# R2–D2: Roman and Rubin – from Data to Discovery

Suvi Gezari (chair, STScI/JHU)  Misty Bentz (Georgia State)  Kishalay De (MIT)
K. Decker French (UIUC)  Aaron Meisner (NOIRLab)  Michelle Ntampaka
(STScI/JHU)  Robert Jedicke (Hawai‘i)  Ekta Patel (UC Berkeley)  Daniel Perley
(Liverpool)  Robyn Sanderson (UPenn)  Christian Aganze (UC San Diego)  Igor
Andreoni (Maryland/JSI)  Eric F. Bell (Michigan)  Edo Berger (Harvard)  Ian
Dell’Antonio (Brown)  Ryan Foley (UC Santa Cruz)  Henry Hsieh (Planetary
Science Institute)  Mansi Kasliwal (Caltech)  Joel Kastner (Rochester
Institute of Technology)  Charles D. Kilpatrick (Northwestern/CIERA)  J. Davy
Kirkpatrick (IPAC)  Casey Lam (UC Berkeley)  Karen Meech (Hawai‘i)  Dante
Minniti (Universidad Andrés Bello)  Ethan O. Nadler (Carnegie/USC)  Daisuke
Nagai (Yale)  Justin Pierel (STScI)  Irene Shivaei (Arizona)  Rachel Street
(LCOGT)  Erik J. Tollerud (STScI)  Benjamin Williams(University of Washington)

##  1 Executive Summary

The NASA Nancy Grace Roman Space Telescope (Roman) and the Vera C. Rubin
Observatory Legacy Survey of Space and Time (Rubin), will transform our view
of the wide-field sky, with similar sensitivities, but complementary in
wavelength, spatial resolution, and time domain coverage. Here we present
findings from the AURA Roman$+$Rubin Synergy Working
group111https://outerspace.stsci.edu/display/RRS/AURA+Roman+Rubin+Synergies+Working+Group,
charged by the STScI and NOIRLab Directors to identify frontier science
questions in General Astrophysics, beyond the well-covered areas of Dark
Energy and Cosmology, that can be uniquely addressed with Roman and Rubin
synergies in observing strategy, data products and archiving, joint analysis,
and community engagement. This analysis was conducted with input from the
community in the form of brief (1-2 paragraph) “science pitches” (see
Appendix), and testimony from “outside experts” (included as co-authors).

Rubin will provide wide, deep, multi-band seeing-limited optical imaging with
a regular cadence of observations. Roman will provide wide, deep, multi-band
diffraction-limited near-infrared imaging with grism and prism low-resolution
spectroscopic capabilities. The core science goals, and technical requirements
to achieve these goals, have been defined separately for Roman
222https://www.stsci.edu/roman/documentation/technical-documentation and Rubin
333https://www.lsst.org/scientists/keynumbers. However, substantial effort has
been made towards examining how these surveys could be combined to improve our
measurements of Dark Energy and Dark Matter, e.g., improvements in photometric
redshifts, weak-lensing shear measurements, the galaxy power spectrum, galaxy
cluster selection, time-delays in strong-lensing systems, and type Ia
supernova distances (Jain et al., 2015; Chary et al., 2020). Here we expand
further into areas of General Astrophysics, and identify a rich and broad
landscape of potential discoveries catalyzed by the combination of exceptional
quality and quantity of Roman and Rubin data, and summarize implementation
requirements that would facilitate this bounty of additional science with
coordination of survey fields, joint coverage of the Galactic plane, bulge,
and ecliptic, expansion of General Investigator and Target of Opportunity
observing modes, co-location of Roman and Rubin data, and timely distribution
of data, transient alerts, catalogs, value-added joint analysis products, and
simulations to the broad astronomical community.

##  2 Milky Way and Solar System Science

###  2.1 What is the composition, physical state, and morphology
of the small bodies in the solar system?

The Sun, Moon, comets and five planets have been observed and studied by
humans since the time they became a distinct species, but new populations of
objects in our solar system continue to be discovered. Within the past few
decades our understanding of our solar system’s formation and evolution has
been revolutionized by e.g. the discovery of the trans-Neptunian objects, the
irregular satellites of giant planets and, just within the past few years, the
detection of the first two macroscopic interstellar objects (ISOs) passing
through the inner solar system. These new classes of objects probe the edges
of our understanding of the formation of our and exoplanetary systems, while
the extreme members in color, physical state, and morphology of long-known
populations test the limits of our ability to model the solar system’s
evolution.

One of Rubin’s key science goals is to generate a catalog of the solar system
that will contain an order of magnitude more objects than are currently known.
Within its vast set of discoveries Rubin is likely to identify extreme objects
within and passing through our solar system such as main belt object cratering
or disruption events, small objects with outrageous morphologies, near-Earth
objects on impact trajectories, interstellar objects, and perhaps even a
distant planet in our own solar system. Roman will provide detailed
observations of these faint, extreme objects to explore how they fit into the
puzzle of our own solar system’s formation and the larger picture of all
planetary systems. High resolution Roman images of ‘active’ small objects will
determine whether the events are due to thermal spin up, impacts, or other
processes. High precision Roman photometry will allow the determination of the
shapes of the faintest and most elongated objects in the solar system e.g. to
determine whether 1I/Oumuamua’s unusual shape remains uncommon when compared
to similar-size objects in our solar system.

Figure 1: Spectrum showing evidence for solid water ice deposits on the
surface of comet 9P/Tempel 1, including a representation of Roman’s prism,
grism, and filter pass bands. Roman’s imaging filters, and spectroscopic
coverage and resolution, are well suited to small body targets and identifying
water ice absorption features at about $1.6\mu$m and $2.0\mu$m. Figure from
Sunshine et al. 2006, modified to include Roman passbands.

Roman and Rubin together will provide spectrophotometric characterization of
interesting targets from the near-UV through the NIR while Roman’s prism and
grism will e.g. enable identifcation of H2O absorption features in asteroids
and comets. If an object is detected on an Earth-impact trajectory, Roman’s
faint limiting magnitude and space-based vantage point will provide the
necessary astrometry to refine its orbit and impact probability. Finally, the
LSST provides the best opportunity in the foreseeable future of detecting a
distant planetary-size object in our own solar system (Sheppard & Trujillo,
2016; Batygin & Brown, 2016). Roman’s unique capabilities will be important to
revealing its properties and establishing its dynamical and evolutionary
trajectory to its current state and location.

Rubin’s all-sky surveying, repeat cadence, and deep limiting magnitude are an
ideal combination for detecting the extreme members of the population of small
objects within and passing through our solar system. For instance, Rubin will
detect asteroid collisions in the main belt (e.g. Bottke et al., 2005);
interstellar objects passing through our solar system (e.g. Meech et al.,
2017); objects with extremely slow and fast rotation rates (e.g. Pravec &
Harris, 2000); those with remarkable colors (e.g. Sheppard, 2010); distant
objects on cometary orbits that have not yet begun to sublimate volatiles
(e.g. Jewitt, 2005) and, perhaps, unknown planets bound to our Sun (e.g.
Mustill et al., 2016).

Holler et al. 2018 and Holler et al. 2021 provide an exhaustive discussion of
the small body science potential with Roman and synergistic opportunities with
Rubin. The two systems will operate in different but overlapping wavelength
regimes that will enable calibrated spectrophotometry from the near-UV to the
near-IR to determine the composition of the most compelling small bodies
identified in our solar system. Roman’s NIR capability makes it a powerful
tool for characterizing active and disrupted objects because it will be
sensitive to larger ejected dust particles that persist longer in a comet’s
and asteroid’s environs than smaller dust particles.

Some of the objects detected by Rubin will be too faint for followup
characterization by ground-based telescopes and their transient nature will
require rapid acquisition and characterization with Roman’s imaging filters,
grism and prism. For instance, the first ISO, 1I/‘Oumuamua, was discovered on
2017 Oct 19 and the last ground-based observations occurred on Nov 23, but
observations with Hubble doubled the time range of observations and were
critical to identifying its non-gravitational acceleration (Micheli et al.,
2018).

One of the more compelling solar system science problems is measuring the
distribution of water using small bodies as tracers. Some asteroids show
evidence of hydration in an absorption feature at about $0.9\mu$m (e.g. Rivkin
et al., 2002). Roman’s low-resolution prism instrument is well-suited to
identifying and characterizing this broad band while the prism, grism and some
of the filters will be important for identifying water-ice absorption features
near $1.6\mu$m and $2.0\mu$m (Figure 1).

See Table of Implementation Requirements: LABEL:non-sidereal, LABEL:ecliptic,
LABEL:roman_mo, LABEL:mpc, LABEL:cross-match-moving, LABEL:precovery.

###  2.2 What are the demographics and formation histories of stars and
substellar objects in the Milky Way?

One of Roman Space Telescope’s core scientific goals is to improve our
understanding of exoplanet demographics through a dedicated microlensing
campaign (Roman Galactic Bulge Time Domain Survey). Roman and Rubin have the
potential to teach us much more about the prevalence and formation of
planetary and substellar mass objects, beyond the baseline Roman program. How
does accretion work during the star/planet formation processes? How does the
low-mass initial mass function (IMF) vary as a function of environmental
parameters such as metallicity? How prevalent are free-floating substellar
objects compared to those orbiting higher-mass stars? These questions can be
addressed through suitable combination of Roman and Rubin
observations/capabilities in the following areas: (1) detection and
characterization of substellar objects via photometry and microlensing, and
(2) exploiting eruptive YSOs to understand the temporal evolution of accretion
during star and planet formation.

At substellar masses, for example free-floating brown dwarfs, our knowledge of
the initial mass function is limited by the small volume over which existing
surveys can detect/discover these cold objects (Kirkpatrick et al., 2021). The
combination of Roman and Rubin data at red-optical and near-infrared
wavelengths will dramatically expand the volume over which we can detect L and
T type brown dwarfs (e.g., Stauffer et al., 2019; Aganze et al., 2021). This
will allow us for the first time to begin probing the low-mass initial mass
function in the Galactic thick disk and halo, and thereby check for any
metallicity dependence of the low-mass star formation process (e.g., Bromm,
2013; Chabrier et al., 2014) [Meisner, Sainio pitches]. As shown in Figure 2,
Roman monitoring of the Roman Galactic Bulge Time Domain Survey footprint will
suffer from temporal gaps in coverage. By filling in these gaps, Rubin can
help constrain models of Roman Galactic Bulge Time Domain Survey microlensing
events, pushing to relatively high masses. Simultaneous Rubin monitoring
during Roman Galactic Bulge Time Domain Survey observations can also break
exoplanet microlensing degeneracies. [Street pitch]

In terms of further understanding star and planet formation, Rubin (through
its wide area, sensitivity and repeat coverage) will transform the study of
young stellar object (YSO) outbursts (e.g., Audard et al., 2014; Elbakyan et
al., 2021) by detecting them in greater numbers and to larger distances (in
more varied environments) than ever before. Such outbursts may account for a
significant fraction of accreted mass during star formation. Pre-existing
Roman imaging (to characterize the quiescent state) and follow-up (both
imaging and spectroscopy) will be needed to understand the physical properties
of erupting YSOs (Figure 3) identified from Rubin. Although YSO outburst decay
time scales can be up to years or even decades, obtaining prompt follow-up
shortly after the eruption is highly valuable for constraining models of these
events. [Ginsburg, Kastner pitches]

See Table of Implementation Requirements: LABEL:too_roman,
LABEL:too_roman_spec, LABEL:rges_rubin, LABEL:plane_roman, LABEL:cross-match,
LABEL:precovery.

Figure 2: Candidate $8.7M_{\odot}$ BH microlensing event OGLE3-ULENS-PAR-02
from the OGLE survey. The black dots show the observable cadence with a
ground-based survey like Rubin to derive a microlensing parallax model (red
line), while the red dashed boxes show expected Roman observing windows to
observe the astrometric microlensing shift. Figure from Wyrzykowski et al.
(2016). Figure 3: High resolution Hubble Space Telescope (Hubble) imaging from
the Hubble Legacy Archive of the nearby YSO PV Cep in outburst, which is one
of the handful of objects with such imaging. The image reveals evidence of a
conical nebula carved out by an outflow. Roman high resolution imaging follow-
up of ${\color[rgb]{0,1,1}{\it Rubin}}$-discovered YSO outbursts will
disambiguate the sources of the eruptions and establish the presence and
morphologies of cavities carved by outflows from these YSOs.

###  2.3 How do compact objects form and evolve in the Milky Way?

As end products of the most massive stars through mass loss and powerful
supernova (SN) explosions, the demographics of neutron stars (NSs) and black
holes (BHs) are central to our understanding of stellar evolution. While NSs
have a hard surface, the gravitational influence of BHs is the only way to
find and characterize them. Historically, BHs have been discovered primarily
via X-ray emission from surrounding hot material accreted from a binary
companion (e.g. Corral-Santana et al. 2016). We are now entering an era where
multiplexed spectrographs like DESI and SDSS-V, and astrometric missions like
Gaia can search for the long-sought population of non-accreting BHs in wide
binaries via radial velocity (e.g. Gu et al. 2019; Wiktorowicz et al. 2020)
and astrometric (e.g. Mashian & Loeb 2017; Yamaguchi et al. 2018) shifts,
respectively. Yet, not only is our understanding of the accreting NS/BH
population demographics severely incomplete (e.g. Gandhi et al. 2020; Jonker
et al. 2021), we have just beginning to detect some of the estimated $\sim
10^{7}-10^{8}$ isolated Galactic BHs (Fender et al., 2013; Olejak et al.,
2020), or the 25,000 BHs expected to have clustered in the central parsec of
the Galactic Center (Miralda-Escudé & Gould, 2000).

###  2.4 Roman and Rubin synergies into the dark universe: Isolated compact
objects

Microlensing is the only way to characterize free-floating Galactic BHs
(Paczynski, 1986). With $\sim 10^{4}$ microlensing events expected during its
Bulge survey (Penny et al., 2019), Roman could find $\sim 100$ BHs and $\sim
300$ NSs (Gould, 2000a; Lam et al., 2020) that would offer a completely new
window into the remnant mass function. As intrinsically massive objects,
NSs/BHs are expected to be characterized by long-lived events (Wiktorowicz et
al., 2019) spanning much longer than the planned $\approx 72$ day observing
seasons. A Rubin survey of the Roman bulge footprint to fill-in the gaps
between the observing seasons (Figure 2) would be crucial for photometric
coverage to constrain the microlensing parallax (Gould, 2000b), in addition to
enhancing the overall rate of planets, substellar and stellar systems
discovered via microlensing [Street pitch]. Also, unlike ground based surveys,
Roman would be uniquely placed to detect the astrometric microlensing signal
(WFIRST Astrometry Working Group et al., 2019a) and offer exquisite
constraints on the lens mass to systematically search for these NS/BH lenses
[Lu, Lam, Wyrzykowski pitches], as well as determine the masses for free-
floating planet candidates. This has now been demonstrated with Hubble follow-
up observations of candidate BH microlensing events from the ground-based OGLE
survey, with the confirmation of astrometric microlensing by a 7.1 M⊙ BH (Sahu
et al., 2022) as well as a catalog of several dark compact objects (Lam et
al., 2022).

See Table of Implementation Requirements: LABEL:rges_rubin,
LABEL:rges_rubin_pre.

Figure 4: The extinction “horizon” in the H, F184 and K bands in the Galactic
plane. Roman and Rubin can together map stars and dust throughout essentially
the entire Milky Way plane, providing an unprecedented three-dimensional view
of our Galaxy (figure from Helou et al. 2019; visual by Robert Hurt).

###  2.5 Roman and Rubin synergies into the dark universe: Accreting compact
objects

With its southern location, the Rubin observatory will be capable of creating
an exquisite time domain movie of outbursting sources in the Galactic plane —
dwarf novae, novae and X-ray binaries (Strader et al., 2018). Roman’s planned
high cadence time domain survey of the Galactic bulge will provide an
excellent complement to Rubin’s capabilities by providing unprecedented
historical photometry of the progenitors, allowing us to map out the poorly
understood roles of these eruptive episodes on their long term evolution
(Johnson et al., 2019; Maccarone et al., 2019). Roman’s Galactic bulge survey
will also detect the most dust obscured events and completely new types of
transient phenomena, where Rubin limits will be very constraining. A wide area
Roman map of the Rubin Galactic plane footprint could routinely enable the
characterization (e.g. distance, evolutionary state) of the progenitor
systems, which remain poorly studied due to their very faint quiescent
counterparts.

See Table of Implementation Requirements: LABEL:plane_roman, LABEL:cross-
match.

###  2.6 What is the distribution of stars and dust in our Galaxy?

What is the distribution of stars and dust throughout the Milky Way, and what
does this tell us about our Galaxy’s formation and evolution? Does the Milky
Way have well-defined spiral arms, and what would it look like to an external
observer? SDSS enabled detailed Milky Way tomography of (mostly) extragalactic
fields (e.g., Jurić et al., 2008), revealing for the first time large-scale
substructures in the Galactic distribution of stars, for instance the
Sagittarius and Monoceros streams (Newberg et al., 2002). Pan-STARRS, covering
3/4 of the sky and most of the Galactic plane, took this one step further,
allowing for novel three-dimensional maps of stars and dust (e.g., Green et
al., 2015). Gaia has now complemented Pan-STARRS with high-accuracy
trigonometric distances over the entire sky, another major step toward making
the ultimate three-dimensional map of stars and dust in our Milky Way Galaxy
(e.g., Green et al., 2019; Helmi et al., 2018; Antoja et al., 2018).

However, Gaia and Pan-STARRS are fundamentally limited in terms of their
depths and wavelength coverage, which does not extend beyond $\sim$1 $\mu$m.
This lack of depth and infrared sensitivity limits the extent to which the
Galactic plane’s distant and/or heavily dust-obscured reaches can be mapped.

The combination of very deep Rubin optical and Roman near-infrared photometry
throughout the Galactic plane can make an unprecedented map of the 3D
distribution of stars and dust in dense regions of the Milky Way. Roman’s
angular resolution is highly complementary to Rubin in terms of the improved
deblending of Rubin sources that it can offer. Roman near-infrared photometry
of the plane can push the 3D map to distances/reddenings at which Rubin has
lost sensitivity (see Figure 4). [Zucker pitch]

Focusing more specifically on the Milky Way bulge/nucleus, the combination of
Roman and Rubin can complete the RR Lyrae census in this central region (e.g.,
Minniti et al., 2016), thereby unraveling mysteries about how the nuclear star
cluster was assembled: did it form through merging of primordial globular
clusters? How old is the Galactic nuclear star cluster? Multi-epoch
Roman/Rubin data can also reveal clusters, streams and kinematics of old
stellar populations near the Galactic center. For all of these Milky Way
mapping applications, Roman’s combination of large field of view plus Hubble-
like angular resolution and depth uniquely enables this science. [Minniti,
Fernández-Trincado pitches]

See Table of Implementation Requirements: LABEL:bulge_rubin,
LABEL:plane_roman, LABEL:cross-match, LABEL:forced.

##  3 Transient Discovery and Transient Hosts

###  3.1 Using gravitationally lensed SNe as a time machine and an independent
measure of $H_{0}$.

One of the most intriguing developments to arise in the era of “precision
cosmology” is the tension between the value of Hubble’s constant of expansion
“$H_{0}$” measured from direct local measurements, and the model-dependent
value inferred from the Cosmological Microwave Background. Understanding if
this tension is real, and not due to systematic errors or model assumptions,
is critical for providing evidence for new physics beyond the standard model.
Gravitationally lensed SNe provide a direct and completely independent measure
of $H_{0}$ from these two methods.

Figure 5: Hubble images of the lensed SN AT 2016jka ($z$=1.95) in the galaxy
cluster lens system MACS J0138.0-2155 ($z$=0.338). Follow-up space-based
imaging from Roman of lensed SN discovered by Rubin is required in order to
resolve the multiple images of the lensed SN, and measure their relative time
delays. Figure from Rodney et al. 2021.

A SN that explodes in a background galaxy that is strongly lensed by a
foreground galaxy or cluster, resulting in multiple images due to
gravitational bending of the SN light, can be used to measure $H_{0}$ by
measuring the relative time delays between each SN image. SNe have several
advantages for so-called “time delay cosmography” compared to other variable
phenomena, such as variable quasars, including the fact that their intrinsic
light curves and luminosities are well determined, especially for the class of
Type Ia SNe. This method has been attempted in the only three known multiply
imaged SNe: Type II SN Refsdal (Kelly et al., 2015), Type Ia SN 2016geu
(Goobar et al., 2017); Type Ia SN AT2016jka (Rodney et al., 2021). While each
of these objects had unique challenges that limited their cosmological
utility, high quality observations for dozens of such systems could, in
principle, yield constraints on $H_{0}$ to better than 1% precision (Birrer &
Treu, 2021). Furthermore, one can use the magnification and time delays of the
lensed SN images as a “time machine”, to study a SN in its earliest explosive
phase (Shu et al., 2018). This will be particularly valuable for SNe in
cluster lens systems where the time delays are on the order of $\sim 100$
days, giving time to predict the date and location of a subsequent SN image to
shed light on the SN progenitor and pre-explosion mass-loss, as well as
probing deviations from a smooth lensing model for the foreground mass
distribution (Goobar et al., 2017).

A large enough sample of lensed SNe, could also be used as a competitive probe
of dark energy. Fortunately, the rates of strongly lensed SNe expected from
Rubin (hundreds: Oguri & Marshall 2010) and Roman (tens: Pierel et al. 2021)
as calculated from mock catalogs, are reaching the levels relevant for such
experiments. Roman and Rubin’s lens samples will also be highly complementary
in redshift, with Roman best suited to detect higher-redshift lensed SNe
($z>0.5$) (Pierel et al., 2021). There are several possible observational
methods for finding and monitoring lensed SNe, from blind time domain searches
by looking for an overluminous SNe (Figure 6), to monitoring of known galaxy-
galaxy or galaxy-cluster strong lens systems (Shu et al., 2018). Roman’s high
angular resolution imaging can be used to identify arcs surrounding the
lensing galaxy, and multi-color imaging is essential to maximize the contrast
of the lens arc and model the magnification. [Holwerda pitch]

Identification of strong lenses at scale can be done with machine learning, a
citizen science approach or from spectroscopic signatures of the blend of
galaxies in a galaxy-galaxy lens system. A vetted list of strong lenses can
then be monitored as a “watch list” in the Rubin transient alert stream for
the signature of a SN going off in the source galaxy. With a pre-calculated
catalog of photo-z’s, when a candidate SN is detected in Rubin with an
overluminous peak absolute magnitude, spectroscopic follow-up can confirm the
SN redshift and trigger follow-up imaging with Roman to confirm the multiple
images.

Figure 6: Figure from Goobar et al. 2017 which demonstrates how a lensed type
Ia SN was identified from being overluminous in its ground-based light curve
measured by the Palomar Transient Factory (PTF). A similar method could be
applied to spectroscopically confirmed SNe (or photometrically identified SNe
with a host-galaxy redshift or photo-z) discovered by Rubin.

See Table of Implementation Requirements: LABEL:rubin_roman_tda,
LABEL:ml_roman, LABEL:watchlist_rubin.

###  3.2 How do galaxies and black holes co-evolve through cosmic time?

The co-evolution of massive black holes and the galaxies that host them is
supported by several observational and computational lines of evidence, yet
the details of the physical mechanisms driving this symbiotic relationship are
still uncertain. A fundamental question in the context of galaxy and black
hole co-evolution is the nature of the black hole seeds (see the recent review
by Volonteri et al. 2021). Observational evidence for $\sim 10^{9}$ solar mass
black holes when the universe is $<1$ Gyr old (Fan et al., 2001) creates
difficulties for models of galaxy evolution in which remnants from the first
stars coalesce to form the central black hole seed. More massive black hole
seeds from, for example, direct collapse of density fluctuations in the early
universe can grow to these large sizes in a shorter time, yet we have no
observational evidence to support such massive seeds nor the existence of the
conditions required for them to form and grow. [Bentz pitch] The wide field,
time domain nature of Rubin will allow for the identification of low- or
intermediate-mass black holes in the local universe via the detection of
stochastic nuclear variability in dwarf galaxies (Baldassare et al., 2018;
Ward et al., 2021), as well as previously dormant black holes in quiescent
galaxies that reveal themselves by tidally disrupting and accreting a star
(Gezari, 2021). The high resolution imaging, and wide area coverage of Roman
will characterize the host galaxies of these accreting central black holes,
including luminous quasars [Lacy pitch]. Thus Rubin and Roman together can map
the properties of black holes and their host galaxies over a large range of
mass scales, providing important constraints on the formation mechanisms of
seeds in the early universe, and their growth through accretion and mergers
over cosmic time (see Figure 7).

Black hole feeding, while easy to identify, still presents numerous challenges
to our understanding of how gas is funneled from large galactic scales down
into the nucleus and ultimately onto the black hole. The smallest relevant
spatial scales cannot be resolved with imaging, and so time domain techniques
such as reverberation mapping (or echo mapping) have been developed (Cackett
et al., 2021). Using time resolution rather than spatial resolution to study
light echoes in AGN allows their internal structures to be mapped. Yet the
number of AGN that have been studied in this way remains small because of the
typical necessity of targeting individual objects and monitoring them over
long time scales. Reverberation mapping studies of a large number of AGN
spanning a more diverse range of black hole and galaxy properties is needed to
improve our understanding of black hole feeding [Homayouni, Lyu, Shen
pitches]. Rubin light curves may provide an important component to facilitate
such studies. If a time domain component is also provided by Roman then the
mapping of AGN structures on spatially-unresolvable scales becomes possible
through the combination of Roman and Rubin photometry (dust torus
reverberation) and/or Roman grism spectroscopy plus Rubin photometry (emission
line reverberation).

Finally, mergers provide a separate but important pathway for the growth of
massive black holes. Galaxy mergers are relatively straightforward to identify
and much progress has been made in understanding how the process unfolds, yet
to date there are few observational constraints on the merger process between
their massive black holes (De Rosa et al., 2019). High-resolution images of
galaxies hosting dual AGN with separations ranging from scales of kpc to sub-
arcsec are needed to inform the pairing and evolution of the merger process
for massive black holes. [Shen, Liu pitches] Rubin monitoring will uncover
dual or blended AGN through their variability, while high resolution Roman
imaging will constrain their apparent separations as well as the merger stage
of the host.

Figure 7: Comparison between observations and simulations showing the growth
of massive black holes within their host galaxies. From Volonteri et al.
(2021).

See Table of Implementation Requirements: LABEL:GI_roman, LABEL:GI_roman_spec,
LABEL:south, LABEL:rubin_roman_tda, LABEL:cross-match.

###  3.3 How do the rates and properties of all classes of transients depend
on their host galaxy environments and stellar populations?

Observations of transient events provide brief windows to study stars, compact
objects, and their underlying environments. Combining observations from Roman
and Rubin will enable studies of transient environments on scales ranging from
the integrated host galaxy, to localization within the host galaxy, to
identification of the progenitor star. These studies will deepen our
understanding of known transients and help to interpret the range of new
transients that will be discovered with Roman and Rubin.

By combining photometry from Roman and Rubin, properties of transient host
galaxies can be determined to help interpret known transients and to more
efficiently prepare for detecting new transients [French pitch]. The stellar
masses, star formation rates, recent star formation histories, photometric
redshifts, structural parameters, color gradients, and levels of dust
extinction can be measured using the combined Rubin $ugrizY$ and Roman
$0.5-2\mu$m photometry for transient host galaxies. Spectroscopic
classification will only be possible for a small sub-set of the transients
discovered with Rubin or Roman. The host galaxy properties of a transient can
be used to interpret the likely class of the transient and to identify the
most interesting transients for follow-up observations. Several studies have
found that given the difference in progenitor systems for type Ia SNe and
core-collapse SNe, the differing stellar populations in the host galaxies
allow for transients to be classified based on the host galaxy information
alone (Baldeschi et al., 2020; Gagliano et al., 2021). Tidal disruption events
(TDEs) occur at high rates in post-starburst, green valley, and centrally-
concentrated galaxies (Arcavi et al., 2014; French et al., 2016; Law-Smith et
al., 2017; Graur et al., 2018; Hammerstein et al., 2021; Arcavi et al., 2021).
Similarly, the host galaxies of changing-look AGN (Dodd et al., 2021) have
also been found to lie preferentially in green valley host galaxies, and may
provide clues on what is driving their extreme changes in accretion rate
[Bentz pitch]. Using photometry from Roman and Rubin especially making use of
Roman’s spatial resolution to identify highly concentrated galaxies, likely
host galaxies of interesting nuclear transients can be pre-identified, such
that new transients can be rapidly flagged for multi-wavelength followup.

On more local scales, resolving the location of a transient within a galaxy
can provide important information about the nature of a transient [Gezari
pitch]. Even a single Roman observation of a transient would provide an
improved localization constraint compared to the information available from
Rubin. Localization with Rubin will be limited to $\sim$0.1 arcsec at best,
whereas Roman can localize transients to few$\times 10^{-3}$ arcsec. If a
transient can be constrained to be consistent with a galaxy nucleus on
milliarcsecond scales, contamination from SNe can be all but removed when
searching for TDEs or other SMBH transients. Similarly, the localization of a
transient to a spiral arm or star forming region within a galaxy indicates the
transient is likely to be associated with the death of a massive star. Good
localization constraints can enable more detailed comparisons of SNe with the
metallicities and stellar populations of their parent environment (e.g.
Galbany et al., 2018). Type Ic SNe are found in more metal-rich environments
and super-luminous SNe (SLSNe) are found in more metal-poor environments
(Galbany et al., 2018; Izzo et al., 2018). These trends are obscured by the
varying conditions across a galaxy, especially when comparing the central
bulge – which may dominate the light in a fiber spectrum – and the conditions
in the outer disk.

On the smallest scales, progenitor stars of SNe imaged years or decades before
the explosion can provide direct links from massive star progenitors to the
types of SNe we observe (Podsiadlowski et al., 1993; Smartt, 2009). We show a
recent example of the likely progenitor for SN 2019yvr in the host galaxy NGC
4666 from Kilpatrick et al. (2021) in Figure 8. While we cannot predict the
locations of all future SNe, many SNe are discovered in nearby galaxy clusters
such as Virgo and Fornax every year, even in the era before Rubin and Roman. A
targeted Roman survey of these nearby clusters would provide deep pre-
explosion imaging of SN to be discovered in the years after the targeted
survey [Kilpatrick pitch]. The IR imaging capabilities of Roman are well-
suited to investigate the progenitors of SNe in dusty star forming regions or
cases of failed SN where the progenitors are obscured in the optical by
circumstellar dust.

Figure 8: Example of the massive progenitor star of the Type Ib SN 2019yvr,
discovered in pre-explosion Hubble imaging. Figure from Kilpatrick et al.
(2021).

See Table of Implementation Requirements: LABEL:south, LABEL:roman_wide,
LABEL:rubin_roman_tda, LABEL:roman_cluster, LABEL:cross-match, LABEL:sed,
LABEL:cross_galaxy.

###  3.4 What are the electromagnetic properties of gravitational wave events,
their relation to binary properties, and implications for r-process
nucleosynthesis?

To address this question we require rapid, deep, and dedicated optical/NIR
follow-up of gravitational wave events and short-duration gamma-ray bursts
(SGRBs). In the case of GW-detected mergers, the sky localizations are
expected to be at best $\sim 10$ deg2 and the distances will be tens to
hundreds of Mpc. The resulting faintness of the optical/NIR emission may
therefore necessitate the use of Rubin and/or Roman, and would benefit from
mutual coordinated follow up to assess the presence of both optical and NIR
emission. For example, in the case of the only existing GW-EM detected merger
to date (GW170817) there appear to be two emission components: one peaking in
the optical on a timescale of $\sim{\rm day}$ and the other peaking in the NIR
on a timescale of $\sim{\rm week}$. Whether this is ubiquitous to NS-NS
mergers (or present in NS-BH mergers) is a key question that will help to
address the origin of these two components.

In the case of SGRBs, the mergers are viewed along the axis of the
relativistic jet and the emission is therefore significantly brighter at early
time. Such events are predominantly located at higher redshifts than GW-
detected events, up to $z\sim 1$, and they therefore provide a complementary
sample of NS-NS/NS-BH mergers to what the GW detectors can find. This sample
can enable studies of redshift evolution in the properties of the mergers, as
well as a different angular view (face-on orientation in SGRBs compared to all
viewing angles in GW-detected mergers). However, the majority of SGRBs are
localized poorly (detections by Fermi), with similar localizations to GW
events. Roman$+$Rubin rapid coordinated follow-up of these events will
increase the SGRB detection rate by at least a factor of three, and will more
than double the NS-NS merger rate when combined with GW-detected events.

Another promising avenue for the detection of NS-NS and NS-BH merger
counterparts is the ”serendipitous” discovery of kilonovae in wide-field
survey data, i.e. independently of external GW or SGRB triggers. These sources
will likely be found at large distances beyond the horizon of the ground-based
GW detectors. They will allow us to study the emission at all viewing angles,
correlations with redshift, and possibly enable yet another type of
cosmological studies (Coughlin et al., 2020). Recent works have shown that a
few tens of kilonovae are expected to be discovered by the Rubin main survey
(Setzer et al., 2019; Cowperthwaite et al., 2019; Andreoni et al., 2022).
Remarkably, Roman will have the capability to detect kilonovae out to redshift
z 1 (Chase et al., 2021).

The key scientific questions that will uniquely be addressed by Roman$+$Rubin
synergy are: (i) How prevalent is $r$-process nucleosynthesis in NS-NS/NS-BH
mergers? What are the typical ejecta masses and velocities? How do these
quantities depend on the properties of the merging objects (NS vs. BH, masses,
mass ratios)? (ii) What is the angular structure of the ejected matter, and
what can this teach us about the merger process? (iii) Do all mergers produce
relativistic jets? (iv) Is there significant evolution in the nature of the
binaries and the properties of the ejected matter as a function of redshift
(at $z\sim 0-1$)?

The key requirement for implementation is rapid target-of-opportunity (TOO)
follow up with both Roman and Rubin covering the larger localization regions
to a sufficient depth to detect (or rule out) an optical/NIR counterpart
(kilonova or afterglow). In practice, this means observations to as faint as
$\sim 24$ AB mag (and potentially fainter for more distant sources) over an
area of up to tens of deg2. We generally expect the optical emission to peak
first, so rapid response with Rubin may need to lead the rapid response with
Roman. However, since we have only one example of an optical/NIR counterpart
of a GW-detected merger, we do not yet know the relation between optical and
NIR emission for the population. Note that in Andreoni et al. (2021) they show
that with proper planning, TOO observations with Rubin would only interrupt
$\sim 1\%$ of the baseline survey. The capabilities of TOO observations with
Roman have also been investigated. However, theoretically we expect the IR to
be more ubiquitous for kilonovae than the optical - it is sensitive to a much
wider range in viewing angle, mass ratio, lanthanide fraction and remnant
lifetime. In fact, some NS-NS and NS-BH mergers may not even be discoverable
by Rubin and only be accessible to Roman, making TOO NIR imaging and
spectroscopy follow-up with Roman even more critical (see Kasliwal et al.
whitepaper in Appendix of Spergel et al. 2013).

See Table of Implementation Requirements: LABEL:too_roman, LABEL:too_rubin,
LABEL:comm_mma.

###  3.5 What end-states of stars exist in nature that have yet to be
discovered?

The mapping between the lives of stars (as we observe them in our Milky Way
Galaxy and nearby galaxies) and the deaths of stars (as we observe them as SNe
or other transients, and as remnants) remains only crudely sketched. The
textbook picture is that a star’s death is controlled primarily by its initial
mass, but this simple picture has slowly been unravelling in recent years.
Observations have been a driving force in driving our understanding of stellar
death, in particular the appreciation that the outcomes of stellar evolution
are much more diverse than what appears in textbooks. The discovery of SNe
that are extremely luminous or extremely feeble, those that are unusually
long-lived or extremely short-lived, those with unusual spectral properties
and so on have all provided new questions about the evolution and end-states
of stars. This revolution remains incomplete due to the limited capabilities
of current facilities, which have limited depth and largely only scan the
optical range.

Roman and Rubin together provide the ability to cover major “blind spots” of
current surveys. Roman’s infrared survey capabilities allow it to survey for
transients that are concealed by dust—whether that dust originates from the
surrounding galaxy environment or from the stellar progenitor. This gives it a
unique ability to probe a range of phenomena not accessible to optical
surveys. In the central regions of LIRGs and ULIRGs, star-formation is at its
most furious and the potential for unusual forms of stellar death due to
enhancement of the high-mass IMF or dynamical interactions are maximized. It
also has the power to survey for self-obscured transients, for which the
optical display is shielded by shells of circumstellar dust synthesized by the
progenitor. Any other end-of-life stellar outcome with a characteristic
temperature that causes it to emit primarily in the infrared range will also
be uniquely detected by Roman—kilonovae (case 3.4) are the classic example,
but there may be other phenomena that peak in this region, as suggested by a
recent Spitzer survey (see Figure 9), including electron-capture SNe from very
low-mass stars, and mergers of massive stars Kasliwal et al. (2017). Finally
very high-redshift transients from early generations of stars will inevitably
be redshifted in the the infrared range.

The synergy with Rubin is critical to this effort, because the phenomena above
would otherwise be lost in a sea of ordinary optical SNe without the benefit
of deep optical limits and without accurate photometric redshift constraints.
Rubin observations close in time with Roman ones will allow genuinely red
transients to be picked out and the Rubin deep reference imaging will permit
rapid estimates of their luminosity and distance, which are key to
appropriately targeting follow-up with other facilities.

See Table of Implementation Requirements: LABEL:south, LABEL:roman_wide,
LABEL:rubin_roman_tda, LABEL:alerts_roman.

Figure 9: Spitzer Infrared Intensive Transients Survey discovery of a new
class of IR transients known as SPRITEs (eSPecially Red Intermediate-
luminosity Transient Events), in comparison to previously known classes of IR-
bright transients. Figure from Kasliwal et al. (2017)

##  4 Galaxies, Large-Scale Structure, and Dark Matter

###  4.1 What is the 3D distribution of small scale structure?

The dozens of ultra-faint dwarf (UFD) galaxies and stellar streams located at
distances up to $\sim$400 kpc from the Milky Way and Andromeda (M31) have
dramatically augmented our understanding of galaxy formation and evolution at
small scales. The continuous stream of precise radial velocity and 6D phase
space information for MW and M31 halo stars are yet another rich dataset with
which we can outline the evolutionary history of the Local Group’s two most
massive galaxies. However, the extent of our knowledge of hierarchical galaxy
formation on the smallest scales is generally rooted in observations of only
these two galaxy halos. Understanding the MW and M31 in a cosmological context
requires a statistically significant sample of similar mass analogs and a
census of their halo substructures at depths and wide-field areas that only
Roman and Rubin can reach.

The Rubin observatory is roughly expected to discover all MW satellites with
$M_{V}<0$ mag, $\mu<32$ mag/arcsec2 in the Southern hemisphere, and it will
nearly double the number of known satellite galaxies around nearby galaxies
($\sim$3-4 Mpc) to put them on roughly equal footing with our current
knowledge of M31’s galaxy luminosity function ($M_{V}\sim-6.5$; E. Bell, Fig.
10; e.g., Drlica-Wagner et al., 2019; Mutlu-Pakdil et al., 2021). It will also
yield the most thorough search for stellar streams in the outer halo ($>$ 50
kpc) of the MW [Belokurov pitch] and around nearby galaxies (Pearson et al.,
2019). Rubin will also enable the detection and characterization of stellar
halos around dwarf and Milky-Way mass galaxies allowing for direct comparison
to numerical simulations of hierarchical assembly [Williams pitch]. Together,
dwarf galaxies, streams and stellar halos provide three unique probes to (i)
reconstruct the dynamical history of the MW and analogous nearby galaxies,
(ii) constrain the properties of dark matter halos ($\sim
10^{6}-10^{12}\,M_{\odot}$), and (iii) to test models of galaxy formation at
small scales (see Bullock & Boylan-Kolchin, 2017).

Figure 10: Observed and predicted galaxy luminosity function for the Milky Way
adapted from Drlica-Wagner et al. (2020). The green vertical line shows
approximate estimates for discovery potential with HSC+Rubin at $\sim$3-4 Mpc.
The orange line indicates predictions for Roman at $\sim$10 Mpc.

Owing to its dramatically improved star-galaxy separation and deep point-
source limits, Roman will be more sensitive still to dwarf galaxies, streams
and stellar halos. Roman will enable follow-up searches of satellite galaxies
around nearby hosts ($\sim$3-4 Mpc), probing nearly 10x deeper ($M_{V}\sim-4$
mag), yielding 10s$-$100s of dwarfs per galaxy system (Bell, 2021). With a
statistical sample of host galaxies and their corresponding satellite
populations at these faint limits, Roman$+$Rubin will allow us to draw the
first statistical connections between the faintest known satellite populations
and their hosts, including (i) correlations between the presence of a massive
satellite and satellite phase space configurations (e.g., Battaglia et al.,
2022; Garavito-Camargo et al., 2021; Patel et al., 2020) and (ii) the
subsequent imprints that satellite galaxies leave throughout their hosts’
stellar halos, including stellar wakes and streams (e.g., Garavito-Camargo et
al., 2019).

Such a large sample of UFDs across a variety of environments will also provide
the most robust constraints on the formation, evolution, and properties of
UFDs (i.e., initial mass functions), the oldest known stellar systems in the
Universe (Simon, 2019). Furthermore, UFDs and any potential evidence for
perturbations and gaps in streams will illuminate the threshold for galaxy
formation in subhalos, star formation in the early Universe, and further our
understanding of reionization (i.e., the near/far connection, Weisz & Boylan-
Kolchin 2019). Roman$+$Rubin will also reveal thousands of strong lens
measurements (Oguri & Marshall, 2010; Collett, 2015; Weiner et al., 2020)
that, along with precise measurements of UFD population statistics, will
sharpen constraints on dark matter properties that influence the abundance and
properties of small subhalos, including its primordial temperature,
interactions, and particle mass (Nadler et al., 2021b, a) [Nadler, Bechtol
pitches].

Fitting a simple Gaussian-process model of turbulence to reconcile the sky
positions of point sources measured by a space-based telescope with their
locations in ground-based surveys can drastically improve astrometry from the
ground. Using Gaia positions and Dark Energy Survey images in the riz bands,
Fortino et al. (2021) reduced astrometric variance due to atmospheric
turbulence by a factor of about 12, resulting in RMS turbulence errors of
$\sim$3-5 mas per exposure. Assuming 20 exposures over a nominal 5-year
baseline this results in an expected proper-motion precision of $\sim$35
uas/yr with current data. This translates to a tangential velocity uncertainty
of $\sim$50 km/s at $\sim$300 kpc from the Galactic center, comparable to
measured orbital speeds of typical MW objects at that distance. Improvement by
a factor $\sim$5 would allow us to determine precise orbits to the virial
radius, while an improvement by a factor $\sim$10 would allow us to measure
internal velocity dispersions of dwarfs (Figure 11).

The biggest improvement in accuracy for this type of joint reduction comes
from an increased density of astrometric sources (Fortino et al., 2021). Roman
will provide much more densely sampled point-source data since a typical Roman
exposure is both 4-6 mag deeper than Gaia and far less affected by extinction.
Roman will be an exquisite astrometric instrument since the core-science bulge
microlensing survey will deliver detailed knowledge of the point-spread
function that is necessary for the success of the core-science weak lensing
survey at high latitudes. In addition, Roman will benefit from using Gaia’s
full extended-mission catalog as its astrometric frame (WFIRST AWG 2018). Thus
we can expect Roman to have far higher source density at similar astrometric
accuracy to Gaia, facilitating a dramatic improvement in turbulence-reduction
modeling for Rubin. Importantly, this strategy requires only catalog-level
astrometric data from Roman to re-reduce the pixel-level data from Rubin.

Figure 11: Proper motion accuracies for different astrometric surveys,
relative to positions and proper motions of various local-group objects.
Figure adapted from WFIRST Astrometry Working Group et al. (2019b).

See Table of Implementation Requirements: LABEL:cross-match, LABEL:sed,
LABEL:astrometry.

###  4.2 Galaxy evolution and large-scale structure through cosmic time.

There is tremendous cosmological information available in the 3D distribution
of galaxies, and together, Roman and Rubin will produce unprecedented,
detailed maps of the sky. Photometric and spectroscopic galaxy surveys
(including galaxy positions, redshifts, fluxes, and shapes) produced by these
two observatories will offer new insight on cosmological models and the
formation of large scale structure (Figure 12).

The astronomical community is interested in a breadth of science cases that
probe large scale structure and galaxy evolution. These include:

Clusters & protoclusters – Rubin with its wider field and bluer imaging,
probes a slightly different population of galaxy clusters than Roman which has
higher resolution and redder imaging. As a result, Roman and Rubin will probe
complementary redshift ranges ($z=0.1-0.2$ and $z=0.5-0.7$, respectively). At
higher redshifts, Rubin’s survey plus Roman’s spectroscopic follow-up can be
useful to identify and characterize galaxy composition of proto-clusters
[Ettori pitch].

Reionization – The combined wavelength coverage of data from Roman and Rubin
in particular Roman’s near-infrared sensitivity and Rubin’s variability
monitoring capabilities, will allow us to select well-controlled samples of
$z>6$ sources. The nature and the global properties of these high-redshift
sources, including galaxies and AGNs, such as the luminosity functions, star-
formation rate densities, or the clustering properties, would be derived with
unprecedented accuracy. The large survey footprint of the combined survey also
benefits from sampling a variety of environment conditions, and allowing us to
explore how different galaxy populations contribute to cosmic reionization.
[Pello pitch]. A combined survey could also extend our ability to identify
quasars out to $z=7-10$, which would allow us to further map cosmic
reionization. [Tee pitch]

Low surface brightness Universe – To study the low surface brightness Universe
statistically, one needs a large area, deep survey. Roman’s high spatial
resolution for object recognition and deblending, when combined with Rubin’s
unprecedented depth for finding faint sources, can provide the observations to
explore this. In this case, having high-quality photometric redshifts will be
crucial because objects will be too faint for spectroscopic follow-up. [Montes
pitch]

Lensing, morphologies, and distances – For many Roman$+$Rubin synergistic
applications, having a robust calibration of Rubin photometric redshifts with
Roman spectroscopy will be imperative [Wallman pitch]. Cosmological parallax
may provide a new probe of cosmological geometry, but such a measurement
requires a new lens sample for parallax measurements. Rubin’s wide survey and
photometric redshifts, plus Roman’s high resolution to model the lenses are
necessary for this work. [Pierce pitch]

See Table of Implementation Requirements: LABEL:south, LABEL:cross-match,
LABEL:errors, LABEL:specz, LABEL:colocate, LABEL:simulation, LABEL:train.

Figure 12: The Roman High Latitude Wide Area Survey will measure spectroscopic
redshifts of tens of millions of galaxies, offering a unique insight into the
3D distribution of large scale structure. [Image credit: Data provided by Z.
Zhai and Y. Wang (Caltech/IPAC); Data visualization by J. DePasquale and D.
Player (STScI). Image from
https://roman.gsfc.nasa.gov/high_latitude_wide_area_survey.html . Rubin survey
strategy is still under consideration, but is expected to include a
complementary and overlapping survey (See https://pstn-051.lsst.io/ for the
March 2021 Rubin Survey Cadence report.)

###  4.3 Data-driven discoveries with Roman and Rubin: Supporting simulations
as parallel data sets

The Roman and Rubin telescopes will generate orders of magnitude more data
than previous missions. Rare astronomical objects will be discoverable in
statistical quantities; common populations will achieve large enough sample
sizes to be sensitive to second-order effects; previously undiscovered
populations will be unveiled for investigation. The majority of these new
discoveries are likely to be data-driven: based on characterizing these huge
data sets using the advanced techniques of modern data science and machine
learning rather than choosing a single model to explain the data.

Examples of discoveries enabled by the data-driven approach include
quantifying the number of dimensions (e.g., distinct physical parameters)
needed to describe a data set, or identifying unusual objects that fall
outside the manifolds that contain most of the data. Such data-driven
discovery requires cross-matched catalogs in addition to the ability to do
cross-catalog queries (that is, searching for targets that meet some criteria
that requires both Roman and Rubin data products, such as optical and near-
infared time domain signatures or image products). With large datasets like
this, the only feasible way will be to co-locate the data sets.

The data-driven approach prescribes a fundamental role for simulations of the
objects being studied. In particular, they are crucial to make quantitative
comparisons with observations by multiple instruments, train machine learning
models, deal with complex uncertainties, and connect observations in detail to
the underlying physical processes that govern them. The intermediate step of
synthetic observation—imprinting the error model of a particular instrument on
the output of a simulation—is the critical yet underappreciated link between
the excitement of discovery and the resolution of scientific questions. To
fully achieve the scientific promise of Roman and Rubin investment in the
development of simulations and synthetic observation pipelines for frontier
science is important, as well as publicizing them among the community,
training its members to use them, and determining what functionalities need to
be added are all key to maximizing the potential of these synergies.

In many of the most exciting potential synergies between these two
observatories, Rubin acts as a discovery engine and Roman as the follow-up
instrument. In this mode, it is crucial to understand the impact of the
selection function on the results of the initial object census and the
subsequent selection of candidates for follow-up [Nadler pitch]. Simulated
catalogs allow the quantification of these effects, extending the impact of
the few yet valuable targeted observations relative to the full population and
lending statistical power to generalizations. Furthermore, detailed
simulations allow for forecasting of follow-up observations to explore
sensitivity to unconstrained physics within the constraints of the initial
census results, reconciling observations by very different instruments to a
common underlying source. Realizing the full potential of synergistic
observations with Roman and Rubin will require community access to a
standardized, maintained suite of flexible instrument models, validated by the
instrument teams, for carrying out synthetic observations.

Enormous and synergistic data sets also pose new challenges for data
exploration, which hinges on responsive tools for visualization. As one pitch
put it,

_How do we most effectively leverage humans’ remarkable abilities to detect
patterns and the unexpected when one instrument may generate petabytes of data
spanning imagery, spectra, diagnostic telemetry, catalogs, MCMC outputs, and
machine learning data products?_ (P.K.G. Williams, “Visualizing Big and Wide
Data in the 21st Century”)

While each mission will develop its own data exploration tools, realizing the
full combined potential of Roman and Rubin will require a coordinated effort
to build an environment where the data can be visualized and explored side-by-
side. The more seamless the transitions between the different data products,
the more power it will have to generate new discoveries.

The vast size of the Roman and Rubin catalogs will allow many measurements to
push to unprecedented sensitivity. This new regime comes with the challenge of
coping with non-Gaussian uncertainties: for example in photometric redshifts,
phase-space modeling, or stellar population modeling. Forward modeling with
simulated data is the preferred tool for meeting these challenges, yet is
currently extremely computationally demanding in almost all cases. New
advances—in computational infrastructure, interpolation algorithms, and
analytical statistics—are needed to speed up forward modeling of Roman$+$Rubin
data for key science questions.

See Table of Implementation Requirements: LABEL:colocate, LABEL:simulation,
LABEL:train, LABEL:forward. See also Chary et al. (2020) for joint pixel-level
analysis infrastructure recommendations.

Acknowledgements: We would like to thank the ex-officio members of our Roman
and Rubin Synergy working group for their guidance and input throughout this
process, including Harry Ferguson (STScI), Željko Ivezić (University of
Washington), Neill Reid (STScI), and Beth Willman (AURA).

##  5 Table of Implementation Requirements

|  Observing Strategy
---|---
1.1 |  Non-sidereal tracking by Roman and Rubin
1.2 |  Target of opportunity follow-up imaging with Roman
1.3 |  Target of opportunity follow-up spectroscopy with Roman
1.4 |  Target of opportunity follow-up imaging with Rubin
1.5 |  Survey observations of the ecliptic by Roman and Rubin
1.6 |  ${\color[rgb]{0,1,1}{\it Rubin}}$ observations of the Roman Galactic Bulge Time Domain Survey to fill in gaps between the observing windows and extend the baseline of the observations, and provide contemporaneous (within 1 day) observations
1.7 |  ${\color[rgb]{0,1,1}{\it Rubin}}$ precursor observations of the Roman Galactic Bulge Time Domain Survey fields to characterize potential microlensing source stars, and provide an astrometric and photometric baseline in order to reliably detect microlensing events
1.8 |  Survey observations of the Galactic Plane and Bulge by Rubin in its Deep Wide Fast Survey
1.9 |  ${\color[rgb]{1,.5,0}{\it Roman}}$ Early Definition Astrophysics survey of 1000 deg2 of the Galactic bulge/plane footprint
1.10 |  Guest Investigator programs for follow-up imaging by Roman
1.11 |  Guest Investigator programs for follow-up spectroscopy by Roman
1.12 |  Guest Investigator programs for follow-up observations by Rubin
1.13 |  Choose Southern Hemisphere fields for the the Roman High Latitude Wide Field Survey and High Latitude Time Domain Survey to overlap with Rubin, including the Rubin Deep Drilling Fields
1.14 |  Complete one sweep of the Roman High Latitude Wide Field Survey within the first year of operations to maximize astrometry potential
1.15 |  Augment the Roman High Latitude Survey to include a slow (1 month cadence) time domain survey
1.16 |  ${\color[rgb]{0,1,1}{\it Rubin}}$ Deep Wide Fast Survey observations of the Roman Time Domain Survey fields
1.17 |  ${\color[rgb]{1,.5,0}{\it Roman}}$ Early Definition Astrophysics survey of nearby galaxy groups and clusters
|  Data Products
2.1 |  Automated identification and specialized image processing for moving objects in the Roman images
2.2 |  Timely reporting of moving object detections by Roman and Rubin to the Minor Planet Center
2.3 |  Cross-matched catalogs, joint source detection, cross-survey forced photometry, and joint astrometric solutions
2.4 |  Cross-matched catalogs, joint source detection, cross-survey forced photometry for moving objects
2.5 |  Timely reporting of transient alerts by Roman to the community
2.6 |  Machine-learning classification of morphology on Roman data to identify structures such as strongly lenses systems
2.7 |  The ability to create a watchlist of interesting targets to be notified when Rubin detects a spatially coincident transient alert
2.8 |  Detailed understanding of instrumental effects, selection functions, and sources of systematic errors for both Roman and Rubin
|  Joint Analysis Methods
3.1 |  Precovery of newly identified Rubin targets in earlier serendipitous Roman observations
3.2 |  Joint value-added catalogs of static targets (deblending, photo-z’s, morphology, colors, SED fitting)
3.3 |  Forced photometry, including ability to forced photometer subsets of the data like individual epochs e.g., for moving (solar system) objects
3.4 |  Forced spectroscopic extraction in Roman using Rubin imaging as input catalog including moving objects
3.5 |  Astrometric cross-calibration of Rubin pixel-level data with turbulence-reduction modeling using Roman catalogs to enable precision proper motion measurements
3.6 |  Catalogs of key galaxy parameters constructed from joint ${\color[rgb]{1,.5,0}{\it Roman}}$+Rubin catalogs should be made early, such that alert brokers can incorporate this information when processing new transients
3.7 |  ${\color[rgb]{1,.5,0}{\it Roman}}$ spectroscopic calibration of Rubin photo-z’s
|  Computing Infrastructure
4.1 |  Coordination and communication of Roman and Rubin with multi-messenger facilities, with a clear set of trigger criteria for TOO observations, and a clear (automated) implementation plan
4.2 |  Co-location of Roman and Rubin data in a common analysis environment to bring compute to the data
4.3 |  Support for simulated data: execution and documentation of a simulation database, up to date (versioned) instrument models for synthetic observations, parallel analysis of real and simulated data, catalog-level error modeling
4.4 |  Train the community in recent computational advances in big data (beyond querying databases, e.g., cloud computing)
4.5 |  New advances—in computational infrastructure, interpolation algorithms, and analytical statistics—are needed to speed up forward modeling of ${\color[rgb]{1,.5,0}{\it Roman}}$+Rubin data

##  6 Appendix: Community Science Pitches

First Author |  Title of Community Science Pitch
---|---
MILKY WAY AND SOLAR SYSTEM SCIENCE
Abrams, Natasha |  Using microlensing distributions to perform Galactic population analysis.
Busa, Innocenza |  Uncovering new sources of Galactic cosmic rays from coordinated optical and near-infrared monitoring of diffuse clouds in the Galactic Bulge.
Chakrabarti, Sukanya |  Direct accelerations measurements of eclipsing binaries to probe dark matter in the Milky Way.
Couperus, Andrew |  ${\color[rgb]{0,1,1}{\it Rubin}}$ observations of the cycles of host stars of ${\color[rgb]{1,.5,0}{\it Roman}}$ detected exoplanets.
De, Kishalay |  Accreting compact objects in the dynamic Galactic plane. What are the demographics of accreting compact objects in the Milky Way?
Eggl, Siegfried |  Joint analysis of Solar System objects from ${\color[rgb]{1,.5,0}{\it Roman}}$ and ${\color[rgb]{0,1,1}{\it Rubin}}$ observations of the ecliptic
Fernández-Trincado, José G. |  Dusting-off the Ancient Relics from the heart of the Milky Way. What is the nature of the Milky Way’s globular clusters?
Ginsburg, Adam |  ${\color[rgb]{1,.5,0}{\it Roman}}$ and ${\color[rgb]{0,1,1}{\it Rubin}}$ observations of young stellar object (YSO) outbursts, and the identification of obscured YSO populations.
Jedicke, Rob |  Characterizing Extreme Members in ${\color[rgb]{0,1,1}{\it Rubin^{\prime}s}}$ Solar System Catalogue. What is the composition, physical state, and morphology of the most unusual small bodies discovered by the LSST?
Kastner, Joel |  How do pre-main sequence stars obtain their final masses? Rapid response Roman observations of ${\color[rgb]{0,1,1}{\it Rubin}}$-detected eruptions of Young Stellar Objects.
Lam, Casey |  Uncovering the hidden isolated black hole population: How many isolated black holes are there in the Milky Way?
Lu, Jessica |  Finding primordial black holes with microlensing.
Meisner, Aaron |  A new window into the Milky Way’s structure and history. What is the nature of star formation at very low masses?
Minniti, Dante |  Old Populations in the Innermost Milky Way: A ${\color[rgb]{1,.5,0}{\it Roman}}$ and ${\color[rgb]{0,1,1}{\it Rubin}}$ search for RR Lyrae in the Galactic Center.
Sainio, Arttu |  ${\color[rgb]{1,.5,0}{\it Roman}}$ observations of the coldest brown dwarfs and rogue exoplanets.
Soares-Furtado, Melinda |  Searching for moons around rogue planets. What percentage of these planetary-mass objects harbor transiting companions, and what are the architectures of these bounded systems?
Street, Rachel |  Characterizing and enhancing the known population of cold exoplanets. What are the masses and dynamics of free-floating planets?
Wyrzykowski, Lukasz |  Astrometric microlensing signal from black holes.
Zucker, Catherine |  Mapping the Milky Way with a ${\color[rgb]{1,.5,0}{\it Roman}}$+${\color[rgb]{0,1,1}{\it Rubin}}$ Galactic Plane survey.
TRANSIENT DISCOVERY AND TRANSIENT HOSTS
Bentz, Misty |  The morphologies and environments of galaxies hosting changing-look AGN. What is the physical mechanism behind rapid and large changes in the accretion rates of active galactic nuclei?
Berger, Edo |  Optical-infrared follow-up of gravitational wave events. What are the electromagnetic properties of gravitational wave events, their relation to binary properties, and implications for r-process nucleosynthesis?
Chase, Eve |  ${\color[rgb]{1,.5,0}{\it Roman}}$ and ${\color[rgb]{0,1,1}{\it Rubin}}$ detection of kilonovae from follow-up of short-duration gamma-ray bursts or a gravitational-wave detections.
Coughlin, Michael |  Identifying and Characterizing the Sources of $r$-process material in ${\color[rgb]{0,1,1}{\it Rubin}}$’s alert stream. What fraction of heavy elements do neutron star mergers produce?
Fox, Ori |  Probing the first stars, the evolution of the early Initial Mass Function (IMF), and the sources of reionization with transient astronomy in the early, high-redshift ($z>6$) universe.
French, Decker |  a) Measuring the Black Hole - Bulge relation across cosmic time. How do galaxies and supermassive black holes co-evolve? b) Connecting transients to their host galaxies. How can we best identify large numbers of transient events?
Gezari, Suvi |  Pinpointing the environments and hosts of ${\color[rgb]{0,1,1}{\it Rubin}}$-discovered transients with ${\color[rgb]{1,.5,0}{\it Roman}}$ imaging. What are the progenitors and/or central engines of cosmic explosions?
Holwerda, Benne |  Using lensed supernova observations as an independent measure of $H_{0}$.
Holz, Daniel |  EM follow-up of GW sources.
Homayouni, Yasaman |  Census of black hole mass and accretion disks with ${\color[rgb]{1,.5,0}{\it Roman}}$-${\color[rgb]{0,1,1}{\it Rubin}}$.
Kilpatrick, Charlie |  Early ${\color[rgb]{1,.5,0}{\it Roman}}$ survey of nearby galaxy groups and clusters in the ${\color[rgb]{0,1,1}{\it Rubin}}$ LSST footprint, especially Fornax and Virgo. Systematically addressing massive star evolution and death.
Lacy, Mark |  Quasar/AGN host galaxy studies in the era of the Nancy Grace ${\color[rgb]{1,.5,0}{\it Roman}}$ Space Telescope and Vera C. ${\color[rgb]{0,1,1}{\it Rubin}}$ Observatory.
Liu, Xin |  Sub-arcsec quasar pairs and lenses from combining ${\color[rgb]{0,1,1}{\it Rubin}}$ and ${\color[rgb]{1,.5,0}{\it Roman}}$.
Lyu, Jianwei |  Infrared reverberation mapping of active galactic nuclei.
Matsuura, Mikako |  How common is dust formation in supernovae?
Mattila, Seppo |  Systematic study of dust emission in ${\color[rgb]{0,1,1}{\it Rubin}}$-discovered transients with ${\color[rgb]{1,.5,0}{\it Roman}}$ imaging.
Perley, Dan |  Unusual and obscured transients at high redshift. What populations of transients remain unseen?
Shen, Yue |  a. The discovery of a large statistical sample of $\sim$kpc-scale dual quasars from ${\color[rgb]{1,.5,0}{\it Roman}}$+${\color[rgb]{0,1,1}{\it Rubin}}$ to improve our understanding of the pairing and evolution of binary SMBHs in galaxy mergers. b. Measuring the time lags from the dusty torus in AGN.
Villar, V. Ashley |  ${\color[rgb]{1,.5,0}{\it Roman}}$+${\color[rgb]{0,1,1}{\it Rubin}}$ observations of a ${\color[rgb]{0,1,1}{\it Rubin}}$ Deep Drilling Field to finally unveil the late-time evolution of stripped-envelope core-collapse supernovae.
Vinko, Jozsef |  Separating low- and high-redshift supernovae in a cosmological survey with ${\color[rgb]{1,.5,0}{\it Roman}}$. How does the cosmic star formation rate behave at very high ($z>6$) redshifts?
Windhorst, Rogier |  Observing the first stars directly with ${\color[rgb]{0,1,1}{\it Rubin}}$ and ${\color[rgb]{1,.5,0}{\it Roman}}$ through cluster caustic transits.
Zhang, Jielai |  Kilonovae: A new window into fundamental physics, element creation, transient astronomy, and cosmology. What is the equation of state of dense dense matter, and are kilonovae responsible for producing more than half the elements heavier than iron?
GALAXIES, LARGE SCALE STRUCTURE, AND DARK MATTER
Bechtol, Keitha |  ${\color[rgb]{1,.5,0}{\it Roman}}$ and ${\color[rgb]{0,1,1}{\it Rubin}}$ coordination to enhance astrophysical probes of dark matter.
Bell, Eric |  Reshaping our understanding of small-scale cosmology and the hierarchical growth of galaxies in the Local Universe.
Belokurov, Vasily |  Detecting and characterising Galactic halo sub-structure with ${\color[rgb]{0,1,1}{\it Rubin}}$ and ${\color[rgb]{1,.5,0}{\it Roman}}$
Birrer, Simon |  Strong lensing tomography with joint ${\color[rgb]{0,1,1}{\it Rubin}}$+${\color[rgb]{1,.5,0}{\it Roman}}$ deblending of strongly lensed arcs. What is the geometry of the Universe?
Blain, Andrew |  The role of large-scale structure in controlling the morphology of the zoo of galaxies.
Dell’Antonio, Ian |  Studying the structure and populations of galaxy clusters with ${\color[rgb]{1,.5,0}{\it Roman}}$ and ${\color[rgb]{0,1,1}{\it Rubin}}$.
Ettori, Stefano |  Proto-clusters and first collapsed structure at z¿2. Identifying and characterizing the galaxy composition of the first structures that form on Mpc scales (with masses ¿1 $\times 10^{13}M_{\odot}$) using ${\color[rgb]{0,1,1}{\it Rubin}}$ imaging and ${\color[rgb]{1,.5,0}{\it Roman}}$ dedicated spectroscopic follow-up.
Laine, Seppo |  Power of multiwavelength SEDs in deciphering stellar stream progenitors. What are the stellar population metallicities, masses, and star formation histories of stellar stream progenitors?
Masters, Daniel |  A complete picture of galaxy evolution driven by the unparalleled statistical power of ${\color[rgb]{1,.5,0}{\it Roman}}$ and ${\color[rgb]{0,1,1}{\it Rubin}}$ photometric data.
Montes, Mireia |  Exploring the low-surface brightness universe statistically with ${\color[rgb]{1,.5,0}{\it Roman}}$ and ${\color[rgb]{0,1,1}{\it Rubin}}$.
Nadler, Ethan |  How does dark matter cluster on the smallest observationally accessible scales, and what does this teach us about dark matter particle properties?
Ntampaka, Michelle |  Enabling modern data analysis methods. ${\color[rgb]{1,.5,0}{\it Roman}}$ and ${\color[rgb]{0,1,1}{\it Rubin}}$ will produce exquisitely detailed observations - how can our data analysis tools rise to the challenge and use these data to their fullest potential?
Patel, Ekta |  Probing the faint end of the galaxy luminosity function beyond the Local Group. How do complete censuses of ultra-faint dwarf galaxies (to $M_{V}>-7$) beyond the Local Group improve our understanding of small-scale structure?
Pello, Roser |  Towards a complete census of ionizing sources during the first billion years. What is the relative contribution of the different galaxy populations to cosmic reionization?
Pierce, Michael |  Measuring cosmological parallax with gravitationally lensed quasars and galaxies.
Rawls, Meredith |  Quantifying the impact of low-Earth-orbit satellite proliferation on ground-based astronomy. How do increasing numbers of bright commercial satellites impact ${\color[rgb]{0,1,1}{\it Rubin}}$’s science programs?
Scognamiglio, Diana |  Relics with ${\color[rgb]{0,1,1}{\it Rubin}}$ & ${\color[rgb]{1,.5,0}{\it Roman}}$ ($R^{3}$): constraining the assembly and cosmic evolution of the most massive galaxies in the Universe. How do the most massive galaxies form? How do they evolve in cosmic time?
Shivaei, Irene |  The effect of environment on galaxy evolution processes at Cosmic Noon ($z\sim 1-3$). How does galaxies morphologies, their distribution of star formation, stellar mass, and dust varies across environment? What is the connection between the AGN and their host galaxies’ morphologies, stellar populations, and environment?
Simon, Josh |  Stellar archaeology in ultra-faint dwarf galaxies.
Tee, Wei Leong, |  The first quasars in the Universe: ${\color[rgb]{1,.5,0}{\it Roman}}$+${\color[rgb]{0,1,1}{\it Rubin}}$ synergy.
Tran, Kim-Vy |  Identifying strong lensing candidates with machine-learning on ${\color[rgb]{0,1,1}{\it Rubin}}$ and modeling the lenses and sources with ${\color[rgb]{1,.5,0}{\it Roman}}$ high resolution imaging and grism spectroscopy.
Wallman, Bruce |  Calibration of ${\color[rgb]{0,1,1}{\it Rubin}}$ photometric redshifts with ${\color[rgb]{1,.5,0}{\it Roman}}$ spectroscopy
Williams, Benjamin |  Fundamental improvement of our observational constraints on the structure and stellar content of the halos of nearby galaxies.
Williams, Peter K. G. |  Visualizing big and wide data in the 21st Century. What are the visualization tools that will empower astronomers to get the most science out of 21st-century datasets?
Yoon, Ilsang |  Evolution of the rest-frame optical galaxy structure and environmental effect up to z=2.5.

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