We here report on the progress of the HHH Workshop, that took place in Dubrovnik in July 2023. After the discovery of a particle that complies with the properties of the Higgs boson of the Standard Model, all Standard Model (SM) parameters are in principle determined. However, in order to verify or falsify the model, the full form of the potential has to be determined. This includes the measurement of the triple and quartic scalar couplings.

Morocco

Hamza Abouabid

Abdesslam Arhrib

United States

Hannah Arnold

Germany

Duarte Azevedo

Croatia

Vuko Brigljevic

United Kingdom

Maggie Chen

Daniel Diaz

Javier Duarte

Netherlands

Tristan Pree

Jaouad Falaki

Dinko Ferencek

Portugal

Pedro. Ferreira

France

Benjamin Fuks

India

Japan

Sanmay Ganguly

Osama Karkout

Marina Kolosova

Jacobo Konigsberg

Greg Landsberg

Canada

China

Bingxuan Liu

CERN

Switzerland

Brian Moser

Margarete Mühlleitner

Andreas Papaefstathiou

Sweden

Roman Pasechnik

Tania Robens

Rui Santos

Brian Sheldon

Gregory Soyez

Marko Stamenkovic

Panagiotis Stylianou

Tatjana Susa

Gilberto Tetlalmatzi-Xolocotzi

Georg Weiglein

Giulia Zanderighi

Rui Zhang

HHH whitepaper

The task of reconstructing particles from low-level detector response data to predict the set of final state particles in collision events represents a set-to-set prediction task requiring the use of multiple features and their correlations in the input data. We deploy three separate set-to-set neural network architectures to reconstruct particles in events containing a single jet in a fully-simulated calorimeter. Performance is evaluated in terms of particle reconstruction quality, properties regression, and jet-level metrics. The results demonstrate that such a high-dimensional end-to-end approach succeeds in surpassing basic parametric approaches in disentangling individual neutral particles inside of jets and optimizing the use of complementary detector information. In particular, the performance comparison favors a novel architecture based on learning hypergraph structure, HGPflow, which benefits from a physically-interpretable approach to particle reconstruction.

Italy

Francesco Di Bello

Israel

Etienne Dreyer

Eilam Gross

Lukas Heinrich

Anna Ivina

Marumi Kado

Nilotpal Kakati

Lorenzo Santi

Jonathan Shlomi

Matteo Tusoni

Reconstructing particles in jets using set transformer and hypergraph prediction networks

Jet classification is an important ingredient in measurements and searches for new physics at particle colliders, and secondary vertex reconstruction is a key intermediate step in building powerful jet classifiers. We use a neural network to perform vertex finding inside jets in order to improve the classification performance, with a focus on separation of bottom vs. charm flavor tagging. We implement a novel, universal set-to-graph model, which takes into account information from all tracks in a jet to determine if pairs of tracks originated from a common vertex. We explore different performance metrics and find our method to outperform traditional approaches in accurate secondary vertex reconstruction. We also find that improved vertex finding leads to a significant improvement in jet classification performance.

Kyle Cranmer

Yaron Lipman

Hadar Serviansky

Haggai Maron

Nimrod Segol

Secondary vertex finding in jets with neural networks

Triple heavy vector boson production, pp → VVV (V = W, Z), has recently been observed for the first time. We propose that precision measurements of this process provide an excellent probe of the first generation light quark Yukawa couplings. Modified quark interactions with the off-shell Higgs in this process lead to a rapid growth of the partonic cross sections with energy, which manifests in an enhanced p  T  distribution of the final state leptons and quarks. We quantify this effect and estimate the present and future 2σ sensitivity to the up, down, and strange Yukawas. In particular, we find that HL-LHC can reach   <math> <mi>O</mi> <mfenced> <mn>400</mn> </mfenced> </math>  $$ \mathcal{O}(400) $$  sensitivity to the down Yukawa relative to the Standard Model value, improving the current sensitivity in this process by a factor of 10, and which can be further improved to   <math> <mi>O</mi> <mfenced> <mn>30</mn> </mfenced> </math>  $$ \mathcal{O}(30) $$  at FCC-hh. This is competitive with and complementary to constraints from global fits and other on-shell probes of the first generation Yukawas. The triboson sensitivity at HL-LHC corresponds to probing dimension-6 SMEFT operators suppressed by an   <math> <mi>O</mi> <mfenced> <mn>1</mn> </mfenced> </math>  $$ \mathcal{O}(1) $$  TeV scale, similarly to other LHC Higgs probes.

Adam Falkowski

Phillippe Gras

Spain

Jose No

Kohsaku Tobioka

Natascia Vignaroli

Tevong You

Light quark Yukawas in triboson final states

In High Energy Physics experiments Particle Flow (PFlow) algorithms are designed to provide an optimal reconstruction of the nature and kinematic properties of the particles produced within the detector acceptance during collisions. At the heart of PFlow algorithms is the ability to distinguish the calorimeter energy deposits of neutral particles from those of charged particles, using the complementary measurements of charged particle tracking devices, to provide a superior measurement of the particle content and kinematics. In this paper, a computer vision approach to this fundamental aspect of PFlow algorithms, based on calorimeter images, is proposed. A comparative study of the state of the art deep learning techniques is performed. A significantly improved reconstruction of the neutral particle calorimeter energy deposits is obtained in a context of large overlaps with the deposits from charged particles. Calorimeter images with augmented finer granularity are also obtained using super-resolution techniques.

Francesco Bello

Michael Pitt

Towards a computer vision particle flow

A dedicated search is presented for new phenomena in inclusive 8- and 10-jet final states with low missing transverse momentum, with and without identification of jets originating from b quarks. The analysis is based on data from proton–proton collisions corresponding to an integrated luminosity of 19.7fb−1 collected with the CMS detector at the LHC at s=8TeV . The dominant multijet background expectations are obtained from low jet multiplicity control samples. Data agree well with the standard model background predictions, and limits are set in several benchmark models. Colorons (axigluons) with masses between 0.6 and 0.75 (up to 1.15) TeV are excluded at 95% confidence level. Similar exclusion limits for gluinos in R -parity violating supersymmetric scenarios are from 0.6 up to 1.1 TeV. These results comprise the first experimental probe of the coloron and axigluon models in multijet final states.

Armenia

Vardan Khachatryan

Albert M Sirunyan

Armen Tumasyan

Austria

Wolfgang Adam

Ece Aşılar

Thomas Bergauer

Johannes Brandstetter

Erica Brondolin

Marko Dragicevic

Janos Erö

Martin Flechl

Markus Friedl

Rudolf Fruehwirth

Vasile Mihai Ghete

Christian Hartl

Natascha Hörmann

Josef Hrubec

Manfred Jeitler

Axel König

Ilse Krätschmer

Dietrich Liko

Takashi Matsushita

Ivan Mikulec

Dinyar Rabady

Navid Rad

Babak Rahbaran

Herbert Rohringer

Jochen Schieck

Josef Strauss

Wolfgang Treberer-Treberspurg

Wolfgang Waltenberger

Claudia-Elisabeth Wulz

Belarus

Vladimir Mossolov

Nikolai Shumeiko

Juan Suarez Gonzalez

Belgium

Sara Alderweireldt

Eddi A De Wolf

Xavier Janssen

Jasper Lauwers

Merijn Van De Klundert

Hans Van Haevermaet

Pierre Van Mechelen

Nick Van Remortel

Alex Van Spilbeeck

Shimaa Abu Zeid

Freya Blekman

Jorgen D'Hondt

Nadir Daci

Isabelle De Bruyn

Kevin Deroover

Natalie Heracleous

Steven Lowette

Seth Moortgat

Lieselotte Moreels

Annik Olbrechts

Quentin Python

Stefaan Tavernier

Walter Van Doninck

Petra Van Mulders

Isis Van Parijs

Hugues Brun

Cécile Caillol

Barbara Clerbaux

Gilles De Lentdecker

Hugo Delannoy

Giuseppe Fasanella

Laurent Favart

Reza Goldouzian

Anastasia Grebenyuk

Georgia Karapostoli

Thomas Lenzi

Alexandre Léonard

Jelena Luetic

Thierry Maerschalk

Andrey Marinov

Aidan Randle-conde

Tomislav Seva

Catherine Vander Velde

Pascal Vanlaer

Ryo Yonamine

Florian Zenoni

Fengwangdong Zhang

Anna Cimmino

Tom Cornelis

Didar Dobur

Alexis Fagot

Guillaume Garcia

Muhammad Gul

Deniz Poyraz

Sinem Salva Diblen

Robert Schöfbeck

Michael Tytgat

Ward Van Driessche

Efe Yazgan

Nicolas Zaganidis

Hamed Bakhshiansohi

Camille Beluffi

Olivier Bondu

Sébastien Brochet

Giacomo Bruno

Search for new phenomena in events with high jet multiplicity and low missing transverse momentum in proton–proton collisions at s=8TeV