# Non-Visual Cooking: Exploring Practices and Challenges of Meal Preparation
by People with Visual Impairments

Franklin Mingzhe Li  Carnegie Mellon UniversityUnited States
<EMAIL_ADDRESS>,  Jamie Dorst  Carnegie Mellon UniversityUnited States
<EMAIL_ADDRESS>,  Peter Cederberg  Carnegie Mellon UniversityUnited
States<EMAIL_ADDRESS>and  Patrick Carrington  Carnegie Mellon
UniversityUnited States<EMAIL_ADDRESS>
(2021)

###### Abstract.

The reliance on vision for tasks related to cooking and eating healthy can
present barriers to cooking for oneself and achieving proper nutrition. There
has been little research exploring cooking practices and challenges faced by
people with visual impairments. We present a content analysis of 122 YouTube
videos to highlight the cooking practices of visually impaired people, and we
describe detailed practices for 12 different cooking activities (e.g., cutting
and chopping, measuring, testing food for doneness). Based on the cooking
practices, we also conducted semi-structured interviews with 12 visually
impaired people who have cooking experience and show existing challenges,
concerns, and risks in cooking (e.g., tracking the status of tasks in
progress, verifying whether things are peeled or cleaned thoroughly). We
further discuss opportunities to support the current practices and improve the
independence of people with visual impairments in cooking (e.g., zero-touch
interactions for cooking). Overall, our findings provide guidance for future
research exploring various assistive technologies to help people cook without
relying on vision.

accessibility, cooking, people with visual impairments, blind, activity of
daily living, assistive technology

††journalyear: 2021††copyright: rightsretained††conference: The 23rd
International ACM SIGACCESS Conference on Computers and Accessibility; October
18–22, 2021; Virtual Event, USA††booktitle: The 23rd International ACM
SIGACCESS Conference on Computers and Accessibility (ASSETS ’21), October
18–22, 2021, Virtual Event, USA††doi: 10.1145/3441852.3471215††isbn:
978-1-4503-8306-6/21/10††ccs: Human-centered computing Empirical studies in
accessibility

##  1\. Introduction

Vision is an important sensory modality for humans. Many activities of daily
living (ADLs), such as cooking and eating, can be difficult without visual
support. Jones et al. (Jones et al., 2019) revealed that people with visual
impairments tend to have poor nutritional status, which is often linked to
problems with buying, preparing, and eating healthy food. People with visual
impairments may have an aversion to cooking due to difficulty accessing visual
information and cues during the cooking process (Bilyk et al., 2009; Kostyra
et al., 2017). This has resulted in people with visual impairments more
frequently eating outside at restaurants or preparing frozen food that may be
calorie-rich. According to the aforementioned Canadian study (Bilyk et al.,
2009), eight out of nine participants stated they “disliked or hated cooking”
because of the time it takes to cook without vision.

Christine Ha, the first blind contestant of MasterChef, won the third season
of the show in 2012 and described the importance of cooking to people with
visual impairments (Wikipedia, 2021; Ha, 2020). To assist visually impaired
individuals to cook independently, there have also been training guidelines
released from blind communities (VisionAware, [n.d.]) and cooking related
assistive technologies that are commercially available (Henkler, 2020). For
example, people with visual impairments could use voice commands to set timers
or use a speaking kitchen thermometer to check the temperature of a steak.
However, little research has explored the practices of people with visual
impairments in cooking and how they leverage different assistive devices for
cooking. Furthermore, there has been little to guide HCI researchers on what
stages or steps in the cooking process may benefit most from support via
technology. In our research, we explore the following research questions:

  * •

RQ1: What are current cooking approaches and techniques employed by people
with visual impairments?

  * •

RQ2: What are the key challenges, concerns, and risks encountered while
preparing meals?

  * •

RQ3: What are potential opportunities for assistive technologies to support
people with visual impairments to cook independently?

To first understand the current cooking experiences of people with visual
impairments (RQ1), we conducted a content analysis of 122 YouTube videos that
feature visually impaired individuals preparing meals. We describe 12
different activities essential to cooking that were summarized from the video
analysis. Based on the findings from the video analysis, we then conducted
semi-structured interviews with 12 visually impaired people who have
experience cooking to better understand RQ2 and RQ3. The interview findings
further illuminate challenges encountered before, during, and after cooking
including: utilizing tools, information access, touching and feeling, safety
and consequence, precision and ambiguity, organizing and tracking, item and
quality inspection, and collaborative cooking and communication. We then
discuss the potential opportunities to support people with visual impairments
while cooking (e.g., zero-touch interactions for cooking, status tracking and
safety monitoring, and collaborative cooking).

##  2\. Background and Related Work

###  2.1. Eating and Cooking for People with Visual Impairments

People with visual impairments’ Activities of Daily Living (e.g., eating and
mobility) and Instrumental Activities of Daily Living (e.g., preparing and
making food) are affected by the loss of vision (Bhowmick and Hazarika, 2017).
Jones et al. (Jones et al., 2019) conducted a survey study with 101 visually
impaired people and found 65% of the participants stated that their visual
impairments made cooking difficult. Due to the difficulty of cooking, Bilyk et
al. (Bilyk et al., 2009) found that people with visual impairments tend to eat
outside or prepared food, which affects healthy eating behaviors. To enable
efficient preparation of meals, Kostyra et al. (Kostyra et al., 2017) further
showed that assistive technologies, such as having equipment with a voice
editor, devices informing about the cooking process, and sensors supporting
pouring fluids, may enable efficient preparation of meals for people with
visual impairments. Therefore, it is important to explore existing cooking
practices and challenges for people with visual impairments and understand
specific cooking processes or steps that certain assistive technologies may
help people with visual impairments in cooking.

###  2.2. Enabling Technology for People with Visual Impairments

Cooking usually requires people with visual impairments to interact with
different interfaces or devices. The traditional way to enable people with
visual impairments to interact with electronic appliances is by adding tactile
markers to them. Beyond this traditional method, prior research also explored
using computer vision (Guo et al., 2016; Fusco et al., 2014; Morris et al.,
2006; Tekin et al., 2011), voice interactions (Abdolrahmani et al., 2018;
Branham and Mukkath Roy, 2019), and 3D printed tactile marking (Guo et al.,
2017; He et al., 2017) to better support people with visual impairments
interacting with different interfaces. For example, VizLens leveraged computer
vision and crowdsourcing to enable people with visual impairments to interact
with different interfaces, such as a microwave oven (Guo et al., 2016). Guo et
al. (Guo et al., 2017) further introduced a crowdsourced fabrication pipeline
to help blind people independently make physical interfaces accessible through
adding a 3D printed augmentation of tactile buttons overlaying the original
panel.

Beyond making appliance interfaces accessible, prior research has also
explored various approaches to improve the accessibility of mobile devices for
people with visual impairments which might help with the cooking process, such
as gestural interactions (e.g., (Kane et al., 2008; Azenkot et al., 2012; Li
et al., 2017)) and screen readers (e.g., (Rodrigues et al., 2015; Inc., 2021;
Leporini et al., 2012; LLC, 2021)). For example, Talkback (LLC, 2021), and
VoiceOver (Inc., 2021) enable people with visual impairments to explore
interface elements on mobile devices through audio feedback. The feasibility
of using mobile devices further allows people with visual impairments to
interact with other IoT devices (Zhou et al., 2017; Saquib et al., 2017).
However, it is unknown how people with visual impairments tend to interact
with mobile devices during cooking or utilize their mobile devices to interact
with different kitchen appliances, and associated challenges and barriers.

###  2.3. Technology for Cooking

In terms of cooking processes, there has been prior research that explored
learning procedures of cooking (Kato and Hasegawa, 2013) and different cooking
techniques (Kusu et al., 2017; Hamada et al., 2005). For example, Kato and
Hasegawa (Kato and Hasegawa, 2013) introduced an interactive sauteed cooking
simulator that could visualize different cooking states (e.g., temperature
changes, browning from burns). This system could help users to better manage
their cooking skills, such as how to cook medium-rare meat (Kato and Hasegawa,
2013). Kusu et al. (Kusu et al., 2017) further proposed a method to calculate
a cooking recipe’s difficulty level during searching and recommend recipes
that match the user’s cooking skills. Although prior research has explored how
to help people with cooking activities, there lacks research and understanding
of what cooking-related learning procedures people with visual impairments
have adopted and what the existing challenges are during these learning
processes. In our work, we showed cooking practices of 12 different cooking
procedures through a YouTube video analysis and uncovered eight themes of
cooking challenges through interviews with people with visual impairments.

##  3\. YouTube Video Analysis: Cooking Practices for People with Visual
Impairments

To understand existing cooking practices and potential risks for people with
visual impairments, we conducted a YouTube video analysis—searching,
filtering, and analyzing YouTube videos related to cooking practices by people
with visual impairments—inspired by prior research on leveraging the richness
of YouTube video contents to understand accessibility needs (Anthony et al.,
2013). Our video analysis consisted of two main steps: 1) searching for
YouTube videos related to cooking practices for people with visual
impairments; 2) analysis and coding procedures.

Table 1. Searching Keywords Searching Keywords
---
Blind Cooking, Blind Person Cooking, Blind Chef, Legally Blind Cooking, Blind
Cooking Food, Blind Cooking Dinner, Blind in the Kitchen, Visually Impaired
Cooking, Visually Impaired Person Cooking, Visually Impaired Chef, Visual
Impairment Cooking

###  3.1. Search Protocol

In the video searching process, we looked for videos focused on cooking
practices for people with visual impairments. To search for relevant videos,
three researchers independently combined visual impairment related keywords
(e.g., blind, visually impaired, visual impairment) and cooking related
keywords (e.g., cook, cooking, chef, kitchen). To come up with these, our
researchers first started with basic searches (e.g., blind cooking) and
gradually included other keyword combinations from candidate video titles or
descriptions. Because each search may generate hundreds of results, we then
followed the same approach as Komkaite et al. (Komkaite et al., 2019) by
stopping our search for videos after the whole page of results started to be
irrelevant.

In total, we initially created a video dataset of 136 relevant videos found by
March 28th, 2021. We then filtered out videos if: 1) the person who cooked in
the video did not have visual impairments; 2) the person only heated frozen
food; 3) it has poor audio and video quality or did not show the person
cooking; 4) videos were duplicated. We then ended up filtering 14 videos and
created the final video dataset of 122 videos (V1 - V122). Among the 122
videos in our dataset, most videos were uploaded in 2020 (35), while others
were uploaded in 2021 (34), 2019 (11), 2018 (10), 2013 (9), 2014 (7), 2017
(6), 2016 (5), 2015 (3), and 2012 (2). The average length of videos was 698
seconds (ranging from 77 seconds to 2694 seconds).

###  3.2. Video Content Analysis

To code the videos, three researchers first open-coded (Charmaz, 2006) the
videos independently. Then, the coders met and discussed their codes. When
there was a conflict, they explained their rationale for their code to each
other and discussed to find a resolution. Eventually, they reached a consensus
and consolidated the list of codes. Afterward, we performed affinity
diagramming (Hartson and Pyla, 2012) to group the codes and identify emerging
themes according to the Cooking Guidelines for people with visual impairments
(VisionAware, [n.d.]). Overall, we established 12 themes and 37 codes. The
following section describes the findings based on the 12 themes.

Table 2. Cooking activities from YouTube video analysis for people with visual impairments. Category |  Activities
---|---
Preparation |  General Safe Cooking Practices
Cutting and Chopping
Measuring
Spreading
Pouring
Cooking |  Placing Pans on a Burner
Baking
Turning Food
Testing Food for Doneness
Tools, Environment, and Recognition |  Tools and Small Appliances
Knowing the Kitchen Space
Tell Things Apart

###  3.3. Results: Cooking Practices of People with Visual Impairments

From the YouTube video analysis, we uncovered unique cooking practices and
procedures of different people with visual impairments (Table 2).

####  3.3.1. General Safe Cooking Practices

For general safe cooking tips and practices, we found that people with visual
impairments usually start everything with gentle actions. This includes using
a slow cooker rather than an open grill to avoid fire flare ups (V18), always
cooking on low to medium heat to avoid injury (V80), and putting all food and
ingredients in a cold pan first before putting the pan on the burner (V4, V6).
In V80, the person with visual impairments mentioned in his video:

> “…For people with visual impairments like me, we might have to make
> different adjustments on the pan with our hands or utensils while cooking, I
> often start with using my hand to feel the temperature and cook at low to
> medium temperature on my pan…”

Beyond starting with gentle actions, we found that people with visual
impairments tend to use hearing and hand-feeling to compensate for vision
needs during cooking. In terms of leveraging hands for feeling during cooking,
we found that people with visual impairments hold their hands over the pan to
gauge temperature (e.g., V11, V23) and use hands to guide food onto utensils
(V15). For hearing, we found that people with visual impairments leveraged
sound to tell if oil is hot or not (V90, V94). For example, one cook with
visual impairments put a small droplet of water into a pan of oil to hear if
it was hot (V90). Furthermore, we found that it is important for people with
visual impairments to memorize the environment in the kitchen, including the
kitchen layout (V1, V17, V46) and which knobs correspond to which burner on
the stove (V9).

Figure 1. Different ways of cutting. (a) Cutting with a vegetable chopper. (b)
Cutting practice of putting the whole hand on top of the knife to cut garlic.
(c) Tucking the fingertips under and in towards the palm of the hand and cut
with the other hand. (d) Having fingers on both sides of the knife to position
where to cut.

####  3.3.2. Cutting and Chopping

For cutting and chopping, we learned that many people with visual impairments
tried using alternate ways to avoid knives, such as using a garlic masher, a
vegetable chopper (Fig. 1(a)), a food processor (e.g., V103, V114), scissors
(e.g., V84, V94), or even by buying pre-minced garlic (V9, V13). Moreover, we
found that people with visual impairments sort cut and uncut food into
different specific locations on the cutting surface (V1, V32). In V1, the
person commented on her practices in the video: “For me, I always place the
cut food at the top right corner of my cutting board, so I can keep track of
the food that I still need to cut.” We also found that people with visual
impairments have different practices of using the knife to cut objects. For
example, we found people perform cutting by 1) having fingers on both sides of
the knife to position where to cut (e.g., V23, V28) (Fig. 1(d)), 2) putting
the whole hand on top of the knife (e.g., V20, V27) (Fig. 1(b)), or 3) tucking
the fingertips under and in towards the palm of the hand and cutting with the
other hand (e.g., V16, V112) (Fig. 1(c)).

####  3.3.3. Measuring

To measure food, we first found that there is a common practice for people
with visual impairments to use hands to weigh meat and measure spices (e.g.,
V12, V35). We further revealed that people with visual impairments highly rely
on using measuring cups with hand assistance to measure liquids (Fig. 2). To
accurately use measuring cups, people with visual impairments either have to
memorize the measurements (V51) or use a braille-labeled set (V10). Finally,
visually impaired cooks also prefer using existing containers or utensils to
measure food, such as using the same can to get a 1:1 ratio of water and soup
(V89) and using the broad side of a knife to measure the size of meat (V96).

Figure 2. Using the hand to direct the oil and measuring cup to measure liquid
(V17).

####  3.3.4. Pouring

For pouring, we found that people with visual impairments use an extra-large
bowl to reduce the difficulty of pouring (V53, V56) and usually pour over the
sink to make sure spills do not matter (V10, V26). To know how much people are
pouring, we revealed that people tend to use their finger over the spout
and/or in containers to know the exact amount (e.g., V16, V21). Another common
practice we discovered is “making sure you poured everything” to ensure all of
the ingredients they want are added to their dish. More specifically, we found
that people first add water to food processor or bowl to get any leftover
ingredients (V35) and then wipe bowls with fingers to make sure they are empty
after pouring (e.g., V30, V59).

####  3.3.5. Placing Pans on a Burner

From the video analysis, we recognized that people with visual impairments
prefer using traditional electric stove top than modern glass top or gas top.
The key reason was the ease of placing pans on the stove top. As a practice,
we found that people with visual impairments tend to either use listening or
feeling to know whether the pan is on the burner. For example, V19 showed that
visually impaired individuals listen to sizzle to tell whether the pan is on
the burner or not after turning on the fire (V19). As another option, we found
people either use hand to feel to make sure the pan is at the center of the
stove top (V96) or use the back of a spatula to feel if the pan is on the
burner (V19).

####  3.3.6. Baking

In baking, we found that visually impaired people have to use their hands to
feel batter texture, check the batter readiness, and shape dough (V41, V88).
Specifically, we found that visually impaired cooks prefer to fold the dough
over parchment paper (V92), use their hands to shape the dough (V121), and
create barriers when rolling it out (V82). Furthermore, we revealed the
importance of keeping things clean during baking. For example, V29 mentioned
the importance of wiping edges of the pan before baking to prevent the burning
of anything that accidentally got on the edges. Finally, we found people with
visual impairments use caution with the oven when baking, such as by pulling
the oven shelf out slightly to avoid burns (V40).

Figure 3. The person uses one hand to feel the food location and another to
turn the food with a spatula

####  3.3.7. Turning Food

For turning food, we learned that people with visual impairments have
different preferences on choosing tools to accomplish the task. This includes
using a large two-prong fork (V55), using two utensils (V83), and using tongs
and spatulas for (V33, V78). While turning food, people with visual
impairments have different practices of keeping track of food locations. For
example, V22 showed that the person always memorizes how much food she put on
the grill and roughly where it is. V16 further showed that people with visual
impairments use one hand to feel for food locations and then use the other
hand to turn the food with a spatula (Fig. 3).

####  3.3.8. Spreading

From the video analysis, we uncovered that using hands to spread butter or jam
is a common practice for people with visual impairments to make sure
everything is evenly distributed (e.g., V43, V63). V88 further showed the
practices of using single-use plastic gloves for spreading. Beyond using
hands, V45 showed the approach of using utensils to spread jam evenly on
bread.

####  3.3.9. Tools and Small Appliances

In terms of tools and small appliances for cooking, we found that visually
impaired individuals often use a virtual assistant to set timers. This
includes using Siri (e.g., V9, V13), Alexa (V1), Google (V4), and analog
timers (V44). In V44, the visually impaired person rotated the analog timer
using her hand position to gauge the correct time. Furthermore, we uncovered
that speaking tools are widely used for different purposes, such as using a
barcode reader to know what is in a can (V5), Aira (Corp, 2021) for
identifying cans, screen readers to read recipes off of a phone (V29), video
magnifiers to read boxes (V17), talking scales (V41) and speaking thermometers
(e.g., V42, V115). We further found that people with visual impairments tend
to use a lot of specific tools for specific tasks. For instance, V36 showed
that the cook used an automatic can opener and listened for when it was done.
Other examples include using a tea strainer to rinse off rice (V84) and
spatula and chopsticks for mixing (V95).

####  3.3.10. Testing Food for Doneness

To test whether the food is done, we found that people with visual impairments
mostly use five different methods. First, they feel to check food doneness,
such as feeling food texture with a spoon (e.g., V9, V86), feeling for fried
food to float to the top with a utensil (V90), and feeling cooking food with
hands (e.g., V16, V39). We also learned that visually impaired people used
measures for checking doneness. For example, they initiated a timer (e.g.,
V19, V54) or used a thermometer to check whether the food is done (V19, V121).
Moreover, we found people with visual impairments also used listening to tell
when food sounds done (e.g., V11, V34) and use smell to check for food
doneness (e.g., V67, V100). For example, V84 mentioned that visually impaired
people could use sound to know whether water is boiling. Finally, we found
that people who learned cooking before losing their vision judge food doneness
on their intuition and prior memories from when they were sighted (V91).

####  3.3.11. Knowing the Kitchen Space

From the YouTube video analysis, we discovered that people with visual
impairments put a lot of effort to organize the kitchen space to reduce
cooking barriers, such as having separate space for cooking and for
ingredients (V10), keeping the container of utensils handle side down so they
can feel the ends (V25), keeping measuring cups nested (V28) and always
placing utensils back to their original places (V3). Furthermore, we realized
visually impaired individuals have different practices to feel the kitchen
space. For example, V22 showed that people use a spatula to feel around a hot
grill to get to know the space.

####  3.3.12. Tell Things Apart

To tell ingredients or tools apart, we found a common practice for people with
visual impairments is adding labels on buttons or objects. For instance,
visually impaired individuals add Braille labels on spice jars (V27, V47) or
bump dot stickers for recognizing buttons (V50). Another approach to tell
things apart is by recognizing the size or shape of containers. For example,
the person from V72 put each ingredient in a different type of bowls to keep
track of different ingredients. Furthermore, people also rely on smell to tell
things apart, such as smelling to tell the differences between spices (V35,
V65). Finally, we found that some people with visual impairments prefer
putting their containers in certain orders. For example, some people prefer to
alphabetically organize spices in their drawers (V50).

##  4\. Interview Study: Challenges of Cooking for People with Visual
Impairments

In the YouTube video analysis, we uncovered existing practices for people with
visual impairments in different cooking procedures. We then leveraged these
findings and conducted semi-structured interviews with visually impaired
people who have experience cooking to better understand their existing cooking
challenges.

Table 3. Participants’ demographic information Participant |  Age |  Gender |  Vision Impairment Description |  Learned Cooking Before or After Vision Loss
---|---|---|---|---
P1 |  25 |  Male |  Totally Blind, Congenital |  After
P2 |  33 |  Male |  Legally Blind, Congenital |  After
P3 |  32 |  Non-binary |  Legally Blind, Congenital |  After
P4 |  19 |  Male |  Totally Blind, Congenital |  After
P5 |  36 |  Male |  Totally Blind, Acquired (7 years) |  Before
P6 |  24 |  Female |  Legally Blind, Congenital |  After
P7 |  22 |  Male |  Totally Blind, Congenital |  After
P8 |  35 |  Male |  Legally Blind, Acquired (1 year) |  Before
P9 |  28 |  Female |  Totally Blind, Congenital |  After
P10 |  31 |  Female |  Legally Blind, Congenital |  After
P11 |  55 |  Male |  Legally Blind, Congenital |  After
P12 |  48 |  Female |  Legally Blind, Congenital |  After

###  4.1. Method: Semi-structured Interview with People with Visual
Impairments

To understand perceptions and challenges of cooking for people with visual
impairments, we conducted semi-structured interviews with 12 visually impaired
people who have experience cooking (Table 3). Our participants have an average
age of 32, with a range from 19 to 55 years old. Five of them are totally
blind, and the rest are legally blind. Furthermore, ten of our participants
are congenitally blind and learned cooking after the vision loss, and the
other two cooked before losing their vision. Participants were recruited
through social platforms (e.g., Reddit, Twitter, Facebook). To participate in
our interview, participants needed to 1) be 18 or above; 2) have visual
impairments; 3) have experience cooking; 4) be able to communicate in English.
The interviews were conducted through Zoom and took around 60 - 75 minutes for
each participant. Participants who completed the interview were compensated by
a $20 Amazon gift card. The entire recruitment and study procedure was
approved by the institutional review board (IRB).

In the interview, we first asked participants about their demographic
information, general reasoning and perceptions about cooking, and any barriers
they encountered when they started cooking. Based on our findings of different
cooking procedures and practices in the YouTube video analysis, we asked our
participants to discuss their perceptions and challenges around different
cooking procedures, such as measuring, pouring, and cutting and chopping.
Afterward, we asked participants to discuss their experiences and challenges
with assistive cooking technologies, cooking with other people, and the
modification of kitchen appliances for accessibility purposes.

Two researchers independently performed open-coding (Charmaz, 2006) on the
interview transcripts. Then, the coders met and discussed their codes. When
there was a conflict, they explained their rationale for their code to each
other and discussed to resolve the conflict. Eventually, they reached a
consensus and consolidated the list of codes. Afterward, they performed
affinity diagramming (Hartson and Pyla, 2012) to group the codes and identify
emerging themes. Overall, we established eight themes and 26 codes. The
results introduced in the findings are organized based on eight themes.

###  4.2. Findings

In this section, we introduce the challenges of cooking derived from our
interviews in eight themes: utilizing tools, information access, touching and
feeling, safety and consequences, precision and ambiguity, organizing and
tracking, item and quality inspection, and collaborative cooking and
communication.

####  4.2.1. Utilizing Tools

From the interviews, we found that people with visual impairments encountered
various challenges when utilizing tools (e.g., cooking utensils, appliances).
In the video analysis, we found that people with visual impairments rely on
either braille markers or bump dots to interact with an interface (Section
3.3.12). In the interview, seven participants commented on the concerns of
messing up tools with dirty hands and food during cooking. For example, P1
commented on the problems of messing up braille tactile markers on kitchen
appliances:

> “…It is very common that I might accidentally have something on my hand,
> especially when I make a dough, then when I touch my Braille markers or bump
> dots, it just stays on the marker and affects me when I want to read the
> markers…”

From the video analysis, we learned that people with visual impairments
usually utilize many tools for different purposes (Section 3.3.9). In the
interview, we found that eight of our participants struggled with having too
many tools for different purposes than are necessary. For example, P12
mentioned that she had to use a vegetable holder on one hand to hold the
vegetable and use another hand to peel it. P4 further commented on his
situation of even using syringes for cooking:

> “…I have too many tools or cookware for different purposes. I even used
> syringes to measure and add soy sauce to my dishes. But having too many
> things really freak me out, it made me look for things all the time…”

During the interview, five participants mentioned that existing tools lack
confirmation and feedback, especially for our participants who learned cooking
before vision loss and were used to leverage vision for confirmation and
feedback. For example, P8 mentioned the challenges of just using his spatula
to tell where food is and whether the food is done:

> “…Feel through tools is hard, I cannot just use my spatula to do everything
> for me, it is really hard for me to use it to tell if the food is ready by
> using tools to feel the texture. That is why I said feel is difficult
> without hand, that is why we need more help with adding more sensors at the
> head of tools to tell me different status, and it can also accomplish basic
> needs…”

####  4.2.2. Information Access

In the interview, participants overall complained about information access
problems with kitchen appliances’ guides and recipes. First, three
participants mentioned the lack of details for guide manuals and instructions
of kitchen appliances. For example, P11 commented on the need for more vocal
descriptions about the interface layout:

> “…I cannot understand existing manuals and instructions for kitchen
> appliances like pressure cookers, it definitely needs more vocal
> descriptions about the position of different parts and the interface layout.
> Especially that the current pressure cookers or rice cookers have so many
> buttons…”

Beyond the manuals, we found our participants complained that recipe content
does not correspond to how people with visual impairments cook. This includes
cookbooks that have too many pictures (P9), wording or cooking language
varying among different recipes which confused visually impaired cooks (P7),
online recipes containing many figures and ads (P1, P3), and the way current
recipes explain different steps requiring lots of vision support (P8). P8
continued with his experiences with recipes like a guessing game:

> “…The descriptions in many recipes are very hard to correspond to the ways
> that I cook now. I was reading a recipe, and it told me to cook the piece of
> chicken until it turns brown and then flip it over. It does not transfer to
> people with visual impairments, it is like a guessing game…”

Furthermore, we realized that existing recipe structures are not friendly to
people with visual impairments. Participants commented that different recipes
they found online tend to have different recipe structures, such as the cook’s
notes, nutrition facts, ingredients and cooking steps. As a consequence, our
participants complained that they always have to go back to check the recipe
(P1) and do not know where to start the recipe (P1, P3). P6 then further
talked about her frustration of starting over from the beginning of the recipe
because of the non-standardized recipe structure:

> “…I use voiceover for apple devices, it typically read the whole recipe.
> However, many recipes do not follow a specific structure which made me
> easily miss information. Once I lose my place, I have to use voiceover to
> read from the beginning again…”

Besides the need for better recipe structures, we also found that our
participants had a difficult time interacting with the recipe while cooking.
Challenges include problems interacting with phones or tablets while cooking
(P1), irrelevant ads and pictures (P10), and worries of getting both physical
recipes and electronic devices dirty while cooking (P10). P10 continued:

> “…I just cannot find a better way to interact with both my braille recipes
> and electronic recipes by getting them dirty and have a better interactive
> interface while cooking. Because my hands often have oil or sauce when I am
> cooking my dishes…”

Therefore, it is necessary for future research to consider how to better
support cooking related information access and better interactive methods
while cooking.

####  4.2.3. Touching and Feeling

In the YouTube video analysis, we mentioned that people with visual
impairments leverage their hands’ feeling for measuring (Section 3.3.3),
placing pans on a burner (Section 3.3.5), baking (Section 3.3.6), and testing
food for “doneness” (Section 3.3.10). From the interview, we found that there
exist various challenges and concerns about this from people with visual
impairments in cooking. First, we found that hand feeling is not sufficient to
tell all the information about certain cooking procedures. For example, P12
commented: “it can be difficult to feel the liquid level if the liquid is room
temperature”. Therefore, P5 complained that he had to put oil in the fridge
first and then use a finger to feel the level. P1 further mentioned his
difficulties in using his hand to feel the pouring speed of liquid:

> “…When I use my fingers to lead the liquid to pour into a container, I can
> tell somethings flows over my finger, but I cannot tell how fast that is,
> and it usually ended up with having too much or too little that got poured,
> because I do not have another hand to feel the weight of the container, I
> have to use one hand to pour and use one hand to lead the liquid…”

Another concern from our participants is that using hand feeling during
cooking might not be appropriate or unsafe. As an explanation, P5 mentioned
his experiences of messing up the decoration of a cake by using his hands.
Moreover, P9 and P12 mentioned that using hands when cooking might cause them
to burn themselves by accidentally touching the hot pan. P12 continued:

> “…Using hands to feel the food on a hot pan is not always easy to do, you
> have to be very very slow and gentle, especially when you want to feel the
> doneness of the food and try to flip it. I have burned myself many times…”

Furthermore, we found that visually impaired individuals complained that using
hands to feel objects affects the efficiency of cooking, that is, the person
cannot use their hand, which is used for feeling and touching, to do other
things. As an example we mentioned previously, the person has to use one hand
to pour and one hand to lead the liquid to the container, which forces them to
place the container at a stable position (P10). Therefore, future research
should consider how to enable hand and finger sensing to help with knowing
more information during different cooking procedures and explore alternative
methods to substitute the need for hand touch and feel in cooking.

####  4.2.4. Safety and Consequence

From the interview, we found that all participants showed strong concerns of
getting burned during different cooking procedures, which need heat detection
assistance while cooking. For example, taking things out of the oven puts the
visually impaired person in danger (P8), and the plate and dish for baking can
be very hot even after taking it out of the oven (P7). P8 explained the danger
of exposed heat sources:

> “…A lot of kitchen appliances have exposed heat sources, it can be very
> dangerous, such as my toaster or the steamer. After I burned myself once, I
> always pay 200% attention when I am cooking…”

Beyond burning, we found participants reported the difficulty of handling
accidents while cooking. This includes accidentally knocking things over (P3),
it being hard to know if there is an oven fire or not (P10), and accidentally
dropping things and making a mess (P5). P5 continued:

> “…I had experiences making scrambled eggs and spilled the egg all over the
> stovetop. However, I did not notice the mess after cooking, and my partner
> told me this situation the next day. I need something to alert me or help me
> to be aware of these things happened…”

Finally, we found that seven participants mentioned that other people walking
by could potentially cause safety concerns, especially for participants who
learned cooking before their vision loss. For example, P8 mentioned: “you may
turn around, and someone might stay behind you, and you hold a knife…It is a
factor that is out of your control with another person while cooking.” Thus,
future research should explore how to help people with visual impairments
detect heat to prevent burns, track accidents, and people walking by.

####  4.2.5. Precision and Ambiguity

In the YouTube video analysis, we introduced the need for measuring (Section
3.3.3) while cooking. From the interview, we found that making precise steps
while cooking can be a huge barrier for people with visual impairments, such
as getting the right amount of baking soda while baking, because using a hand
to feel the level in a measuring cup of powders is challenging (P1). P7
further mentioned the problem with always getting more during spreading:

> “…Because it is hard to know how much butter I already spread on my bread, I
> always ended up with taking more than needed…”

P11 further continued with commenting on the difficulty of decorating the
food:

> “…Decorating the cake or dishes is extremely hard for me. It took a lot of
> practice to get used to frosting or adding decorations. And it is very easy
> to get messed up…”

To reduce cooking risks, we showed different safe cooking tips in Section
3.3.1. However, we found that our participants complained about the long
learning curve of new methods which makes people stick with their familiar way
of cooking. P7 mentioned: “every person has their own of doing things.” P3
further commented on the hardness of adopting advice from other people:

> “…It is really difficult to get advice from other people when I am already
> used to doing things in a certain way. I know my current way of cutting and
> chopping might not be the recommended way by blind cooking guidelines, but I
> am already used to it, I tried using the recommended method of cutting and
> chopping, and I cut myself while learning it…”

According to participants’ responses, another challenge is following cooking
steps or procedures precisely according to a recipe or instructions. P10
mentioned that a simple mistake during cooking could end up ruining the dish,
such as measuring baking soda and tracking the leavening time during baking.
All participants mentioned the correlation between the tedious work of
following all cooking steps and procedures and why they do not like to cook.
Therefore, future research should explore different tracking and measuring
methods to help people with visual impairments follow steps easier and should
create various teaching techniques to reduce the learning curve of different
safe cooking methods.

####  4.2.6. Organizing and Tracking

In Section 3.3.11, we showed that people put a lot of effort into organizing
their kitchen space to reduce barriers. From the interviews, we uncovered
various challenges in organizing the kitchen and tracking objects. First, we
found that participants complained about the challenge of finding previously
used objects or cookware due to the mental load during cooking. P1 commented:
“It is really easy for me to forget where my used knives are.” P10 also
mentioned that it is hard to find previously used ingredients. P3 then
commented on the need of having a system that continuously tracks the kitchen
environment:

> “…Cooking itself is a very complicated task that requires people to do
> multi-tasking and keep tracking of different things. For me, I sometimes
> forgot where my used knives are, and I had to be very conscious about my
> actions to prevent being cut by touching my used knife. Therefore, I think I
> need a tracker that can track the positions of different utensils and
> cookware for convenience and safety…”

Furthermore, we found that cooking is not a simple binary task that will
guarantee successful and tasty dishes if you follow the recipe. We also
realized people with visual impairments have a hard time tracking the status
of tasks in progress. Five participants commented that it is hard to know when
things are done. They have to constantly check the food. P1 further commented
on this problem and tended to overcook the food:

> “…Knowing whether the food is done is such a complicated project for me
> without any vision. To make sure I do not get diarrhea afterward, I often
> overcooked the food, such as shrimp. Although I can make sure it is totally
> cooked, but it affected the texture and the taste of the food…”

We also found that participants have challenges tracking and organizing parts
of the food, such as organizing the cut and uncut parts of a large volume of
food, and memorizing which parts of the food on the pan need to be flipped
(P10). Therefore, P1 said that she had to wash her hands a lot and touch
everything to check the food. P6 continued with her challenges:

> “…The pan is big. If only a portion of the food on the pan needs to be
> turned over, it would be very difficult. I usually had to use hand and smell
> to check if the food at the center of the pan is burned or not…”

Finally, participants mentioned that they often have a hard time knowing and
remembering where to clean while cooking. Specifically, six participants
complained about remembering where they need to clean afterward, or they have
to clean things right after use. P4 further commented that it is also very
common for him to not know or not be aware of where he has to clean. Overall,
future works should explore how to help people with visual impairments to
better organize and track things through cooking.

####  4.2.7. Item and Quality Inspection

From the interview, we realized that people with visual impairments often have
concerns about inspecting an item or food. First of all, we found that
participants have a hard time checking food quality, such as whether the bread
is moldy. P5 commented on the difficulty of checking food quality just with
smell and touch, even with computer vision assistance:

> “…Knowing the food quality is hard just by smelling and touch. Such as
> knowing whether a piece of bread is moldy or whether bananas or pineapples
> are ripe. I even tried with some apps that could object recognition, but I
> realized it just told me the object it is…”

Furthermore, we found that our participants complained about the task of
telling whether things got peeled or cleaned thoroughly. P8 commented on
cleaning and peeling non-smooth vegetables:

> “…Different vegetables have different difficulties in cleaning and peeling.
> Vegetables like carrots are very easy to peel. But non-smooth vegetables are
> hard to tell if they got peeled or cleaned completely. Ginger is a hard one,
> also spots on potatoes are hard too…”

In the interviews, nine participants reported having a hard time inspecting an
accidental mix of unwanted things, such as eggshells and bugs in flour (P1).
P1 continued: “I had many times of having eggshells in my scrambled eggs
without noticing, it was disgusting.” Overall, we showed the existing
challenges of items and quality inspection. Future research should explore how
to bridge the gap of the challenges we introduced to people with visual
impairments in cooking.

####  4.2.8. Collaborative Cooking and Communication

From interviewing people with visual impairments, we found that ten out of
twelve participants mentioned that they do not like cooking with another
person because of continuous communication needs. “Cooking with another person
needs me to constantly speak out things.” said P4. P3 further complained about
collaborative cooking processes:

> “…I have to verbally confirm steps and actions with another person all the
> time. Sometimes they might just do something in a way that I do not like,
> and they might not inform me because cooking and talking is not common for
> any person…”

In the interview, we found that eight participants leveraged apps for remote
assistance while cooking, such as Aira (Corp, 2021) or Be My Eyes (Wiberg,
2021). However, our participants complained about making sure the item is in
the camera’s field of view. P1 continued:

> “…Remote assistance definitely needs more improvements, I often do not know
> whether the thing I am talking about is in the camera’s field of view. More
> importantly, my hands are often dirty while cooking, and I always tried not
> to touch my electronic device. This leads to more concerns about using
> remote assistance…”

Moreover, we found that our participants complained about appliance
modification barriers in a communal space. P8 commented on this problem: “the
biggest thing about using a shared kitchen, even the simple one like adding
bump dots or braille markers, it is hard for me to just add these markers on
different appliances, because they are shared.” Therefore, future research
should explore how to improve the social acceptability of adding tactile
markers on appliances or other alternative ways for people to interact with
different appliances.

##  5\. Discussion and Future Work

From the results of our interviews with people with visual impairments, we
uncovered existing challenges and gaps for visually impaired people in
cooking. In this section, we further discuss zero-touch interactions for
cooking, status tracking and safety monitoring, collaborative cooking, and
contextual inquiry and learning process in-depth.

###  5.1. Zero-touch Interactions for Cooking

From the results, we showed that our participants often rely on touching and
feeling when cooking (e.g., measuring, checking for food doneness, spreading),
and interacting with touch-based electronic devices might be inconvenient
because they often have food on their hands. As an input method, prior
research has explored speech (Choi et al., 2020; Branham and Mukkath Roy,
2019; Abdolrahmani et al., 2020; Storer et al., 2020) and mid-air gestures
(Dim et al., 2016) as input options for people with visual impairments.
However, many of the existing interaction paradigms still require people to
physically touch the interfaces (e.g., (Ye et al., 2014)), especially to
explore elements on the screen. Although visually impaired individuals can
currently leverage voice control to input certain commands (setting a timer),
future research should explore how to best integrate zero-touch interactions
with electronic devices alongside existing practices utilizing touch to
manipulate ingredients and cooking tools.

###  5.2. Status Tracking and Safety Monitoring

From our interviews, we highlighted that people with visual impairments have
various difficulties in tracking the status of tasks in progress (Section
4.2.6), protecting themselves from getting burned during different cooking
procedures (Section 4.2.4), and handling accidents while cooking (Section
4.2.4). Prior work has explored using thermography to detect heat sources for
energy auditing (e.g., (Mauriello et al., 2017; Mauriello et al., 2019)). A
similar approach could be applied in the kitchen to detect heat hazards.
Beyond heat detection, prior research also explored using computer vision
approaches to opportunistically capture actions and provide proactive
reminders to users with visual impairments (Kianpisheh et al., 2019). Similar
approaches using computer vision or audio (Laput et al., 2018) could be
applied in the kitchen to track object locations or track user’s actions and
status of cooking steps (e.g., boiling water).

###  5.3. Collaborative Cooking

The majority of our participants complained about cooking with other people
because they have to constantly speak about their actions and ask about other
people’s actions. Cooking while constantly talking might not be a natural
behavior for people both with and without visual impairments. However, three
participants did mention the benefit of having another person to accomplish
tasks that rely on vision, such as checking expiration dates and food doneness
(P10). Prior research has explored collaborative behaviors for people with
visual impairments for other purposes, such as gaming (Gonçalves et al.,
2020), use of tangible interfaces (Chibaudel et al., 2020), music composition
(Omori and Yairi, 2013), and creating accessible home environments (Branham
and Kane, 2015). Therefore, future research should explore the collaborative
cooking behaviors for people with visual impairments and further create
guidelines and training methods to reduce safety concerns as well as both
mental and physical effort.

###  5.4. Contextual Inquiry and Learning Process

In this paper, we described our research approach using YouTube Video analysis
to understand more about the practices of cooking by people with visual
impairments. In general, video analyses of this type have the benefit of
allowing researchers to gain a broad understanding of the practices of
assistive technology use for many different users. This has been exemplified
in prior work in HCI and Accessibility (e.g., (Anthony et al., 2013; Hourcade
et al., 2015)). In future research, a contextual inquiry study is well suited
to gather a deeper understanding of individual users’ practices and challenges
(e.g., (Dosono et al., 2015; Li et al., 2019; Krome et al., 2016; Kianpisheh
et al., 2019)), which may then be aggregated to understand themes across those
individuals. As it relates to the research opportunities we discussed above, a
contextual inquiry study may enable future researchers to deeply explore the
practices and challenges of zero-touch interactions, status and safety
monitoring systems, and collaborative cooking. For example, conducting a
contextual inquiry study at a participant’s kitchen could identify more status
and safety monitoring concerns that might not be recognized by the
participants with visual impairments and are not included in the camera’s
field of view in YouTube videos. An example of this might be watching a
visually impaired cook show how she cuts vegetables in front of the camera
while, off-camera, cooking chicken in a pan. In this case, neither the
audience nor researchers may recognize any potential safety concerns with the
pan or other objects in the surroundings from the video. Beyond only
interviewing visually impaired individuals, we realized it would also be
beneficial for future research to conduct interviews with teachers or trainers
who work at community centers and help people with visual impairments with
their activities of daily living and meal preparation. This may generate more
common practices of cooking by people with visual impairments.

##  6\. Limitations

All of the participants in our study were either legally blind or totally
blind. We think people with low vision may have different cooking strategies
and challenges that utilize their visual perceptive abilities. In our study,
we focused on understanding the existing practices and challenges of visually
impaired people in their cooking. However, we did also find that people with
visual impairments have different learning experiences with cooking.
Therefore, people might encounter different challenges at different stages of
learning to cook.

##  7\. Conclusion

In this paper, we describe the results of content analyses with 122 YouTube
videos featuring visually impaired people preparing meals and uncovered unique
cooking practices for people with visual impairments (e.g., use hands to weigh
meat and measure spices, use sound and smell to test if the food is done). We
then present findings from semi-structured interviews with 12 visually
impaired participants and highlight existing challenges encountered while
cooking from their perspectives (e.g., tracking the status of tasks in
progress, telling whether ingredients are peeled or cleaned thoroughly). We
then discussed potential opportunities to integrate technology with the
existing practices for cooking, status tracking, safety monitoring, and
collaborative cooking. Overall, our findings provide guidance for future
research exploring various assistive technologies to help people cook without
relying on vision.

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[b]Giacomo Magni

# SMEFT analysis of the electroweak sector: challenges beyond dimension 6

Raquel Gomez-Ambrosio

###### Abstract

In these proceedings we present the latest developments in our effort to
include vector boson scattering (VBS) measurements into global SMEFT fits of
LHC data. We present some updates to our initial study of arXiv:2101.03180 as
well as comment on a possible roadmap for the inclusion of higher orders
beyond dimension 6 in the SMEFT and on the interpretation of VBS data in other
EFT frameworks.

##  1 Introduction

In the past few years a significant amount of work has been done to extend the
effort to make a global fit for the dimension 6 EFT basis to all the LHC
dataset. After the initial attempts where only a few operators were considered
and only Higgs channels included, we now have more sophisticated tools, able
to fit a large number of operators simultaneously [1, 2].

In arXiv:2101.03180 [3], we presented a fit of the LHC Run-2 electroweak
sector, including the relevant operators of the dimension 6 SMEFT basis at the
linear level. We found that VBS data, albeit still affected by relatively
large experimental uncertainties due to the limited statistics, can still show
an improvement with respect to the diboson dataset only. It has been shown in
previous works that the main constraints to LHC fits come from Higgs data, and
that diboson measurements have a relatively low impact on the final results,
as shown in [2, 1]. Nevertheless, the inclusion of diboson and VBS data in
this context represents a test for the electroweak symmetry breaking mechanism
(EWSB) and the results show that the former is in very good shape. In figure 1
we show a summary of our results where we observe that adding the VBS
information helps to improve the precision of the diboson fit. This fact hints
that, once more precise VBS results are available (in particular, more
differential data), the VBS can improve the VV fit substantially.

Figure 1: Comparison of the magnitude of $95\%$ CL intervals (top) and
posterior distribution samples (bottom) in three different settings: the VV
only, the combined VV plus VBS marginalised and individual fits, at the linear
level in the EFT expansion. The operators are in the Warsaw basis [3]

##  2 Two-dimensional fits

In order to go one step further in our analysis, we have now incorporated a
series of two-dimensional fits, in which only two operators are varied while
all the rest are set to their SM values. These are shown in figure 2, where we
appreciate the complementarity between the diboson and VBS channels. In the
top row we show results for the $\mathcal{O}_{WWW}$ operator, the pure
contribution to gauge couplings. VBS naturally complements diboson processes
in this case as it contains the interplay between triple and quartic gauge
couplings. If something was to be _different_ with the electroweak symmetry
breaking mechanism, it would be likely to show up here, for example if the
Higgs would not be an $SU(2)$ doublet, the ratio between these couplings would
not necessarily be proportional to the Higgs vacuum expectation value and
tensions would appear in this particular degree of freedom. The
$\mathcal{O}_{\varphi W}$ and $\mathcal{O}_{\varphi B}$ operators, enter
always by means of an $HVV$ coupling. In the bottom row we show results for
these two: in the case of diboson processes, such operators contribute as
contamination in the s-channel whereas in VBS they enter through Higgs boson
exchanges in the t-channel, helping to remove their degeneracy. The last
example shows the case of the $\mathcal{O}_{\varphi WB}$ operator, which
accounts for the mixing in the neutral sector. This operator is deemed to be
very well understood and constrained from electroweak precision measurements,
but it is is still very interesting to see the complementarity between diboson
and VBS in this case.

These plots show the region containing the $95\%$ of the posterior
distribution samples obtained after the $\chi^{2}$ minimization. Whenever one
degree of freedom is not affecting a specific process this region appears as
band, while when both the two operators are constrained the CL bounds appear
as an ellipse. In a two dimensional model at liner level in the EFT expansion
is always possible to find out a linear combination of Wilson coefficients
that cancels exactly the EFT contribution. This sort of flat direction
coincide with the major axes of the ellipses presented here.

Figure 2:  We display the regions containing the $95\%$ of the posteriors
distribution samples obtained for different data subsets and for the complete
dataset (VV+VBS). Operator definitions are given in [3].

##  3 Moving to dimension 8 and computational bottlenecks

Now that the global fits with the full dimension 6 basis at linear and
quadratic level are well underway, it is time to start thinking on adding
dimension 8 operators [4]:

$\sigma_{EFT}=\sigma_{SM}+\frac{1}{\Lambda^{2}}\sigma_{int,6}+\frac{1}{\Lambda^{4}}(\sigma_{sq,6}+\sigma_{int,8})+\dots$
(1)

Many options are available in the market, including the full dimension 8 basis
[5, 6], as well as some works selecting a subset of process dependent
operators. These can be used to estimate the effects of the dimension 8
contributions in particular channels, as shown in refs.[7, 8]. For the case of
VBS processes, an ad-hoc basis [9] was defined to account for anomalous
quartic gauge couplings (aQGCs), and it has been frequently adopted by the
experimental collaborations. Several analyses including bounds on dimension 8
operators111These analyses include only this restricted set of dimension 8
operators, and no dimension 6 contribution. have been released by the
experimental collaborations in recent years. To name a few: [10, 11, 12, 13].

Adopting this approach has the drawback that the basis is not complete as it
neglects by definition any contribution affecting triple gauge couplings: for
example operators of the form
$\mathcal{O}_{8}\sim(H^{\dagger}H)\epsilon_{ijk}W_{\mu\nu}^{i}W_{\nu\rho}^{j}W_{\rho\mu}^{k}\,$
are not included.

To address this issue and given that this basis has now been widely used and
experimental constraints are available, we suggest a three-step procedure:

  1. 1.

Perform a fit for the VBS channels including the dimension 6 basis as well as
the aQCG one.

  2. 2.

Extract the relevant dimension 8 operators from the complete basis and repeat
the procedure.

  3. 3.

Compare results and draw conclusions based on their consistency.

Furthermore including dimension 8 terms from the aQGC basis, could be very
interesting, since it allows for interactions that are traditionally forbidden
by gauge invariance in the SM, for example vertices of the form
$Z_{\mu}Z_{\nu}Z_{\rho}Z_{\sigma}$ or
$Z_{\mu}Z_{\nu}\gamma_{\rho}\gamma_{\sigma}$. Non-SM-like couplings are
important to ensure that we are not missing a possible trace of new physics.
The available experimental bounds show that neutral aTCGs are very unlikely,
but results on aQCGs are still not completely ruling them out.

Additionally, it is important to consider different scenarios for the EWSB
mechanism, which might shed light on the shape of the Higgs potential. For
example, in the HEFT Lagrangian (2), a non-linear representation for the Higgs
field is adopted. The physical $h$ field is deemed to be independent from the
goldstone bosons $(w^{+},w^{-})$, for example by arranging them as a phase of
the covariant derivative. The EFT expansion then, is not performed by adding
higher dimensional operators regularized but a cut off scale. Instead, the
expansion is in terms of $h/v$, leading to a different power counting:

$\mathcal{L}_{HEFT}=\left(1+a\frac{h}{v}+b\frac{h^{2}}{v^{2}}+\dots\right)\partial_{\mu}w^{+}\partial^{\mu}w^{-}+\frac{1}{2}\partial_{\mu}h\partial^{\mu}h+\dots$
(2)

This model has been studied in the particular context of VBS, the paradigmatic
example of unitary violations, in different publications,[14, 15, 16]. However
extending it to reproduce the full LHC setup still represents some technical
challenge: most Monte Carlo generators assume BSM models to be an extension of
the SM, whereas in this Lagrangian the $SU(2)xU(1)$ structure is removed and
reproducing experimental results becomes non-trivial. Specifically for a
channel like VBS with a 6-fermion final state. This particular theory would
particularly benefit from the Pseudo Observable approach that we discuss in
the following section.

##  4 Computational Bottlenecks and Pseudo Observables

At this stage it is important to reflect on the computational demands of
performing such a global fit. The fitting tool used here, SMEFiT [2] is based
on a sampling procedure, and can perform a quadratic fit with
$\mathcal{O}(50)$ operators in a a couple hours working on multiple CPU. The
bottleneck though is on the prediction side. Reproducing accurately the
experimental definitions, parton shower set-up and selection cuts, which are
different for each collaborations, is a non-trivial exercise. To put it
quantitatively: reproducing the SM prediction for a certain diboson process
takes a couple of CPU hours for the generation and showering of
$\mathcal{O}(500k)$ events, plus a few minutes for the analysis of the output
files with Rivet222This is the most accurate approach as experimental data are
published in HepData in the native Rivet format (.yoda).. The same procedure
for a VBS process (6-particle final state) takes a few days already at LO.

Once the SM theory prediction agrees with the experimental one, one can move
onto generating the different EFT predictions, for the case of $n$ operators,
one needs to scale the CPU time (and disk storage) by $n$ for a linear
prediction. Moving to quadratic terms, the situation worsens dramatically: we
need to calculate additional $n$ predictions for each operator interfering
with itself, as well as $\binom{n}{2}$ cross-terms. This tendency is
illustrated in Figure 3.

Figure 3: Number of terms in the EFT expansion for a given observable. On the
left we see the tendency up to 59 operators, the dimension 6 case assuming
flavour universality. On the right we see how the numbers would grow as we add
higher dimensional contributions.

The main alternative to circumvent this issue would be to define new
quantities which are easier to reproduce from the theory side, namely certain
Pseudo Observables [17, 18, 19]. Some works have pointed in this direction
recently, with the main issues being that such observables are process-
dependent (one needs to find the _optimal observable_ for a given process),
and that, if a certain technique is used to process the data (typically a
neural network), the details of the former would have to be passed on to the
theory community alongside with the results. This issue has been discussed in
the past given that published data for differential distributions at LHC often
rely on machine learning techniques, and it is difficult to estimate their
impact on the exact final event selection. This question has been highlighted
again recently in [20, 21], where some solutions where proposed. Another
interesting approach is that of Matrix Element methods, a type of multivariate
analysis techniques where predictions are compared at the level of the
$S$-matrix elements, independent of phase space definitions. A recent
interesting proposal in this direction is that of [22].

Last but not least, there is the issue of _precision_. It would be important
to revisit our understanding of precision measurements and the proton
structure, in the context of the EFT interpretations, to ensure we are not
overlooking some new physics effects. Some works including the SMEFT effects
in PDF fits have recently been presented in [23]. On this note, it would be
also interesting to perform combined studies to understand the impact of the
uncertainties that can be associated to the determination of the SM
parameters, in the context of non-SM-like scenarios like HEFT.

##  5 Conclusions

Now that global fits of the dimension 6 basis in the LHC dataset are well
underway, it is time to look into the near future and (re)design our strategy
for the analysis of the forthcoming runs. The main challenges we find are two:
On the more technical level, the inclusion of higher orders in the EFT
expansion, needs of large computational resources. This issue might be
circumvented in the future if the theory and experimental communities agree on
a common framework for the publication of data and statistical models. On the
broader side of the question, we have to ensure that our results don’t fall
into a _self fulfilled prophecy_. By assuming Standard-Model-like structures
we might be missing information on strongly coupled new physics. Understanding
the shape of the Higgs potential through direct $HH$ and $HHH$ production
measurements seems to be slightly out of reach given the HL-LHC projections
and alternative approaches should also be considered. This task should be a
priority for the collider phenomenology community, and EFT searches in the EW
sector should illustrate this fact.

##  6 Acknowledgements

We are very thankful to our collaborators J. J. Ethier and J. Rojo.

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