# Measurements of photon and jet production properties with ATLAS

, on behalf of the ATLAS Collaboration
University of Pennsylvania (US)
E-mail

###### Abstract:

Summary of recent ATLAS measurements of jet and photon production using
proton–proton ($pp$) collisions from the Large Hadron Collider. The charged-
particle multiplicity in jets, and jet charge measurements are presented using
8 TeV $pp$ collisions. Differential measurements of jet and photon cross-
sections are shown for 7, 8 and 13 TeV $pp$ collisions.

##  1 Introduction

Quantum chromodynamics (QCD) is an incredibly successful theory which
describes the interaction and resulting kinematics of colored objects. When
modeling a proton–proton ($pp$) collision at the Large Hadron Collider, there
are four general contributions:  1) hard-scatter,  2) fragmentation,  3)
hadronisation, and  4) underlying event.  The hard-scatter process models the
scattering of the two incoming partons, and is described by perturbative QCD
calculations. Current calculations are generally accurate out to next-to-next-
to-leading order (NNLO), and are also dependent on the parton distribution
function (PDF) of the incoming protons. Fragmentation takes the outgoing hard-
scatter partons to the non-perturbative regime through parton emission and
splitting. Upon entering the non-perturbative regime, where quarks are
confined, hadronisation evolves them to the state observed by the detector.
The underlying event, which includes color-connected beam–beam remnants and
multiple-parton interactions within the colliding protons, is also described
by non-perturbative models.

Photons provide a good measurement of the hard-scatter, constraining both the
accuracy of the perturbative calculation as well as the proton PDF. Measured
in the ATLAS [1] electromagnetic calorimeter, the clusters are calibrated
using a multivariate analysis trained on simulations. An additional data-
driven correction is also applied, resulting in well calibrated photons.
Figure 1 shows the Z-boson decaying into two electrons, which have a similar
calibration applied [2]. The data are seen to agree well with the known
position of the Z-boson mass peak.

Figure 1:  In (a) the calibrated dielectron mass, dominated by the
$Z\rightarrow ee$ process, is shown for data and MC simulation [2]. In (b) the
absolute jet selection efficiency is shown, as well as the ratio of data and
MC simulation [4].

Because partons can not be measured directly, the resulting particles from the
fragmentation and hadronisation simulation are clustered into _particle-level
jets_. The ATLAS collaboration predominantly uses the anti-$k_{t}$ clustering
algorithm [3], with matching radius $\Delta R=0.4$. Particle-level jets
consider all charged particles with lifetime $c\tau>30$ ps, except for muons,
as inputs to the clustering algorithm. _Detector-level jets_ are formed by
clustering topological energy clusters from both the hadronic and
electromagnetic calorimeters. The resulting detector-level jet is further
calibrated using several data-driven techniques, resulting in a well measured
energy scale and resolution. As seen in figure 1, jets with
$p_{\mathrm{T}}>20$ GeV have a selection efficiency $>98\%$ [4], which is well
modeled by the MC simulation.

For both jet and photon measurements, detector efficiency and resolution
effects are corrected (unfolded) using MC simulation, resulting in a
measurement at the particle-level. In general, the fiducial acceptance of the
particle-level prediction is very similar to the event selection applied to
data, which minimizes the dependence of the unfolding method on the MC
simulation. This allows data to confront PDF models, perturbative
calculations, as well as the modeling of factorisation and hadronisation.

Recent ATLAS measurements of charged-particle multiplicity in jets [5], as
well as jet charge [6], confront the simulation of jet properties such as
flavour identification and hadronisation. The jet charge, along with
differential measurements of jet [7] and photon [8, 9] production, provide a
probe of the PDF. Finally, differential jet and photon production are
important for testing perturbative QCD calculations.

##  2 Jet properties

Figure 2:  In (a) is the charged-particle track multiplicity for three
different ranges of jet $p_{\mathrm{T}}$ [5]. In (b) the jet charge is shown
for different flavours of parton which initiated the jet [6].

Two recent ATLAS results study the properties of jets:  1) charged-particle
multiplicity in jets, and  2) jet charge.  Both measurements consider well-
balanced dijet events produced in 8 TeV $pp$ collisions. Individual jets are
selected with absolute pseudo-rapidity $|\eta|<2.1$ and transverse momentum
$p_{\mathrm{T}}>50$ GeV. In addition, the ratio of transverse momentum of the
leading jet with the sub-leading jet must be less than
$p_{\mathrm{T,1}}/p_{\mathrm{T,2}}<1.5$. Of the two highest $p_{\mathrm{T}}$
jets, the jet with the smaller (larger) absolute pseudo-rapidity is classified
as the _central_ (_forward_) jet. Due to PDF effects, the forward jets are
less likely to be associated with gluons.

Within the selected jets, charged-particle tracks are considered if they fall
within the inner tracking detector, $|\eta|<2.5$, and have
$p_{\mathrm{T}}>500$ MeV. An example of the charged-particle track
multiplicity $\langle n_{\mathrm{charged}}\rangle$ is shown in figure 2, where
data are compared with predictions by Pythia8 [10] and Herwig++ [11]. Both MC
generators show differences compared to the data, over a range of jet
$p_{\mathrm{T}}$ values.

Figure 3:  In (a) is the average charged-particle track multiplicity as a
function of jet $p_{\mathrm{T}}$ [5]. In (b) the average charged-particle
track multiplicity is shown as a function of jet $p_{\mathrm{T}}$ for
different flavours of parton which initiated the jet [6].

The average value of the charged-particle multiplicity distribution is shown
as a function of jet $p_{\mathrm{T}}$ in figure 3. In general, various tunes
of Pythia6 [12] and Pythia8 overestimate the data, while Herwig++
underestimates the data. Within the uncertainties, Pythia6 using the P2012
RadLo tune shows the best description across the full jet $p_{\mathrm{T}}$
range.

The average charged-particle multiplicity for different jet flavours is also
extracted, shown in figure 3. This is extracted using the measured values of
$\langle n_{\mathrm{charged}}^{c/f}\rangle$ as inputs and solving the linear
equations:

$\displaystyle\langle n_{\mathrm{charged}}^{c}\rangle$
$\displaystyle=f_{q}^{c}\langle
n_{\mathrm{charged}}^{q}\rangle+f_{g}^{c}\langle
n_{\mathrm{charged}}^{g}\rangle$ $\displaystyle\langle
n_{\mathrm{charged}}^{f}\rangle$ $\displaystyle=f_{q}^{f}\langle
n_{\mathrm{charged}}^{q}\rangle+f_{g}^{f}\langle
n_{\mathrm{charged}}^{g}\rangle$

where $f_{q}^{c/f}$ ($f_{g}^{c/f}$) is the fraction of events from quark
(gluon) initiated jets in the central ($c$) or forward ($f$) region,
determined using MC simulation. Two different theory predictions are
considered: a leading-order (LO) prediction using Pythia8 with the AU2 tune,
and a next-to-next-to-next-to-leading-order (N3LO) calculation [13, 14]. While
the N3LO calculation better describes the data at lower values of jet
$p_{\mathrm{T}}$, both predictions overestimate the data for larger values of
jet $p_{\mathrm{T}}$.

Useful as a tool for flavour discrimination as well as studying hadronisation,
the jet charge ($Q_{J}$) is defined as:

$Q_{J}=\frac{1}{(p_{\mathrm{T}J})^{k}}\sum_{i\in
Tracks}q_{i}\times(p_{\mathrm{T},i})^{\kappa}$

where $p_{\mathrm{T}J}$ is the transverse momentum of the jet, $q_{i}$
($p_{\mathrm{T},i}$) is the charge (transverse momentum) of the track, and
$\kappa$ is a regularisation parameter. Lower values of $\kappa$ enhance the
jet charge’s sensitivity to low-$p_{\mathrm{T}}$ tracks, while in the limit
$\kappa\rightarrow\infty$ only the highest-$p_{\mathrm{T}}$ track is
considered. An example of the jet charge distribution for different flavours
of jets is shown in figure 2. While the mean of the different distributions
clearly reflects the charge of various jet flavours, the width of the
distribution lowers the expected purity when using the jet charge as a flavour
discriminant.

The average jet charge is shown in figure 4 for central jets, and in figure 4
for forward jets. Theory predictions by Pythia8 using the AU2 tune, and
Herwig++ using the EE3 tune, are also considered. The Pythia8 prediction
generally follows the data better at low jet $p_{\mathrm{T}}$, while Herwig++
describes the data better at high jet $p_{\mathrm{T}}$.

Figure 4:  The average jet charge is shown as a function of jet
$p_{\mathrm{T}}$ for (a) central jets, and (b) forward jets [6]. For both
cases, the result using three different values of the regularisation parameter
$\kappa$ is shown.

##  3 Probing the hard scatter with jets

Figure 5:  Summary of inclusive jet and dijet measurements for two values of
the jet radius parameter ($R=0.4$ and $R=0.6$), compared with NLO theory
predictions [15]. The top half of the figure shows the inclusive jet results
in different ranges of jet rapidity, while the bottom half shows the dijet
results in different ranges of $y^{*}=|y_{1}-y_{2}|/2$.

Figure 6:  Inclusive jet cross-sections measured with 13 TeV $pp$ collisions
[7], compared with NLOJet++ theory predictions using the CT10 PDF set.

Inclusive jet and dijet measurements provide a good test of perturbative QCD
predictions, as well as PDF models. A summary of measurements made using 7 TeV
$pp$ collisions [15] is shown in figure 5, including NLO predictions by
NLOJet++ [16] using the CT10 PDF set [17]. An impressive agreement of the
theory prediction with data is seen over a large kinematic range. While the
experimental uncertainties are currently similar to the theory uncertainties,
they are expected to continue being reduced. Recent updates to various PDF
sets already reduce their component of the theory uncertainty, which is now
approaching the same magnitude as the renormalisation and factorisation scale
uncertainty in some kinematic regions. This coincides well with the expected
release of NNLO predictions in the near future, which will further reduce the
theory uncertainty.

A recent differential measurement of inclusive jet production using 13 TeV
$pp$ collisions is shown in figure 6. In this preliminary measurement of
low-$p_{\mathrm{T}}$ central jets ($|y|<0.5$), the theory describes the data
well within the assigned uncertainties. The CT10, MMHT [18], and NNPDF3 [19]
PDF sets are considered for the NLOJet++ calculation.

##  4 Probing the hard scatter with photons

Inclusive photon production provides a similar test of perturbative QCD and
PDF models. Both photons produced in the matrix element (direct production)
and during the fragmentation process are considered. To reduce the background
resulting from photons produced during hadronisation, an isolation requirement
is applied. An example of the isolation profile of photons produced in 13 TeV
$pp$ collisions is shown in figure 7, where the isolation of the signal
photons is shown after subtracting the background template [20].

Figure 7:  In (a) the photon isolation profile from 13 TeV $pp$ collisions is
shown [20], with the background template in purple, the background subtracted
data in black, and signal photon isolation profiles from Pythia and Sherpa. In
(b) is a preliminary energy distribution of isolated photons [9], again using
13 TeV $pp$ collisions, compared with the shape prediction made by Sherpa.

A preliminary differential measurement of photon transverse energy unfolded to
the particle-level is shown in figure 7. Photons with absolute pseudo-rapidity
$|\eta|<2.37$ are considered, excluding the crack in the detector between
$1.37<|\eta|<1.56$ where photons are reconstructed poorly. Within the
uncertainties, the LO Sherpa [21] prediction describes the shape of the data.

The measurement of differential cross-sections as a function of photon
transverse energy and pseudo-rapidity using 8 TeV $pp$ collisions is shown in
figures 8 and 9. Both figures show the ratio of NLO JetPhox [22] using the
CT10 PDF set with data, while the first figure also considers the
approximately NNLO prediction by PeTeR [23], and the second considers the LO
predictions by Pythia8 and Herwig++. In general, only the PeTeR prediction is
within 10% of the data over the kinematic range where statistical uncertainty
is not dominant. Of the LO predictions, Pythia is within 10% the data for
larger values of absolute pseudo-rapidity.

Figure 8:  The differential photon cross-sections as a function of transverse
energy ($E_{\mathrm{T}}$) and absolute pseudo-rapidity, using 8 TeV $pp$
collisions, are shown as a ratio of theory with data [8]. Theory predictions
from JetPhox and PeTeR are considered.  Figure 9:  The differential photon
cross-sections as a function of transverse energy ($E_{\mathrm{T}}$) and
absolute pseudo-rapidity, using 8 TeV $pp$ collisions, are shown as a ratio of
theory with data [8]. Theory predictions from JetPhox, Pythia, and Herwig++
are considered.

##  5 Summary

Several recent jet and photon measurements from ATLAS are summarized,
providing good tests of perturbative calculations, PDF models, and
fragmentation/hadronisation simulation in MC generators. While a good
description of jet properties such as charged-particle multiplicity and charge
is seen, there is no single MC generator which performs best in all
situations.

As the LHC moves into a mode of operation which focuses on collecting a larger
data sample, jet and photon cross-sections will become increasingly important.
The experimental uncertainties are already similar to the PDF uncertainties,
and both will be reduced further when the full analysis of the 13 TeV $pp$
collision sample is complete. This highlights the importance of the NNLO
predictions that will soon be available, which will reduce the now non-
negligible renormalisation and factorisation scale uncertainty.

Going forward, the properties and precision measurements of jets and photons
will be crucial for fine-tuning descriptions of the Standard Model, and
providing precise background estimates for new physics searches. In summary,
it is an exciting time for both jet and photon physics.

## References

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# Boosting Architectural Generation via Prompts: Report

Xin Zhang  and  Wenwen Liu  HUSTCN

(2018)

###### Abstract.

In the realm of AI architectural design, the importance of prompts is becoming
increasingly prominent. With advancements in artificial intelligence and
large-scale model technology, more design tasks are being delegated to machine
learning algorithms. This necessitates a method for designers to guide
algorithms in producing their desired designs. Prompts serve as a guiding and
motivational mechanism, playing a crucial role in AI-generated architectural
design. This paper categorizes and summarizes common vocabulary used in
architectural design, discussing how to craft effective prompts and their
impact on the quality and creativity of generated results. Through careful
prompt design, designers can better control the generated architectural design
images, thereby achieving designs that are more aligned with requirements and
innovative.

Archtectural design, Prompts

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##  1\. Introduction

In the current field of architectural design (mcguire1983theory, ;
groat2013architectural, ; garlan1994exploiting, ; machairas2014algorithms, ;
demirbacs2003focus, ; caetano2020computational, ; xu2017taxonomy, ;
kotnik2010digital, ; hollberg2016lca, ; aliakseyeu2006computer, ;
akin1996frames, ; lomas2007architectural, ), the development of artificial
intelligence 2D/3D technology (Rae2021ScalingLM, ; Xu2022GroupViTSS, ;
li2023towards, ; Thoppilan2022LaMDALM, ; Brown2020LanguageMA, ;
Liu2022FewShotPF, ) has brought designers both new challenges and
opportunities. With the increasing maturity of large-scale model technology
(rombach2022stable, ; openai2023gpt4, ; Radford2021LearningTV, ;
croitoru2023diffusion, ), the application of machine learning algorithms
(he2016deep, ; vaswani2017attention, ; he2017mask, ) in generating
architectural images is becoming more widespread (li2023sketch, ;
Nauata2020HouseGANRG, ; li2024generating, ; chaillou2020archigan, ;
Para2021SketchGenGC, ). However, a significant challenge for designers in this
process is how to effectively guide algorithms to generate the designs they
envision. In this regard, Prompts (jia2022visual, ; zhou2022learning, ;
liu2022design, ; li2022align, ; hao2024optimizing, ) serve as a crucial
mechanism for guiding and motivating.

Prompts are not merely simple words or phrases; they act as communication
bridges between designers and machine learning algorithms. Well-designed
Prompts can accurately convey design intent and requirements, guiding
algorithms to generate architectural images that align with design objectives.
In this process, Prompt design needs to consider multiple aspects such as
spatial perception, material selection, and functional layout to ensure that
the generated designs meet client needs while also demonstrating innovation
and uniqueness. Moreover, Prompts can serve as sources of creative inspiration
during the design process, stimulating algorithms to generate more imaginative
and innovative design solutions.

When using Prompts to guide architectural image generation, designers need to
possess deep professional knowledge and extensive design experience. Only by
fully understanding the complexity and diversity of architectural design can
designers effectively design Prompts that guide algorithms to generate designs
that meet requirements. Therefore, Prompt design is not just about arranging
words; it also reflects architectural design concepts and aesthetic
principles. Designers need to continuously explore and optimize Prompt design
methods to better harness the potential of artificial intelligence in
architectural design, injecting more novel and unique elements into designs.

This article provides an in-depth summary and classification of commonly used
vocabulary in architectural design, aiming to explore how to design Prompts
that effectively guide the generation process and analyze their impact on the
quality and creativity of generated results.

##  2\. Architectural Prompts

The categorized architectural prompts summarize key elements of the
architectural design process, spanning from visual perspectives to technical
details, and from design concepts to creators. ”Perspectives and viewpoints”
focus on the observation angles, prompting consideration of how to present
buildings and how viewers interact with them. ”Lighting Control” centers on
utilizing light to shape space and emotional experiences. ”Rendering Effects”
concentrates on using rendering techniques to showcase the appearance and
atmosphere of architectural designs. ”Architectural Types” encompass
classifications and types of buildings, including residential, commercial,
cultural, etc. ”Design Styles” cover different design aesthetics, such as
modernism, classicism, etc., impacting the appearance and ambiance of
buildings. ”Building Materials” discusses the selection and application of
materials, crucial for both the structure and appearance of buildings.
”Architectural Landscapes” focuses on the relationship between buildings and
their surrounding environments, including landscape design and urban planning.
Lastly, ”Architects” highlights the individual design styles of architects;
their concepts and creativity shape the appearance and significance of
different buildings.

###  2.1. Perspectives and viewpoints

Three Point Perspective, Aerial Shot, Close-Up, Long Shot, Panoramic View,
Dutch Angle, Overhead Shot, High Angle, Low Angle, Point of View (POV), Fish-
Eye View, Macro View, Microscopic View, Wide Shot, Full Shot, Medium Shot,
Extreme Close-Up, Tilt Shot, Crane Shot, Handheld Shot, Static Shot, Tracking
Shot, Zoom Shot, Split Screen, Mirror Shot, POV Shot, Reverse Shot,
Establishing Shot, Cutaway Shot, Two Shot, One Shot, Insert Shot, Reaction
Shot, Subjective Shot, Objective Shot, Top-Down View, Bottom-Up View, Slanted
Angle, Diagonal View, Symmetrical View, Asymmetrical View, Side View, Frontal
View.

###  2.2. Lighting Control

Bright sunlight, Soft glow, Harsh light, Dim illumination, Radiant beams,
Faint gleam, Vivid brightness, Subtle radiance, Intense luminosity, Muted
hues, Shining rays, Glimmering light, Sparkling brilliance, Twinkling
luminescence, Gleaming brightness, Dazzling brilliance, Blinding glare,
Flickering candlelight, Dappled sunlight, Glistening dewdrops, Scattered
light, Illuminated room, Fluorescent lighting, Incandescent bulbs, Ambient
glow, Transparent sheen, Opaque shadows, Translucent glow, Blurred vision,
Clear brightness, Prismatic colors, Refracted light, Reflective surfaces,
Penetrating rays, Piercing brightness, Drenched in light, Saturated colors,
Patchy illumination, Spotty brightness, Streaked light, Glinting reflections,
Glaring sunlight, Searing heat, Illuminated landscape, Radiated warmth,
Beaming sunlight, Radiating heat, Glancing reflections, Streaming sunlight,
Cascading beams, Sizzling heat, Sputtering flames, Glowing embers, Lit room,
Unlit space, Darkened corners, Mellow glow, Muted tones, Reflected light,
Diffused illumination, Sharp contrast, Crisp brightness, Precise spotlight,
Focused beams, Blurred vision, Distorted shadows, Focused illumination,
Vibrant colors, Alight with sunshine, Sunlit room, Moonlit night, Starlit sky,
Torchlit path, Candlelit dinner, Streetlit evening, Firelit ambiance, Daylit
scene, Nightlit cityscape, Shaded nook, Overcast sky, Sun-drenched landscape,
Sun-soaked atmosphere, Light-filled room, Light-splashed surface, Light-
dappled forest, Light-streaked sky, Light-flooded space, Light-infused
atmosphere, Light-suffused ambiance, Light-imbued surroundings, Light-bathed
scenery, Light-gilded landscape.

###  2.3. Rendering Effects

Modern, sleek, minimalist, vibrant, dynamic, futuristic, elegant,
sophisticated, spacious, airy, luxurious, contemporary, stylish, innovative,
geometric, iconic, striking, majestic, picturesque, serene, inviting,
seamless, luminous, illuminated, tranquil, polished, refined, detailed,
textured, immersive, captivating, artistic, dramatic, panoramic, expansive,
panoramic, urban, organic, naturalistic, harmonious, balanced, integrated,
sustainable, eco-friendly, technologically advanced, sleek lines, clean
design, seamless integration, open concept, fluidity, monumental, grandiose,
awe-inspiring, monumental, grandiose, awe-inspiring, ornate, historic,
classical, timeless, traditional, cultural, eclectic, dynamic lighting,
dramatic shadows, reflective surfaces, transparent elements, panoramic views,
focal points, landmark, iconic skyline, futuristic skyline, high-rise,
skyscraper, landmark, iconic, cutting-edge, avant-garde, sculptural, abstract,
industrial, adaptive reuse, mixed-use, community-oriented, inclusive,
accessible, user-friendly, high-tech, energy-efficient, green spaces, outdoor
integration, sustainable materials, renewable energy, smart technology,
interactive elements, community-centric, collaborative spaces, multipurpose
areas, flexible layouts, ergonomic design, seamless transitions, iconic
silhouette, dynamic composition, bold colors, soft hues, subtle contrasts,
natural materials, harmonious palette, immersive experience, inviting
atmosphere.

###  2.4. Architectural Types

Hospital, school, university, library, museum, gallery, office building,
apartment complex, residential building, condominium, townhouse, villa,
mansion, cottage, farmhouse, bungalow, duplex, triplex, skyscraper, high-rise,
tower block, shopping mall, retail store, supermarket, department store,
boutique, hotel, motel, hostel, inn, guesthouse, lodge, resort, spa,
restaurant, cafe, diner, bar, nightclub, theater, cinema, concert hall,
stadium, arena, gymnasium, sports complex, swimming pool, recreation center,
community center, church, mosque, temple, synagogue, chapel, cathedral,
monastery, shrine, mausoleum, cemetery, crematorium, crematory, funeral home,
warehouse, factory, manufacturing plant, workshop, garage, hangar, barn,
greenhouse, conservatory, laboratory, research facility, observatory,
planetarium, clinic, medical center, nursing home, assisted living facility,
rehabilitation center, orphanage, shelter, halfway house, prison, jail,
courthouse, police station, fire station, embassy, consulate, government
building, town hall, city hall, municipal building, post office, bank,
financial institution, insurance company, stock exchange, brokerage firm, data
center, telecommunications center, power plant, energy facility, water
treatment plant, sewage treatment plant, recycling center, landfill, waste
management facility, military base, barracks, training facility, command
center, missile silo, airfield, airport, seaport, dockyard, lighthouse,
observatory, weather station, radio station, television station, broadcasting
studio, satellite facility, telecommunications tower. Art studio, art gallery,
exhibition hall, performance venue, concert hall, music studio, recording
studio, rehearsal space, dance studio, ballet studio, theater, opera house,
playhouse, amphitheater, auditorium, lecture hall, conference center,
convention center, meeting space, seminar room, classroom, laboratory,
research center, innovation hub, coworking space, incubator, accelerator,
startup hub, maker space, hackerspace, library, archives, reading room, study
hall, digital library, e-library, e-learning center, tutoring center, language
school, vocational school, trade school, technical college, community college,
liberal arts college, business school, law school, medical school, engineering
school, architecture school, design school, art school, conservatory, cooking
school, culinary institute, hospitality school, boarding school, day care
center, preschool, kindergarten, elementary school, middle school, high
school, boarding school, military academy, sports academy, athletic training
center, sports complex, stadium, arena, gymnasium, fitness center, yoga
studio, martial arts dojo, wellness center, rehabilitation center, physical
therapy clinic, medical clinic, health center, dental clinic, eye clinic,
chiropractic clinic, holistic health center, veterinary clinic, pet hospital,
animal shelter, wildlife sanctuary, botanical garden, arboretum, nature
reserve, wildlife preserve, zoo, aquarium, petting zoo, farm, ranch, vineyard,
winery, brewery, distillery, tasting room, wine cellar, cellar door, wine bar,
cocktail lounge, brewpub, taproom, distillery bar, speakeasy, distillery
tasting room, distillery bar, gastropub, bistro, brasserie, trattoria,
pizzeria, sushi bar, noodle bar, food court, food truck park, street food
market, farmers market, artisanal market, flea market, craft fair, antique
market, vintage market, pop-up shop, boutique hotel, boutique inn, boutique
resort.

###  2.5. Design Styles

Art Deco, Minimalist, Brutalist, Gothic, Baroque, Neoclassical, Contemporary,
Victorian, Renaissance, Modernist, Islamic, Romanesque, Tudor, Bauhaus,
Industrial, Eclectic, Mediterranean, Prairie Style, Colonial, Federalist, Art
Nouveau, Mid-century Modern, Postmodern, International Style,
Deconstructivist, Craftsman, Shingle Style, High-Tech, Organic, Futurist,
Expressionist, Streamline Moderne, Neo-Gothic, Neo-Renaissance, Neo-Futurist,
Neo-Victorian, Neo-Georgian, Neo-Colonial, Neo-Baroque, Neo-Byzantine, Neo-
Moorish, Neo-Rococo, Neo-Palladian, Neo-Classical Revival, Neo-Tudor, Neo-
Romanesque, Neo-Medieval, Neo-Contemporary, Neo-Industrial, Neo-Traditional,
Neo-Modern, Neo-Art Deco, Neo-Eclectic, Neo-Cubist, Neo-Constructivist, Neo-
Expressionist, Neo-Primitivist, Neo-Surrealist, Neo-Rocaille, Neo-
Enlightenment, Neo-Structuralist, Neo-Functional, Neo-Folk, Neo-Rural, Neo-
Suburban, Neo-Urban, Neo-Cosmopolitan, Neo-Nomadic.

###  2.6. Building Materials

Concrete, Glass, Steel, Brick, Wood, Stone, Aluminum, Copper, Ceramic, Marble,
Granite, Terracotta, Timber, Plastic, Bamboo, Composite materials, Plaster,
Fabric, Corrugated metal, Limestone, Slate, Stucco, Rubber, Asphalt,
Fiberglass, Plexiglass, Cor-ten steel, Zinc, Wrought iron, Titanium,
Polycarbonate, Carbon fiber, Glass fiber, Polyurethane foam, Gypsum, Resin,
Clay, Straw, Adobe, Rammed earth, Stainless steel, Copper alloy, Brass,
Bronze, Recycled materials, Terrazzo, Vinyl, Polyvinyl chloride (PVC),
Acrylic, Engineered wood, Particle board, Medium-density fiberboard (MDF),
Oriented strand board (OSB), Laminate, Leather, Cork, Linoleum, Rattan,
Bamboo, Hempcrete, Reclaimed wood, Salvaged materials, Gabion, Photovoltaic
panels, Solar tiles, Green roof, Living wall, Reflective glass, Insulated
concrete forms (ICF), Reinforced concrete, Prefabricated panels, Composite
panels, Synthetic turf, Rubberized asphalt, Self-healing concrete, Transparent
concrete, Corrugated plastic, Structural insulated panels (SIPs), Translucent
polycarbonate, Fiberglass reinforced panels (FRP), Glulam, Cross-laminated
timber (CLT), Metal mesh, Expanded metal mesh, Metal cladding, Glass block,
Cob, Rammed earth, Cellulose insulation, Aerogel, Structural steel, Engineered
stone, Fibre-reinforced polymer (FRP), Hemp fiber, Recycled steel, Bamboo
composite, Cast iron, Fiber cement, Poured-in-place concrete, Rubber roofing,
Natural fibers, Aluminum composite panel (ACP), Gypsum board, Galvanized
steel, Reflective roofing, Composite shingles, Polyethylene, Ceramic tile,
Cellular concrete.

###  2.7. Architectural Landscapes

Park, Garden, Plaza, Courtyard, Waterfront, Promenade, Esplanade, Boardwalk,
Canal, Riverwalk, Water feature, Fountain, Pond, Lake, Stream, Waterfall,
Bridge, Overpass, Underpass, Viaduct, Arch, Tunnel, Pathway, Trail, Walkway,
Alley, Avenue, Boulevard, Street, Sidewalk, Promontory, Outlook, Observation
deck, Terrace, Patio, Deck, Balcony, Veranda, Porch, Roof garden, Rooftop
terrace, Green roof, Sky garden, Arboretum, Botanical garden, Conservatory,
Greenhouse, Horticultural display, Woodland, Forest, Grove, Glade, Meadow,
Field, Prairie, Pasture, Orchard, Vineyard, Farm, Ranch, Estate, Estate
garden, Estate parkland, Nature reserve, Wildlife sanctuary, Wetland, Marsh,
Bog, Fen, Swamp, Dune, Desert garden, Rock garden, Alpine garden, Zen garden,
Japanese garden, Chinese garden, Mediterranean garden, Tropical garden, Formal
garden, Informal garden, Sculpture garden, Labyrinth, Maze, Topiary garden,
Parkland, Urban park, City park, National park, State park, Provincial park,
Regional park, Community park, Neighborhood park, Memorial park, Sculpture
park, Heritage park, Recreational park, Sports park, Adventure park, Water
park, Theme park, Botanic park, Cultural park, Historical park, Tourist park,
Picnic area, Campground, Outdoor theater, Amphitheater, Bandstand, Performance
space, Event venue, Picnic shelter, Barbecue area, Playground, Skate park, Dog
park.

###  2.8. Architects

Frank Lloyd Wright, Le Corbusier, Zaha Hadid, Antoni Gaudí, Ludwig Mies van
der Rohe, I. M. Pei, Renzo Piano, Louis Kahn, Walter Gropius, Oscar Niemeyer,
Eero Saarinen, Richard Meier, Philip Johnson, Frank Gehry, Jean Nouvel, Norman
Foster, Tadao Ando, Alvar Aalto, Rem Koolhaas, Santiago Calatrava, Bjarke
Ingels, Daniel Libeskind, Moshe Safdie, César Pelli, Herzog de Meuron, Robert
Venturi, Denise Scott Brown, Charles Correa, Peter Zumthor, Jean Prouvé,
Rafael Moneo, Jeanne Gang, SANAA (Kazuyo Sejima and Ryue Nishizawa), Toyo Ito,
Thom Mayne, Álvaro Siza Vieira, Richard Rogers, Marcel Breuer, Jørn Utzon,
Skidmore, Owings Merrill (SOM), SOMA (Fernando Romero), Bernard Tschumi,
Steven Holl, Bjarke Ingels Group (BIG), Richard Neutra, Paul Rudolph, Kengo
Kuma, Cesar Manrique, Paul Williams, Fumihiko Maki, Paul Andreu, Arthur
Erickson, Kenzo Tange, William Van Alen, Paul Revere Williams, Charles-Édouard
Jeanneret-Gris, Marion Mahony Griffin, Charles and Ray Eames, Eileen Gray,
Walter Burley Griffin, Carlo Scarpa, Shigeru Ban, Wang Shu, Richard
Buckminster Fuller, Gerrit Rietveld, Louis Sullivan, Gordon Bunshaft, Aldo
Rossi, Aldo van Eyck, Frei Otto, Peter Eisenman, Jean Michel Wilmotte, David
Chipperfield, Richard England, Henry Hobson Richardson, Kazimir Malevich,
Eliel Saarinen, Albert Speer, Oscar Wilde, Will Alsop, Ricardo Bofill, Cedric
Price, Dominique Perrault, Ove Arup, Rafael Viñoly, Charles Garnier, Richard
Horden, David Adjaye, Winy Maas, Michel Rojkind, Robert Adam, Michael Graves,
Tange Kenzō.

##  3\. Examples

Figure 1. Examples: A Bar.  Figure 2. Examples: A Ice and Snow Building.

In this section, as shown in Fig. 1 and Fig. 2, we demonstrate how finely
crafted (a) and diverse prompts (b) can generate more refined architectural
images compared to simple prompts.

Prompts of Fig. 1:  A Bar, Bird’s-Eye View, stormy sky illumination, Smooth
Shadow Transitions, Bauhaus, terrace, Glass, Japanese Garden, Recreation Park,
Waterfront Walkways, Sunflower, Banyan, Wisteria cascades like a waterfall,
with strands of purple flowers covering the entire wall, creating a dreamlike
scene, Warm, abstract interpretation, Victor Horta.

Prompts of Fig. 2:An Ice and Snow Building, Bird’s-Eye View, foggy glow,
Refined Surface Details, Bauhaus, security camera, Metal, Dutch Garden, Urban
Park, Streams, Forsythia, Yew, Grape vines wrap around the walls of a
countryside cottage, bearing clusters of translucent grapes in autumn, as if a
gift from nature, Morning, high detail, Toyo Ito.

Prompts 3: A Department Store, Elevation View, underwater light, Ambient
Occlusion, Modernism, columns, Limestone, Japanese Garden, Urban Squares,
Canals, Rose, Silk Tree, Boston ivy covers the entire exterior of the
building, transforming it into a lively green ecosystem, Rainbow, innovative
design, Vincent Callebaut.

Prompts 4: A Theater, Perspective, penumbra, Varied Texture Details,
Brutalism, wind chimes, Concrete, English Garden, Greenways, Boardwalks,
Hydrangea, Cypress, Morning glory spreads along the wall under the morning
sun, with its blooming flowers like stars nestled among the green leaves,
Snowy, high detail, Harry Seidler.

##  4\. Conclusion

In summary, the advancement of artificial intelligence in architectural design
presents both challenges and opportunities. Prompts play a pivotal role in
guiding machine learning algorithms to generate designs aligned with
designers’ visions. Well-crafted Prompts convey design intent effectively,
inspiring innovative solutions. However, their success relies on designers’
expertise and understanding of architectural complexities. Continuous
exploration and refinement of Prompt design methods are essential to fully
leverage AI’s potential in architectural design. This article provides a
thorough exploration of common architectural vocabulary, aiming to empower
designers in crafting effective Prompts that enhance the quality and
creativity of generated results.

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