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

Grad student

Since 2019 we study the sky (and more!) mostly with AI

Postdoc

LSST:

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

20Tb of data every night. That is equivalent to

8,000 high definition movies

4,000 hours of tiktok videos

every night for 10 years

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

“The most important feature of any telescope is the imagination with which it is used.”

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

In 2026 we will begin observing the sky with 1000 images every night for 10 years

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

Are We There YET????!!!!

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

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

Until the 2010s we would see 1 in a month

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

2025

edge computing

more data

but also

information-rich data

SKA

(2025)

edge computing

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.... ?)

edge computing

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.... ?)

edge computing

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

2025

edge computing

Other surveys will be even larger!

"BUT BIG DATA DOES NOT MEAN BIG SCIENCE"

Yang Huang,
University of Chinese Academy of Sciences

SpecCLIP talk @UNIVERSAI

IAU workshop Greece June 2025

rapid accutare alert release

Data Products

federica bianco - fbianco@udel.edu

data right holders only

federica bianco - fbianco@udel.edu

federica bianco - fbianco@udel.edu

world public!

10Million alerts per night!

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

INFRASTRUCTURE FOR DM IS CPU BASED

Rubin Observatory Data Management Team

federica bianco - fbianco@udel.edu

template

difference

96% accurate

Tatiana Acero-Cuellar, UNIDEL fellow, LSSTC data science fellow

template

difference

92% accurate

Tatiana Acero-Cuellar, UNIDEL fellow, LSSTC data science fellow

template

difference

Improving the efficiency of transient detections with Neural Networks

template

difference

Saliency maps: what pixels matter?

template

difference

95% accurate

Acero-Cuellar et al. DESC submitted

Tatiana Acero-Cuellar

UNIDEL fellow,

LSST Data Science Fellow

FASTlab Flash highlight

What is the network learning?

What can we learn from the AI?

template

difference

template

Tatiana Acero-Cuellar, UNIDEL fellow, LSSTC data science fellow

Saliency maps: what pixels matter?

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)

Saliency maps: what pixels matter?

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)

DP1

Saliency maps: what pixels matter?

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)

DP1

Text

tests on injection in cosmos fiels

alerts

tests on injection in cosmos fiels

alerts

tests on injection in cosmos fiels

alerts

Saliency maps: what pixels matter?

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)

DP1

Saliency maps: what pixels matter?

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)

DP1

Saliency maps: what pixels matter?

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)

DP1

Saliency maps: what pixels matter?

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)

DP1

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!

survey optimization

Discovery Engine

10M alerts/night

Community Brokers

target observation managers

Pitt-Google

Broker

BABAMUL

federica bianco - fbianco@udel.edu

the new astronomy discovery chain

Operation Simulator (OpSim)

simulates the catalog of LSST observations + observation properties

Metric Analysi Framwork (MAF)

Python API to interact with OpSims specifying science performance on a science case with a metric

Lynne Jones

Peter Yoachim

~100s simulations

~1000s MAFs

Rubin LSST survey design

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

85% submissins led by SC members

Rubin LSST survey design

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

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

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

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

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

80,000

←Dimmer Brighter →

0.01 0.1 1 10 100

Rubin LSST survey design

TDA =>

~800 per field

10 seasons, with each 6 months

2 visits per night (within ~30 min for Solar System Science)

revisit time => 4.5 nights

This will scatter significantly (weather, moon, ...)

The original survey plan didn't lead to good time domain astronomy (TDA) outcomes:

2 intranight obs in same filer +

2 intranight obs in another filter ~5 day later

←Dimmer Brighter →

Text

From Law et al. 2021

Rubin LSST survey design up to 2018

# pairs of observations (1e5)

time gaps (days)

Eric C. Bellm et al 2022 ApJS 258 13

Proposed 3 intranight obs

2 within 1 hour in different filters

1 at 4-8 hours separation w repeat filter

Intranight color (near instantaneous)

Intranight rate of change (~hour time scales)

Presto-Color, Bianco+ 2019

Introducing Rolling Cadence

Current plan: rolling 8 out of the 10 years

newer simulations ->

<-bad good ->

newer simulations ->

4 – 24 hour gaps between epochs will enable kilonova parameter estimation

Kilonovae in LSST Wide Fast Deep

Andreoni+ 2022a

https://arxiv.org/pdf/2411.04793

Rubin ToO program

https://lssttooworkshop.github.io/

Rubin ToO program

https://lssttooworkshop.github.io/

Rubin ToO program

newer simulations ->

<-bad good ->

Real Observatory Performance??

TDA is most sensitive to efficiency and cannot recover by just adding survey time

https://pstn-056.lsst.io/

Proposed reduction to 6 rolling years (3 2-year cycles) to improve intrasurvey uniformity

https://pstn-056.lsst.io/

8y rolling

no rolling

6y rolling

~7% loss in KN characterization

Proposed reduction to 6 rolling years (3 2-year cycles) to improve intrasurvey uniformity

Rubin LSST Science Collaborations

Text

https://arxiv.org/abs/2208.04499

Transietsand variabel stars SC

TVS Science Collaboration

join TVS! no fees no minimum req

Chairs: Igor Andreoni,

Sara Bonito

Shar Daniels

NSF Graduate Student Fellow

University of Delaware

TVS Science Collaboration

Fast Transient Subgroup

Fast Transients MetricsCOordinations and White Paper

Continuous readout

astronomical images for rapid transients

Shar Daniels

NSF Graduate Fellow

CHECK OUT THE POSTER!

Challange

follow up

federica bianco - fbianco@udel.edu

Rubin will see ~1000 SN every night!

Credit: Alex Gagliano IAIFI fellow MIT/CfA

Photometric Classification of transients

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

Photometric Classification of transients

Kepler EB

LSST (simulated) EB

Photometric Classification of transients

Kepler EB

LSST (simulated) EB

LSST Deep Drilling Fields

Kaggle PLAsTiCC challenge

AVOCADO classifier

https://arxiv.org/abs/1907.04690

LSST Wide Fast Deep (main survey)

Photometric Classification of transients

Photometric classification of subtypes may simply not be possible

Spectroscopic classification at scale will not be possible

Rest+Andreoni

Fortino

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

Willow Fox Fortino

UDelaware

When they go high, we go low

Classification power vs spectral resolution for SNe subtypes

Adapting Transformer architecture (Vaswani et al. 2017)

Kaggle PLAsTiCC challenge

AVOCADO classifier

https://arxiv.org/abs/1907.04690

Classification from sparse data: Lightcurves

Viswani+ 2017 Attention is all you need

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

data embedding

classification head

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

Challange

data encoding

visualizatoin and concept credit: Alex Razim

Kaicheng Zhang et al 2016 ApJ 820 67

SN 2011fe

deSoto+2024

Boone 2017

7% of LSST data

Boone 2017

7% of LSST data

The rest

Kaggle PLAsTiCC challenge

AVOCADO classifier

https://arxiv.org/abs/1907.04690

Lochner et al 2018

https://arxiv.org/pdf/1812.00515.pdf

Kaggle PLAsTiCC challenge

AVOCADO classifier

https://arxiv.org/abs/1907.04690

Lochner et al 2018

https://arxiv.org/pdf/1812.00515.pdf

Text

Dr. Somayeh HKhakpash

LSST Catalyst Fellow

Lehigh University Visiting Prof.

Text

https://arxiv.org/pdf/2405.01672

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

Siddharth Chaini

NASA FINESST Fellow

anomaly detection

Challenge

Most classifiers for variable stars use Random Forest (not distance based)

In distance based classification, optimal distances can be found for the class of interest: flexible, customizable, efficient

https://arxiv.org/pdf/2403.12120.pdf

Astronomy and computing

Check out the poster

Siddharth Chaini

NASA FINESST Fellow

Siddarth Chiaini, UDelaware

FASTlab Flash highlight

Siddarth Chiaini, UDelaware

FASTlab Flash highlight

Siddarth Chiaini, UDelaware

FASTlab Flash highlight

Siddarth Chiaini, UDelaware

FASTlab Flash highlight

Siddarth Chiaini, UDelaware

FASTlab Flash highlight

Text

This distance based methods can find true out of set anomalies, not just unusal presentatios of the usual physicis!

And its "explainable"!

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

Challenge

LEO satellites

The LSST

Science Collaborations

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

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

credit: AMNH Dark Universe

https://docs.google.com/presentation/d/1s784GMcz_dJtofI7UxRpLbkGMt3YXFT1VshOBZn9a7s/edit?usp=sharing

ISN'T FREE

ethics of AI

Challange + Opportunity

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!

the butterfly effect

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?)

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?)

Challange

echological AI

←Dimmer Brighter →

0.01 0.1 1 10 100

stellar sexplosions

stellar eruptions

stellar variability

trained extensively on large amounts of data to solve generic problems

Foundational AI models

We use the ILSVRC-2012 ImageNet dataset with 1k classes
and 1.3M images, its superset ImageNet-21k with
21k classes and 14M images and JFT with 18k classes and
303M high-resolution images.

Typically, we pre-train ViT on large datasets, and fine-tune to (smaller) downstream tasks. For
this, we remove the pre-trained prediction head and attach a zero-initialized D × K feedforward
layer, where K is the number of downstream classe

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!!