LSST:

The Vera C. Rubin Observatory Legacy Survey of Space and Time

A new, transformational observatory is about to start building a legacy for humanity vith a movie of the night sky

just as human-made satellites are about to forever change it

A new, transformational observatory is about to start building a legacy for humanity vith a movie of the night sky

Cosa c'è in un nome?

Cosa c'è in un nome?

Il primo osservatorio terrestre in USA ad essere dedicato a una astronoma:

Dott.ssa Veral C. Rubin

Cosa c'è in un nome?

Il primo osservatorio terrestre in USA ad essere dedicato a una astronoma:

Dott.ssa Veral C. Rubin

8,000 film ad alta definizione

4,000 ore su tiktok 

ogni notte per 10 anni

8,000 film ad alta definizione

4,000 ore su tiktok 

ogni notte per 10 anni

Investigare la materia oscura e l'energia oscura

image credit ESO-Gaia

Una mappa della Via Lattica e del Volume Locale fino ad Andromeda

17Miliardi di stelle : colore, positione, velocita, e variabilità

Un inventorio del Sistema Solare senza precedenti

dai asteroidi in rotta di collisione con la terra fino alla Oort Cloud

Exploring the Transients and Variable Universe

10M alerts every night shared with the world

60 seconds after observation

Per farlo bisogna:

Obbiettivo: transformare l'astronomia in quattro aree chiave

Per farlo bisogna avere:

Dark skies - Cerro Pachon Chile

Obbiettivo: transformare l'astronomia in quattro aree chiave

Obbiettivo: transformare l'astronomia in quattro aree chiave

Per farlo bisogna avere:

Dark skies - Cerro Pachon Chile

Telescopio - 8m

May 2022 - Telescope Mount Assembly

F/D = 1.23 

F/D = 1.23 

The M1M3 mirror is actively supported by 156 pneumatic figure control actuators that resist loads (gravitational, wind, dynamic, etc.) and provide the active optics figure control.

https://www.spiedigitallibrary.org/conference-proceedings-of-spie/9150/1/Overview-of-the-LSST-active-optics-system/10.1117/12.2056553.full

3.2 Gigapixels:

La fotocamera CCD piu large del mondo - nel Guinnes dei primati!

Obbiettivo: transformare l'astronomia in quattro aree chiave

Per farlo bisogna avere:

Dark skies - Cerro Pachon Chile

Telescopio - 8m

Fotocamera - 3.2Gpixels

1996-1998 Tony Tyson, Roger Angel

2008

in compagnia di

Astronaut Reid Wiseman (Arthemis),

Zhoran Mandami,

Papa Leo XIV,

Olympionica Alysa Liu,

Benicio Del Toro.......

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
 

 678 immagini raccolte in sette ore.  

Abbiamo stimato 10-15 oggetti interstellari nei 10 anni di LSST...

ma ne abbiamo già visto uno!

e 230 autori - 9 aprile 2026

11,000+ nuovi asteroid scoperti da Rubin

The Vera C. Rubin Observatory Data Preview 1

https://arxiv.org/pdf/2603.23786

30 Giugno 2025

DP1 publication

54 pagine, 121 autori

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

8 nuove supernovae e 3 già note

0.2'' / pixel, 6 filtri (ugrizy),

r~24 (1 immagine) r~27 (10 anni di immagini)

Data Preview 1 è la preview del film galattico del secolo...

 with this much data we need Artificial Intelligence

 with this much data we need Artificial Intelligence

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)

Alerce

Ampel

The immutable skies

10 stars explode in the universe every second

Until the 1900s we would see 1 in a century

Until the 1980s we would see 1 in a decade

With the Vera C. Rubin Observatory we will see 1000 every night !

  0.01         0.1           1           10          100     

stellar sexplosions

stellar eruptions

stellar variability

  0.01         0.1           1           10          100     

Why do we study stellar explosions?

we are made of stars

The nitrogen in our DNA, the calcium in our teeth, the iron in our blood, the carbon in our apple pies were made in the interiors of collapsing stars.

We are made of starstuff.

― Carl Sagan, Cosmos

Farthest: 10.5 billion years ago

3 billion years after the Big Bang

redshift 4

Why do we study stellar explosions?

they are the best tool to "measure" the Universe

largest explosion on earth 10,000,000 erg

typical supernova....

Why do we study stellar explosions?

a unique opportunity to study extreme energy events

who'sploded?

7% of LSST data

7% of LSST data

The rest

visualizatoin and concept credit: Alex Razim

Siddharth Chaini​ 

NASA FINESST Fellow

Siddharth Chaini​ 

NASA FINESST Fellow

Can we study unusual SNe with Rubin data alone?

Data Driven Templates for rare classes of supernovae arising from massive stars: can we tell them apart from sparse LSST data lightcurves?

Creating templates from over 1000 photometry of "Stripped Envelope" Supernovae

Dr. Somayeh Khakpash

2025

Big data?? Ma Certo!

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

Scopriremo fisica nuova?

Come possiamo rappresentare e visualizzare i nostri dati per isolare phenomeni nuovi?

Evenly sampled Kepler time series

Sparse, unevenly sampled Kepler time series

2D T-SNE projection of feature space

Boyajian’s star

Why do we study the night sky?

Due cose riempiono l’animo di ammirazione e venerazione sempre nuova e crescente, quanto piú spesso e piú a lungo la riflessione si occupa di esse: il cielo stellato sopra di me, e la legge morale in me. [...]La prima comincia dal posto che io occupo nel mondo sensibile esterno, ed estende la connessione in cui mi trovo a una grandezza interminabile, con mondi e mondi, e sistemi di sistemi; e poi ancora ai tempi illimitati del loro movimento periodico, del loro principio e della loro durata

Trainare in modello AI come ResNet-50 su una generica GPU (e.g., NVIDIA V100) su una collezione di dati come ImageNet (1.2M immagini) ha un'emissione di ~100-150 kg CO₂ equivalente a un breve volo passeggeri. 

Ma quanti ne treiniamo prima della versione finale?

Creiamo communita scientifiche fondate sui principi di empatia, compassione, e giustizia

Le LSST Science Collaborations sono fondate su questi principi

https://lsstdiscoveryalliance.org/lsst-science-collaborations/

Il futuro siamo noi

Mentre le utopie vivono nei sogni e le distopie vivono negli incubi, le us-topie sono quello che creiamo noi, da svegli

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

7 bands

sparse data

Award #2308016

Award #2123264

Rubin Rhapsodies:

a project to give access to LSST data through sound

7 bands

sparse data

Award #2308016

Award #2123264

Rubin Rhapsodies:

a project to give access to LSST data through sound

7 bands

sparse data

Award #2308016

Award #2123264

Rubin Rhapsodies:

a project to give access to LSST data through sound

Multi-city Urban Observatory Network

Studying cities as complex systems through imaging data

Multi-city Urban Observatory Network

Dobler, Bianco, et al 2021, in Remote Sensing

Studying cities as complex systems through imaging data

• energy demand and consumption
• ecology of flora and fauna
• urban metabolism
• circadiem rhythms

Multi-city Urban Observatory Network

Testing the performance of MetaAI SAM on astronomical objects

Instead of building our own specialized AI, can we adapt the models that the industry produces?

That would save a lot of computational resources and computational resources have an environmental footprint!

Award #2123264

Rodiat Ayinde and Tatiana Acero Cuellar are applying the computer vision models they developed for astronomy to geography

Tatiana Acero Cuellar

What's even harder to study than stellar explosions?

Shar Daniels is a NSF Graduate Research Fellow.

They use telescopes and cameras in innovative ways to show the stars in their time evolution at milliseconds rate

and uses cutting edge AI (transformers) to discover new physical phenomena

NSF Graduate Research

Fellowship Program

time: 1 pixel = 3.0 milliseconds

What's even harder to study than stellar explosions?

Any phenomenon that changes rapidly, in less than hours, is a technological challenge in astronomy

What's even harder to study than stellar explosions?

Stellar flares are short lived (~minutes) brightening events caused by magnetic reconnections in stars' atmospheres. Stellar flare impact planetary habitability. Fast and unpredictable, they are hard to study and their physical properties, like temperature, are poorly constrained.

Award #2308016

Light Echoes

Light Echoes

supernova, star eruption, stellar merger

Light Echoes

Light Echoes

supernova, star eruption, stellar merger, stellar variability

Light Echoes

supernova, star eruption, stellar merger, stellar variability

Light Echoes

Light Echoes

η-Carinae light echoes

Rest et al. (w Bianco) 2012Natur.482..375R​

Light Echoes

η-Carinae light echoes

Frew 2004, Smith & Frew 2011

Light Echoes

η-Carinae light echoes

• very faint signal
• very rare

Light echoes are like a time machine: 

but they are so hard to find!

Xiaolong Li LSST Catalyst Fellow.

• change over time
• can have almost any shape

AILE: the first AI-based platform for the detection and study of Light Echoes

Award #2108841

Li et al. 2019

AILE: the first AI-based platform for the detection and study of Light Echoes

Tatiana Acero Cuellar is a UNIDEL fellow:

she is Building simulated light echo images to help train AI models

If light echoes are too rare to build large training set to train AI, can we generate realistic light echo images with simulations?

Award #2108841