Related papers: MadMax, or Where Boosted Significances Come From
At the Large Hadron Collider, numerous physics processes expected within the standard model and theories beyond it give rise to very high momentum particles decaying to multihadronic final states. Development of algorithms for efficient…
With the LHC close to complete its 8 TeV run, the experimental searches have already started to probe the vast beyond-the-standard Model scenery. Providing next-to-leading order (NLO) predictions for the major new physics discovery channels…
Machine-learning techniques have become fundamental in high-energy physics and, for new physics searches, it is crucial to know their performance in terms of experimental sensitivity, understood as the statistical significance of the…
Advances in machine learning have led to an emergence of new paradigms in the analysis of large data which could assist traditional approaches in the search for new physics amongst the immense Standard Model backgrounds at the Large Hadron…
The extended infomax algorithm for independent component analysis (ICA) can separate sub- and super-Gaussian signals but converges slowly as it uses stochastic gradient optimization. In this paper, an improved extended infomax algorithm is…
The softmax function is a cornerstone of multi-class classification, integral to a wide range of machine learning applications, from large-scale retrieval and ranking models to advanced large language models. However, its computational cost…
Iterative algorithms with feedback are amongst the most powerful and versatile optimization methods for phase retrieval. Among these, the hybrid input-output algorithm has demonstrated practical solutions to giga-element nonlinear phase…
In this report we present the status of the MAgnetized Disk and Mirror Axion eXperiment (MADMAX), the first dielectric haloscope for the direct search of dark matter axions in the mass range of 40 to 400 $\mu$eV. MADMAX will consist of…
In time for the first tests on LHC data we introduce a set of improvements and tests of purely kinematic top tagging algorithms. First, we show how different jet algorithms can be used for different transverse momentum regimes. Combining…
We explore supersymmetric theories in which the Higgs mass is boosted by the non-decoupling D-terms of an extended $U(1)_X$ gauge symmetry, defined here to be a general linear combination of hypercharge, baryon number, and lepton number.…
Deep generative models (DGMs) are effective on learning multilayered representations of complex data and performing inference of input data by exploring the generative ability. However, little work has been done on examining or empowering…
We address the task of predicting the gain of using RAG (retrieval augmented generation) for question answering with respect to not using it. We study the performance of a few pre-retrieval and post-retrieval predictors originally devised…
The Retrieval-Augmented Generation (RAG) framework introduces a retrieval module to dynamically inject retrieved information into the input context of large language models (LLMs), and has demonstrated significant success in various NLP…
We propose a new search strategy for heavy top partners at the early stages of the LHC run-II, based on lepton-jet final states. Our results show that final states containing a boosted massive jet and a hard lepton, in addition to a top…
We present some highlights on the complementaries of the Higgs and SUSY searches at the LHC, using the 8 and 13 TeV results. In particular, we discuss the constraints that can be obtained on the MSSM parameters by the determination of the…
The jet-jet profile, or detailed manner, in which transverse energy and mass are distributed around the jet-jet system resulting from the hadronic decay of a $Z$ boson in the process Higgs$\to ZZ$ at a proton-proton collider energy of…
We present nonparametric algorithms for estimating optimal individualized treatment rules. The proposed algorithms are based on the XGBoost algorithm, which is known as one of the most powerful algorithms in the machine learning literature.…
Retrieval-Augmented Generation (RAG) has recently emerged as a method to extend beyond the pre-trained knowledge of Large Language Models by augmenting the original prompt with relevant passages or documents retrieved by an Information…
Motivated by the possible 95 GeV diphoton excess, we investigate the capability of the Type-I Two-Higgs-Doublet Model (2HDM-I) to explain this signal under current theoretical and experimental constraints. Using full Monte Carlo (MC)…
The di-top final state is an important search channel for additional Higgs bosons at the LHC. In this channel, large signal--background interference contributions can strongly distort a resonance peak as it would be expected from a pure…