE: the first AI-based platform for the detection and study of Light Echoes
YOLO + attention mechanism
precision 80% at 70% recall with a training set of 19 light echo examples!
NSF Award #2108841
Detecting and studying light echoes in the era of Rubin and Artificial Intelligence
P.I. Bianco
Xiaolong Li et al. almost submitted
From Light Echoes to Polluting Plumes
Ian Heffner, UD MSDS
improved image
subtraction through PCA
Pessimal AI problem:
- small training data
- inaccurate labels
- imbalance classes
- diverse morphology
- low SNR
- complex BG
Urban Observatory -
Urban Plumes
Urban Observatory -
Urban Plumes
Urban Observatory -
Urban Plumes
differenced image + AI
team: Bea Steers, Jon Kastelan, Jack Tsai, Greg Dobler
https://cuspcapstones.github.io/Detection-of-polluting-plumes-ejected-from-NYC-buildings_2018
Research Inclusion: sonification of LSST lightcurves
Sid Patel, UD undergrad summer research project
Sonification: Data → Sound
New way of understanding data
- Can be complementary to visualizations
Gives access to people who cannot
interpret data visuallySounds cool! Good for public outreach
Research Inclusion: sonification of LSST lightcurves
Presented at 2021 Rubin Project Community Workshop (300 ppl)
Started a new Rubin Working Group
Rubin Rhapsodies
Riley Clarke, UD