A5072503522- Particle physics theoretical and experimental studies
- Particle Detector Development and Performance
- Computational Physics and Python Applications
- High-Energy Particle Collisions Research
- Black Holes and Theoretical Physics
- Cosmology and Gravitation Theories
- Quantum Chromodynamics and Particle Interactions
- Dark Matter and Cosmic Phenomena
- Neutrino Physics Research
- Stellar, planetary, and galactic studies
- Astrophysics and Cosmic Phenomena
- Anomaly Detection Techniques and Applications
- Distributed and Parallel Computing Systems
- Galaxies: Formation, Evolution, Phenomena
- Astronomy and Astrophysical Research
- Radiation Detection and Scintillator Technologies
- Gamma-ray bursts and supernovae
- Algorithms and Data Compression
- Medical Imaging Techniques and Applications
- Particle Accelerators and Free-Electron Lasers
- Generative Adversarial Networks and Image Synthesis
- Noncommutative and Quantum Gravity Theories
- Radio Astronomy Observations and Technology
- Scientific Research and Discoveries
- Astrophysics and Star Formation Studies
Rutgers Sexual and Reproductive Health and Rights
2012-2025
Rutgers, The State University of New Jersey
2015-2024
HES-SO Genève
2023
Universität Hamburg
2023
Heidelberg University
2023
Deutsches Elektronen-Synchrotron DESY
2023
University of Geneva
2023
Institut National de Physique Nucléaire et de Physique des Particules
2022
Centre National de la Recherche Scientifique
2022
Université Grenoble Alpes
2022
This document proposes a collection of simplified models relevant to the design new-physics searches at LHC and characterization their results. Both ATLAS CMS have already presented some results in terms models, we encourage them continue expand this effort, which supplements both signature-based benchmark model interpretations. A is defined by an effective Lagrangian describing interactions small number new particles. Simplified can equally well be described masses cross-sections. These...
We give a general definition of gauge mediated supersymmetry breaking which encompasses all the known mediation models. In particular, it includes both models with messengers as well direct A formalism for computing soft terms in generic model is presented. Such necessary strongly-coupled where perturbation theory cannot be used. It allows us to identify features entire class and distinguish them from specific signatures various subclasses.
We analyze the SDSS Lyα forest PF(k, z) measurement to determine linear theory power spectrum. Our analysis is based on fully hydrodynamic simulations, extended using hydro-particle-mesh simulations. account for effect of absorbers with damping wings, which leads an increase in slope break degeneracy between mean level absorption and spectrum without significant use external constraints. infer amplitude Δ(kp = 0.009 s km-1, zp 3.0) 0.452 neff(kp, zp) -2.321 (possible systematic errors are...
We present the physics program of Belle II experiment, located on intensity frontier SuperKEKB $e^+e^-$ collider. collected its first collisions in 2018, and is expected to operate for next decade. It anticipated collect 50/ab collision data over lifetime. This book outcome a joint effort collaborators theorists through theory interface platform (B2TiP), an that commenced 2014. The aim B2TiP was elucidate potential impacts program, which includes wide scope topics: B physics, charm, tau,...
Recently, the ATLAS and CMS collaborations have announced exciting hints for a Standard Model-like Higgs boson at mass of approximately 125 GeV. In this paper, we explore potential consequences MSSM low scale SUSY-breaking. As is well-known, GeV implies either extremely heavy stops (>~ 10 TeV), or near-maximal stop mixing. We review quantify these statements, investigate implications models low-scale SUSY breaking such as gauge mediation where A-terms are small messenger scale. For models,...
We leverage recent breakthroughs in neural density estimation to propose a new unsupervised ANOmaly detection with Density Estimation (ANODE) technique. By estimating the conditional probability of data signal region and sidebands, interpolating latter into region, fully data-driven likelihood ratio versus background can be constructed. This is broadly sensitive overdensities that could due localized anomalies. In addition, unique potential benefit ANODE method directly estimated using...
We introduce a potentially powerful new method of searching for physics at the LHC, using autoencoders and unsupervised deep learning. The key idea autoencoder is that it learns to map ``normal'' events back themselves, but fails reconstruct ``anomalous'' has never encountered before. reconstruction error can then be used as an anomaly threshold. demonstrate effectiveness this QCD jets background boosted top R-parity violating (RPV) gluino signal. show significantly improve signal over when...
In the original version of this manuscript, an error was introduced on pp352. '2.7nb:1.6nb' has been corrected to '2.4nb:1.3nb' in current online and printed version. doi:10.1093/ptep/ptz106.
We apply computer vision with deep learning -- in the form of a convolutional neural network (CNN) to build highly effective boosted top tagger. Previous work (the "DeepTop" tagger Kasieczka et al) has shown that CNN-based can achieve comparable performance state-of-the-art conventional taggers based on high-level inputs. Here, we introduce number improvements DeepTop tagger, including architecture, training, image preprocessing, sample size and color pixels. Our final CNN outperforms BDTs...
We propose a new model-agnostic search strategy for physics beyond the standard model (BSM) at LHC, based on novel application of neural density estimation to anomaly detection. Our approach, which we call Classifying Anomalies THrough Outer Density Estimation (CATHODE), assumes BSM signal is localized in region (defined e.g. using invariant mass). By training conditional estimator collection additional features outside region, interpolating it into and sampling from it, produce events that...
First-principle simulations are at the heart of high-energy physics research program. They link vast data output multi-purpose detectors with fundamental theory predictions and interpretation. This review illustrates a wide range applications modern machine learning to event generation simulation-based inference, including conceptional developments driven by specific requirements particle physics. New ideas tools developed interface will improve speed precision forward simulations, handle...
We introduce CaloFlow, a fast detector simulation framework based on normalizing flows. For the first time, we demonstrate that flows can reproduce many-channel calorimeter showers with extremely high fidelity, providing fresh alternative to computationally expensive GEANT4 simulations, as well other state-of-the-art frameworks GANs and VAEs. Besides usual histograms of physical features images showers, new metric for judging quality generative modeling: performance classifier trained...
Well-trained classifiers and their complete weight distributions provide us with a well-motivated practicable method to test generative networks in particle physics. We illustrate benefits for distribution-shifted jets, calorimeter showers, reconstruction-level events. In all cases, the classifier weights make powerful of network, identify potential problems density estimation, relate them underlying physics, tie comprehensive precision uncertainty treatment networks.
Abstract Machine learning-based anomaly detection (AD) methods are promising tools for extending the coverage of searches physics beyond Standard Model (BSM). One class AD that has received significant attention is resonant detection, where BSM assumed to be localized in at least one known variable. While there have been many proposed identify such a signal make use simulated or detected data different ways, not yet study methods’ complementarity. To this end, we address two questions....
Simulating particle detector response is the single most expensive step in Large Hadron Collider computational pipeline. Recently it was shown that normalizing flows can accelerate this process while achieving unprecedented levels of accuracy, but scaling approach up to higher resolutions relevant for future upgrades leads prohibitive memory constraints. To overcome problem, we introduce Inductive CaloFlow (icaloflow), a framework fast simulation based on an inductive series trained pattern...
CALOFLOW is a new and promising approach to fast calorimeter simulation based on normalizing flows. Applying the photon charged pion ≥ant showers of Dataset 1 Fast Calorimeter Simulation Challenge 2022, we show how it can produce high-fidelity samples with sampling time that several orders magnitude faster than ≥ant. We demonstrate fidelity using shower images, histograms high level features, aggregate metrics such as classifier trained distinguish from samples.
Physics beyond the Standard Model that is resonant in one or more dimensions has been a longstanding focus of countless searches at colliders and beyond. Recently, many new strategies for have developed, where sideband information can be used conjunction with modern machine learning, order to generate synthetic datasets representing background. Until now, this approach was only able accommodate relatively small number dimensions, limiting breadth search sensitivity. Using recent innovations...
Abstract We present a novel, data-driven analysis of Galactic dynamics, using unsupervised machine learning — in the form density estimation with normalizing flows to learn underlying phase space distribution 6 million nearby stars from Gaia DR3 catalog. Solving equilibrium collisionless Boltzmann equation, we calculate for first time ever model-free, unbinned estimate local acceleration and mass fields within 3 kpc sphere around Sun. As our approach makes no assumptions about symmetries,...
Whenever invertible generative networks are needed for LHC physics, normalizing flows show excellent performance. In this work, we investigate their performance fast calorimeter shower simulations with increasing phase space dimension. We use and expressive coupling spline transformations applied to the CaloChallenge datasets. addition base flow architecture also employ a VAE compress dimensionality train network in latent space. evaluate our on several metrics, including high-level...
We measure the power spectrum, PF(k, z), of transmitted flux in Lyα forest using 3035 high-redshift quasar spectra from Sloan Digital Sky Survey. This sample is almost 2 orders magnitude larger than any previously available data set, yielding statistical errors ~0.6% and ~0.005 on, respectively, overall amplitude logarithmic slope z). unprecedented requires a correspondingly careful analysis possible systematic contaminations it. For this purpose we reanalyze raw to make use information not...