Andrzej Novák

This white paper presents the current status of the R&D for Analysis Facilities (AFs) and attempts to summarize the views on the future direction of these facilities. These views have been collected through the High Energy Physics (HEP) Software Foundation's (HSF) Analysis Facilities forum, established in March 2022, the Analysis Ecosystems II workshop, that took place in May 2022, and the WLCG/HSF pre-CHEP workshop, that took place in May 2023. The paper attempts to cover all the aspects of an analysis facility.

Recent searches for rare and BSM Higgs boson decays with the ATLAS and CMS experiments on data collected at √ = 13 TeV between 2016 and 2018 are presented.These represent previously inaccessible probes of the Higgs properties, enabled by advanced analysis and machine learning methods, as well as the large amount of data collected during the Run2 of the LHC.Topics covered include standard model Higgs decays with a branching fraction magnitude equal to that into charm quarks or lower along with searches for decays into yet unobserved particles or decays of Higgs-like and extended Higgs sector particles.

The Continuous Valued Number System (CVNS) is a novel analog digit number system which employs digit level analog modular arithmetic. The information redundancy among the digits, allows efficient binary operations using analog circuitry with arbitrary accuracy, which in turn reduces the area and the number of required interconnections. CVNS theory can open up a new approach for performing digital arithmetic with classical analog elements, such as current comparators and current mirrors, and with arbitrary precision. Addition in the CVNS is digit wise and digits do not intercommunicate. In this paper the two operand CVNS adder complexity is compared with similar CVNS adders, as well as conventional threshold adders. Comparisons show that the CVNS adder is more area efficient than conventional threshold logic adders.

Deep learning is a standard tool in the field of high-energy physics, facilitating considerable sensitivity enhancements for numerous analysis strategies. In particular, in identification of physics objects, such as jet flavor tagging, complex neural network architectures play a major role. However, these methods are reliant on accurate simulations. Mismodeling can lead to non-negligible differences in performance in data that need to be measured and calibrated against. We investigate the classifier response to input data with injected mismodelings and probe the vulnerability of flavor tagging algorithms via application of adversarial attacks. Subsequently, we present an adversarial training strategy that mitigates the impact of such simulated attacks and improves the classifier robustness. We examine the relationship between performance and vulnerability and show that this method constitutes a promising approach to reduce the vulnerability to poor modeling.

Abstract Dielectric axion haloscopes, such as the Madmax experiment, are promising concepts for the direct search for dark matter axions. A reliable simulation is a fundamental requirement for the successful realisation of the experiments. Due to the complexity of the simulations, the demands on computing resources can quickly become prohibitive. In this paper, we show for the first time that modern deep learning techniques can be applied to aid the simulation and optimisation of dielectric haloscopes.

Deep learning is a standard tool in the field of high-energy physics, facilitating considerable sensitivity enhancements for numerous analysis strategies. In particular, in identification of physics objects, such as jet flavor tagging, complex neural network architectures play a major role. However, these methods are reliant on accurate simulations. Mismodeling can lead to non-negligible differences in performance in data that need to be measured and calibrated against. We investigate the classifier response to input data with injected mismodelings and probe the vulnerability of flavor tagging algorithms via application of adversarial attacks. Subsequently, we present an adversarial training strategy that mitigates the impact of such simulated attacks and improves the classifier robustness. We examine the relationship between performance and vulnerability and show that this method constitutes a promising approach to reduce the vulnerability to poor modeling.

A search for standard model Higgs bosons produced with transverse momentum greater than 450 GeV and decaying to charm quark-antiquark pairs is performed using proton-proton collision data collected by the CMS experiment at the LHC at 13 TeV.The search is inclusive in the Higgs boson production mode.Highly Lorentz-boosted Higgs bosons are reconstructed as single large-radius jets and are identified using a dedicated tagging technique based on a Deep Neural Network.The method is validated with Z to charm quark-antiquark pair decays and this process is observed for the first time in association with jets at a hadron collider.

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.

Identification of bottom and charm quarks is crucial for most physics analyses at the CMS Experiment. Advancement and proliferation of deep learning techniques as well as hardware developments have facilitated their use in high energy physics and CMS is successfully employing them to classify jets originating from bottom quarks with unprecedented performance. Furthermore, the improvements have been sufficient to begin meaningfully identifying charm jets as well.

Der Flugverkehr haelt dank intelligenter Ausruestungssysteme fuer die Flugzeuge einen aussergewoehnlich hohen Sicherheitsstandard. Angesichts des wachsenden Umfangs des Flugbetriebs verschiebt sich so das Potenzial moeglicher Zwischenfaelle oder Unfaelle vorrangig in Richtung des menschlichen Faktors. Die heutigen Flugzeugsteuersysteme werden als FMS (Flight Management System) bezeichnet. FMS ist ein vollautomatisches Steuersystem, das in der Lage ist, ein Flugzeug nach einem programmierten Plan zu lenken, die augenblickliche Situation einzuschaetzen und auf die aktuellen Flugbedingungen zu reagieren. Bordeigene Kollisionswarnsysteme wurden urspruenglich in den USA entwickelt. Seit den 1980er Jahren wurde im Rahmen der ICAO an einer Standardisierung der Kollisionswarnsysteme auf dem Niveau der Formulierung vereinheitlichter Systemanforderungen gearbeitet. Die ICAO bezeichnet das Kollisionswarnsystem als ACAS (Airborne Collision Avoidance System). Dieses System ist technisch so eingestellt, dass es die Beobachtung des Luftraumes in der Umgebung des Flugzeuges und die Koordination mit kooperierenden ACAS-Einrichtungen anderer Flugzeuge sicherstellt und alle fuer die Darstellung der Situation notwendigen Daten und Berechnungen liefert. Kollisionswarnsysteme kommunizieren untereinander, um Vorschlaege zur Loesung eines Konflikts zu koordinieren. Sie kommunizieren auch mit Bodenstationen und geben Informationen fuer die Steuerung des Flugbetriebs an sie weiter. Das System ACAS kann an Bord eines Flugzeuges selbststaendig und unabhaengig von Bodeneinrichtungen arbeiten. Wenn sich ein Zusammenstoss abzeichnet, sendet es Warnsignale aus, die Informationen ueber die Annaeherung und gleichzeitig Empfehlungen zur Vermeidung des drohenden Zwischenfalls enthalten. ACAS-Systeme sind das letzte moegliche vorbeugende Mittel gegen Kollisionen von Flugzeugen in der Luft. Die technischen Errungenschaften des Systems bewirken eine deutliche Zunahme der Sicherheit von Fluegen. ACAS kann jedoch nicht alle potentiellen Kollisionsgefahren ausschliessen; es kann im Gegenteil selbst zu einer Gefahrenquelle werden. Daher ist es wichtig, dass die Flugsteuerung niemals allein vom Betrieb des ACAS-Systems abhaengt. ABSTRACT IN ENGLISH: Air transport shows an exceptionally high standard of safety thanks to aircrafts' intelligent systems' equipment. With the growing air traffic volume, the possibility of incidents and accidents concerns mainly the human factor. FMS (Flight Management System) is a fully automatic control system with the ability to guide an aircraft according to pre-programmed processes, evaluate immediate situations and react to particular flight conditions. Airborne warning and collision avoidance systems were first developed in the USA. Within ICAO the standardization of airborne collision avoidance systems on the level of formulating the unified system requirements has been carried out. ACAS (Airborne Collision Avoidance System) is designed so that it ensures monitoring of airspace in the area around an aircraft, coordinates between co-operating ACAS devices from other aircrafts and provides all calculations necessary to display the situation. The ACAS systems communicate to each other so that they would be able to coordinate conflicts solutions. They are able to communicate with ground stations and provide information for air traffic operation control. The ACAS systems are able to work in the aircraft on their own and independently to ground facilities. The ACAS systems make warnings which provide information on potential approach and at the same time recommend solutions to imminent threat of crash with another aircraft. ACAS systems are the tools of the last possible chance to prevent collision of aircrafts during flights. The technological advancement of the systems contributes to improved flight safety. However, ACAS systems cannot guarantee prevention of all possible aircrafts collisions and on the contrary can sometimes be a cause of other potential dangers. Then it is important that the processes of flight controls would not depend only on the ACAS systems.