The full simulation of particle colliders incurs a significant computational cost. Among the most resource-intensive steps are detector simulations. It is expected that future developments, such as higher collider luminosities and highly granular calorimeters, will increase the computational resource requirement for simulation beyond availability. One possible solution is generative neural...
Experimental uncertainties related to the calibration of hadronic objects (particularly the jet energy scale and resolution) can limit the precision of physics analyses at the LHC, and so improvements in performance have the potential to broadly increase the impact of results. Such settings are among most promising for cutting-edge machine learning and artificial intelligence algorithms at the...
This talk will overview the usage of boosted multi-prong jet tagging in CMS and how such taggers are calibrated. It will highlight a new method for calibrating the tagging of multi-prong jets using the Lund Jet Plane to correct the substructure of simulated jets. The method is shown to significantly improve the data-simulation agreement of substructure observables.
The use of machine learning for collider data generation has become a significant area of study within particle physics. This interest arises from the increasing computational difficulties associated with traditional Monte Carlo simulation methods, especially in the context of future high-luminosity colliders. Representing collider data as particle clouds introduces several advantageous...
The identification of heavy-flavour jets (tagging) remains a critical task at hadron colliders. A key signature of such jets is the displaced decay vertices left by boosted b- and c-hadrons. While existing tagging algorithms leveraged manually designed algorithms to identify and fit vertices, they were succeeded by edge-classification based Graph Neural Networks (GNNs) that, despite...
Simulation of calorimeter response is a crucial part of detector study for modern high energy. The computational cost of conventional MC-based simulation becoming a major bottleneck with the increasingly large and high granularity design. We propose a 2-step generative model for fast calorimeter simulation based on Vector-Quantized Variational Autoencoder (VQ-VAE). This model achieves a fast...
Physics measurements in the highly Lorentz-boosted regime, including the search for the Higgs boson or beyond standard model particles, are a critical part of the LHC physics program. In the CMS Collaboration, various boosted-jet tagging algorithms, designed to identify hadronic jets originating from a massive particle decaying to bb̅ or cc̅, have been developed and deployed in a variety of...
Inspired by the recent successes of language modelling and computer vision machine learning techniques, we study the feasibility of repurposing these developments for particle track reconstruction in the context of high energy physics. In particular, drawing from developments in the field of language modelling we showcase the performance of multiple implementations of the transformer model,...
At experiments at the LHC, a growing reliance on fast Monte Carlo applications will accompany the high luminosity and detector upgrades of the Phase 2 era. Traditional FastSim applications which have already been developed over the last decade or more may help to cope with these challenges, as they can achieve orders of magnitude greater speed than standard full simulation applications....
Machine learning-based simulations, especially calorimeter simulations, are promising tools for approximating the precision of classical high energy physics simulations with a fraction of the generation time. Nearly all methods proposed so far learn neural networks that map a random variable with a known probability density, like a Gaussian, to realistic-looking events. In many cases, physics...
Tree structure is a natural way to represent particle decays in high energy physics. The possibility of reconstructing the entire decay tree that ends in stable particles entering the detector is an interesting and potentially beneficial task. [The interesting and extremely helpful task is to reconstruct the entire decay process, starting from the leaf nodes, which are the reconstructed...
Accurately reconstructing particles from detector data is a critical challenge in experimental particle physics. The detector's spatial resolution, specifically the calorimeter's granularity, plays a crucial role in determining the quality of the particle reconstruction. It also sets the upper limit for the algorithm's theoretical capabilities. Super-resolution techniques can be explored as a...
Most searches at the LHC employ an analysis pipeline consisting of various discrete components, each individually optimized and later combined to provide relevant features used to discriminate SM background from potential signal. These are typically high-level features constructed from particle four-momenta. However, the combination of individually optimized tasks doesn't guarantee an optimal...
Photons are important objects at collider experiments. For example, the
Higgs boson is studied with high precision in the diphoton decay channel. For this purpose, it is crucial to achieve the best possible spatial resolution for photons and to discriminate against other particles which mimic the photon signature, mostly Lorentz-boosted $\pi^0\to\gamma\gamma$ decays.
In this talk, a study...
In this work we introduce ν²-Flows, an extension of the ν-Flows method to final states containing multiple neutrinos. The architecture can natively scale for all combinations of object types and multiplicities in the final state for any desired neutrino multiplicities. In ttbar dilepton events, the momenta of both neutrinos and correlations between them are reconstructed more accurately than...
Axion-like particles (ALPs) arise in beyond the Standard Model theories with global symmetry breaking. Several experiments have been constructed and proposed to look for them at different energy scales. We focus here on beam-dump experiments looking for GeV scale ALPs with macroscopic decay lengths. In this work we show that using ML we can reconstruct the ALP properties (mass and lifetime)...
Supervised learning has been used successfully for jet classification and to predict a range of jet properties, such as mass and energy. Each model learns to encode jet features, resulting in a representation that is tailored to its specific task. But could the common elements underlying such tasks be combined in a single model trained to extract features generically? To address this question,...
We present CoCo (Contrastive Combinatorics) a new approach using contrastive learning to solve object assignment in HEP. By utilizing contrastive objectives, CoCo aims to pull jets originating from the same parent closer together in an embedding space while pushing unrelated jets apart.
This approach can be extended natively to have multiple objectives for each subsequent particle in a decay...
Last year we proposed a novel hypergraph-based algorithm (HGPflow) for one-shot prediction of particle cardinality, class, and kinematics in a dataset of single jets. This approach has the advantage of introducing energy conservation as an inductive bias, promoting both interpretability and performance gains at the particle and jet levels. We now deploy an upgraded version of HGPflow to the...
We study scalable machine learning models for full event reconstruction in high-energy electron-positron collisions based on a highly granular detector simulation. Particle-flow (PF) reconstruction can be formulated as a supervised learning task using tracks and calorimeter clusters or hits. We compare a graph neural network and kernel-based transformer and demonstrate that both avoid...
The Bert pretraining paradigm has proven to be highly effective in many domains including natural language processing, image processing and biology. To apply the Bert paradigm the data needs to be described as a set of tokens, and each token needs to be labelled. To date the Bert paradigm has not been explored in the context of HEP. The samples that form the data used in HEP can be described...
The reconstruction of physical observables in hadron collider events from recorded experimental quantities poses a repeated task in almost any data analysis at the LHC. While the experiments record hits in tracking detectors and signals in the calorimeters, which are subsequently combined into particle-flow objects, jets, muons, electrons, missing transverse energy, or similar high-level...
The NA61/SHINE experiment is a prominent venture in high-energy physics, located at the SPS accelerator within CERN. Recently, the experiment's physics program underwent expansion, necessitating a comprehensive overhaul of its detector configuration. This upgrade is primarily geared towards augmenting the event flow rate, elevating it from 80Hz to 1kHz. This enhancement involves a substantial...
State-of-the-art (SoTA) deep learning models have achieved tremendous improvements in jet classification performance while analyzing low-level inputs, but their decision-making processes have become increasingly opaque. We introduce an analysis model (AM) that combines several phenomenologically motivated neural networks to circumvent the interpretability issue while maintaining high...
The basic signal of the ATLAS calorimeters are three-dimensional clusters of topologically connected cell signals formed by following signal significance patterns. These topo-clusters provide measures of their shape, location and signal character which are employed to apply a local hadronic calibration. The corresponding multi-dimensional calibration functions are determined by training...
Particle jets exhibit tree-like structures through stochastic showering and hadronization. The hierarchical nature of these structures aligns naturally with hyperbolic space, a non-Euclidean geometry that captures hierarchy intrinsically. Drawing upon the foundations of geometric learning, we introduce hyperbolic transformer models tailored for tasks relevant to jet analyses, such as...
The upcoming high-luminosity upgrade of the LHC will lead to a factor of five increase in instantaneous luminosity during proton-proton collisions. Consequently, the experiments situated around the collider ring, such as the CMS experiment, will record approximately ten times more data. Furthermore, the luminosity increase will result in significantly higher data complexity, thus making more...
The High Luminosity upgrade to the LHC will deliver unprecedented luminosity to the experiments, culminating in up to 200 overlapping proton-proton collisions. In order to cope with this challenge several elements of the CMS detector are being completely redesigned and rebuilt. The Level-1 Trigger is one such element; it will have a 12.5 microsecond window in which to process protons colliding...
We present the preparation, deployment, and testing of an autoencoder trained for unbiased detection of new physics signatures in the CMS experiment Global Trigger test crate FPGAs during LHC Run 3. The Global Trigger makes the final decision whether to readout or discard the data from each LHC collision, which occur at a rate of 40 MHz, within a 50 ns latency. The Neural Network makes a...
In the search for exotic events involving displaced particles at HL-LHC, the triggering at the level-1 (L1) system will pose a significant challenge. This is particularly relevant in scenarios where low mass long-lived particles (LLPs) are coupled to a Standard Model (SM)-like 125 GeV Higgs boson and they decay into jets. The complexity arises from the low hadronic activity resulting from LLP...
Based on: JHEP 09 (2023) 084:
Hadronization is a critical step in the simulation of high-energy particle and nuclear physics experiments. As there is no first principles understanding of this process, physically-inspired hadronization models have a large number of parameters that are fit to data. Deep generative models are a natural replacement for classical techniques, since they are more...
In this talk, we introduce a method for efficiently generating jets in the field of High Energy Physics.
Our model is designed to generate ten different types of jets, expanding the versatility of
jet generation techniques.
Beyond the kinematic features of the jet constituents, our model also excels in generating
informative features that provide insight into the types of jet constituents,...
In particle physics, precise simulations of the interaction processes in calorimeters are essential for scientific discovery. However, accurate simulations using GEANT4 are computationally very expensive and pose a major challenge for the future of particle physics. In this study, we apply the CaloPointFlow model, a novel generative model based on normalizing flows, to fast and high-fidelity...
Building on the success of PC-JeDi we introduce PC-Droid, a substantially improved diffusion model for the generation of jet particle clouds. By leveraging a new diffusion formulation, studying more recent integration solvers, and training on all jet types simultaneously, we are able to achieve state-of-the-art performance for all types of jets across all evaluation metrics. We study the...
In High Energy Physics, detailed and time-consuming simulations are used for particle interactions with detectors. To bypass these simulations with a generative model, it needs to be able to generate large point clouds in a short time while correctly modeling complex dependencies between the particles.
For non-sparse problems on a regular grid, such a model would usually use (De-)Convolution...
Simulating particle physics data is a crucial yet computationally expensive aspect of analyzing data at the LHC. Typically, in fast simulation methods, we rely on a surrogate calorimeter model to generate a set of reconstructed objects. This work demonstrates the potential to generate these reconstructed objects in a single step, effectively replacing both the calorimeter simulation and...
Calorimeter response simulation is a critical but computationally consuming part of many physics analyses at the Large Hadron Collider. The simulation time and resource consumption can be effectively reduced by the usage of neural networks. Denosing diffusion models are emerging as the state-of-the-art for various generative tasks ranging from images to sets. We propose a new graph-based...
Transformers have become the primary architecture for natural language processing. In this study, we explore their use for auto-regressive density estimation in high-energy jet physics. We draw an analogy between sentences and words in natural language and jets and their constituents. Specifically, we investigate density estimation for light QCD jets and hadronically decaying boosted top jets....
Diffusion generative models are a recent type of generative models that excel in various tasks, including those in collider physics and beyond. Thanks to their stable training and flexibility, these models can easily incorporate symmetries to better represent the data they generate. In this talk, I will provide an overview of diffusion models' key features and highlight their practical...
Generative machine learning models are a promising avenue to resolve computing challenges by replacing intensive full simulations of particle detectors. We introduce CaloDiffusion, a denoising diffusion model that generates calorimeter showers, trained on the public CaloChallenge datasets. Our algorithm employs 3D cylindrical convolutions that take advantage of symmetries in the underlying...
Given the recent success of diffusion models in image generation, we study their applicability to generating LHC phase space distributions. We find that they achieve percent level precision comparable to INNs. To further enhance the interpretability of our results we quantify our training uncertainty by developing Bayesian versions. In this talk, diffusion models are introduced and discussed...
In High Energy Physics, generating physically meaningful parton configurations from a collision reconstructed within a detector is a critical step for many complex analysis tasks such as the Matrix Element Method computation and Bayesian inference on parameters of interest. This contribution introduces a novel approach that employs generative machine learning architectures, Transformers...
Simulating showers of particles in highly-granular detectors is a key frontier in the application of machine learning to particle physics. Achieving high accuracy and speed with generative machine learning models would enable them to augment traditional simulations and alleviate a major computing constraint.
This work achieves a major breakthrough in this task by directly generating a...
The matrix element method remains a crucial tool for LHC inference in scenarios with limited event data. We enhance our neural network-based framework, now dubbed MEMeNNto, by optimizing phase-space integration techniques and introducing an acceptance function. Additionally, employing new architectures, like transformer and diffusion models, allows us to better handle complex jet combinatorics...
The simulation of particle interactions with detectors plays a central role in many high energy physics experiments. In the simulation pipeline, the most computationally expensive process is calorimeter shower generation. Looking into the future, as the size and granularity of calorimeters increase and we approach the high luminosity operational phase of the LHC, the severity of the simulation...
Theory predictions for the LHC require precise numerical phase-space integration and generation of unweighted events. We combine machine-learned multi-channel weights with a normalizing flow for importance sampling to improve classical methods for numerical integration. By integrating buffered training for potentially expensive integrands, VEGAS initialization, symmetry-aware channels, and...
We present a novel, data-driven analysis of Galactic dynamics, using unsupervised machine learning -- in the form of density estimation with normalizing flows -- to learn the underlying phase space distribution of 6 million nearby stars from the Gaia DR3 catalog. Solving the collisionless Boltzmann equation with the assumption of approximate equilibrium, we calculate -- for the first time ever...
Utilizing 21cm tomography provides a unique opportunity to directly investigate the astrophysical and fundamental aspects of early stages of our Universe's history, spanning the Epoch of Reionization (EoR) and Cosmic Dawn (CD). Due to the non-Gaussian nature of signals that trace this period of the Universe, methods based on summary statistics omit important information about the underlying...
Cosmic inflation is a process in the early Universe responsible for the generation of cosmic structures. The dynamics of the scalar field driving inflation is determined by its self-interaction potential and is coupled to the gravitational dynamics of the FLRW-background. In addition, perturbations of the inflaton field can be computed by numerical solution of the so-called mode equations....
We introduce the revamped HEPML Living Review: a more accessible website dedicated to the interplay of High-Energy Physics and Machine Learning. Featuring a new 'Recent' section and more anticipated features, we actively seek and encourage ongoing community input, envisioning this platform as a dynamic and continuously evolving exchange.
Improving the identification of jets initiated from gluon or quark will impact the precision of several analysis in the ATLAS collaboration physics program. Current identification algorithms (taggers) take as inputs high-level jet kinematic and substructure variables as the number of tracks associated to the jet or the jet width. We present a novel approach to tag quark- and gluon-initiated...
Top-performing jet networks often compromise infrared and collinear (IRC) safety, leading to a dilemma between pursuing high experimental performance and good theoretical interpretability. In this talk, we present an innovative modification of the classic Transformer self-attention block (whose token is per-particle input) to ensure full IRC safety. By integrating this recipe into Particle...
Energy correlators, which are are correlation functions of the energy flow operator, are theoretically clean observables which can be used to improve various measurements. In this talk, we discuss ongoing work exploring the benefits of combining them with Machine Learning.
Weakly supervised methods have emerged as a powerful tool for model agnostic anomaly detection at the LHC. While these methods have shown remarkable performance on specific signatures such as di-jet resonances, their application in a more model-agnostic manner requires dealing with a larger number of potentially noisy input features. We show that neural networks struggle with noisy input...
We employ the diffusion framework to generate background enriched templates to be used in a downstream Anomaly Detection task (generally with CWoLa). We show how Drapes can provide an analogue to many different methods of template generation, common in literature, and show good performance on the public RnD LHCO dataset.
High-energy collisions at the Large Hadron Collider (LHC) provide valuable insights into open questions in particle physics. However, detector effects must be corrected before measurements can be compared to certain theoretical predictions or measurements from other detectors. Methods to solve this inverse problem of mapping detector observations to theoretical quantities of the underlying...
The radiation pattern within quark- and gluon-initiated jets (jet substructure) is used extensively as a precision probe of the strong force and for optimizing event generators for particle physics. Jet substructure measurements in electron-proton collisions are of particular interest as many of the complications present at hadron colliders are absent.
In this contribution, a detailed study...
Machine learning--based anomaly detection (AD) methods are promising tools for extending the coverage of searches for physics beyond the Standard Model (BSM). One class of AD methods that has received significant attention is resonant anomaly detection, where the BSM is assumed to be localized in at least one known variable. While there have been many methods proposed to identify such a BSM...
Experimental data on a wide range of jet observables measured in heavy ion collisions provide a rich picture of the modification of jets as perturbative probes and of the properties of the created quark-gluon plasma. However, their interpretation is often limited by the assumptions of specific quenching models, and it remains a challenge to establish model-independent statements about the...
Progress in the theoretical understanding of parton branching dynamics that occurs within an expanding QGP relies on detailed and fair comparisons with experimental data for reconstructed jets. Such validation is only meaningful when the computed object, be it analitically or via event generation, accounts for the complexity of experimentally reconstructed jets. The reconstruction of jets in...
In many well-motivated models of the electroweak scale, cascade decays of new particles can result in highly boosted hadronic resonances (e.g. $Z/W/h$). This can make these models rich and promising targets for recently developed resonant anomaly detection methods powered by modern machine learning. We demonstrate this using the state-of-the-art CATHODE method applied to supersymmetry...
Searching for non-resonant signals at the LHC is a relatively underexplored, yet challenging approach to discover new physics. These signals could arise from off-shell effects or final states with significant missing energy. This talk explores the potential of using weakly supervised anomaly detection to identify new non-resonant phenomena at the LHC. Our approach extends existing resonant...
We present improvements to model agnostic resonant anomaly detection based on normalizing flows.
Semivisible jets are a novel signature of dark matter scenarios where the dark sector is confining and couples to the Standard Model via a portal. They consist of jets of visible hadrons intermixed with invisible stable particles that escape detection. In this work, we use normalized autoencoders to tag semivisible jets in proton-proton collisions at the CMS experiment. Unsupervised models are...
The development of precise and computationally efficient simulations is a central challenge in modern physics. With the advent of deep learning, new methods are emerging from the field of generative models. Recent applications to the generation of calorimeter images showed promising results motivating the application in astroparticle physics. In this contribution, we introduce a...
The properties of hot and/or dense nuclear matter are studied in the laboratory via Heavy-Ion Collisions (HIC) experiments. Of particular interest are the intermediate energy heavy-ion collisions that create strongly interacting matter of moderate temperatures and high densities where interesting structures in the QCD phase diagram such as a first order phase transition from a gas of hadrons...
caloutils is a Python package built to simplify and streamline the handling, processing, and analysis of 4D point cloud data derived from calorimeter showers in high-energy physics experiments. The package includes tools to map between continuous point clouds and discrete calorimeter cells.
Furthermore, the library contains models for evaluating the performance of generative models of...
In this talk I will present a recent strategy to perform a goodness-of-fit test via two-sample testing, powered by machine learning. This approach allows to evaluate the discrepancy between a data sample of interest and a reference sample, in an unbiased and statistically sound fashion. The model leverages the ability of classifiers to estimate the density ratio of the data-generating...
I will summarize the results of the CaloChallenge, a HEP community challenge on generating calorimeter showers with deep generative models that took place in 2022/2023.
We propose a new method based on machine learning to play the devil's advocate and investigate the impact of unknown systematic effects in a quantitative way. This method proceeds by reversing the measurement process and using the physics results to interpret systematic effects under the Standard Model hypothesis.
We explore this idea with two alternative approaches, one relies on a...
Machine learning based jet tagging techniques have greatly enhanced the sensitivity of measurements and searches involving boosted final states at the LHC. However, differences between the Monte-Carlo simulations used for training and data lead to systematic uncertainties on tagger performance. This talk presents the performance of boosted top and W boson taggers when applied on data sets...
Deep neural network based classifiers allow for efficient estimation of likelihood ratios in high dimensional spaces. Classifier-based cuts are thus being used to process experimental data, for example in top tagging. To efficiently investigate new theory, it is essential to estimate the behavior of these cuts efficiently. We suggest circumventing the full simulation of the experimental setup...
Applications of Machine Learning to physics beyond the Standard Model are becoming increasingly invaluable for theorists. As a leading proposal for a theory of quantum gravity, string theory gives rise to a plethora of 4-dimensional EFTs upon compactification, the so-called string landscape. For decades, a prohibiting factor in analysing these EFTs has been the computational cost of standard...
Neural networks are a powerful tool for an ever-growing list of tasks. However, their enormous complexity often complicates developing theories describing how these networks learn. In our recent work, inspired by the development of statistical mechanics, we have studied the use of collective variables to explain how neural networks learn, specifically, the von Neumann entropy and Trace of the...
This talk will be about our work on using machine learning to understand Calabi-Yau metrics. These extra-dimensional metrics determine aspects of the low-energy EFTs arising from string theory which have been unavailable for several decades prior to works using machine learning methods.
In particle physics, the search for phenomena outside the well-established predictions of the Standard Model (SM) is of great importance. For more than four decades, the SM has been the established theory of fundamental particles and their interactions. However, some aspects of nature remain elusive to the explanatory power of the SM. Thus, researchers' attention turns to the pursuit of new...
Exploring innovative methods and emerging technologies holds the promise of enhancing the capabilities of LHC experiments and contributing to scientific discoveries. In this work, we propose a new strategy for anomaly detection at the LHC based on unsupervised quantum machine learning algorithms. To accommodate the constraints on the problem size dictated by the limitations of current quantum...
Neural Networks (NN), the backbones of Deep Learning, create field theories through their output ensembles at initialization. Certain limits of NN architecture give rise to free field theories via Central Limit Theorem (CLT), whereas other regimes give rise to weakly coupled, and non-perturbative field theories, via small, and large deviations from CLT. I will present a systematic construction...
Recognizing symmetries in data allows for significant boosts in neural network training. In many cases, however, the underlying symmetry is present only in an idealized dataset, and is broken in the training data, due to effects such as arbitrary and/or non-uniform detector bin edges. Standard approaches, such as data augmentation or equivariant networks fail to represent the nature of the...
We have developed an end-to-end data analysis framework, HEP ML Lab (HML), based on Python for signal-background analysis in high-energy physics research. It offers essential interfaces and shortcuts for event generation, dataset creation, and method application.
With the HML API, a large volume of collision events can be generated in sequence under different settings. The representations...
We present a class of Neural Networks which extends the notion of Energy Flow Networks (EFNs) to higher-order particle correlations. The structure of these networks is inspired by the Energy-Energy Correlators of QFT, which are particularly robust against non-perturbative corrections. By studying the response of our models to the presence and absence of non-perturbative hadronization, we can...
A measurement of novel event shapes quantifying the isotropy of collider events is presented, made using 140 fb$^{−1}$ of proton-proton collisions with $\sqrt{s}$=13 TeV centre-of-mass energy recorded with the ATLAS detector at CERN's Large Hadron Collider. These event shapes are defined as the Energy-Mover's Distance between collider events and isotropic reference geometries, evaluated by...
As the performance of the Large Hadron Collider (LHC) continues to improve in terms of energy reach and instantaneously luminosity, ATLAS faces an increasingly challenging environment. High energy proton-proton ($pp$) interactions, known as hard scatters, are produced in contrast to low energy inelastic proton-proton collisions referred to as pile-up. From the perspective of data analyses,...
Jet formation algorithms that utilise eigenvalues of the similarity matrix offer a innovative take on the definition of a jet. This is referred to as spectral clustering. It solves the clustering problem in a non-greedy manner, and so may find more optimal solutions that straightforward agglomerative algorithms. However, the eigenvalue problem is computationally expensive, so in this study...
On average, during Run 2 of the Large Hadron Collider (LHC), 30-50 simultaneous vertices yielding charged and neutral showers, otherwise known as pileup, were recorded per event. This number is expected to only increase at the High Luminosity LHC with predicted values as high as 200. As such, pileup presents a salient problem that, if not checked, hinders the search for new physics as well as...
We present a model-agnostic search for new physics in the dijet final state using five different novel machine-learning techniques. Other than the requirement of a narrow dijet resonance, minimal additional assumptions are placed on the signal hypothesis. Signal regions are obtained utilizing multivariate machine learning methods to select jets with anomalous substructure. A collection of...
Neural network models that rely on jet substructure are commonly trained assuming jet constituents at truth level or smeared by parameterized detector response. However, the performance in such simplified circumstances may translate poorly to actual collider experiments. We investigate the impact by comparing large-R jet tagging using smeared particle-level jets versus jets built using...
Machine learning algorithms have the capacity to discern intricate features directly from raw data. We demonstrated the performance of top taggers built upon three machine learning architectures: a BDT that uses jet-level variables (high-level features, HLF) as input, while a CNN (ResNet) trained on the jet image, and a GNN (LorentzNet) trained on the particle cloud representation of a jet...
Full statistical models encapsulate the complete information of an experimental result, including the likelihood function given observed data. Their proper publication is of vital importance for a long lasting legacy of the LHC. Major steps have been taken towards this goal; a notable example being ATLAS release of statistical models with the pyhf framework. However, even the likelihoods are...
Some machine learning methods that have been developed for particle physics applications are actually completely general with regards to the data. In this talk, I will show how ANODE and CATHODE, originally created to search for anomalies in particle physics, can be used to search for stellar streams in the Milky Way using data from the Gaia space telescope. Stellar streams are important...
