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1. The Forward Physics Facility at the High-Luminosity LHC

   https://doi.org/10.1088/1361-6471/ac865e  

   Feng, Jonathan L ; Kling, Felix ; Reno, Mary Hall ; Rojo, Juan ; Soldin, Dennis ; Anchordoqui, Luis A ; Boyd, Jamie ; Ismail, Ahmed ; Harland-Lang, Lucian ; Kelly, Kevin J ; et al ( January 2023 , Journal of Physics G: Nuclear and Particle Physics)

   Abstract High energy collisions at the High-Luminosity Large Hadron Collider (LHC) produce a large number of particles along the beam collision axis, outside of the acceptance of existing LHC experiments. The proposed Forward Physics Facility (FPF), to be located several hundred meters from the ATLAS interaction point and shielded by concrete and rock, will host a suite of experiments to probe standard model (SM) processes and search for physics beyond the standard model (BSM). In this report, we review the status of the civil engineering plans and the experiments to explore the diverse physics signals that can be uniquely probed in the forward region. FPF experiments will be sensitive to a broad range of BSM physics through searches for new particle scattering or decay signatures and deviations from SM expectations in high statistics analyses with TeV neutrinos in this low-background environment. High statistics neutrino detection will also provide valuable data for fundamental topics in perturbative and non-perturbative QCD and in weak interactions. Experiments at the FPF will enable synergies between forward particle production at the LHC and astroparticle physics to be exploited. We report here on these physics topics, on infrastructure, detector, and simulation studies, and on future directions to realize the FPF’s physics potential. 

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2. The Deep Learning Epilepsy Detection Challenge: Design, Implementation, and Test of a New Crowd-Sourced AI Challenge Ecosystem

   Kiral, Isabell ; Roy, Subhrajit ; Mummert, Todd ; Braz, Alan ; Tsay, Jason ; Tang, Jianbin ; Asif, Umar ; Schaffter, Thomas ; Mehmet, Eren ; Picone, Joseph ; et al ( April 2019 , Challenges in Machine Learning Competitions for All (CiML))

   null (Ed.)

   The DeepLearningEpilepsyDetectionChallenge: design, implementation, andtestofanewcrowd-sourced AIchallengeecosystem Isabell Kiral*, Subhrajit Roy*, Todd Mummert*, Alan Braz*, Jason Tsay, Jianbin Tang, Umar Asif, Thomas Schaffter, Eren Mehmet, The IBM Epilepsy Consortium◊ , Joseph Picone, Iyad Obeid, Bruno De Assis Marques, Stefan Maetschke, Rania Khalaf†, Michal Rosen-Zvi† , Gustavo Stolovitzky† , Mahtab Mirmomeni† , Stefan Harrer† * These authors contributed equally to this work † Corresponding authors: rkhalaf@us.ibm.com, rosen@il.ibm.com, gustavo@us.ibm.com, mahtabm@au1.ibm.com, sharrer@au.ibm.com ◊ Members of the IBM Epilepsy Consortium are listed in the Acknowledgements section J. Picone and I. Obeid are with Temple University, USA. T. Schaffter is with Sage Bionetworks, USA. E. Mehmet is with the University of Illinois at Urbana-Champaign, USA. All other authors are with IBM Research in USA, Israel and Australia. Introduction This decade has seen an ever-growing number of scientific fields benefitting from the advances in machine learning technology and tooling. More recently, this trend reached the medical domain, with applications reaching from cancer diagnosis [1] to the development of brain-machine-interfaces [2]. While Kaggle has pioneered the crowd-sourcing of machine learning challenges to incentivise data scientists from around the world to advance algorithm and model design, the increasing complexity of problem statements demands of participants to be expert data scientists, deeply knowledgeable in at least one other scientific domain, and competent software engineers with access to large compute resources. People who match this description are few and far between, unfortunately leading to a shrinking pool of possible participants and a loss of experts dedicating their time to solving important problems. Participation is even further restricted in the context of any challenge run on confidential use cases or with sensitive data. Recently, we designed and ran a deep learning challenge to crowd-source the development of an automated labelling system for brain recordings, aiming to advance epilepsy research. A focus of this challenge, run internally in IBM, was the development of a platform that lowers the barrier of entry and therefore mitigates the risk of excluding interested parties from participating. The challenge: enabling wide participation With the goal to run a challenge that mobilises the largest possible pool of participants from IBM (global), we designed a use case around previous work in epileptic seizure prediction [3]. In this “Deep Learning Epilepsy Detection Challenge”, participants were asked to develop an automatic labelling system to reduce the time a clinician would need to diagnose patients with epilepsy. Labelled training and blind validation data for the challenge were generously provided by Temple University Hospital (TUH) [4]. TUH also devised a novel scoring metric for the detection of seizures that was used as basis for algorithm evaluation [5]. In order to provide an experience with a low barrier of entry, we designed a generalisable challenge platform under the following principles: 1. No participant should need to have in-depth knowledge of the specific domain. (i.e. no participant should need to be a neuroscientist or epileptologist.) 2. No participant should need to be an expert data scientist. 3. No participant should need more than basic programming knowledge. (i.e. no participant should need to learn how to process fringe data formats and stream data efficiently.) 4. No participant should need to provide their own computing resources. In addition to the above, our platform should further • guide participants through the entire process from sign-up to model submission, • facilitate collaboration, and • provide instant feedback to the participants through data visualisation and intermediate online leaderboards. The platform The architecture of the platform that was designed and developed is shown in Figure 1. The entire system consists of a number of interacting components. (1) A web portal serves as the entry point to challenge participation, providing challenge information, such as timelines and challenge rules, and scientific background. The portal also facilitated the formation of teams and provided participants with an intermediate leaderboard of submitted results and a final leaderboard at the end of the challenge. (2) IBM Watson Studio [6] is the umbrella term for a number of services offered by IBM. Upon creation of a user account through the web portal, an IBM Watson Studio account was automatically created for each participant that allowed users access to IBM's Data Science Experience (DSX), the analytics engine Watson Machine Learning (WML), and IBM's Cloud Object Storage (COS) [7], all of which will be described in more detail in further sections. (3) The user interface and starter kit were hosted on IBM's Data Science Experience platform (DSX) and formed the main component for designing and testing models during the challenge. DSX allows for real-time collaboration on shared notebooks between team members. A starter kit in the form of a Python notebook, supporting the popular deep learning libraries TensorFLow [8] and PyTorch [9], was provided to all teams to guide them through the challenge process. Upon instantiation, the starter kit loaded necessary python libraries and custom functions for the invisible integration with COS and WML. In dedicated spots in the notebook, participants could write custom pre-processing code, machine learning models, and post-processing algorithms. The starter kit provided instant feedback about participants' custom routines through data visualisations. Using the notebook only, teams were able to run the code on WML, making use of a compute cluster of IBM's resources. The starter kit also enabled submission of the final code to a data storage to which only the challenge team had access. (4) Watson Machine Learning provided access to shared compute resources (GPUs). Code was bundled up automatically in the starter kit and deployed to and run on WML. WML in turn had access to shared storage from which it requested recorded data and to which it stored the participant's code and trained models. (5) IBM's Cloud Object Storage held the data for this challenge. Using the starter kit, participants could investigate their results as well as data samples in order to better design custom algorithms. (6) Utility Functions were loaded into the starter kit at instantiation. This set of functions included code to pre-process data into a more common format, to optimise streaming through the use of the NutsFlow and NutsML libraries [10], and to provide seamless access to the all IBM services used. Not captured in the diagram is the final code evaluation, which was conducted in an automated way as soon as code was submitted though the starter kit, minimising the burden on the challenge organising team. Figure 1: High-level architecture of the challenge platform Measuring success The competitive phase of the "Deep Learning Epilepsy Detection Challenge" ran for 6 months. Twenty-five teams, with a total number of 87 scientists and software engineers from 14 global locations participated. All participants made use of the starter kit we provided and ran algorithms on IBM's infrastructure WML. Seven teams persisted until the end of the challenge and submitted final solutions. The best performing solutions reached seizure detection performances which allow to reduce hundred-fold the time eliptologists need to annotate continuous EEG recordings. Thus, we expect the developed algorithms to aid in the diagnosis of epilepsy by significantly shortening manual labelling time. Detailed results are currently in preparation for publication. Equally important to solving the scientific challenge, however, was to understand whether we managed to encourage participation from non-expert data scientists. Figure 2: Primary occupation as reported by challenge participants Out of the 40 participants for whom we have occupational information, 23 reported Data Science or AI as their main job description, 11 reported being a Software Engineer, and 2 people had expertise in Neuroscience. Figure 2 shows that participants had a variety of specialisations, including some that are in no way related to data science, software engineering, or neuroscience. No participant had deep knowledge and experience in data science, software engineering and neuroscience. Conclusion Given the growing complexity of data science problems and increasing dataset sizes, in order to solve these problems, it is imperative to enable collaboration between people with differences in expertise with a focus on inclusiveness and having a low barrier of entry. We designed, implemented, and tested a challenge platform to address exactly this. Using our platform, we ran a deep-learning challenge for epileptic seizure detection. 87 IBM employees from several business units including but not limited to IBM Research with a variety of skills, including sales and design, participated in this highly technical challenge. 

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3. Search for point sources of ultra-high-energy photons with the Telescope Array surface detector

   https://doi.org/10.1093/mnras/stz3618  

   Abbasi, R.U. ( February 2020 , Monthly notices of the Royal Astronomical Society)

   Ultra-high-energy (UHE) photons are an important tool for studying the high-energy Universe. A plausible source of photons with exa-eV (EeV) energy is provided by UHE cosmic rays (UHECRs) undergoing the Greisen–Zatsepin–Kuzmin process (Greisen 1966; Zatsepin & Kuzmin 1966) or pair production process (Blumenthal 1970) on a cosmic background radiation. In this context, the EeV photons can be a probe of both UHECR mass composition and the distribution of their sources (Gelmini, Kalashev & Semikoz 2008; Hooper, Taylor & Sarkar 2011). At the same time, the possible flux of photons produced by UHE protons in the vicinity of their sources by pion photoproduction or inelastic nuclear collisions would be noticeable only for relatively near sources, as the attenuation length of UHE photons is smaller than that of UHE protons; see, for example, Bhattacharjee & Sigl (2000) for a review. There also exists a class of so-called top-down models of UHECR generation that efficiently produce the UHE photons, for instance by the decay of heavy dark-matter particles (Berezinsky, Kachelriess & Vilenkin 1997; Kuzmin & Rubakov 1998) or by the radiation from cosmic strings (Berezinsky, Blasi & Vilenkin 1998). The search for the UHE photons was shown to be the most sensitive method of indirect detection of heavy dark matter (Kalashev & Kuznetsov 2016, 2017; Kuznetsov 2017; Kachelriess, Kalashev & Kuznetsov 2018; Alcantara, Anchordoqui & Soriano 2019). Another fundamental physics scenario that could be tested with UHE photons (Fairbairn, Rashba & Troitsky 2011) is the photon mixing with axion-like particles (Raffelt & Stodolsky 1988), which could be responsible for the correlation of UHECR events with BL Lac type objects observed by the High Resolution Fly’s Eye (HiRes) experiment (Gorbunov et al. 2004; Abbasi et al. 2006). In most of these scenarios, a clustering of photon arrival directions, rather than diffuse distribution, is expected, so point-source searches can be a suitable test for photon - axion-like particle mixing models. Finally, UHE photons could also be used as a probe for the models of Lorentz-invariance violation (Coleman & Glashow 1999; Galaverni & Sigl 2008; Maccione, Liberati & Sigl 2010; Rubtsov, Satunin & Sibiryakov 2012, 2014). The Telescope Array (TA; Tokuno et al. 2012; Abu-Zayyad et al. 2013c) is the largest cosmic ray experiment in the Northern Hemisphere. It is located at 39.3° N, 112.9° W in Utah, USA. The observatory includes a surface detector array (SD) and 38 fluorescence telescopes grouped into three stations. The SD consists of 507 stations that contain plastic scintillators, each with an area of 3 m2 (SD stations). The stations are placed in the square grid with 1.2 km spacing and cover an area of ∼700 km2. The TA SD is capable of detecting extensive air showers (EASs) in the atmosphere caused by cosmic particles of EeV and higher energies. The TA SD has been operating since 2008 May. A hadron-induced EAS significantly differs from an EAS induced by a photon because the depth of the shower maximum Xmax for a photon shower is larger, and a photon shower contains fewer muons and has a more curved front (see Risse & Homola 2007 for a review). The TA SD stations are sensitive to both muon and electromagnetic components of the shower and therefore can be triggered by both hadron-induced and photon-induced EAS events. In the present study, we use 9 yr of TA SD data for a blind search for point sources of UHE photons. We utilize the statistics of the SD data, which benefit from a high duty cycle. The full Monte Carlo (MC) simulation of proton-induced and photon-induced EAS events allows us to perform the photon search up to the highest accessible energies, E ≳ 1020 eV. As the main tool for the present photon search, we use a multivariate analysis based on a number of SD parameters that make it possible to distinguish between photon and hadron primaries. While searches for diffuse UHE photons were performed by several EAS experiments, including Haverah Park (Ave et al. 2000), AGASA (Shinozaki et al. 2002; Risse et al. 2005), Yakutsk (Rubtsov et al. 2006; Glushkov et al. 2007, 2010), Pierre Auger (Abraham et al. 2007, 2008a; Bleve 2016; Aab et al. 2017c) and TA (Abu-Zayyad et al. 2013b; Abbasi et al. 2019a), the search for point sources of UHE photons has been done only by the Pierre Auger Observatory (Aab et al. 2014, 2017a). The latter searches were based on hybrid data and were limited to the 1017.3 < E < 1018.5 eV energy range. In the present paper, we use the TA SD data alone. We perform the searches in five energy ranges: E > 1018, E > 1018.5, E > 1019, E > 1019.5 and E > 1020 eV. We find no significant evidence of photon point sources in all energy ranges and we set the point-source flux upper limits from each direction in the TA field of view (FOV). The search for unspecified neutral particles was also previously performed by the TA (Abbasi et al. 2015). The limit on the point-source flux of neutral particles obtained in that work is close to the present photon point-source flux limits. 

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4. Searching for long-lived particles beyond the Standard Model at the Large Hadron Collider

   https://doi.org/10.1088/1361-6471/ab4574  

   Alimena, Juliette ; Beacham, James ; Borsato, Martino ; Cheng, Yangyang ; Vidal, Xabier Cid ; Cottin, Giovanna ; Curtin, David ; De Roeck, Albert ; Desai, Nishita ; Evans, Jared A. ; et al ( September 2020 , Journal of Physics G: Nuclear and Particle Physics)

   Particles beyond the Standard Model (SM) can generically have lifetimes that are long compared to SM particles at the weak scale. When produced at experiments such as the Large Hadron Collider (LHC) at CERN, these long-lived particles (LLPs) can decay far from the interaction vertex of the primary proton–proton collision. Such LLP signatures are distinct from those of promptly decaying particles that are targeted by the majority of searches for new physics at the LHC, often requiring customized techniques to identify, for example, significantly displaced decay vertices, tracks with atypical properties, and short track segments. Given their non-standard nature, a comprehensive overview of LLP signatures at the LHC is beneficial to ensure that possible avenues of the discovery of new physics are not overlooked. Here we report on the joint work of a community of theorists and experimentalists with the ATLAS, CMS, and LHCb experiments—as well as those working on dedicated experiments such as MoEDAL, milliQan, MATHUSLA, CODEX-b, and FASER—to survey the current state of LLP searches at the LHC, and to chart a path for the development of LLP searches into the future, both in the upcoming Run 3 and at the high-luminosity LHC. The work is organized around the current and future potential capabilities of LHC experiments to generally discover new LLPs, and takes a signature-based approach to surveying classes of models that give rise to LLPs rather than emphasizing any particular theory motivation. We develop a set of simplified models; assess the coverage of current searches; document known, often unexpected backgrounds; explore the capabilities of proposed detector upgrades; provide recommendations for the presentation of search results; and look towards the newest frontiers, namely high-multiplicity ‘dark showers’, highlighting opportunities for expanding the LHC reach for these signals.

    

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5. Searches for Heavy Resonances with Substructure

   https://doi.org/10.1146/annurev-nucl-102419-055402  

   Maksimović, Petar ( September 2022 , Annual Review of Nuclear and Particle Science)

   In the past decade, the Large Hadron Collider (LHC) has probed a higher energy scale than ever before. Most models of physics beyond the standard model (BSM) predict the production of new heavy particles; the LHC results have excluded lower masses of such particles. This makes the high-mass regions especially interesting for current and future searches. In most BSM scenarios of interest, the new heavy resonances decay to standard model particles. In a subset of these models, the new particles have large couplings to the top quark, the W and Z bosons, or the Higgs boson. The top quark and W, Z, and Higgs bosons often decay to quarks, giving rise to jets of particles with substructure; event selection based on substructure is used to suppress standard model backgrounds. This review covers the key concepts in experimental searches based on the jet substructure and discusses recent results from the ATLAS and CMS experiments. 

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