Rehm, Florian Vallecorsa, Sofia Saletore, Vikram Pabst, Hans Chaibi, Adel Codreanu, Valeriu Borras, Kerstin Krücker, Dirk

Deep learning is finding its way into high energy physics by replacing traditional Monte Carlo simulations. However, deep learning still requires an excessive amount of computational resources. A promising approach to make deep learning more efficient is to quantize the parameters of the neural networks to reduced precision. Reduced precision compu...

Huang, R Armengaud, E Augier, C Barabash, AS Bellini, F Benato, G Beno t, A Beretta, M Bergé, L Billard, J
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CUPID-Mo is a cryogenic detector array designed to search for neutrinoless double-beta decay (0νββ) of 100Mo. It uses 20 scintillating 100Mo-enriched Li2MoO4 bolometers instrumented with Ge light detectors to perform active suppression of α backgrounds, drastically reducing the expected background in the 0νββ signal region. As a result, pileup even...

Gilbert, Dustin A Murray, Peyton D De Rojas, Julius Dumas, Randy K Davies, Joseph E Liu, Kai

The first order reversal curve (FORC) method is a magnetometry based technique used to capture nanoscale magnetic phase separation and interactions with macroscopic measurements using minor hysteresis loop analysis. This makes the FORC technique a powerful tool in the analysis of complex systems which cannot be effectively probed using localized te...

Alonso-Monsalve, Saúl Douqa, Dana Jesús-Valls, César Lux, Thorsten Pina-Otey, Sebastian Sánchez, Federico Sgalaberna, Davide Whitehead, Leigh H.

Deep learning tools are being used extensively in high energy physics and are becoming central in the reconstruction of neutrino interactions in particle detectors. In this work, we report on the performance of a graph neural network in assisting with particle flow event reconstruction. The three-dimensional reconstruction of particle tracks produc...

Di Bello, Francesco Armando Ganguly, Sanmay Gross, Eilam Kado, Marumi Pitt, Michael Santi, Lorenzo Shlomi, Jonathan
Published in
The European Physical Journal C

In High Energy Physics experiments Particle Flow (PFlow) algorithms are designed to provide an optimal reconstruction of the nature and kinematic properties of the particles produced within the detector acceptance during collisions. At the heart of PFlow algorithms is the ability to distinguish the calorimeter energy deposits of neutral particles f...

Barlow, Roger John

This is the write-up of a set of lectures given at the Asia Europe Pacific School of High Energy Physics in Quy Nhon, Vietnam in September 2018, to an audience of PhD students in all branches of particle physics They cover the different meanings of 'probability', particularly frequentist and Bayesian, the binomial, Poisson and Gaussian distribution...

Pata, Joosep Duarte, Javier Vlimant, Jean-Roch Pierini, Maurizio Spiropulu, Maria

In general-purpose particle detectors, the particle-flow algorithm may be used to reconstruct a comprehensive particle-level view of the event by combining information from the calorimeters and the trackers, significantly improving the detector resolution for jets and the missing transverse momentum. In view of the planned high-luminosity upgrade o...

Kasieczka, Gregor Nachman, Benjamin Shih, David Amram, Oz Andreassen, Anders Benkendorfer, Kees Bortolato, Blaz Brooijmans, Gustaaf Canelli, Florencia Collins, Jack H.
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A new paradigm for data-driven, model-agnostic new physics searches at colliders is emerging, and aims to leverage recent breakthroughs in anomaly detection and machine learning. In order to develop and benchmark new anomaly detection methods within this framework, it is essential to have standard datasets. To this end, we have created the LHC Olym...

Philcox, Oliver H. E. Ivanov, Mikhail M. Zaldarriaga, Matias Simonovic, Marko Schmittfull, Marcel

Creating accurate and low-noise covariance matrices represents a formidable challenge in modern-day cosmology. We present a formalism to compress arbitrary observables into a small number of bins by projection into a model-specific subspace that minimizes the prior-averaged log-likelihood error. The lower dimensionality leads to a dramatic reductio...

Wunsch, Stefan Jörger, Simon Wolf, Roger Quast, Günter
Published in
Computing and Software for Big Science

Data analysis in science, e.g., high-energy particle physics, is often subject to an intractable likelihood if the observables and observations span a high-dimensional input space. Typically the problem is solved by reducing the dimensionality using feature engineering and histograms, whereby the latter allows to build the likelihood using Poisson ...