no code implementations • 1 Feb 2024 • Eyup B. Unlu, Marçal Comajoan Cara, Gopal Ramesh Dahale, Zhongtian Dong, Roy T. Forestano, Sergei Gleyzer, Daniel Justice, Kyoungchul Kong, Tom Magorsch, Konstantin T. Matchev, Katia Matcheva
Models based on vision transformer architectures are considered state-of-the-art when it comes to image classification tasks.
no code implementations • 21 Jan 2024 • Konstantin T. Matchev, Katia Matcheva, Pierre Ramond, Sarunas Verner
Theoretical physicists describe nature by i) building a theory model and ii) determining the model parameters.
1 code implementation • 30 Nov 2023 • Zhongtian Dong, Marçal Comajoan Cara, Gopal Ramesh Dahale, Roy T. Forestano, Sergei Gleyzer, Daniel Justice, Kyoungchul Kong, Tom Magorsch, Konstantin T. Matchev, Katia Matcheva, Eyup B. Unlu
Our results show that the $\mathbb{Z}_2\times \mathbb{Z}_2$ EQNN and the QNN provide superior performance for smaller parameter sets and modest training data samples.
1 code implementation • 30 Nov 2023 • Roy T. Forestano, Marçal Comajoan Cara, Gopal Ramesh Dahale, Zhongtian Dong, Sergei Gleyzer, Daniel Justice, Kyoungchul Kong, Tom Magorsch, Konstantin T. Matchev, Katia Matcheva, Eyup B. Unlu
In this paper, we perform a fair and comprehensive comparison between classical graph neural networks (GNNs) and equivariant graph neural networks (EGNNs) and their quantum counterparts: quantum graph neural networks (QGNNs) and equivariant quantum graph neural networks (EQGNN).
no code implementations • 31 Oct 2023 • Konstantin T. Matchev, Katia Matcheva, Pierre Ramond, Sarunas Verner
The discovery process of building new theoretical physics models involves the dual aspect of both fitting to the existing experimental data and satisfying abstract theorists' criteria like beauty, naturalness, etc.
no code implementations • 16 Oct 2023 • Eyup B. Unlu, Roy T. Forestano, Konstantin T. Matchev, Katia Matcheva
We describe a machine-learning-based surrogate model for reproducing the Bayesian posterior distributions for exoplanet atmospheric parameters derived from transmission spectra of transiting planets with typical retrieval software such as TauRex.
no code implementations • 14 Sep 2023 • Roy T. Forestano, Konstantin T. Matchev, Katia Matcheva, Alexander Roman, Eyup B. Unlu, Sarunas Verner
We design loss functions which probe the subalgebra structure either during the deep learning stage of symmetry discovery or in a subsequent post-processing stage.
no code implementations • 15 Aug 2023 • Roy T. Forestano, Konstantin T. Matchev, Katia Matcheva, Eyup B. Unlu
The next generation of telescopes will yield a substantial increase in the availability of high-resolution spectroscopic data for thousands of exoplanets.
no code implementations • 10 Jul 2023 • Roy T. Forestano, Konstantin T. Matchev, Katia Matcheva, Alexander Roman, Eyup B. Unlu, Sarunas Verner
Recent work has applied supervised deep learning to derive continuous symmetry transformations that preserve the data labels and to obtain the corresponding algebras of symmetry generators.
no code implementations • 10 Feb 2023 • Roy T. Forestano, Konstantin T. Matchev, Katia Matcheva, Alexander Roman, Eyup B. Unlu, Sarunas Verner
Recent work has used deep learning to derive symmetry transformations, which preserve conserved quantities, and to obtain the corresponding algebras of generators.
no code implementations • 2 Feb 2023 • Alexander Roman, Roy T. Forestano, Konstantin T. Matchev, Katia Matcheva, Eyup B. Unlu
We develop a deep learning methodology for the simultaneous discovery of multiple nontrivial continuous symmetries across an entire labelled dataset.
no code implementations • 13 Jan 2023 • Roy T. Forestano, Konstantin T. Matchev, Katia Matcheva, Alexander Roman, Eyup Unlu, Sarunas Verner
We design a deep-learning algorithm for the discovery and identification of the continuous group of symmetries present in a labeled dataset.
no code implementations • 15 Nov 2022 • Zhongtian Dong, Kyoungchul Kong, Konstantin T. Matchev, Katia Matcheva
We demonstrate the use of symbolic regression in deriving analytical formulas, which are needed at various stages of a typical experimental analysis in collider phenomenology.
no code implementations • 4 Oct 2022 • Kyoungchul Kong, Konstantin T. Matchev, Stephen Mrenna, Prasanth Shyamsundar
We propose an intuitive, machine-learning approach to multiparameter inference, dubbed the InferoStatic Networks (ISN) method, to model the score and likelihood ratio estimators in cases when the probability density can be sampled but not computed directly.
no code implementations • 7 Jan 2022 • Konstantin T. Matchev, Katia Matcheva, Alexander Roman
Transit spectroscopy is a powerful tool to decode the chemical composition of the atmospheres of extrasolar planets.
no code implementations • 22 Dec 2021 • Konstantin T. Matchev, Katia Matcheva, Alexander Roman
The physical characteristics and atmospheric chemical composition of newly discovered exoplanets are often inferred from their transit spectra which are obtained from complex numerical models of radiative transfer.
no code implementations • 21 May 2021 • Doojin Kim, Kyoungchul Kong, Konstantin T. Matchev, Myeonghun Park, Prasanth Shyamsundar
The choice of optimal event variables is crucial for achieving the maximal sensitivity of experimental analyses.
1 code implementation • 31 Aug 2020 • Konstantin T. Matchev, Prasanth Shyamsundar
We introduce a new machine-learning-based approach, which we call the Independent Classifier networks (InClass nets) technique, for the nonparameteric estimation of conditional independence mixture models (CIMMs).
1 code implementation • 3 Aug 2015 • Won Sang Cho, James S. Gainer, Doojin Kim, Sung Hak Lim, Konstantin T. Matchev, Filip Moortgat, Luc Pape, Myeonghun Park
Reconstructed mass variables, such as $M_2$, $M_{2C}$, $M_T^\star$, and $M_{T2}^W$, play an essential role in searches for new physics at hadron colliders.
High Energy Physics - Phenomenology High Energy Physics - Experiment
no code implementations • 19 Mar 2014 • James S. Gainer, Joseph Lykken, Konstantin T. Matchev, Stephen Mrenna, Myeonghun Park
We extend the study of Higgs boson couplings in the "golden" $gg\to H \to ZZ^\ast \to 4\ell$ channel in two important respects.
High Energy Physics - Phenomenology High Energy Physics - Experiment
1 code implementation • 7 Jan 2014 • Won Sang Cho, James S. Gainer, Doojin Kim, Konstantin T. Matchev, Filip Moortgat, Luc Pape, Myeonghun Park
The tests are able to determine: 1) whether the decays in the event are two-body or three-body, 2) if the decay is two-body, whether the intermediate resonances in the two decay chains are the same, and 3) the exact sequence in which the visible particles are emitted from each decay chain.
High Energy Physics - Phenomenology High Energy Physics - Experiment
no code implementations • 2 Oct 2012 • Paul Avery, Dimitri Bourilkov, Mingshui Chen, Tongguang Cheng, Alexey Drozdetskiy, James S. Gainer, Andrey Korytov, Konstantin T. Matchev, Predrag Milenovic, Guenakh Mitselmakher, Myeonghun Park, Aurelijus Rinkevicius, Matthew Snowball
The importance of the H -> ZZ -> 4l "golden" channel was shown by its major role in the discovery, by the ATLAS and CMS collaborations, of a Higgs-like boson with mass near 125 GeV.
High Energy Physics - Phenomenology High Energy Physics - Experiment