https://app.sli.do/event/98bGw3kwqEeca4sGb882RS

when did the first Neural Network in astronomy review came out?

CSP: Constraint Satisfaction Problems

Rubin Observatory

Site: Cerro Pachon, Chile

Funding: US NSF + DOE

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:

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

2) a large field-of-view for sky-scanning speed - 10 deg2

3) high spatial resolution, high quality images - 0.2''/pixels

4) process images in realtime and offline to produce 10M nightly alerts and catalogs of all 37B objects

>=18000 sq degrees

~800 visits per field

2 visits per night (within ~30 min for asteroids)

+ 5x10sq deg Deep Drilling Fields with ~8000 visits

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

The DOE LSST Camera - 3.2 Gigapixel

3024 science raft amplifier channels

Camera and Cryostat integration completed at SLAC in May 2022,

Shutter and filter auto-changer integrated into camera body

LSSTCam undergoing final stages of testing at SLAC

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

April 17
 

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

Current plan: rolling 8 out of the 10 years

Discovery Engine

10M alerts/night

Community Brokers

target observation managers

Pitt-Google

Broker

BABAMUL

the new astronomy discovery chain

AI for survey design

To this day, transient astronomy heavily relies on spectra

Rubin will see ~1000 SN every night!

Credit: Alex Gagliano  IAIFI fellow MIT/CfA

well... it depends

2025

(2026)

Is the data gonna also be better?

visualizatoin and concept credit: Alex Razim

visualizatoin and concept credit: Alex Razim

SN 2011fe

7% of LSST data

7% of LSST data

The rest

Photometric Classification of transients

Photometric Classification of transients

Kepler satellite EB

LSST (simulated) EB

KIC 3858884: A hybrid δ Scuti pulsator in a highly eccentric eclipsing binary Maceroni+2014

lightcurves make really bad tensors

is transient data AI ready?

lightcurves make really bad tensors

• Variable sizes of data vectors

is transient data AI ready?

lightcurves make really bad tensors

• Variable sizes of data vectors

is transient data AI ready?

• Variable sizes of data vectors
• Uneven sampling

• Variable sizes of data vectors
• Uneven sampling

lightcurves make really bad tensors

• Variable sizes of data vectors

is transient data AI ready?

• Variable sizes of data vectors
• Uneven sampling

• Variable sizes of data vectors
• Uneven sampling

• Variable sizes of data vectors
• Uneven sampling
• Different sampling at different wavelengths

lightcurves make really bad tensors

is transient data AI ready?

• Variable sizes of data vectors
• Uneven sampling
• Different sampling at different wavelengths
• Phase gaps can be months long over ~1 year 

lightcurves make really bad tensors

is transient data AI ready?

• Variable sizes of data vectors
• Uneven sampling
• Different sampling at different wavelengths
• Phase gaps can be months long over ~1 year 
• Multiple relevant time scales

lightcurves make really bad tensors

is transient data AI ready?

• Variable sizes of data vectors
• Uneven sampling
• Different sampling at different wavelengths
• Phase gaps can be months long over ~1 year 
• Multiple relevant time scales
• Aleatory and Epistemic Heteroscedastic uncertainties

• Variable sizes of data vectors
• Uneven sampling
• Different sampling at different wavelengths
• Phase gaps can be months long over ~1 year 
• Multiple relevant time scales

Willow Fox Fortino

UDelaware

Optimal deep learning architectures for transients' spectral classification

As seen in Muthukrishna+2019

Kaggle PLAsTiCC challenge

AVOCADO classifier

https://arxiv.org/abs/1907.04690

The PLAsTiCC challenge winnre, Kyle Boone was a grad student at Berkeley, and did not sue a Neural Network!

Hlozek et al, 2020

DATA CURATION IS THE BOTTLE NECK

models contributed by the community were in

- different format (spectra, lightcurves, theoretical, data-driven)

- the people that contributed the models were included in 1 paper at best

- incompleteness

- systematics

- imbalance

khakpash+ 2024 showed that the models were biased for SN Ibc

AVOCADO, SCONE, all these models are trained on a biased dataset and are being currently used for classification

Ibc data-driven templates vs PLAsTiCC

Dr. Somayeh Khakpash

LSSTC Catalyst Fellow, Rutgers

on the job market!

Kaggle PLAsTiCC challenge

AVOCADO classifier

https://arxiv.org/abs/1907.04690

Text

Classification from sparse data: Lightcurves

Classification from sparse data: Lightcurves

Kaggle PLAsTiCC challenge

AVOCADO classifier

https://arxiv.org/abs/1907.04690

Text

Classification from sparse data: Lightcurves

without redshift

with redshift

Methodological issues with these approaches

CNNs are not designed to ingest uncertainties. Passing them as an image layer "works" but it is not clear why since the convolution on the flux and error space are averaged after the first layer

Kaggle PLAsTiCC challenge

AVOCADO classifier

https://arxiv.org/abs/1907.04690

Classification from sparse data: Lightcurves

without redshift

with redshift

Kaggle PLAsTiCC challenge

AVOCADO classifier

https://arxiv.org/abs/1907.04690

Classification from sparse data: Lightcurves

without redshift

with redshift

Photo-z

Text

Addressing sparsity

Boone19

Qu22

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  

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  

Hajdinjak+2021

When they go high, we go low... spectra classification at low resolution

Astrophysical spectra require the capture of enough photons at each wavelength: 

large telescopes

long exposure times

bright objects

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

When they go high, we go low

Classification power vs spectral resolution for SNe subtypes

FASTlab Flash highlight

Kaggle PLAsTiCC challenge

AVOCADO classifier

https://arxiv.org/abs/1907.04690

Classification from sparse data: Lightcurves

AI was transformed in 2017 by this paper

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

When they go high, we go low

Classification power vs spectral resolution for SNe subtypes

AI

ISN'T FREE

https://www.epa.gov/energy/greenhouse-gas-equivalencies-calculator#results

https://www.epa.gov/energy/greenhouse-gas-equivalencies-calculator#results

Willow Fox Fortino

UDelaware

As seen in Muthukrishna+2019

FASTlab Flash highlight

Text

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

we badly need better benchmark datasets

why not images too? 

lightcurve latent space rep

image

latent space rep

SN 2018cow

SN 2018cow

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?)

Challenges in Space-Based Observations

• Limited Field of View: Space telescopes often have smaller fields of view compared to ground-based surveys.
   

• Data Latency: Delays in data transmission and processing can affect rapid follow-up.
   

• Resource Allocation: Competition for telescope time can limit observations of certain transients.... LETS NOT TRIGGER 3 ToOs ON THE SAME TRANSIENT!!

GRB 080319B, the brightest optical burst ever observed

SWIFT

rapid response

SWIFT

HST, Chandra, SPITZER

...

Kepler, K2, TESS

high precision dense time series