Publications

Order by: publication date number of citations

• Functional flows for complex effective actions

  Friederike Ihssen, Jan M. Pawlowski

  SciPost Phys. 15, 074 (2023) · published 31 August 2023 | Toggle abstract · pdf

  In the present work we set up a general functional renormalisation group framework for the computation of complex effective actions. For explicit computations we consider both flows of the Wilsonian effective action and the one-particle irreducible (1PI) effective action. The latter is based on an appropriate definition of a Legendre transform for complex actions, and we show its validity by comparison to exact results in zero dimensions, as well as a comparison to results for the Wilsonian effective action. In the present implementations of the general approaches, the flow of the Wilsonian effective action has a wider range of applicability and we obtain results for the effective potential of complex fields in $\phi^4$-theories from zero up to four dimensions. These results are also compared with results from the 1PI effective action within its range of applicability. The complex effective action also allows us to determine the location of the Lee-Yang zeros for general parameter values. We also discuss the extension of the present results to general theories including QCD.

  2 citations

• Search for scalar induced gravitational waves in the international pulsar timing array data release 2 and NANOgrav 12.5 years datasets

  Virgile Dandoy, Valerie Domcke, Fabrizio Rompineve

  SciPost Phys. Core 6, 060 (2023) · published 30 August 2023 | Toggle abstract · pdf

  We perform a Bayesian search in the latest Pulsar Timing Array (PTA) datasets for a stochastic gravitational wave (GW) background sourced by curvature perturbations at scales $10^5$ Mpc$^{-1}\lesssim$ k$\lesssim 10^8$ Mpc$^{-1}$. These re-enter the Hubble horizon at temperatures around and below the QCD crossover phase transition in the early Universe. We include a stochastic background of astrophysical origin in our search and properly account for constraints on the curvature power spectrum from the overproduction of primordial black holes (PBHs). We find that the International PTA Data Release 2 significantly favors the astrophysical model for its reported common-spectrum process, over the curvature-induced background. On the other hand, the two interpretations fit the NANOgrav 12.5 years dataset equally well. We then set new upper limits on the amplitude of the curvature power spectrum at small scales. These are independent from, and competitive with, indirect astrophysical bounds from the abundance of PBH dark matter. Upcoming PTA data releases will provide the strongest probe of the curvature power spectrum around the QCD epoch.

  26 citations

• Anomaly Awareness

  Charanjit K. Khosa, Veronica Sanz

  SciPost Phys. 15, 053 (2023) · published 8 August 2023 | Toggle abstract · pdf

  We present a new algorithm for anomaly detection called Anomaly Awareness. The algorithm learns about normal events while being made aware of the anomalies through a modification of the cost function. We show how this method works in different Particle Physics situations and in standard Computer Vision tasks. For example, we apply the method to images from a Fat Jet topology generated by Standard Model Top and QCD events, and test it against an array of new physics scenarios, including Higgs production with EFT effects and resonances decaying into two, three or four subjets. We find that the algorithm is effective identifying anomalies not seen before, and becomes robust as we make it aware of a varied-enough set of anomalies.

  4 citations

• Cornering extended Starobinsky inflation with CMB and SKA

  Tanmoy Modak, Lennart Röver, Björn Malte Schäfer, Benedikt Schosser, Tilman Plehn

  SciPost Phys. 15, 047 (2023) · published 3 August 2023 | Toggle abstract · pdf

  Starobinsky inflation is an attractive, fundamental model to explain the Planck measurements, and its higher-order extension may allow us to probe quantum gravity effects. We show that future CMB data combined with the 21cm intensity map from SKA will meaningfully probe such an extended Starobinsky model. A combined analysis will provide a precise measurement and intriguing insight into inflationary dynamics, even accounting for correlations with astrophysical parameters.

  2 citations

• High-precision muon decay predictions for ALP searches

  Pulak Banerjee, Antonio Coutinho, Tim Engel, Andrea Gurgone, Adrian Signer, Yannick Ulrich

  SciPost Phys. 15, 021 (2023) · published 21 July 2023 | Toggle abstract · pdf

  We present an improved theoretical prediction of the positron energy spectrum for the polarised Michel decay $\mu^+\to e^+ \nu_e\bar{\nu}_\mu$. In addition to the full next-to-next-to-leading order correction of order $\alpha^2$ in the electromagnetic coupling, we include logarithmically enhanced terms at even higher orders. Logarithms due to collinear emission are included at next-to-leading accuracy up to order $\alpha^4$. At the endpoint of the Michel spectrum, soft photon emission results in large logarithms that are resummed up to next-to-next-to-leading logarithmic accuracy. We apply our results in the context of the MEG II and Mu3e experiments to estimate the impact of the theory error on the branching ratio sensitivity for the lepton-flavour-violating decay $\mu^+\to e^+ X$ of a muon into an axion-like particle $X$.

  4 citations

• Analytic neutrino oscillation probabilities

  Chee Sheng Fong

  SciPost Phys. 15, 013 (2023) · published 18 July 2023 | Toggle abstract · pdf

  In the work, we derive exact analytic expressions for $(3+N)$-flavor neutrino oscillation probabilities in an arbitrary matter potential in term of matrix elements and eigenvalues of the Hamiltonian. With the analytic expressions, we demonstrate that nonunitary and nonstandard neutrino interaction scenarios are physically distinct: they satisfy different identities and can in principle be distinguished experimentally. The analytic expressions are implemented in a public code NuProbe, a tool for probing new physics through neutrino oscillations.

  1 citation

• A symbol and coaction for higher-loop sunrise integrals

  Andreas Forum, Matt von Hippel

  SciPost Phys. Core 6, 050 (2023) · published 12 July 2023 | Toggle abstract · pdf

  We construct a symbol and coaction for the $l$-loop sunrise family of integrals, both for equal-mass and generic-mass cases. These constitute the first concrete examples of symbols and coactions for integrals involving Calabi-Yau threefolds and higher. In order to achieve a symbol of finite length, we recast the differential equations satisfied by these integrals in a unipotent form. We augment the integrals in a natural way by including ratios of maximal cuts $\tau_i$. We discuss the relationship of this construction to constructions of symbols and coactions for multiple polylogarithms and elliptic multiple polylogarithms, and its connection to notions of transcendental weight.

• New machine learning techniques for simulation-based inference: InferoStatic nets, kernel score estimation, and kernel likelihood ratio estimation

  Kyoungchul Kong, Konstantin T. Matchev, Stephen Mrenna, Prasanth Shyamsundar

  SciPost Phys. Codebases 14 (2023) · published 7 July 2023 | Toggle abstract · pdf

  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. The ISN uses a backend neural network that models a scalar function called the inferostatic potential $\varphi$. In addition, we introduce new strategies, respectively called Kernel Score Estimation (KSE) and Kernel Likelihood Ratio Estimation (KLRE), to learn the score and the likelihood ratio functions from simulated data. We illustrate the new techniques with some toy examples and compare to existing approaches in the literature. We mention en passant some new loss functions that optimally incorporate latent information from simulations into the training procedure.

  1 citation

• Codebase release 0.1 for infstat

  Kyoungchul Kong, Konstantin T. Matchev, Stephen Mrenna, Prasanth Shyamsundar

  SciPost Phys. Codebases 14-r0.1 (2023) · published 7 July 2023 | Toggle abstract · src

  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. The ISN uses a backend neural network that models a scalar function called the inferostatic potential $\varphi$. In addition, we introduce new strategies, respectively called Kernel Score Estimation (KSE) and Kernel Likelihood Ratio Estimation (KLRE), to learn the score and the likelihood ratio functions from simulated data. We illustrate the new techniques with some toy examples and compare to existing approaches in the literature. We mention en passant some new loss functions that optimally incorporate latent information from simulations into the training procedure.

  1 citation

• Exponential enhancement of dark matter freezeout abundance

  Bibhushan Shakya

  SciPost Phys. Proc. 12, 070 (2023) · published 5 July 2023 | Toggle abstract · pdf

  A novel paradigm for thermal dark matter (DM), termed "bouncing dark matter", is presented. In canonical thermal DM scenarios, the DM abundance falls exponentially as the temperature drops below the mass of DM, until thermal freezeout occurs. This note explores a broader class of thermal DM models that are exceptions to this rule, where the DM abundance can deviate from the exponentially falling curve, and even rise exponentially, while in thermal equilibrium. Such scenarios can feature present day DM annihilation cross sections much larger than the canonical thermal target, improving the prospects for indirect detection of DM annihilation signals.