Loukas Gouskos

This document a outlines a set of simplified models for dark matter and its interactions with Standard Model particles. It is intended to summarize the main characteristics that these simplified models have when applied to dark matter searches at the LHC, and to provide a number of useful expressions for reference. The list of models includes both s-channel and t-channel scenarios. For s-channel, spin-0 and spin-1 mediations are discussed, and also realizations where the Higgs particle provides a portal between the dark and visible sectors. The guiding principles underpinning the proposed simplified models are spelled out, and some suggestions for implementation are presented.

How to represent a jet is at the core of machine learning on jet physics. Inspired by the notion of point clouds, we propose a new approach that considers a jet as an unordered set of its constituent particles, effectively a ``particle cloud.'' Such a particle cloud representation of jets is efficient in incorporating raw information of jets and also explicitly respects the permutation symmetry. Based on the particle cloud representation, we propose ParticleNet, a customized neural network architecture using Dynamic Graph Convolutional Neural Network for jet tagging problems. The ParticleNet architecture achieves state-of-the-art performance on two representative jet tagging benchmarks and is improved significantly over existing methods.

Based on the established task of identifying boosted, hadronically decaying top quarks, we compare a wide range of modern machine learning approaches. Unlike most established methods they rely on low-level input, for instance calorimeter output. While their network architectures are vastly different, their performance is comparatively similar. In general, we find that these new approaches are extremely powerful and great fun.

In this White Paper we present and discuss a concrete proposal for the consistent interpretation of Dark Matter searches at colliders and in direct detection experiments. Based on a specific implementation of simplified models of vector and axial-vector mediator exchanges, this proposal demonstrates how the two search strategies can be compared on an equal footing.

Jet flavour identification algorithms are of paramount importance to maximise the physics potential of future collider experiments. This work describes a novel set of tools allowing for a realistic simulation and reconstruction of particle level observables that are necessary ingredients to jet flavour identification. An algorithm for reconstructing the track parameters and covariance matrix of charged particles for an arbitrary tracking sub-detector geometries has been developed. Additional modules allowing for particle identification using time-of-flight and ionizing energy loss information have been implemented. A jet flavour identification algorithm based on a graph neural network architecture and exploiting all available particle level information has been developed. The impact of different detector design assumptions on the flavour tagging performance is assessed using the FCC-ee IDEA detector prototype.

We present performance studies of a full-length prototype for the CASTOR quartz-tungsten sampling calorimeter, installed in the very forward region of the CMS experiment at the LHC. The response linearity and energy resolution, the uniformity, as well as the showers’ spatial properties in the prototype have been studied with electrons, pions and muons of various energies. A special study was also carried out for testing the light-output with a 90-degree cut of the quartz plates of the calorimeter. The data were taken during the CASTOR test beam at CERN/SPS in 2007.

In order to stimulate new engagement and trigger some concrete studies in areas where further work would be beneficial towards fully understanding the physics potential of an $e^+e^-$ Higgs / Top / Electroweak factory, we propose to define a set of focus topics. The general reasoning and the proposed topics are described in this document.

We present results of the performance of the second prototype of the CASTOR quartz–tungsten sampling calorimeter, to be installed in the very forward region of the CMS experiment at the LHC. The energy linearity and resolution, as well as the spatial resolution of the prototype to electromagnetic and hadronic showers are studied with E=20–200 GeV electrons, E=20–350 GeV pions, and E=50, 150 GeV muons from beam tests carried out at CERN/SPS in 2004. The responses of the calorimeter using two different types of photodetectors (avalanche photodiodes APDs, and photomultiplier tubes PMTs) are compared.

Even though jet substructure was not an original design consideration for the Large Hadron Collider (LHC) experiments, it has emerged as an essential tool for the current physics program. We examine the role of jet substructure on the motivation for and design of future energy Frontier colliders. In particular, we discuss the need for a vibrant theory and experimental research and development program to extend jet substructure physics into the new regimes probed by future colliders. Jet substructure has organically evolved with a close connection between theorists and experimentalists and has catalyzed exciting innovations in both communities. We expect such developments will play an important role in the future energy Frontier physics program.

Abstract The Higgs bosons and the top quark decay into rich and diverse final states, containing both light and heavy quarks, gluons, photons as well as W and Z bosons. This article reviews the challenges involved in reconstructing Higgs and top events at the FCC-ee and identifies the areas where novel developments are needed. The precise identification and reconstruction of these final states at the FCC-ee rely on the capability of the detector to provide excellent flavour tagging, jet energy and angular resolution, and global kinematic event reconstruction. Excellent flavour tagging performance requires low-material vertex and tracking detectors, and advanced machine learning techniques as successfully employed in LHC experiments. In addition, the Z pole run will provide abundant samples of heavy flavour partons that can be used for calibration of the tagging algorithms. For the reconstruction of jets, leptons, and missing energy, particle-flow algorithms are crucial to explore the full potential of the highly granular tracking and calorimeter systems, and give access to excellent energy–momentum resolution and precise identification of heavy bosons in their hadronic decays. This enables, among many other key elements, the reconstruction of Higgsstrahlung processes with leptonically and hadronically decaying Z bosons, and an almost background-free identification of top quark pair events. Exploiting the full available kinematic constraints together with exclusive jet clustering algorithms will allow for the optimisation of global event reconstruction with kinematic fitting techniques.

The European Committee for Future Accelerators (ECFA) Early Career Researcher's (ECR) panel, which represents the interests of the ECR community to ECFA, officially began its activities in January 2021. In the first two years, the panel has defined its own internal structure, responded to ECFA requests for feedback, and launched its own initiatives to better understand and support the diverse interests of early career researchers. This report summarises the panel composition and structure, as well as the different activities the panel has been involved with during the first two years of its existence.

We propose a novel strategy for disentangling proton collisions at hadron colliders such as the LHC that considerably improves over the current state of the art. Employing a metric inspired by optimal transport problems as the cost function of a graph neural network, our algorithm is able to compare two particle collections with different noise levels and learns to flag particles originating from the main interaction amidst products from up to 200 simultaneous pileup collisions. We thereby sidestep the critical task of obtaining a ground truth by labeling particles and avoid arduous human annotation in favor of labels derived in situ through a self-supervised process. We demonstrate how our approach---which, unlike competing algorithms, is trivial to implement---improves the resolution in key objects used in precision measurements and searches alike and present large sensitivity gains in searching for exotic Higgs boson decays at the High-Luminosity LHC.

Even though jet substructure was not an original design consideration for the Large Hadron Collider (LHC) experiments, it has emerged as an essential tool for the current physics program. We examine the role of jet substructure on the motivation for and design of future energy frontier colliders. In particular, we discuss the need for a vibrant theory and experimental research and development program to extend jet substructure physics into the new regimes probed by future colliders. Jet substructure has organically evolved with a close connection between theorists and experimentalists and has catalyzed exciting innovations in both communities. We expect such developments will play an important role in the future energy frontier physics program.

We present preliminary results of the performance of prototype III of the CASTOR quartz-tungsten sampling calorimeter, to be installed in the very forward region of the CMS experiment at the LHC. The energy linearity and resolution, as well as the spatial resolution of the prototype to electromagnetic and hadronic showers are studied with E= 10−200 GeV electrons, E= 20−350 GeV pions, and E= 50, 150 GeV muons in beam tests carried out at CERN/SPS in 2007.

A group of Early-Career Researchers (ECRs) has been given a mandate from the European Committee for Future Accelerators (ECFA) to debate the topics of the current European Strategy Update (ESU) for Particle Physics and to summarise the outcome in a brief document [1]. A full-day debate with 180 delegates was held at CERN, followed by a survey collecting quantitative input. During the debate, the ECRs discussed future colliders in terms of the physics prospects, their implications for accelerator and detector technology as well as computing and software. The discussion was organised into several topic areas. From these areas two common themes were particularly highlighted by the ECRs: sociological and human aspects; and issues of the environmental impact and sustainability of our research.

The European Committee for Future Accelerators (ECFA) Early-Career Researchers (ECR) Panel was invited by the ECFA Detector R&D Roadmap conveners to collect feedback from the European ECR community. A working group within the ECFA ECR panel held a Townhall Meeting to get first input, and then designed and broadly circulated a detailed survey to gather feedback from the larger ECR community. A total of 473 responses to this survey were received, providing a useful overview of the experiences of ECRs in instrumentation training and related topics. This report summarises the feedback received, and is intended to serve as an input to the ECFA Detector R&D Roadmap process.

Abstract In high energy physics (HEP), analysis metadata comes in many forms—from theoretical cross-sections, to calibration corrections, to details about file processing. Correctly applying metadata is a crucial and often time-consuming step in an analysis, but designing analysis metadata systems has historically received little direct attention. Among other considerations, an ideal metadata tool should be easy to use by new analysers, should scale to large data volumes and diverse processing paradigms, and should enable future analysis reinterpretation. This document, which is the product of community discussions organised by the HEP Software Foundation, categorises types of metadata by scope and format and gives examples of current metadata solutions. Important design considerations for metadata systems, including sociological factors, analysis preservation efforts, and technical factors, are discussed. A list of best practices and technical requirements for future analysis metadata systems is presented. These best practices could guide the development of a future cross-experimental effort for analysis metadata tools.

Jet flavour identification algorithms are of paramount importance to maximise the physics potential of future collider experiments. This work describes a novel set of tools allowing for a realistic simulation and reconstruction of particle level observables that are necessary ingredients to jet flavour identification. An algorithm for reconstructing the track parameters and covariance matrix of charged particles for an arbitrary tracking sub-detector geometries has been developed. Additional modules allowing for particle identification using time-of-flight and ionizing energy loss information have been implemented. A jet flavour identification algorithm based on a graph neural network architecture and exploiting all available particle level information has been developed. The impact of different detector design assumptions on the flavour tagging performance is assessed using the FCC-ee IDEA detector prototype.

We propose a novel strategy for disentangling proton collisions at hadron colliders such as the LHC that considerably improves over the current state of the art. Employing a metric inspired by optimal transport problems as the cost function of a graph neural network, our algorithm is able to compare two particle collections with different noise levels and learns to flag particles originating from the main interaction amidst products from up to 200 simultaneous pileup collisions. We thereby sidestep the critical task of obtaining a ground truth by labeling particles and avoid arduous human annotation in favor of labels derived in situ through a self-supervised process. We demonstrate how our approach - which, unlike competing algorithms, is trivial to implement - improves the resolution in key objects used in precision measurements and searches alike and present large sensitivity gains in searching for exotic Higgs boson decays at the High-Luminosity LHC.

The second workshop on the HEP Analysis Ecosystem took place 23-25 May 2022 at IJCLab in Orsay, to look at progress and continuing challenges in scaling up HEP analysis to meet the needs of HL-LHC and DUNE, as well as the very pressing needs of LHC Run 3 analysis. The workshop was themed around six particular topics, which were felt to capture key questions, opportunities and challenges. Each topic arranged a plenary session introduction, often with speakers summarising the state-of-the art and the next steps for analysis. This was then followed by parallel sessions, which were much more discussion focused, and where attendees could grapple with the challenges and propose solutions that could be tried. Where there was significant overlap between topics, a joint discussion between them was arranged. In the weeks following the workshop the session conveners wrote this document, which is a summary of the main discussions, the key points raised and the conclusions and outcomes. The document was circulated amongst the participants for comments before being finalised here.

The European Committee for Future Accelerators (ECFA) Early Career Researcher's (ECR) panel, which represents the interests of the ECR community to ECFA, officially began its activities in January 2021. In the first two years, the panel has defined its own internal structure, responded to ECFA requests for feedback, and launched its own initiatives to better understand and support the diverse interests of early career researchers. This report summarises the panel composition and structure, as well as the different activities the panel has been involved with during the first two years of its existence.

Based on the established task of identifying boosted, hadronically decaying top quarks, we compare a wide range of modern machine learning approaches.We find that they are extremely powerful and great fun.

Based on the established task of identifying boosted, hadronically decaying top quarks, we compare a wide range of modern machine learning approaches.We find that they are extremely powerful and great fun.

Based on the established task of identifying boosted, hadronically decaying top quarks, we compare a wide range of modern machine learning approaches.We find that they are extremely powerful and great fun.

Το αντικείμενο της διατριβής είναι η αναζήτηση υπερσυμμετρικών σωματιδίων σε συγκρούσεις πρωτονίων σε συνολική ενέργεια σύγκρουσης 7 και 8 TeV. Τα δεδομένα συλλέχθηκαν με το πείραμα Compact Muon Solenoid (CMS) κατά τη πρώτη περίοδο λειτουργίας (2010-2012) του επιταχυντή Large Hadron Collider (LHC), αντιστοιχούν σε ολοκληρωμένη φωτεινότητα (integrated luminosity) 4.98 fb-1 και 19.3 fb-1, αντίστοιχα. Η μελέτη εστιάζεται στα κανάλια διάσπασης των υπερσυμμετρικών σωματιδίων που οδηγούν στην παραγωγή γεγονότων με ένα απομονωμένο, ενεργητικό, φορτισμένο λεπτόνιο. Η συγκεκριμένη υπογραφή προσφέρει ένα αξιόπιστο κανάλι παρατήρησης στο αδρονικό περιβάλλον του LHC, διατηρώντας παράλληλα υψηλό κλάσμα διακλάδωσης (branching fraction). Η πρώτη ανάλυση, που αφορά δεδομένα από συγκρούσεις στα 7 TeV, εστιάζεται στην αναζήτηση της υπερσυμμετρίας σε γενικές (inclusive) διαδικασίες. Η δεύτερη ανάλυση, βασισμένη στα δεδομένα των 8 TeV, σχεδιάστηκε για την ανίχνευση υπερσυμμετρικών top squark από την παραγωγή gluinos, , και την ακόλουθη διάσπασή τους μέσω της διαδικασίας . Τα δεδομένα που συλλέχθηκαν στα 7 και 8 TeV αντιστοιχούν σε ολοκληρωμένη φωτεινότητα (integrated luminosity) 4.98 fb-1 και 19.3 fb-1, αντιστοίχα. Και οι δύο αναλύσεις δείχνουν πλήρη συμφωνία των δεδομένων, τόσο των παρατηρούμενων αριθμών γεγονότων όσο και των χαρακτηριστικών τους, με τις προβλέψεις του Καθιερωμένου Προτύπου σε όλες στις περιοχές αναζήτησης. Τα αποτελέσματα χρησιμοποιούνται για τον υπολογισμό κατωτάτων ορίων στις μάζες των υπερσυμμετρικών σωματιδίων. Τέλος, γίνεται μελέτη της παρατηρησιμότητας της διαδικασίας σε μελλοντικές συγκρούσεις πρωτονίων στον LHC σε ολική ενέργεια 14 TeV, δινοντας έμφαση στην επίδραση των διαφορετικών συνθηκών λήψης και των διαφορετικών αναβαθμίσεων του CMS, στην αποδοτικότητα της ανακατασκευής των φυσικών αντικειμένων και στην ευαισθησία (sensitivity) της έρευνας.