Distribution of jet girth of CMS data, SM multijet MC events, and various signal samples. Events are required to pass the trigger requirements and also have 4 jets with $p_\mathrm{T}>100\mathrm{GeV}$. The distribution from various processes have their total count normalized to 1.

Distribution of uGNN output score of CMS data, SM multijet MC events, and various signal samples. Events are required to pass the trigger requirements and also have 4 jets with $p_\mathrm{T}>100\mathrm{GeV}$. The distribution from various processes have their total count normalized to 1.

Distribution of aGNN output score of CMS data, SM multijet MC events, and various signal samples. Events are required to pass the trigger requirements and also have 4 jets with $p_\mathrm{T}>100\mathrm{GeV}$. The distribution from various processes have their total count normalized to 1.

Template fit of the DeepJet discriminator used to determine the b jet fraction of the non-EMJ tagged jets in data events that pass the 1-EMJ selection of the event selection criteria defined in the paper.

The EJ tagger misidentification probability for b quark jets and light jets as a function of jet $p_\mathrm{T}$ for the taggers u-tag 1, u-tag 2, and u-tag 3, as defined in the paper, evaluated using data and generator-level flavor information from simulated samples in events containing a high-$p_\mathrm{T}$ photon.

The EJ tagger misidentification probability for b quark jets and light jets as a function of jet $p_\mathrm{T}$ for the taggers uGNN tag 1, uGNN tag 2, and uGNN tag 3, as defined in the paper, evaluated using data and generator-level flavor information from simulated samples in events containing a high-$p_\mathrm{T}$ photon.

The EJ tagger misidentification probability for b quark jets and light jets as a function of jet $p_\mathrm{T}$ for the taggers u-tag 4 and u-tag 5, as defined in the paper, evaluated using data and generator-level flavor information from simulated samples in events containing a high-$p_\mathrm{T}$ photon.

The expected and observed 95% CL upper limits on the production cross section for various signal models in the unflavored scenario model-generic and GNN EMJ tagging methods.

The expected and observed 95% CL upper limits on the production cross section for various signal models in the flavor-aligned scenario model-generic and GNN EMJ tagging methods.

The observed yield of events in data satisfying the validation selection criteria with at least two jets passing the corresponding validation tag, and the estimation based on the misidentification rate calculated using validation events with exactly one jet passing the validation tagger scaled by the factor given in Equation (4) of the paper.

The estimated number of events from the background prediction based on control samples in data and the observed event yields.

Distribution of event $H_\mathrm{T}$ of CMS data, SM multijet MC events, and various signal samples. Events are required to pass the trigger requirements and also have 4 jets with $p_\mathrm{T}>100\mathrm{GeV}$. The distribution from various processes have their total count normalized to 1.

Distribution of leading jet $p_\mathrm{T}$ of CMS data, SM multijet MC events, and various signal samples. Events are required to pass the trigger requirements and also have 4 jets with $p_\mathrm{T}>100\mathrm{GeV}$. The distribution from various processes have their total count normalized to 1.

Distribution of second leading jet $p_\mathrm{T}$ of CMS data, SM multijet MC events, and various signal samples. Events are required to pass the trigger requirements and also have 4 jets with $p_\mathrm{T}>100\mathrm{GeV}$. The distribution from various processes have their total count normalized to 1.

Distribution of third leading jet $p_\mathrm{T}$ of CMS data, SM multijet MC events, and various signal samples. Events are required to pass the trigger requirements and also have 4 jets with $p_\mathrm{T}>100\mathrm{GeV}$. The distribution from various processes have their total count normalized to 1.

Distribution of forth leading jet $p_\mathrm{T}$ of CMS data, SM multijet MC events, and various signal samples. Events are required to pass the trigger requirements and also have 4 jets with $p_\mathrm{T}>100\mathrm{GeV}$. The distribution from various processes have their total count normalized to 1.

Distribution of Track $d_{xy}$ of CMS data, SM multijet MC events, and various signal samples. Events are required to pass the trigger requirements and also have 4 jets with $p_\mathrm{T}>100\mathrm{GeV}$. The distribution from various processes have their total count normalized to 1.

Distribution of track $D_{N}$ of CMS data, SM multijet MC events, and various signal samples. Events are required to pass the trigger requirements and also have 4 jets with $p_\mathrm{T}>100\mathrm{GeV}$. The distribution from various processes have their total count normalized to 1.

Distribution of track $d_{xy}$ of CMS data, SM multijet MC events, and various signal samples. Events are required to pass the trigger requirements and also have 4 jets with $p_\mathrm{T}>100\mathrm{GeV}$. The distribution from various processes have their total count normalized to 1.

Distribution of jet $\overline{\tau}_{2/1}$ of CMS data, SM multijet MC events, and various signal samples. Events are required to pass the trigger requirements and also have 4 jets with $p_\mathrm{T}>100\mathrm{GeV}$. The distribution from various processes have their total count normalized to 1.

Distribution of $|\Delta\eta|$ between jet and tracks of CMS data, SM multijet MC events, and various signal samples. Events are required to pass the trigger requirements and also have 4 jets with $p_\mathrm{T}>100\mathrm{GeV}$. The distribution from various processes have their total count normalized to 1.

Distribution of $|\Delta\phi|$ between jet and tracks of CMS data, SM multijet MC events, and various signal samples. Events are required to pass the trigger requirements and also have 4 jets with $p_\mathrm{T}>100\mathrm{GeV}$. The distribution from various processes have their total count normalized to 1.

Distribution of track $d_{xy}$ of CMS data, SM multijet MC events, and various signal samples. Events are required to pass the trigger requirements and also have 4 jets with $p_\mathrm{T}>100\mathrm{GeV}$. The distribution from various processes have their total count normalized to 1.

Distribution of track $p_\mathrm{T}$ of CMS data, SM multijet MC events, and various signal samples. Events are required to pass the trigger requirements and also have 4 jets with $p_\mathrm{T}>100\mathrm{GeV}$. The distribution from various processes have their total count normalized to 1.

Distribution of track $d_{z}$ of CMS data, SM multijet MC events, and various signal samples. Events are required to pass the trigger requirements and also have 4 jets with $p_\mathrm{T}>100\mathrm{GeV}$. The distribution from various processes have their total count normalized to 1.

Distribution of track $p_\mathrm{T}$ fraction of CMS data, SM multijet MC events, and various signal samples. Events are required to pass the trigger requirements and also have 4 jets with $p_\mathrm{T}>100\mathrm{GeV}$. The distribution from various processes have their total count normalized to 1.

The EJ tagger misidentification probability for b quark jets and light jets as a function of jet $p_\mathrm{T}$ for the taggers a-tag 1, a-tag 2, a-tag 3, and a-tag 4, as defined in the paper, evaluated using data and generator-level flavor information from simulated samples in events containing a high-$p_\mathrm{T}$ photon.

The EJ tagger misidentification probability for b quark jets and light jets as a function of jet $p_\mathrm{T}$ for the taggers aGNN tag 1, aGNN tag 2, and aGNN tag 3, as defined in the paper, evaluated using data and generator-level flavor information from simulated samples in events containing a high-$p_\mathrm{T}$ photon.

Signal acceptance of signal models in the unflavored scenario with using the designated cut sets with the model agnostic and GNN EJ tagging methods.

Signal acceptance of signal models in the flavor-aligned scenario with using the designated cut sets with the model agnostic and GNN EJ tagging methods.

Tagging efficiency and its uncertainty of the taggers used to select signal jets in the unflavored scenario, as a function of the $p_\mathrm{T}$ and $|\eta|$ of the signal jet.

Tagging efficiency and its uncertainty of the taggers used to select signal jets in the unflavored scenario, as a function of the $p_\mathrm{T}$ and $|\eta|$ of the signal jet.

Tagging efficiency and its uncertainty of the taggers used to select signal jets in the unflavored scenario, as a function of the $p_\mathrm{T}$ and $|\eta|$ of the signal jet.

Tagging efficiency and its uncertainty of the taggers used to select signal jets in the unflavored scenario, as a function of the $p_\mathrm{T}$ and $|\eta|$ of the signal jet.

Tagging efficiency and its uncertainty of the taggers used to select signal jets in the unflavored scenario, as a function of the $p_\mathrm{T}$ and $|\eta|$ of the signal jet.

Tagging efficiency and its uncertainty of the taggers used to select signal jets in the unflavored scenario, as a function of the $p_\mathrm{T}$ and $|\eta|$ of the signal jet.

Tagging efficiency and its uncertainty of the taggers used to select signal jets in the unflavored scenario, as a function of the $p_\mathrm{T}$ and $|\eta|$ of the signal jet.

Tagging efficiency and its uncertainty of the taggers used to select signal jets in the unflavored scenario, as a function of the $p_\mathrm{T}$ and $|\eta|$ of the signal jet.

Tagging efficiency and its uncertainty of the taggers used to select signal jets in the unflavored scenario, as a function of the $p_\mathrm{T}$ and $|\eta|$ of the signal jet.

Tagging efficiency and its uncertainty of the taggers used to select signal jets in the unflavored scenario, as a function of the $p_\mathrm{T}$ and $|\eta|$ of the signal jet.

Tagging efficiency and its uncertainty of the taggers used to select signal jets in the flavor-aligned scenario, as a function of the $p_\mathrm{T}$ and $|\eta|$ of the signal jet.

Tagging efficiency and its uncertainty of the taggers used to select signal jets in the flavor-aligned scenario, as a function of the $p_\mathrm{T}$ and $|\eta|$ of the signal jet.

Tagging efficiency and its uncertainty of the taggers used to select signal jets in the flavor-aligned scenario, as a function of the $p_\mathrm{T}$ and $|\eta|$ of the signal jet.

Tagging efficiency and its uncertainty of the taggers used to select signal jets in the flavor-aligned scenario, as a function of the $p_\mathrm{T}$ and $|\eta|$ of the signal jet.

Tagging efficiency and its uncertainty of the taggers used to select signal jets in the flavor-aligned scenario, as a function of the $p_\mathrm{T}$ and $|\eta|$ of the signal jet.

Tagging efficiency and its uncertainty of the taggers used to select signal jets in the flavor-aligned scenario, as a function of the $p_\mathrm{T}$ and $|\eta|$ of the signal jet.

Tagging efficiency and its uncertainty of the taggers used to select signal jets in the flavor-aligned scenario, as a function of the $p_\mathrm{T}$ and $|\eta|$ of the signal jet.

Tagging efficiency and its uncertainty of the taggers used to select signal jets in the flavor-aligned scenario, as a function of the $p_\mathrm{T}$ and $|\eta|$ of the signal jet.

Tagging efficiency and its uncertainty of the taggers used to select signal jets in the flavor-aligned scenario, as a function of the $p_\mathrm{T}$ and $|\eta|$ of the signal jet.

Distribution of jet-associated tracks of jets in SM multijet processes that pass the basic kinematic selection, the uGNN tag 1 tagging criteria, and aGNN tag 1 EMJ tagging criteria as a function of $\Delta R$ between the jet and track of interest and $\mathrm{sign}(d_{xy})\cdot\ln\left( 1+\left\lvert\frac{d_{xy}}{1\mathrm{cm}}\right\rvert\right)$, with $d_{xy}$ being the transverse impact parameter of the track of interest.

Example cut flow used for selecting signals events using the u-set 4 selection criteria. Both background and MC events has been normalized to unity.

Example cut flow used for selecting signals events using the u-set 1 selection criteria. Both background and MC events has been normalized to unity.

Example cut flow used for selecting signals events using the u-set 2 selection criteria. Both background and MC events has been normalized to unity.

Example cut flow used for selecting signals events using the u-set 3 selection criteria. Both background and MC events has been normalized to unity.

Example cut flow used for selecting signals events using the u-set 5 selection criteria. Both background and MC events has been normalized to unity.

Example cut flow used for selecting signals events using the uGNN set 3 selection criteria. Both background and MC events has been normalized to unity.

Example cut flow used for selecting signals events using the uGNN set 1 selection criteria. Both background and MC events has been normalized to unity.

Example cut flow used for selecting signals events using the uGNN set 2 selection criteria. Both background and MC events has been normalized to unity.

Example cut flow used for selecting signals events using the a-set 3 selection criteria. Both background and MC events has been normalized to unity.

Example cut flow used for selecting signals events using the a-set 1 selection criteria. Both background and MC events has been normalized to unity.

Example cut flow used for selecting signals events using the a-set 2 selection criteria. Both background and MC events has been normalized to unity.

Example cut flow used for selecting signals events using the a-set 4 selection criteria. Both background and MC events has been normalized to unity.

Example cut flow used for selecting signals events using the a-set 5 selection criteria. Both background and MC events has been normalized to unity.

Example cut flow used for selecting signals events using the aGNN set 3 selection criteria. Both background and MC events has been normalized to unity.

Example cut flow used for selecting signals events using the aGNN set 1 selection criteria. Both background and MC events has been normalized to unity.

Example cut flow used for selecting signals events using the aGNN set 2 selection criteria. Both background and MC events has been normalized to unity.