Rubin LSST:

It's About (space and) Time

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

Deputy Project Scientist for Construction

LSST Survey Scientist

federica b. bianco

she/her

8,000 high definition movies

4,000 hours of tiktok videos

every night for 10 years

what's in a name?

what's in a name?

The first ground-based national US observatory named after a woman, Dr. Vera C. Rubin 

what's in a name?

The first ground-based national US observatory named after a woman, Dr. Vera C. Rubin 

Building an unprecedented catalog of Solar System Objects

LSST Science Drivers

Mapping the Milky Way and Local Volume

Probing Dark Energy and Dark Matter

Exploring the Transient Optical Sky

To accomplish this, we need:

Objective: to provide a science-ready dataset to transform the 4 key science area

To accomplish this, we need:

1) Dark skies - Cerro Pachon Chile

Objective: to provide a science-ready dataset to transform the 4 key science area

To accomplish this, we need:

1) Dark skies - Cerro Pachon Chile

2) a large telescope mirror to be sensitive - 8m (6.7m)

Objective: to provide a science-ready dataset to transform the 4 key science area

May 2022 - Telescope Mount Assembly

3.2 Gigapixels:

We built the largest (declassified) camera ever built 

to look farther and wider into the sky than ever before

0.2'' / pixel, 6 filgers (ugrizy)

1996-1998 Tony Tyson, Roger Angel

How it started

with Zhoran Mandami,

Astronaut Reid Wiseman,

Activist Zabib Musa Loro, 

Pope Leo XIV,

Olympic medalist Alysa Liu,

Benicio Del Toro.......

2008

2017

Eye to the sky…on-sky engineering tests have begun at @nsfgov–@energy Rubin Observatory using the world’s largest digital camera!🔭

April 17 2025
 

Eye to the sky…on-sky engineering tests have begun at

 Rubin Observatory using the world’s largest digital camera!

June 23 2025
 

First Look party here at UD with 213 people signed up!

 678 separate images taken in just over seven hours of observing time.  Trifid nebula (top right) and the Lagoon nebula, which are several thousand light-years away from Earth. | NSF-DOE Vera C. Rubin Observatory

The Vera C. Rubin Observatory Data Preview 1

https://arxiv.org/pdf/2603.23786

June 30, 2025

DP1 release!

https://arxiv.org/pdf/2507.22864v2

>25% DIA detections
 

https://arxiv.org/pdf/2507.22864v2

>25% DIA detections
 

1 AGN

https://arxiv.org/pdf/2507.22864v2

>25% DIA detections
 

1 AGN

Galactic variables

https://arxiv.org/pdf/2507.22864v2

>25% DIA detections
 

new AGNs/TDEs

1 AGN

Galactic variables

2025

Will we get more data???

HELL YEAH!

2025

Will we get more data???

SKA

(2025)

Rubin LSST Transients by the numbers

17B stars (x10) Ivezic+19​

~10 million QSO (x10) Mary Loli+21

~50k Tidal Disruption Events (from ~150) Brickman+ 2020

~10k SuperLuminous Supernovae (from ~200)Villar+ 2018

~400 strongly lensed SN Ia (from 10) Ardense+24

~50 kilonovae (from 2) Setzer+19, Andreoni+19   (+ ToO)

> 10 Interstellar Objects fom 2....    ?)

Rubin LSST Transients by the numbers

17B stars (x10) Ivezic+19​

~10 million QSO (x10) Mary Loli+21

~50k Tidal Disruption Events (from ~150) Brickman+ 2020

~10k SuperLuminous Supernovae (from ~200)

~400 strongly lensed SN Ia (from 10) Ardense+24

~50 kilonovae (from 2) Setzer+19, Andreoni+19   (+ ToO)

> 10 Interstellar Objects fom 2....    ?)

Rubin LSST Transients by the numbers

17B stars (x10) Ivezic+19​

~10 million QSO (x10) Mary Loli+21

~50k Tidal Disruption Events (from ~150) Brickman+ 2020

~10k SuperLuminous Supernovae (from ~200)

~400 strongly lensed SN Ia (from 10) Ardense+24

~50 kilonovae (from 2) Setzer+19, Andreoni+19   (+ ToO)

> 10 Interstellar Objects fom 2....    ?)

Rubin LSST Transients by the numbers

17B stars (x10) Ivezic+19​

~10 million QSO (x10) Mary Loli+21

~50k Tidal Disruption Events (from ~150) Brickman+ 2020

~10k SuperLuminous Supernovae (from ~200) Villar+ 2018

~400 strongly lensed SN Ia (from 10) Ardense+24

~50 kilonovae (from 2) Setzer+19, Andreoni+19   (+ ToO)

> 10 Interstellar Objects fom 2....    ?)

Rubin LSST Transients by the numbers

17B stars (x10) Ivezic+19​

~10 million QSO (x10) Mary Loli+21

~50k Tidal Disruption Events (from ~150) Brickman+ 2020

~10k SuperLuminous Supernovae (from ~200) Villar+ 2018

~400 strongly lensed SN Ia (from 10) Ardense+24

~50 kilonovae (from 2) Setzer+19, Andreoni+19   (+ ToO)

> 10 Interstellar Objects fom 2....    ?)

Rubin LSST Transients by the numbers

17B stars (x10) Ivezic+19​

~10 million QSO (x10) Mary Loli+21

~50k Tidal Disruption Events (from ~150) Brickman+ 2020

~10k SuperLuminous Supernovae (from ~200) Villar+ 2018

~400 strongly lensed SN Ia (from 10) Ardense+24

~50 kilonovae (from 2) Setzer+19, Andreoni+19   (+ ToO)

> 10 Interstellar Objects fom 2....    ?)

SKA

(2025)

17B stars (x10) Ivezic+19​

~10 million QSO (x10) Mary Loli+21

~50k Tidal Disruption Events (from ~150) Brickman+ 2020

~10k SuperLuminous Supernovae (from ~200) Villar+ 2018

~400 strongly lensed SN Ia (from 10) Ardense+24

~50 kilonovae (from 2) Setzer+19, Andreoni+19   (+ ToO)

> 10 Interstellar Objects fom 2....    ?)

True Novelties!

Rubin LSST Transients by the numbers

"BUT BIG DATA DOES NOT MEAN BIG SCIENCE"

Yang Huang,
University of Chinese Academy of Sciences

SpecCLIP talk @UNIVERSAI

IAU workshop Greece June 2025

https://rubinobservatory.org/for-scientists/data-products/alerts-and-brokers

in 60 seconds:

Difference Image Analysis

template

in 60 seconds:

Difference Image Analysis

template

difference image

in 60 seconds:

Difference Image Analysis

template

difference image

Rubin Observatory Data Management Team 

Saliency maps: what pixels matter? 

search

template

difference

Acero-Cuellar et al. DESC submitted

Tatiana Acero-Cuellar 

UNIDEL fellow

LSST Data Science Fellow

The Rubin LSST ML-Reliability Score (aka real-bogus)

accuracy 98.06%, purity 97.87%, completeness of 98.27%... on simulated data

- requires instantaneous inference

- limited computational resources (CPU)

- evolving data quality

- limited ground truth data (e.g. no variable stars in training)

CHECK OUT THE POSTER!

POI/Variables

Ampel

Alerce

Ampel

ALERTS HAVE STARTED!

Why not always? We deliver alerts on DDF+Virgo when we run the survey in survey mode (=not engineering tests)

Why not everywhere? Limited templates available - template foorprint increasing

~7M alerts per night

*Conservative ML-reliability scores for now because the infrastructure (Rubin and brokers) is still under development

classifications by ALeRce and Lasair

ALERTS HAVE STARTED!

Current plan: rolling 8 out of the 10 years

https://www.lsst.org/content/charge-survey-cadence-optimization-committee-scoc

Rubin has involved the community to an unprecedented level in survey design this is a uniquely "democratic" process!

Survey Cadence Optimization Committee

Rubin LSST survey design

  0.01         0.1           1           10          100     

Rubin LSST survey design

2017

80,000

Rubin LSST survey design

Rubin has involved the community to an unprecedented level in survey design this is a uniquely "democratic" process!

2019

80,000

Rubin LSST survey design

Rubin has involved the community to an unprecedented level in survey design this is a uniquely "democratic" process!

2023

80,000

Rubin LSST survey design

Rubin has involved the community to an unprecedented level in survey design this is a uniquely "democratic" process!

2024

80,000

Rubin LSST survey design

Rubin has involved the community to an unprecedented level in survey design this is a uniquely "democratic" process!

2024

2024

80,000

Rubin ToO program

Rubin ToO program

Continuous readout

astronomical images for rapid transients

Shar Daniels

NSF Graduate Fellow

CHECK OUT THE POSTER!

visualizatoin and concept credit: Alex Razim

SN 2011fe

7% of LSST data

7% of LSST data

The rest

Kaggle PLAsTiCC challenge

AVOCADO classifier

https://arxiv.org/abs/1907.04690

Kaggle PLAsTiCC challenge

AVOCADO classifier

https://arxiv.org/abs/1907.04690

Text

Dr. Somayeh HKhakpash

LSST Catalyst Fellow

Lehigh University Visiting Prof.

Text

we introduce

Gaussian process Optimized Photometric Regression of Extragalactic Archival Ultraviolet-infrared eXplosions, a.k.a GOPREAUX—

a Python package for Gaussian Process Regression of multi-wavelength transient photometry. [...]

This allows for predictions of light curves [...] at higher redshifts, where the rest-frame UV emission is redshifted into the observer-frame optical or infrared.

Gaussian processes work by imposing a kernel that represents the covariance in the data (how data depend on time or time/wavelength). Imposing the same kernel for different time-domain phenomena is principally incorrect

=> bias toward known classes

Methodological issues with these approaches

Neural processes replace the imposed kernel with a learned one  

Siddharth Chaini​ 

NASA FINESST Fellow

Siddharth Chaini​ 

NASA FINESST Fellow

Rubin will see ~1000 SN every night!

Credit: Alex Gagliano  IAIFI fellow MIT/CfA

Willow Fox Fortino

UDelaware

When they go high, we go low

Classification power vs spectral resolution for SNe subtypes

FASTlab Flash highlight

Willow Fox Fortino

UDelaware

When they go high, we go low

Classification power vs spectral resolution for SNe subtypes

FASTlab Flash highlight

Willow Fox Fortino

UDelaware

Text

A new AI-based classifier for SN spectra at low resolution

Ally Baldelli

UDelaware

Text

Upgrading ABC-SN

• - building robustness to redshift
• - building robustness to galaxy contamination
• - add non-SN transients

CHECK OUT THE POSTER!

This ensamble distance method excells at identifying out of sample anomalies!

DiMMAD: Distance Multi-Metric Anomaly Detection

NSF award

2219731

NASA FINESST Fellow

Siddharth Chaini 

Check out the poster

https://arxiv.org/pdf/2510.23702

Text

Are we prepared to discover new physics?

Text

Are we prepared to discover new physics?

A community of practice funded on principles of Equity, Inclusivity, Cooperation

Rubin LSST Science Collaborations

Text

https://arxiv.org/abs/2208.04499 

Transietsand variabel stars SC

is a word I am borrowing from Margaret Atwood to describe the fact that the future is us. 

However loathsome or loving we are, so will we be. 

Whereas utopias are the stuff of dream dystopias are the stuff of nightmares, ustopias are what we create together when we are wide awake

https://www.youtube.com/watch?v=QO3nY_u6hos

US-TOPIA

thank you!

University of Delaware

Department of Physics and Astronomy

Biden School of Public Policy and Administration

Data  Science Institute

federica bianco

fbianco@udel.edu

https://slides.com/federicabianco/uvacolloquium2026

AI

ISN'T FREE

Knowledge is power

• Astrophysical data is a sandbox. It has no social value, no privacy risk. We can safely learn about how bias builds into algorithm and how to correct it

Knowledge is power

• Astrophysical data is a sandbox. It has no social value, no privacy risk. We can safely learn about how bias builds into algorithm and how to correct it
• Ethics of AI is a critical element of the education of a technologist

With great power comes grteat responsibility

"Sharing is caring"

• Astrophysical data is a sandbox. It has no social value, no privacy risk. We can safely learn about how bias builds into algorithm and how to correct it
• Ethics of AI is a critical element of the education of a technologist
• AI is a transferable skill - use if for good!
   

We use astrophyiscs as a neutral and safe sandbox to learn how to develop and apply powerful tool. 

Deploying these tools in the real worlds can do harm.

Ethics of AI is essential training that all data scientists shoudl receive.

https://www.theverge.com/21298762/face-depixelizer-ai-machine-learning-tool-pulse-stylegan-obama-bias

Why does this AI model whitens Obama face?

Simple answer: the data is biased. The algorithm is fed more images of white people

But really, would the opposite have been acceptable? The bias is in society

models are neutral, the bias is in the data (or is it?)

https://www.theverge.com/21298762/face-depixelizer-ai-machine-learning-tool-pulse-stylegan-obama-bias

Why does this AI model whitens Obama face?

Simple answer: the data is biased. The algorithm is fed more images of white people

But really, would the opposite have been acceptable? The bias is in society

models are neutral, the bias is in the data (or is it?)

models are neutral, the bias is in the data (or is it?)

https://www.theverge.com/21298762/face-depixelizer-ai-machine-learning-tool-pulse-stylegan-obama-bias

Why does this AI model whitens Obama face?

Simple answer: the data is biased. The algorithm is fed more images of white people

Joy Boulamwini

models are neutral, the bias is in the data (or is it?)