arXiv subjects
Publications and source records attributed to Marco Rocco.
We present a fully differential computation of $e^+\,e^-\to\gamma\,\gamma$ at next-to-next-to-leading order in QED. The process has been implemented into McMule, completing its set of next-to-next-to-leading-order calculations for the most important $2 \to 2$ processes. The results allow for generic applications to electron-positron colliders with centre-of-mass energies up to a few GeV, particularly for luminosity measurements.
We present disperon QED, a method to deal with data input in loop processes in Monte Carlo codes. It relies on dispersion relations, automated tools such as OpenLoops, effective field theory methods and a threshold subtraction. We motivate this method and apply it to the process $ee\to\pi\pi$ in McMule to deal with hadronic vacuum polarisation insertions in two-loop contributions as well as the vector form factor of the pion within the form-factor scalar QED approximation. The generality of this method for more complicated processes is emphasised.
We present the results of Phase I of an ongoing review of Monte Carlo tools relevant for low-energy hadronic cross sections. This includes a detailed comparison of Monte Carlo codes for electron-positron scattering into a muon pair, pion pair, and electron pair, for scan and radiative-return experiments. After discussing the various approaches that are used and effects that are included, we show differential cross sections obtained with AfkQed, BabaYaga@NLO, KKMC, MCGPJ, McMule, Phokhara, and Sherpa, for scenarios that are inspired by experiments providing input for the dispersive evaluation of the hadronic vacuum polarisation.
A recently proposed experiment, MUonE, aims to extract the hadronic vacuum polarisation contribution to the muon g-2 from muon-electron scattering at low energy. The extrapolation requires that both experimental and theoretical uncertainties do not exceed 10 ppm. This corresponds, at least, to next-to-next-to-leading-order (NNLO) QED corrections to $e \mu \to e \mu$. I will discuss the implementation of a Monte Carlo integrator for this process in the McMule framework arXiv:2212.06481, which provides infrared-safe differential results at said order in QED. An approximation of the MUonE setup provides some phenomenological results and sheds light on the need for beyond-NNLO corrections, which are currently under study within McMule.
We consider the production of a vector boson ($Z$, $W^\pm$ or $γ^*$) at next-to-next-to-leading order in the strong coupling constant $α_{\rm S}$. We impose a transverse-momentum cutoff, $q_{\rm T}^{\rm cut}$, on the vector boson produced in the $qg$-initiated channel. We then compute the power corrections in the cutoff, up to the second power, of the real-virtual interference contribution to the cumulative cross section at order $α_{\rm S}^2$. Other terms with the same kinematics, originating from the subtraction method applied to the double-real contribution, have been also considered. The knowledge of such power corrections is a required ingredient in order to reduce the dependence on the transverse-momentum cutoff of the QCD cross sections at next-to-next-to-leading order, when the $q_{\rm T}$-subtraction method is applied. In addition, the study of the dependence of the cross section on $q_{\rm T}^{\rm cut}$ allows as well for an understanding of its behaviour in the small transverse-momentum limit, giving hints on the structure at all orders in $α_{\rm S}$ and on the identification of universal patterns. Our result are presented in an analytic form, using the process-independent procedure described in a previous paper for the calculation of the all-order power corrections in $q_{\rm T}^{\rm cut}$.
In this paper we present a framework for developing POWHEG BOX generators using MadGraph5_aMC@NLO for the computation of the matrix elements. Within this framework, all the flexibility of MadGraph5_aMC@NLO for the generation of matrix elements for Standard Model processes and for several of its extensions can be exploited, as well as all features of the POWHEG BOX framework, including the possibility of multijet merging without a merging scale (using the so called MiNLO approach). As a proof of concept, we develop a generator for the production of a spin-0 Higgs-like boson in association with up to two jets, with CP-violating couplings.
We consider the production of a colourless system at next-to-leading order in the strong coupling constant $α_s$. We impose a transverse-momentum cutoff, qtcut, on the colourless final state and we compute the power corrections for the inclusive cross section in the cutoff, up to the fourth power. The study of the dependence of the cross section on qtcut allows for an understanding of its behaviour at the boundaries of the phase space, giving hints on the structure at all orders in $α_s$ and on the identification of universal patterns. The knowledge of such power corrections is also a required ingredient in order to reduce the dependence on the transverse-momentum cutoff of the QCD cross sections at higher orders, when the qt-subtraction method is applied. We present analytic results for both Drell--Yan vector boson and Higgs boson production in gluon fusion and we illustrate a process-independent procedure for the calculation of the all-order power corrections in the cutoff. In order to show the impact of the power-correction terms, we present selected numerical results and discuss how the residual dependence on qtcut affects the total cross section for Drell--Yan Z production and Higgs boson production via gluon fusion at the LHC.
Computing equilibria of games is a central task in computer science. A large number of results are known for \emph{Nash equilibrium} (NE). However, these can be adopted only when coalitions are not an issue. When instead agents can form coalitions, NE is inadequate and an appropriate solution concept is \emph{strong Nash equilibrium} (SNE). Few computational results are known about SNE. In this paper, we first study the problem of verifying whether a strategy profile is an SNE, showing that the problem is in $\mathcal{P}$. We then design a spatial branch--and--bound algorithm to find an SNE, and we experimentally evaluate the algorithm.
Sponsored Search Auctions (SSAs) arguably represent the problem at the intersection of computer science and economics with the deepest applications in real life. Within the realm of SSAs, the study of the effects that showing one ad has on the other ads, a.k.a. externalities in economics, is of utmost importance and has so far attracted the attention of much research. However, even the basic question of modeling the problem has so far escaped a definitive answer. The popular cascade model is arguably too idealized to really describe the phenomenon yet it allows a good comprehension of the problem. Other models, instead, describe the setting more adequately but are too complex to permit a satisfactory theoretical analysis. In this work, we attempt to get the best of both approaches: firstly, we define a number of general mathematical formulations for the problem in the attempt to have a rich description of externalities in SSAs and, secondly, prove a host of results drawing a nearly complete picture about the computational complexity of the problem. We complement these approximability results with some considerations about mechanism design in our context.
Sponsored search auctions constitute one of the most successful applications of microeconomic mechanisms. In mechanism design, auctions are usually designed to incentivize advertisers to bid their truthful valuations and to assure both the advertisers and the auctioneer a non-negative utility. Nonetheless, in sponsored search auctions, the click-through-rates (CTRs) of the advertisers are often unknown to the auctioneer and thus standard truthful mechanisms cannot be directly applied and must be paired with an effective learning algorithm for the estimation of the CTRs. This introduces the critical problem of designing a learning mechanism able to estimate the CTRs at the same time as implementing a truthful mechanism with a revenue loss as small as possible compared to an optimal mechanism designed with the true CTRs. Previous work showed that, when dominant-strategy truthfulness is adopted, in single-slot auctions the problem can be solved using suitable exploration-exploitation mechanisms able to achieve a per-step regret (over the auctioneer's revenue) of order $O(T^{-1/3})$ (where T is the number of times the auction is repeated). It is also known that, when truthfulness in expectation is adopted, a per-step regret (over the social welfare) of order $O(T^{-1/2})$ can be obtained. In this paper we extend the results known in the literature to the case of multi-slot auctions. In this case, a model of the user is needed to characterize how the advertisers' valuations change over the slots. We adopt the cascade model that is the most famous model in the literature for sponsored search auctions. We prove a number of novel upper bounds and lower bounds both on the auctioneer's revenue loss and social welfare w.r.t. to the VCG auction and we report numerical simulations investigating the accuracy of the bounds in predicting the dependency of the regret on the auction parameters.
Mobile geo-location advertising, where mobile ads are targeted based on a user's location, has been identified as a key growth factor for the mobile market. As with online advertising, a crucial ingredient for their success is the development of effective economic mechanisms. An important difference is that mobile ads are shown sequentially over time and information about the user can be learned based on their movements. Furthermore, ads need to be shown selectively to prevent ad fatigue. To this end, we introduce, for the first time, a user model and suitable economic mechanisms which take these factors into account. Specifically, we design two truthful mechanisms which produce an advertisement plan based on the user's movements. One mechanism is allocatively efficient, but requires exponential compute time in the worst case. The other requires polynomial time, but is not allocatively efficient. Finally, we experimentally evaluate the tradeoff between compute time and efficiency of our mechanisms.
The computational characterization of game-theoretic solution concepts is a central topic in artificial intelligence, with the aim of developing computationally efficient tools for finding optimal ways to behave in strategic interactions. The central solution concept in game theory is Nash equilibrium (NE). However, it fails to capture the possibility that agents can form coalitions (even in the 2-agent case). Strong Nash equilibrium (SNE) refines NE to this setting. It is known that finding an SNE is NP-complete when the number of agents is constant. This hardness is solely due to the existence of mixed-strategy SNEs, given that the problem of enumerating all pure-strategy SNEs is trivially in P. Our central result is that, in order for a game to have at least one non-pure-strategy SNE, the agents' payoffs restricted to the agents' supports must, in the case of 2 agents, lie on the same line, and, in the case of n agents, lie on an (n - 1)-dimensional hyperplane. Leveraging this result, we provide two contributions. First, we develop worst-case instances for support-enumeration algorithms. These instances have only one SNE and the support size can be chosen to be of any size-in particular, arbitrarily large. Second, we prove that, unlike NE, finding an SNE is in smoothed polynomial time: generic game instances (i.e., all instances except knife-edge cases) have only pure-strategy SNEs.
Computing a Nash equilibrium (NE) is a central task in computer science. An NE is a particularly appropriate solution concept for two-agent settings because coalitional deviations are not an issue. However, even in this case, finding an NE is PPAD-complete. In this paper, we combine path following algorithms with local search techniques to design new algorithms for finding exact and approximate NEs. We show that our algorithms largely outperform the state of the art and that almost all the known benchmark game classes are easily solvable or approximable (except for the GAMUT CovariantGameRand class).