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The majority of experiments in fundamental science today are designed to be multi-purpose: their aim is not simply to measure a single physical quantity or process, but rather to enable increased precision in the measurement of a number of different observable quantities of a natural system, to extend the search for new phenomena, or to exclude a larger phase space of candidate theories. Most of the time, a combination of the above goals is pursued; this breadth of scope adds a layer of complexity to the already demanding task of designing the measurement apparatus in an optimal way, by defining suitable geometries and choosing the most advantageous materials and appropriate detection technologies. The precise definition of a global optimality criterion may then require experimentalists to find a consensus on the relative scientific worth of those goals. In this work, we discuss the problem of formulating a utility function for multipurpose experiments, as an enabling step to employ artificial intelligence tools to explore the design space and assist humans in finding solutions at the Pareto front., Comment: 27 pages, 5 figures, 4 tables; prepared for submission to Physics Open

This document is comprised of a collection of updated preliminary parameters for the key parts of the muon collider. The updated preliminary parameters follow on from the October 2023 Tentative Parameters Report. Particular attention has been given to regions of the facility that are believed to hold greater technical uncertainty in their design and that have a strong impact on the cost and power consumption of the facility. The data is collected from a collaborative spreadsheet and transferred to overleaf.

We have applied an operator-overloading forward-mode algorithmic differentiation tool to the Monte-Carlo particle simulation toolkit Geant4. Our differentiated version of Geant4 allows computing mean pathwise derivatives of user-defined outputs of Geant4 applications with respect to user-defined inputs. This constitutes a major step towards enabling gradient-based optimization techniques in high-energy physics, as well as other application domains of Geant4. This is a preliminary report on the technical aspects of applying operator-overloading AD to Geant4, as well as a first analysis of some results obtained by our differentiated Geant4 prototype. We plan to follow up with a more refined analysis.

An open scientific challenge is how to classify events with reliable measures of uncertainty, when we have a mechanistic model of the data-generating process but the distribution over both labels and latent nuisance parameters is different between train and target data. We refer to this type of distributional shift as generalized label shift (GLS). Direct classification using observed data $\mathbf{X}$ as covariates leads to biased predictions and invalid uncertainty estimates of labels $Y$. We overcome these biases by proposing a new method for robust uncertainty quantification that casts classification as a hypothesis testing problem under nuisance parameters. The key idea is to estimate the classifier's receiver operating characteristic (ROC) across the entire nuisance parameter space, which allows us to devise cutoffs that are invariant under GLS. Our method effectively endows a pre-trained classifier with domain adaptation capabilities and returns valid prediction sets while maintaining high power. We demonstrate its performance on two challenging scientific problems in biology and astroparticle physics with data from realistic mechanistic models., Comment: 26 pages, 19 figures, code available at https://github.com/lee-group-cmu/lf2i

In this document we consider the problem of finding the optimal layout for the array of water Cherenkov detectors proposed by the SWGO collaboration to study very-high-energy gamma rays in the southern hemisphere. We develop a continuous model of the secondary particles produced by atmospheric showers initiated by high-energy gamma rays and protons, and build an optimization pipeline capable of identifying the most promising configuration of the detector elements. The pipeline employs stochastic gradient descent to maximize a utility function aligned with the scientific goals of the experiment. We demonstrate how the software is capable of finding the global maximum in the high-dimensional parameter space, and discuss its performance and limitations., Comment: 40 pages, 23 figures

In this article we examine recent developments in the research area concerning the creation of end-to-end models for the complete optimization of measuring instruments. The models we consider rely on differentiable programming methods and on the specification of a software pipeline including all factors impacting performance -- from the data-generating processes to their reconstruction and the extraction of inference on the parameters of interest of a measuring instrument -- along with the careful specification of a utility function well aligned with the end goals of the experiment. Building on previous studies originated within the MODE Collaboration, we focus specifically on applications involving instruments for particle physics experimentation, as well as industrial and medical applications that share the detection of radiation as their data-generating mechanism., Comment: 70 pages, 17 figures. To be submitted to journal

We describe a software package, TomOpt, developed to optimise the geometrical layout and specifications of detectors designed for tomography by scattering of cosmic-ray muons. The software exploits differentiable programming for the modeling of muon interactions with detectors and scanned volumes, the inference of volume properties, and the optimisation cycle performing the loss minimisation. In doing so, we provide the first demonstration of end-to-end-differentiable and inference-aware optimisation of particle physics instruments. We study the performance of the software on a relevant benchmark scenario and discuss its potential applications. Our code is available on Github., Comment: V3: Updated to published version; 29 pages content + appendix + refs

In recent years novel inference techniques have been developed based on the construction of non-linear summary statistics with neural networks by minimising inferencemotivated losses. One such technique is inferno (P. de Castro and T. Dorigo, Comp. Phys. Comm. 244 (2019) 170) which was shown on toy problems to outperform classical summary statistics for the problem of confidence interval estimation in the presence of nuisance parameters. In order to test and benchmark the algorithm in a real world application, a full, systematics-dominated analysis produced by the CMS experiment, "Measurement of the top-antitop production cross section in the tau+jets channel in pp collisions at sqrt(s) = 7 TeV" (CMS Collaboration, The European Physical Journal C, 2013) is reproduced with CMS Open Data. The application of the inferno-powered neural network architecture to this analysis demonstrates the potential to reduce the impact of systematic uncertainties in real LHC analyses. This work also exemplifies the extent to which LHC analyses can be reproduced with open data., Comment: 19 pages, 8 figures

Prediction algorithms, such as deep neural networks (DNNs), are used in many domain sciences to directly estimate internal parameters of interest in simulator-based models, especially in settings where the observations include images or complex high-dimensional data. In parallel, modern neural density estimators, such as normalizing flows, are becoming increasingly popular for uncertainty quantification, especially when both parameters and observations are high-dimensional. However, parameter inference is an inverse problem and not a prediction task; thus, an open challenge is to construct conditionally valid and precise confidence regions, with a guaranteed probability of covering the true parameters of the data-generating process, no matter what the (unknown) parameter values are, and without relying on large-sample theory. Many simulator-based inference (SBI) methods are indeed known to produce biased or overly confident parameter regions, yielding misleading uncertainty estimates. This paper presents WALDO, a novel method to construct confidence regions with finite-sample conditional validity by leveraging prediction algorithms or posterior estimators that are currently widely adopted in SBI. WALDO reframes the well-known Wald test statistic, and uses a computationally efficient regression-based machinery for classical Neyman inversion of hypothesis tests. We apply our method to a recent high-energy physics problem, where prediction with DNNs has previously led to estimates with prediction bias. We also illustrate how our approach can correct overly confident posterior regions computed with normalizing flows., Comment: 15 pages, 10 figures, code available at https://github.com/lee-group-cmu/lf2i

The full optimization of the design and operation of instruments whose functioning relies on the interaction of radiation with matter is a super-human task, given the large dimensionality of the space of possible choices for geometry, detection technology, materials, data-acquisition, and information-extraction techniques, and the interdependence of the related parameters. On the other hand, massive potential gains in performance over standard, "experience-driven" layouts are in principle within our reach if an objective function fully aligned with the final goals of the instrument is maximized by means of a systematic search of the configuration space. The stochastic nature of the involved quantum processes make the modeling of these systems an intractable problem from a classical statistics point of view, yet the construction of a fully differentiable pipeline and the use of deep learning techniques may allow the simultaneous optimization of all design parameters. In this document we lay down our plans for the design of a modular and versatile modeling tool for the end-to-end optimization of complex instruments for particle physics experiments as well as industrial and medical applications that share the detection of radiation as their basic ingredient. We consider a selected set of use cases to highlight the specific needs of different applications., Comment: 109 pages, 32 figures. To be submitted to Reviews in Physics

In this paper we report on the current status of studies on the expected performance for a detector designed to operate in a muon collider environment. Beam-induced backgrounds (BIB) represent the main challenge in the design of the detector and the event reconstruction algorithms. The current detector design aims to show that satisfactory performance can be achieved, while further optimizations are expected to significantly improve the overall performance. We present the characterization of the expected beam-induced background, describe the detector design and software used for detailed event simulations taking into account BIB effects. The expected performance of charged-particle reconstruction, jets, electrons, photons and muons is discussed, including an initial study on heavy-flavor jet tagging. A simple method to measure the delivered luminosity is also described. Overall, the proposed design and reconstruction algorithms can successfully reconstruct the high transverse-momentum objects needed to carry out a broad physics program., Comment: contribution to Snowmass 2021

In the path towards a muon collider with center of mass energy of 10 TeV or more, a stage at 3 TeV emerges as an appealing option. Reviewing the physics potential of such muon collider is the main purpose of this document. In order to outline the progression of the physics performances across the stages, a few sensitivity projections for higher energy are also presented. There are many opportunities for probing new physics at a 3 TeV muon collider. Some of them are in common with the extensively documented physics case of the CLIC 3 TeV energy stage, and include measuring the Higgs trilinear coupling and testing the possible composite nature of the Higgs boson and of the top quark at the 20 TeV scale. Other opportunities are unique of a 3 TeV muon collider, and stem from the fact that muons are collided rather than electrons. This is exemplified by studying the potential to explore the microscopic origin of the current $g$-2 and $B$-physics anomalies, which are both related with muons., Comment: 73 pages, 28 figures; Contribution to Snowmass 2021

The perspective of designing muon colliders with high energy and luminosity, which is being investigated by the International Muon Collider Collaboration, has triggered a growing interest in their physics reach. We present a concise summary of the muon colliders potential to explore new physics, leveraging on the unique possibility of combining high available energy with very precise measurements., Comment: 21 pages, 7 figures; Contribution to Snowmass 2021

Among the post-LHC generation of particle accelerators, the muon collider represents a unique machine with capability to provide very high energy leptonic collisions and to open the path to a vast and mostly unexplored physics programme. However, on the experimental side, such great physics potential is accompanied by unprecedented technological challenges, due to the fact that muons are unstable particles. Their decay products interact with the machine elements and produce an intense flux of background particles that eventually reach the detector and may degrade its performance. In this paper, we present technologies that have a potential to match the challenging specifications of a muon collider detector and outline a path forward for the future R&D efforts., Comment: Contribution to Snowmass 2021, 27 pages, 15 figures

The performance demands of future particle-physics experiments investigating the high-energy frontier pose a number of new challenges, forcing us to find improved solutions for the detection, identification, and measurement of final-state particles in subnuclear collisions. One such challenge is the precise measurement of muon momentum at very high energy, where an estimate of the curvature provided by conceivable magnetic fields in realistic detectors proves insufficient for achieving good momentum resolution when detecting, e.g., a narrow, high mass resonance decaying to a muon pair. In this work we study the feasibility of an entirely new avenue for the measurement of the energy of muons based on their radiative losses in a dense, finely segmented calorimeter. This is made possible by exploiting spatial information of the clusters of energy from radiated photons in a regression task. The use of a task-specific deep learning architecture based on convolutional layers allows us to treat the problem as one akin to image reconstruction, where images are constituted by the pattern of energy released in successive layers of the calorimeter. A measurement of muon energy with better than 20% relative resolution is shown to be achievable for ultra-TeV muons., Comment: V2 Updating to journal version

The unsupervised search for overdense regions in high-dimensional feature spaces, where locally high population densities may be associated with anomalous contaminations to an otherwise more uniform population, is of relevance to applications ranging from fundamental research to industrial use cases. Motivated by the specific needs of searches for new phenomena in particle collisions, we propose a novel approach that targets signals of interest populating compact regions of the feature space. The method consists in a systematic scan of subspaces of a standardized copula of the feature space, where the minimum p-value of a hypothesis test of local uniformity is sought by gradient descent. We characterize the performance of the proposed algorithm and show its effectiveness in several experimental situations., Comment: 58 pages, 18 figures, 11 tables. To be submitted to Computer Physics Communications

Between the years 2015 and 2019, members of the Horizon 2020-funded Innovative Training Network named "AMVA4NewPhysics" studied the customization and application of advanced multivariate analysis methods and statistical learning tools to high-energy physics problems, as well as developed entirely new ones. Many of those methods were successfully used to improve the sensitivity of data analyses performed by the ATLAS and CMS experiments at the CERN Large Hadron Collider; several others, still in the testing phase, promise to further improve the precision of measurements of fundamental physics parameters and the reach of searches for new phenomena. In this paper, the most relevant new tools, among those studied and developed, are presented along with the evaluation of their performances., Comment: 101 pages, 21 figures, submitted to Elsevier. [v2]: Updated to published version (in 'Reviews in Physics')

The performance demands of future particle-physics experiments investigating the high-energy frontier pose a number of new challenges, forcing us to find new solutions for the detection, identification, and measurement of final-state particles in subnuclear collisions. One such challenge is the precise measurement of muon momenta at very high energy, where the curvature provided by conceivable magnetic fields in realistic detectors proves insufficient to achieve the desired resolution. In this work we show the feasibility of an entirely new avenue for the measurement of the energy of muons based on their radiative losses in a dense, finely segmented calorimeter. This is made possible by the use of the spatial information of the clusters of deposited photon energy in the regression task. Using a homogeneous lead-tungstate calorimeter as a benchmark, we show how energy losses may provide significant complementary information for the estimate of muon energies above 1 TeV., Comment: 20 pages, 12 figures

In this work we discuss the impact of nuisance parameters on the effectiveness of machine learning in high-energy physics problems, and provide a review of techniques that allow to include their effect and reduce their impact in the search for optimal selection criteria and variable transformations. The introduction of nuisance parameters complicates the supervised learning task and its correspondence with the data analysis goal, due to their contribution degrading the model performances in real data, and the necessary addition of uncertainties in the resulting statistical inference. The approaches discussed include nuisance-parameterized models, modified or adversary losses, semi-supervised learning approaches, and inference-aware techniques., Comment: 43 pages, 5 figures. v1: original review manuscript. v2: text improvement/fixes from review process

A high-statistics determination of the differential cross section of elastic muon-electron scattering as a function of the transferred four-momentum squared, $d \sigma_{el}(\mu e \to \mu e)/dq^2$, has been argued to provide an effective constraint to the hadronic contribution to the running of the fine-structure constant, $\Delta \alpha_{had}$, a crucial input for precise theoretical predictions of the anomalous magnetic moment of the muon. An experiment called ``MUonE'' is being planned at the north area of CERN for that purpose. We consider the geometry of the detector proposed by the MUonE collaboration and offer a few suggestions on the layout of the passive target material and on the placement of silicon strip sensors, based on a fast simulation of elastic muon-electron scattering events and the investigation of a number of possible solutions for the detector geometry., Comment: 54 pages, 25 figures

Complex computer simulations are commonly required for accurate data modelling in many scientific disciplines, making statistical inference challenging due to the intractability of the likelihood evaluation for the observed data. Furthermore, sometimes one is interested on inference drawn over a subset of the generative model parameters while taking into account model uncertainty or misspecification on the remaining nuisance parameters. In this work, we show how non-linear summary statistics can be constructed by minimising inference-motivated losses via stochastic gradient descent such they provided the smallest uncertainty for the parameters of interest. As a use case, the problem of confidence interval estimation for the mixture coefficient in a multi-dimensional two-component mixture model (i.e. signal vs background) is considered, where the proposed technique clearly outperforms summary statistics based on probabilistic classification, which are a commonly used alternative but do not account for the presence of nuisance parameters., Comment: Code available at https://github.com/pablodecm/paper-inferno . Version updates: - v2: fixed typos, improve text, link to code and a better synthetic experiment

This review covers results of searches for new elementary particles that decay into boson pairs (dibosons), performed at the CERN Large Hadron Collider in proton-proton collision data collected by the ATLAS and CMS experiments at 7-, 8-, and 13-TeV center-of-mass energy until the year 2017. The available experimental results of the analysis of final states including most of the possible two-object combinations of W and Z bosons, photons, Higgs bosons, and gluons place stringent constraints on a variety of theoretical ideas that extend the standard model, pushing into the multi-TeV region the scale of allowed new physics phenomena., Comment: 76 pages, 16 figures. To be published in Progress of Particle and Nuclear Physics

For data sets populated by a very well modeled process and by another process of unknown probability density function (PDF), a desired feature when manipulating the fraction of the unknown process (either for enhancing it or suppressing it) consists in avoiding to modify the kinematic distributions of the well modeled one. A bootstrap technique is used to identify sub-samples rich in the well modeled process, and classify each event according to the frequency of it being part of such sub-samples. Comparisons with general MVA algorithms will be shown, as well as a study of the asymptotic properties of the method, making use of a public domain data set that models a typical search for new physics as performed at hadronic colliders such as the Large Hadron Collider (LHC)., Comment: 8 pages, 5 figures. Proceedings of the XIIth Quark Confinement and Hadron Spectrum conference, 28/8-2/9 2016, Thessaloniki, Greece

In this document we study the effect of anomalous Higgs boson couplings on non-resonant pair production of Higgs bosons ($HH$) at the LHC. We explore the space of the five parameters $\kappa_{\lambda}$, $\kappa_{t}$, $c_2$, $c_g$, and $c_{2g}$ in terms of the corresponding kinematics of the final state, and describe a partition of the space into a limited number of regions featuring similar phenomenology in the kinematics of $HH$ final state. We call clusters the sets of points belonging to the same region; to each cluster corresponds a representative point which we call a benchmark. We discuss a possible technique to estimate the sensitivity of an experimental search to the kinematical differences between the phenomenology of the benchmark points and the rest of the parameter space contained in the corresponding cluster. We also provide an analytical parametrization of the cross-section modifications that the variation of anomalous couplings produces with respect to standard model $HH$ production along with a recipe to translate the results into other parameter-space bases. Finally, we provide a preliminary analysis of variations in the topology of the final state within each region based on recent LHC results., Comment: 20 pages, 10 figures, LHC Higgs Cross Section Working Group report http://cds.cern.ch/record/2199287

New physics theories often depend on a large number of free parameters. The precise values of those parameters in some cases drastically affect the resulting phenomenology of fundamental physics processes, while in others finite variations can leave it basically invariant at the level of detail experimentally accessible. When designing a strategy for the analysis of experimental data in the search for a signal predicted by a new physics model, it appears advantageous to categorize the parameter space describing the model according to the corresponding kinematical features of the final state. A multi-dimensional test statistic can be used to gauge the degree of similarity in the kinematics of different models; a clustering algorithm using that metric may then allow the division of the space into homogeneous regions, each of which can be successfully represented by a benchmark point. Searches targeting those benchmark points are then guaranteed to be sensitive to a large area of the parameter space. In this document we show a practical implementation of the above strategy for the study of non-resonant production of Higgs boson pairs in the context of extensions of the standard model with anomalous couplings of the Higgs bosons. A non-standard value of those couplings may significantly enhance the Higgs pair production cross section, such that the process could be detectable with the data that the Large Hadron Collider will collect in Run 2., Comment: Editorial changes, improvements in figures and changes in the appendix

The CMS experiment obtained a large number of groundbreaking results from the analysis of 7- and 8-TeV proton-proton collisions produced so far by the Large Hadron Collider at CERN. In this brief summary only a sample of those results will be discussed. A new particle with mass m(H) = 125.3 +- 0.4(stat.) +- 0.5(syst.) GeV and characteristics compatible with those expected for a standard model Higgs boson has been observed in its decays to photon pairs, WW pairs, and ZZ pairs. Searches for the rare decays B_d -> mu mu and B_s -> mu mu have allowed to set limits on the branching fractions which are close to standard model predictions, strongly constraining new physics models. The top quark has been studied with great detail, obtaining among other results the world's best measurement of its mass as m(top) = 173.49 +- 0.43(stat. + JES) +- 0.98(syst.) GeV. New physics models have been strongly constrained with the available data., Comment: 13 pages. Proceedings of the 1st International Conference on Frontier Physics, Kolymbari, Crete (GR) June 2012

The CMS collaboration has recently produced results of a number of searches for new physics processes using data collected during the 2011 run of the Large Hadron Collider. Up to 5 inverse femtobarns of proton-proton collisions at 7 TeV centre-of-mass energy have been used to search for the standard model Higgs boson in five different decay modes, divided in 42 independent sub-channels. The combination of the results has allowed CMS to set 95% confidence-level limits on the Higgs boson mass, constraining it to lay in the region 114.4600 GeV. An excess of events with a local significance of 3.1 standard deviations is observed for M(H)=124 GeV; the global significance of observing such an effect anywhere in the search range 110-600 GeV is estimated to be 1.5 standard deviations. A number of signatures of supersymmetric particles have also been investigated, significantly restricting the parameter space of natural low-scale theories. A search for the rare decays of neutral bottom mesons to muon pairs, Bs to mu mu and Bd to mu mu, has achieved the tightest limits to date, and is approaching the sensitivity to measure the standard model branching ratios. As it happens, though, the highly informative results extracted from 2011 data produce more questions than answers; this doubles expectations for the 2012 run of LHC, which will conclusively answer several of them., Comment: 33 pages, 20 figures. Contribution to the Proceedings of the 50th International Winter Meeting on Nuclear Physics, Bormio (Italy) 23-27 January 2012

The prospects for the search of the Standard Model Higgs boson with the CMS experiment at the LHC are presented. The analyses rely on a full simulation of the detector response and emphasis is put on explicit strategies for the measurement of experimental and background systematics from data. The discovery reach is presented as a function of the Higgs boson mass. A new complete strategy is presented for the early searches and for the control of systematics at very low luminosities of O(1/fb)., Comment: 2 pages, 5 figures. To appear in the proceedings of XXIX Physics in Collision, Kobe, Japan, Aug 30-Sep 2, 2009

The D0 and CDF collaborations recently published two independent analyses that both claim to represent the observation of the Omega_b particle, a baryon made up by a (bss) quark combination. Both signals are estimated to exceed the statistical significance of five standard deviations; however, the mass measurements derived from the candidates differ by over six standard deviations, accounting for estimated systematics. Measured rates also appear to differ, although they remain compatible within the large uncertainties. In this paper the author recomputes the significance of the D0 result, showing that it was considerably overestimated in the original publication; he then investigates with a pseudoexperiment-based approach which, among different hypotheses, appears the most likely cause of the observed discrepancy between the D0 and CDF signals., Comment: 14 pages, 12 figures; uses included cmspaper style files

The energy measurement of jets produced by b-quarks at hadron colliders suffers from biases due to the peculiarities of the hadronization and decay of the originating B hadron. The impact of these effects can be estimated by reconstructing the mass of Z boson decays into pairs of b-quark jets. From a sample of 584 pb-1 of data collected by the CDF experiment in 1.96 TeV proton-antiproton collisions at the Tevatron collider, we show how the Z signal can be identified and measured. Using the reconstructed mass of Z candidates we determine a jet energy scale factor for b-quark jets with a precision better than 2%. This measurement allows a reduction of one of the dominant source of uncertainty in analyses based on high transverse momentum b-quark jets. We also determine, as a cross-check of our analysis, the Z boson cross section in hadronic collisions using the b-bbar final state as sigma x B(Z->b-bbar) = 1578 +636 -410 pb., Comment: 35 pages, 9 figures, submitted to Nuclear Instruments and Methods in Physics Research Section A

The CDF and D0 experiments at the Tevatron have searched for the Standard Model Higgs boson in data collected between 2001 and 2004. Upper limits have been placed on the production cross section times branching ratio to b-antib pairs or WW pairs as a function of the Higgs boson mass. Projections indicate that the Tevatron experiments have a chance of discovering a MH = 115 GeV Higgs with the total dataset foreseen by 2009, or excluding it at 95% C.L. up to a mass of 135 GeV., Comment: 6 pages, 7 figures, proceedings of "40th Rencontres de Moriond on QCD and High Energy Interactions", to be published by World Scientific