D. Krücker

The first measurement of lepton-jet momentum imbalance and azimuthal correlation in lepton-proton scattering at high momentum transfer is presented. These data, taken with the H1 detector at HERA, are corrected for detector effects using an unbinned machine learning algorithm (multifold), which considers eight observables simultaneously in this first application. The unfolded cross sections are compared with calculations performed within the context of collinear or transverse-momentum-dependent factorization in quantum chromodynamics as well as Monte Carlo event generators.

Cross sections for elastic photoproduction of J/ψ and ϒ mesons are presented. For J/ψ mesons the dependence on the photon-proton centre-of-mass energy Wγp is analysed in an extended range with respect to previous measurements of 26≤Wγp≤285GeV. The measured energy dependence is parameterized as σγp∝Wγpδ with δ=0.83±0.07. The differential cross section dσ/dt for J/ψ mesons is derived, its dependence on Wγp and on t is analysed and the effective trajectory (in terms of Regge theory) is determined to be α(t)=(1.27±0.05)+(0.08±0.17)·t/GeV2. Models based on perturbative QCD and on pomeron exchange are compared to the data.

Abstract Charged particle multiplicity distributions in positron-proton deep inelastic scattering at a centre-of-mass energy $$\sqrt{s}=319$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msqrt> <mml:mi>s</mml:mi> </mml:msqrt> <mml:mo>=</mml:mo> <mml:mn>319</mml:mn> </mml:mrow> </mml:math> GeV are measured. The data are collected with the H1 detector at HERA corresponding to an integrated luminosity of 136 pb $$^{-1}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow /> <mml:mrow> <mml:mo>-</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:msup> </mml:math> . Charged particle multiplicities are measured as a function of photon virtuality $$Q^2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>Q</mml:mi> <mml:mn>2</mml:mn> </mml:msup> </mml:math> , inelasticity y and pseudorapidity $$\eta $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>η</mml:mi> </mml:math> in the laboratory and the hadronic centre-of-mass frames. Predictions from different Monte Carlo models are compared to the data. The first and second moments of the multiplicity distributions are determined and the KNO scaling behaviour is investigated. The multiplicity distributions as a function of $$Q^2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>Q</mml:mi> <mml:mn>2</mml:mn> </mml:msup> </mml:math> and the Bjorken variable $$x_{\mathrm{bj}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>x</mml:mi> <mml:mi>bj</mml:mi> </mml:msub> </mml:math> are converted to the hadron entropy $$S_{\mathrm{hadron}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>S</mml:mi> <mml:mi>hadron</mml:mi> </mml:msub> </mml:math> , and predictions from a quantum entanglement model are tested.

Measurements of the kinematic distributions of J/ψ mesons produced in p–C, p–Ti and p–W collisions at $\sqrt{s}=41.6~\mathrm{GeV}$ in the Feynman-x region −0.34<x F <0.14 and for transverse momentum up to p T =5.4 GeV/c are presented. The x F and p T dependencies of the nuclear suppression parameter, α, are also given. The results are based on 2.4×105 J/ψ mesons reconstructed in both the e + e − and μ + μ − decay channels. The data have been collected by the HERA-B experiment at the HERA proton ring of the DESY laboratory. The measurement explores the negative region of x F for the first time. The average value of α in the measured x F region is 0.981±0.015. The data suggest that the strong nuclear suppression of J/ψ production previously observed at high x F turns into an enhancement at negative x F .

Abstract A precision measurement of jet cross sections in neutral current deep-inelastic scattering for photon virtualities $$5.5&lt;Q^2 &lt;80\,\mathrm {GeV}^2 $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>5.5</mml:mn><mml:mo>&lt;</mml:mo><mml:msup><mml:mi>Q</mml:mi><mml:mn>2</mml:mn></mml:msup><mml:mo>&lt;</mml:mo><mml:mn>80</mml:mn><mml:mspace /><mml:msup><mml:mrow><mml:mi>GeV</mml:mi></mml:mrow><mml:mn>2</mml:mn></mml:msup></mml:mrow></mml:math> and inelasticities $$0.2&lt;y&lt;0.6$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>0.2</mml:mn><mml:mo>&lt;</mml:mo><mml:mi>y</mml:mi><mml:mo>&lt;</mml:mo><mml:mn>0.6</mml:mn></mml:mrow></mml:math> is presented, using data taken with the H1 detector at HERA, corresponding to an integrated luminosity of $$290\,\mathrm {pb}^{-1}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>290</mml:mn><mml:mspace /><mml:msup><mml:mrow><mml:mi>pb</mml:mi></mml:mrow><mml:mrow><mml:mo>-</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math> . Double-differential inclusive jet, dijet and trijet cross sections are measured simultaneously and are presented as a function of jet transverse momentum observables and as a function of $$Q^2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mi>Q</mml:mi><mml:mn>2</mml:mn></mml:msup></mml:math> . Jet cross sections normalised to the inclusive neutral current DIS cross section in the respective $$Q^2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mi>Q</mml:mi><mml:mn>2</mml:mn></mml:msup></mml:math> -interval are also determined. Previous results of inclusive jet cross sections in the range $$150&lt;Q^2 &lt;15{,}000\,\mathrm {GeV}^2 $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>150</mml:mn><mml:mo>&lt;</mml:mo><mml:msup><mml:mi>Q</mml:mi><mml:mn>2</mml:mn></mml:msup><mml:mo>&lt;</mml:mo><mml:mn>15</mml:mn><mml:mo>,</mml:mo><mml:mn>000</mml:mn><mml:mspace /><mml:msup><mml:mrow><mml:mi>GeV</mml:mi></mml:mrow><mml:mn>2</mml:mn></mml:msup></mml:mrow></mml:math> are extended to low transverse jet momenta $$5&lt;P_\mathrm{T}^\mathrm{jet} &lt;7\,\mathrm {GeV} $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>5</mml:mn><mml:mo>&lt;</mml:mo><mml:msubsup><mml:mi>P</mml:mi><mml:mrow><mml:mi>T</mml:mi></mml:mrow><mml:mi>jet</mml:mi></mml:msubsup><mml:mo>&lt;</mml:mo><mml:mn>7</mml:mn><mml:mspace /><mml:mi>GeV</mml:mi></mml:mrow></mml:math> . The data are compared to predictions from perturbative QCD in next-to-leading order in the strong coupling, in approximate next-to-next-to-leading order and in full next-to-next-to-leading order. Using also the recently published H1 jet data at high values of $$Q^2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mi>Q</mml:mi><mml:mn>2</mml:mn></mml:msup></mml:math> , the strong coupling constant $$\alpha _s(M_Z)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msub><mml:mi>α</mml:mi><mml:mi>s</mml:mi></mml:msub><mml:mrow><mml:mo>(</mml:mo><mml:msub><mml:mi>M</mml:mi><mml:mi>Z</mml:mi></mml:msub><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:math> is determined in next-to-leading order.

The strong coupling constant αs is determined from inclusive jet and dijet cross sections in neutral-current deep-inelastic ep scattering (DIS) measured at HERA by the H1 collaboration using next-to-next-to-leading order (NNLO) QCD predictions. The dependence of the NNLO predictions and of the resulting value of αs(mZ) at the Z-boson mass mZ are studied as a function of the choice of the renormalisation and factorisation scales. Using inclusive jet and dijet data together, the strong coupling constant is determined to be αs(mZ)=0.1157(20)exp(29)th . Complementary, αs(mZ) is determined together with parton distribution functions of the proton (PDFs) from jet and inclusive DIS data measured by the H1 experiment. The value αs(mZ)=0.1142(28)tot obtained is consistent with the determination from jet data alone. The impact of the jet data on the PDFs is studied. The running of the strong coupling is tested at different values of the renormalisation scale and the results are found to be in agreement with expectations.

The HERA-B Outer Tracker is a large system of planar drift chambers with about 113000 read-out channels. Its inner part has been designed to be exposed to a particle flux of up to 2.10^5 cm^-2 s^-1, thus coping with conditions similar to those expected for future hadron collider experiments. 13 superlayers, each consisting of two individual chambers, have been assembled and installed in the experiment. The stereo layers inside each chamber are composed of honeycomb drift tube modules with 5 and 10 mm diameter cells. Chamber aging is prevented by coating the cathode foils with thin layers of copper and gold, together with a proper drift gas choice. Longitudinal wire segmentation is used to limit the occupancy in the most irradiated detector regions to about 20 %. The production of 978 modules was distributed among six different laboratories and took 15 months. For all materials in the fiducial region of the detector good compromises of stability versus thickness were found. A closed-loop gas system supplies the Ar/CF4/CO2 gas mixture to all chambers. The successful operation of the HERA-B Outer Tracker shows that a large tracker can be efficiently built and safely operated under huge radiation load at a hadron collider.

Ratios of the ψ′ over the J/ψ production cross sections in the dilepton channel for C, Ti and W targets have been measured in 920 GeV proton-nucleus interactions with the HERA-B detector at the HERA storage ring. The ψ′ and J/ψ states were reconstructed in both the μ+μ- and the e+e- decay modes. The measurements covered the kinematic range -0.35≤xF≤0.1 with transverse momentum pT≤4.5 GeV/c. The angular dependence of the ratio has been used to measure the difference of the ψ′ and J/ψ polarization. All results for the muon and electron decay channels are in good agreement: their ratio, averaged over all events, is Rψ′(μ)/Rψ′(e)=1.00±0.08±0.04. This result constitutes a new, direct experimental constraint on the double ratio of branching fractions, (B′(μ)B(e))/(B(μ)B′(e)), of ψ′ and J/ψ in the two channels. The ψ′ to J/ψ production ratio is almost constant in the covered xF range and shows a slow increase with pT.

In this paper we describe the operation and performance of the HERA-B Outer Tracker, a 112674 channel system of planar drift tube layers. The performance of the HERA-B Outer Tracker system fullfilled all requirements for stable and efficient operation in a hadronic environment, thus confirming the adequacy of the honeycomb drift tube technology and of the front-end readout system. The detector was stably operated with a gas gain of 30000 in an Ar/CF4/CO2 (65:30:5) gas mixture, yielding a good efficiency for triggering and track reconstruction, larger than 95 % for tracks with momenta above 5 GeV/c. The hit resolution of the drift cells was 300 to 320 micrometers and the relative momentum resolution can be described as: sigma(p)/p (in %) = (1.61 +- 0.02) + (0.0051 +- 0.0006) p. At the end of the HERA-B running no aging effects in the Outer Tracker cells were observed.

A study of the angular distributions of leptons from decays of J/ψ's produced in p-C and p-W collisions at $\sqrt{s}=41.6\mbox{~GeV}$ has been performed in the J/ψ Feynman-x region −0.34<x F <0.14 and for J/ψ transverse momenta up to 5.4 GeV/c. The data were collected by the HERA-B experiment at the HERA proton ring of the DESY laboratory. The results, based on a clean selection of 2.3×105 J/ψ's reconstructed in both the e + e − and μ + μ − decay channels, indicate that J/ψ's are produced polarized. The magnitude of the effect is maximal at low p T . For p T >1 GeV/c a significant dependence on the reference frame is found: the polar anisotropy is more pronounced in the Collins-Soper frame and almost vanishes in the helicity frame, where, instead, a significant azimuthal anisotropy arises.

The HERA-B Outer Tracker consists of drift tubes folded from polycarbonate foil and is operated with Ar/CF4/CO2 as drift gas. The detector has to stand radiation levels which are similar to LHC conditions. The first prototypes exposed to radiation in HERA-B suffered severe radiation damage due to the development of self-sustaining currents (Malter effect). In a subsequent extended R&D program major changes to the original concept for the drift tubes (surface conductivity, drift gas, production materials) have been developed and validated for use in harsh radiation environments. In the test program various aging effects (like Malter currents, gain loss due to anode aging and etching of the anode gold surface) have been observed and cures by tuning of operation parameters have been developed.

The Breit frame provides a natural frame to analyze lepton-proton scattering events. In this reference frame, the parton model hard interactions between a quark and an exchanged boson defines the coordinate system such that the struck quark is back-scattered along the virtual photon momentum direction. In Quantum Chromodynamics (QCD), higher order perturbative or non-perturbative effects can change this picture drastically. As Bjorken-$x$ decreases below one half, a rather peculiar event signature is predicted with increasing probability, where no radiation is present in one of the two Breit-frame hemispheres and all emissions are to be found in the other hemisphere. At higher orders in $\alpha_s$ or in the presence of soft QCD effects, predictions of the rate of these events are far from trivial, and that motivates measurements with real data. We report on the first observation of the empty current hemisphere events in electron-proton collisions at the HERA collider using data recorded with the H1 detector at a center-of-mass energy of 319 GeV. The fraction of inclusive neutral-current DIS events with an empty hemisphere is found to be $0.0112 \pm 3.9\,\%_\text{stat} \pm 4.5\,\%_\text{syst} \pm 1.6\,\%_\text{mod}$ in the selected kinematic region of $150< Q^2<1500$ GeV$^2$ and inelasticity $0.14< y<0.7$. The data sample corresponds to an integrated luminosity of 351.1 pb$^{-1}$, sufficient to enable differential cross section measurements of these events. The results show an enhanced discriminating power at lower Bjorken-$x$ among different Monte Carlo event generator predictions.

The H1 Collaboration reports the first measurement of the 1-jettiness event shape observable $\tau_1^b$ in neutral-current deep-inelastic electron-proton scattering (DIS). The observable $\tau_1^b$ is equivalent to a thrust observable defined in the Breit frame. The data sample was collected at the HERA $ep$ collider in the years 2003-2007 with center-of-mass energy of $\sqrt{s}=319\,\text{GeV}$, corresponding to an integrated luminosity of $351.1\,\text{pb}^{-1}$. Triple differential cross sections are provided as a function of $\tau_1^b$, event virtuality $Q^2$, and inelasticity $y$, in the kinematic region $Q^2>150\,\text{GeV}^{2}$. Single differential cross section are provided as a function of $\tau_1^b$ in a limited kinematic range. Double differential cross sections are measured, in contrast, integrated over $\tau_1^b$ and represent the inclusive neutral-current DIS cross section measured as a function of $Q^2$ and $y$. The data are compared to a variety of predictions and include classical and modern Monte Carlo event generators, predictions in fixed-order perturbative QCD where calculations up to $\mathcal{O}(\alpha_s^3)$ are available for $\tau_1^b$ or inclusive DIS, and resummed predictions at next-to-leading logarithmic accuracy matched to fixed order predictions at $\mathcal{O}(\alpha_s^2)$. These comparisons reveal sensitivity of the 1-jettiness observable to QCD parton shower and resummation effects, as well as the modeling of hadronization and fragmentation. Within their range of validity, the fixed-order predictions provide a good description of the data. Monte Carlo event generators are predictive over the full measured range and hence their underlying models and parameters can be constrained by comparing to the presented data.

The H1 Collaboration at HERA reports the first measurement of groomed event shape observables in deep inelastic electron-proton scattering (DIS) at $\sqrt{s}=319$ GeV, using data recorded between the years 2003 and 2007 with an integrated luminosity of $351$ pb$^{-1}$. Event shapes provide incisive probes of perturbative and non-perturbative QCD. Grooming techniques have been used for jet measurements in hadronic collisions; this paper presents the first application of grooming to DIS data. The analysis is carried out in the Breit frame, utilizing the novel Centauro jet clustering algorithm that is designed for DIS event topologies. Events are required to have squared momentum-transfer $Q^2 > 150$ GeV$^2$ and inelasticity $ 0.2 < y < 0.7$. We report measurements of the production cross section of groomed event 1-jettiness and groomed invariant mass for several choices of grooming parameter. Monte Carlo model calculations and analytic calculations based on Soft Collinear Effective Theory are compared to the measurements.

The simulation of calorimeter showers presents a significant computational challenge, impacting the efficiency and accuracy of particle physics experiments. While generative ML models have been effective in enhancing and accelerating the conventional physics simulation processes, their application has predominantly been constrained to fixed detector readout geometries. With CaloPointFlow we have presented one of the first models that can generate a calorimeter shower as a point cloud. This study describes CaloPointFlow II, which exhibits several significant improvements compared to its predecessor. This includes a novel dequantization technique, referred to as CDF-Dequantization, and a normalizing flow architecture, referred to as DeepSet- Flow. The new model was evaluated with the fast Calorimeter Simulation Challenge (CaloChallenge) Dataset II and III.

Abstract The main challenge of quantum computing on its way to scalability is the erroneous behaviour of current devices. Understanding and predicting their impact on computations is essential to counteract these errors with methods such as quantum error mitigation. Thus, it is necessary to construct and evaluate accurate noise models. However, the evaluation of noise models does not yet follow a systematic approach, making it nearly impossible to estimate the accuracy of a model for a given application. Therefore, we developed and present a systematic approach to benchmarking noise models for quantum computing applications. It compares the results of hardware experiments to predictions of noise models for a representative set of quantum circuits.&amp;#xD;We also construct a noise model containing five types of quantum noise and optimize its parameters using a series of training circuits. We compare its accuracy to other noise models by volumetric benchmarks involving typical variational quantum circuits. The model can easily be expanded by adding new quantum channels.

The inclusive production cross sections of the strange vector mesons K*0, K*0bar, and phi have been measured in interactions of 920 GeV protons with C, Ti, and W targets with the HERA-B detector at the HERA storage ring. Differential cross sections as a function of rapidity and transverse momentum have been measured in the central rapidity region and for transverse momenta up to pT=3.5 GeV/c. The atomic number dependence is parametrised as sigma(pA) = sigma(pN)*A**alpha, where sigma(pN) is the proton-nucleon cross section. Within the phase space accessible, alpha(K*0) = 0.86+/-0.03, alpha(K*0bar) = 0.87+/-0.03, and alpha(phi) = 0.96+/-0.02. The total proton-nucleon cross sections, determined by extrapolating the differential measurements to full phase space, are sigma(pN->K*0) = 5.06+/-0.54 mb, sigma(pN->K*0bar) = 4.02+/-0.45 mb, and sigma(pN->phi) = 1.17+/-0.11 mb. The Cronin effect is observed for the first time for vector mesons containing strange quarks; compared to the measurements of Cronin et al. for K+- mesons, the measured values of alpha for phi mesons coincide with those of K- mesons for all transverse momenta, while the enhancement for K*0 / K*0bar mesons is smaller.

The inclusive production cross sections of the charmed mesons D0,D+,Ds + and D*+ have been measured in interactions of 920 GeV protons on C, Ti, and W targets with the HERA-B detector at the HERA storage ring. Differential cross sections as a function of transverse momentum and Feynman’s x variable are given for the central rapidity region and for transverse momenta up to pT=3.5 GeV/c. The atomic mass number dependence and the leading to non-leading particle production asymmetries are presented as well.

ψ F < 0.15 is presented.Both µ + µ - and e + e -J/ψ decay channels are observed with an overall statistics of about 15000 χc events, which is by far the largest available sample in pA collisions.The result is Rχ c = 0.188 ± 0.013st +0.024 -0.022 sys averaged over the different materials, when no J/ψ and χc polarisations are considered.The χc1 to χc2 production ratio R12 = Rχ c 1 /Rχ c 2 is measured to be 1.02 ± 0.40, leading to a cross section ratio σ(χ c1 ) σ(χ c2 ) = 0.57 ± 0.23.The dependence of Rχ c on the Feynman-x of the J/ψ, x J/ψ F , and its transverse momentum, p J/ψ T , is studied, as well as its dependence on the atomic number, A, of the target.For the first time, an extensive study of possible biases on Rχ c and R12 due to the dependence of acceptance on the polarization states of J/ψ and χc is performed.By varying the polarisation parameter, λ obs , of all produced J/ψ's by two sigma around the value measured by HERA-B, and considering the maximum variation due to the possible χc1 and χc2 polarisations, it is shown that Rχ c could change by a factor between 1.02 and 1.21 and R12 by a factor between 0.89 and 1.16.

If new phenomena beyond the Standard Model will be discovered at the LHC, the properties of the new particles could be determined with data from the High-Luminosity LHC and from a future linear collider like the ILC. We discuss the possible interplay between measurements at the two accelerators in a concrete example, namely a full SUSY model which features a small $$ \widetilde{\tau }_1$$ -LSP mass difference. Various channels have been studied using the Snowmass 2013 combined LHC detector implementation in the Delphes simulation package, as well as simulations of the ILD detector concept from the Technical Design Report. We investigate both the LHC and the ILC capabilities for discovery, separation and identification of various parts of the spectrum. While some parts would be discovered at the LHC, there is substantial room for further discoveries at the ILC. We finally highlight examples where the precise knowledge about the lower part of the mass spectrum which could be acquired at the ILC would enable a more in-depth analysis of the LHC data with respect to the heavier states.

Abstract Exclusive photoproduction of $${{\rho ^0}} (770)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msup> <mml:mi>ρ</mml:mi> <mml:mn>0</mml:mn> </mml:msup> <mml:mrow> <mml:mo>(</mml:mo> <mml:mn>770</mml:mn> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> </mml:math> mesons is studied using the H1 detector at the ep collider HERA. A sample of about 900,000 events is used to measure single- and double-differential cross sections for the reaction $$\gamma p \rightarrow \pi ^{+}\pi ^{-}Y$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>γ</mml:mi> <mml:mi>p</mml:mi> <mml:mo>→</mml:mo> <mml:msup> <mml:mi>π</mml:mi> <mml:mo>+</mml:mo> </mml:msup> <mml:msup> <mml:mi>π</mml:mi> <mml:mo>-</mml:mo> </mml:msup> <mml:mi>Y</mml:mi> </mml:mrow> </mml:math> . Reactions where the proton stays intact ( $${{{m_Y}} {=}m_p}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub> <mml:mi>m</mml:mi> <mml:mi>Y</mml:mi> </mml:msub> <mml:mo>=</mml:mo> <mml:msub> <mml:mi>m</mml:mi> <mml:mi>p</mml:mi> </mml:msub> </mml:mrow> </mml:math> ) are statistically separated from those where the proton dissociates to a low-mass hadronic system ( $$m_p{&lt;}{{m_Y}} {&lt;}10~{{\text {GeV}}} $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub> <mml:mi>m</mml:mi> <mml:mi>p</mml:mi> </mml:msub> <mml:mo>&lt;</mml:mo> <mml:msub> <mml:mi>m</mml:mi> <mml:mi>Y</mml:mi> </mml:msub> <mml:mo>&lt;</mml:mo> <mml:mn>10</mml:mn> <mml:mspace /> <mml:mtext>GeV</mml:mtext> </mml:mrow> </mml:math> ). The double-differential cross sections are measured as a function of the invariant mass $$m_{\pi \pi }$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>m</mml:mi> <mml:mrow> <mml:mi>π</mml:mi> <mml:mi>π</mml:mi> </mml:mrow> </mml:msub> </mml:math> of the decay pions and the squared 4-momentum transfer t at the proton vertex. The measurements are presented in various bins of the photon–proton collision energy $${{W_{\gamma p}}} $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>W</mml:mi> <mml:mrow> <mml:mi>γ</mml:mi> <mml:mi>p</mml:mi> </mml:mrow> </mml:msub> </mml:math> . The phase space restrictions are $$0.5\le m_{\pi \pi } \le 2.2~{{\text {GeV}}} $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>0.5</mml:mn> <mml:mo>≤</mml:mo> <mml:msub> <mml:mi>m</mml:mi> <mml:mrow> <mml:mi>π</mml:mi> <mml:mi>π</mml:mi> </mml:mrow> </mml:msub> <mml:mo>≤</mml:mo> <mml:mn>2.2</mml:mn> <mml:mspace /> <mml:mtext>GeV</mml:mtext> </mml:mrow> </mml:math> , $$\vert t\vert \le 1.5~{{\text {GeV}^2}} $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mrow> <mml:mo>|</mml:mo> <mml:mi>t</mml:mi> <mml:mo>|</mml:mo> </mml:mrow> <mml:mo>≤</mml:mo> <mml:mn>1.5</mml:mn> <mml:mspace /> <mml:msup> <mml:mtext>GeV</mml:mtext> <mml:mn>2</mml:mn> </mml:msup> </mml:mrow> </mml:math> , and $$20 \le W_{\gamma p} \le 80~{{\text {GeV}}} $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>20</mml:mn> <mml:mo>≤</mml:mo> <mml:msub> <mml:mi>W</mml:mi> <mml:mrow> <mml:mi>γ</mml:mi> <mml:mi>p</mml:mi> </mml:mrow> </mml:msub> <mml:mo>≤</mml:mo> <mml:mn>80</mml:mn> <mml:mspace /> <mml:mtext>GeV</mml:mtext> </mml:mrow> </mml:math> . Cross section measurements are presented for both elastic and proton-dissociative scattering. The observed cross section dependencies are described by analytic functions. Parametrising the $${m_{\pi \pi }}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>m</mml:mi> <mml:mrow> <mml:mi>π</mml:mi> <mml:mi>π</mml:mi> </mml:mrow> </mml:msub> </mml:math> dependence with resonant and non-resonant contributions added at the amplitude level leads to a measurement of the $${{\rho ^0}} (770)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msup> <mml:mi>ρ</mml:mi> <mml:mn>0</mml:mn> </mml:msup> <mml:mrow> <mml:mo>(</mml:mo> <mml:mn>770</mml:mn> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> </mml:math> meson mass and width at $$m_\rho = 770.8{}^{+2.6}_{-2.7}~({\text {tot.}})~{{\text {MeV}}} $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub> <mml:mi>m</mml:mi> <mml:mi>ρ</mml:mi> </mml:msub> <mml:mo>=</mml:mo> <mml:mn>770.8</mml:mn> <mml:msubsup> <mml:mrow /> <mml:mrow> <mml:mo>-</mml:mo> <mml:mn>2.7</mml:mn> </mml:mrow> <mml:mrow> <mml:mo>+</mml:mo> <mml:mn>2.6</mml:mn> </mml:mrow> </mml:msubsup> <mml:mspace /> <mml:mrow> <mml:mo>(</mml:mo> <mml:mtext>tot.</mml:mtext> <mml:mo>)</mml:mo> </mml:mrow> <mml:mspace /> <mml:mtext>MeV</mml:mtext> </mml:mrow> </mml:math> and $$\Gamma _\rho = 151.3 {}^{+2.7}_{-3.6}~({\text {tot.}})~{{\text {MeV}}} $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub> <mml:mi>Γ</mml:mi> <mml:mi>ρ</mml:mi> </mml:msub> <mml:mo>=</mml:mo> <mml:mn>151.3</mml:mn> <mml:msubsup> <mml:mrow /> <mml:mrow> <mml:mo>-</mml:mo> <mml:mn>3.6</mml:mn> </mml:mrow> <mml:mrow> <mml:mo>+</mml:mo> <mml:mn>2.7</mml:mn> </mml:mrow> </mml:msubsup> <mml:mspace /> <mml:mrow> <mml:mo>(</mml:mo> <mml:mtext>tot.</mml:mtext> <mml:mo>)</mml:mo> </mml:mrow> <mml:mspace /> <mml:mtext>MeV</mml:mtext> </mml:mrow> </mml:math> , respectively. The model is used to extract the $${{\rho ^0}} (770)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msup> <mml:mi>ρ</mml:mi> <mml:mn>0</mml:mn> </mml:msup> <mml:mrow> <mml:mo>(</mml:mo> <mml:mn>770</mml:mn> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> </mml:math> contribution to the $$\pi ^{+}\pi ^{-}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msup> <mml:mi>π</mml:mi> <mml:mo>+</mml:mo> </mml:msup> <mml:msup> <mml:mi>π</mml:mi> <mml:mo>-</mml:mo> </mml:msup> </mml:mrow> </mml:math> cross sections and measure it as a function of t and $${W_{\gamma p}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>W</mml:mi> <mml:mrow> <mml:mi>γ</mml:mi> <mml:mi>p</mml:mi> </mml:mrow> </mml:msub> </mml:math> . In a Regge asymptotic limit in which one Regge trajectory $$\alpha (t)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>α</mml:mi> <mml:mo>(</mml:mo> <mml:mi>t</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> dominates, the intercept $$\alpha (t{=}0) = 1.0654\ {}^{+0.0098}_{-0.0067}~({\text {tot.}})$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>α</mml:mi> <mml:mrow> <mml:mo>(</mml:mo> <mml:mi>t</mml:mi> <mml:mo>=</mml:mo> <mml:mn>0</mml:mn> <mml:mo>)</mml:mo> </mml:mrow> <mml:mo>=</mml:mo> <mml:mn>1.0654</mml:mn> <mml:mspace /> <mml:msubsup> <mml:mrow /> <mml:mrow> <mml:mo>-</mml:mo> <mml:mn>0.0067</mml:mn> </mml:mrow> <mml:mrow> <mml:mo>+</mml:mo> <mml:mn>0.0098</mml:mn> </mml:mrow> </mml:msubsup> <mml:mspace /> <mml:mrow> <mml:mo>(</mml:mo> <mml:mtext>tot.</mml:mtext> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> </mml:math> and the slope $$\alpha ^\prime (t{=}0) = 0.233 {}^{+0.067 }_{-0.074 }~({\text {tot.}}) ~{{\text {GeV}^{-2}}} $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msup> <mml:mi>α</mml:mi> <mml:mo>′</mml:mo> </mml:msup> <mml:mrow> <mml:mo>(</mml:mo> <mml:mi>t</mml:mi> <mml:mo>=</mml:mo> <mml:mn>0</mml:mn> <mml:mo>)</mml:mo> </mml:mrow> <mml:mo>=</mml:mo> <mml:mn>0.233</mml:mn> <mml:msubsup> <mml:mrow /> <mml:mrow> <mml:mo>-</mml:mo> <mml:mn>0.074</mml:mn> </mml:mrow> <mml:mrow> <mml:mo>+</mml:mo> <mml:mn>0.067</mml:mn> </mml:mrow> </mml:msubsup> <mml:mspace /> <mml:mrow> <mml:mo>(</mml:mo> <mml:mtext>tot.</mml:mtext> <mml:mo>)</mml:mo> </mml:mrow> <mml:mspace /> <mml:msup> <mml:mtext>GeV</mml:mtext> <mml:mrow> <mml:mo>-</mml:mo> <mml:mn>2</mml:mn> </mml:mrow> </mml:msup> </mml:mrow> </mml:math> of the t dependence are extracted for the case $$m_Y{=}m_p$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub> <mml:mi>m</mml:mi> <mml:mi>Y</mml:mi> </mml:msub> <mml:mo>=</mml:mo> <mml:msub> <mml:mi>m</mml:mi> <mml:mi>p</mml:mi> </mml:msub> </mml:mrow> </mml:math> .

Fast data generation based on Machine Learning has become a major research topic in particle physics. This is mainly because the Monte Carlo simulation approach is computationally challenging for future colliders, which will have a significantly higher luminosity. The generation of collider data is similar to point cloud generation with complex correlations between the points. In this study, the generation of jets with up to 30 constituents with Normalising Flows using Rational Quadratic Spline coupling layers is investigated. Without conditioning on the jet mass, our Normalising Flows are unable to model all correlations in data correctly, which is evident when comparing the invariant jet mass distributions between ground truth and generated data. Using the invariant mass as a condition for the coupling transformation enhances the performance on all tracked metrics. In addition, we demonstrate how to sample the original mass distribution by interpolating the empirical cumulative distribution function. Similarly, the variable number of constituents is taken care of by introducing an additional condition on the number of constituents in the jet. Furthermore, we study the usefulness of including an additional mass constraint in the loss term. On the \texttt{JetNet} dataset, our model shows state-of-the-art performance combined with fast and stable training.

The mid-rapidity (dσpN/dy at y=0) and total (σpN) production cross sections of Jψ mesons are measured in proton–nucleus interactions. Data collected by the HERA-B experiment in interactions of 920 GeV/c protons with carbon, titanium and tungsten targets are used for this analysis. The Jψ mesons are reconstructed by their decay into lepton pairs. The total production cross section obtained is σpNJ/ψ=663±74±46 nb/nucleon. In addition, our result is compared with previous measurements.

A measurement of the polarization of Λ and Λ¯ baryons produced in pC and pW collisions at s=41.6GeV has been performed with the HERA-B spectrometer. The measurements cover the kinematic range of 0.6GeV/c<p⊥<1.2GeV/c in transverse momentum and −0.15<xF<0.01 in Feynman-x. The polarization results from the two different targets agree within the statistical error. In the combined data set, the largest deviation from zero, +0.054±0.029, is measured for xF≲−0.07. Zero polarization is expected at xF=0 in the absence of nuclear effects. The polarization results for the Λ agree with a parametrization of previous measurements which were performed at positive xF values, where the Λ polarization is negative. Results of Λ¯ polarization measurements are consistent with zero.

Abstract We introduce two new loss functions designed to directly optimize the statistical significance of the expected number of signal events when training neural networks and decision trees to classify events as signal or background. The loss functions are designed to directly maximize commonly used estimates of the statistical significance, <?CDATA $s/\sqrt{s+b}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mrow> <mml:mi>s</mml:mi> <mml:mo>/</mml:mo> <mml:mrow> <mml:msqrt> <mml:mrow> <mml:mi>s</mml:mi> <mml:mo>+</mml:mo> <mml:mi>b</mml:mi> </mml:mrow> </mml:msqrt> </mml:mrow> </mml:mrow> </mml:mrow> </mml:math> , and the so-called Asimov estimate, Z a . We consider their use in a toy search for Supersymmetric particles with 30 fb −1 of 14 TeV data collected at the LHC. In the case that the search for this model is dominated by systematic uncertainties, it is found that the loss function based on Z a can outperform the binary cross entropy in defining an optimal search region. The same approach is applied to a boosted decision tree by modifying the objective function used in gradient tree boosting.

Deep learning is finding its way into high energy physics by replacing traditional Monte Carlo simulations. However, deep learning still requires an excessive amount of computational resources. A promising approach to make deep learning more efficient is to quantize the parameters of the neural networks to reduced precision. Reduced precision computing is extensively used in modern deep learning and results to lower execution inference time, smaller memory footprint and less memory bandwidth. In this paper we analyse the effects of low precision inference on a complex deep generative adversarial network model. The use case which we are addressing is calorimeter detector simulations of subatomic particle interactions in accelerator based high energy physics. We employ the novel Intel low precision optimization tool (iLoT) for quantization and compare the results to the quantized model from TensorFlow Lite. In the performance benchmark we gain a speed-up of 1.73x on Intel hardware for the quantized iLoT model compared to the initial, not quantized, model. With different physics-inspired self-developed metrics, we validate that the quantized iLoT model shows a lower loss of physical accuracy in comparison to the TensorFlow Lite model.

This report summarizes the work of the Energy Frontier New Physics working group of the 2013 Community Summer Study (Snowmass).

Data generation based on Machine Learning has become a major research topic in particle physics. This is due to the current Monte Carlo simulation approach being computationally challenging for future colliders, which will have a significantly higher luminosity. The generation of collider data is similar to point cloud generation, but arguably more difficult as there are complex correlations between the points which need to be modelled correctly. A refinement model consisting of normalising flows and transformer encoders is presented. The normalising flow output is corrected by a transformer encoder, which is adversarially trained against another transformer encoder discriminator/critic. The model reaches state-of-the-art performance while yielding a stable training.

The prospect of quantum computing with a potential exponential speed-up compared to classical computing identifies it as a promising method in the search for alternative future High Energy Physics (HEP) simulation approaches. HEP simulations, such as employed at the Large Hadron Collider at CERN, are extraordinarily complex and require an immense amount of computing resources in hardware and time. For some HEP simulations, classical machine learning models have already been successfully developed and tested, resulting in several orders of magnitude speed-up. In this research, we proceed to the next step and explore whether quantum computing can provide sufficient accuracy, and further improvements, suggesting it as an exciting direction of future investigations. With a small prototype model, we demonstrate a full quantum Generative Adversarial Network (GAN) model for generating downsized eight-pixel calorimeter shower images. The advantage over previous quantum models is that the model generates real individual images containing pixel energy values instead of simple probability distributions averaged over a test sample. To complete the picture, the results of the full quantum GAN model are compared to hybrid quantum-classical models using a classical discriminator neural network.

The parameters of the electroweak theory are determined in a combined electroweak and QCD analysis using all deep-inelastic $$e^+p$$ and $$e^-p$$ neutral current and charged current scattering cross sections published by the H1 Collaboration, including data with longitudinally polarised lepton beams. Various fits to Standard Model parameters in the on-shell scheme are performed. The mass of the W boson is determined as $$m_W=80.520\pm 0.115~\mathrm {GeV} $$ . The axial-vector and vector couplings of the light quarks to the Z boson are also determined. Both results improve the precision of previous H1 determinations based on HERA-I data by about a factor of two. Possible scale dependence of the weak coupling parameters in both neutral and charged current interactions beyond the Standard Model is also studied. All results are found to be consistent with the Standard Model expectations.

Signals of QCD instanton-induced processes are searched for in neutral current deep-inelastic scattering at the electron-proton collider HERA in the kinematic region defined by the Bjorken-scaling variable $$x > 10^{-3}$$ , the inelasticity $$0.2< y < 0.7$$ and the photon virtuality $$150< Q^2 < 15000$$ GeV $$^2$$ . The search is performed using H1 data corresponding to an integrated luminosity of 351 pb $$^{-1}$$ . No evidence for the production of QCD instanton-induced events is observed. Upper limits on the cross section for instanton-induced processes between 1.5 and 6 pb, at $$95\,\,\%$$ confidence level, are obtained depending on the kinematic domain in which instantons could be produced. Compared to earlier publications, the limits are improved by an order of magnitude and for the first time are challenging predictions.

The strong coupling constant $\alpha_s(M_Z)$ is determined from inclusive jet and dijet cross sections in neutral-current deep-inelastic $ep$ scattering (DIS) measured at HERA by the H1 collaboration using next-to-next-to-leading order (NNLO) QCD predictions. The dependence of the NNLO predictions and of the resulting value of $\alpha_s(M_Z)$ at the $Z$-boson mass $m_Z$ are studied as a function of the choice of the renormalisation and factorisation scales. Using inclusive jet and dijet data together, the strong coupling constant is determined to be $\alpha_s(M_Z)=0.1166\,(19)_{\rm exp}\,(24)_{\rm th}$. Complementary, $\alpha_s(M_Z)$ is determined together with parton distribution functions of the proton (PDFs) from jet and inclusive DIS data measured by the H1 experiment. The value $\alpha_s(M_Z)=0.1147\,(25)_{\rm tot}$ obtained is consistent with the determination from jet data alone. The impact of the jet data on the PDFs is studied. The running of the strong coupling is tested at different values of the renormalisation scale and the results are found to be in agreement with expectations.

In this paper we promote the use of Support Vector Machines (SVM) as a machine learning tool for searches in high-energy physics. As an example for a new- physics search we discuss the popular case of Supersymmetry at the Large Hadron Collider. We demonstrate that the SVM is a valuable tool and show that an automated discovery- significance based optimization of the SVM hyper-parameters is a highly efficient way to prepare an SVM for such applications. A new C++ LIBSVM interface called SVM-HINT is developed and available on Github.

The HERA-B Outer Tracker is a large detector with 112 674 drift chamber channels.It is exposed to a particle flux of up to 2 • 10 5 cm -2 s -1 thus coping with conditions similar to those expected for the LHC experiments.The front-end readout system, based on the ASD-8 chip and a customized TDC chip, is designed to fulfil the requirements on low noise, high sensitivity, rate tolerance, and high integration density.The TDC system is based on an ASIC which digitizes the time in bins of about 0.5 ns within a total of 256 bins.The chip also comprises a pipeline to store data from 128 events which is required for a deadtime-free trigger and data acquisition system.We report on the development, installation, and commissioning of the front-end electronics, including the grounding and noise suppression schemes, and discuss its performance in the HERA-B experiment.

The precise simulation of particle transport through detectors remains a key element for the successful interpretation of high energy physics results. However, Monte Carlo based simulation is extremely demanding in terms of computing resources. This challenge motivates investigations of faster, alternative approaches for replacing the standard Monte Carlo technique. We apply Generative Adversarial Networks (GANs), a deep learning technique, to replace the calorimeter detector simulations and speeding up the simulation time by orders of magnitude. We follow a previous approach which used three-dimensional convolutional neural networks and develop new two-dimensional convolutional networks to solve the same 3D image generation problem faster. Additionally, we increased the number of parameters and the neural networks representational power, obtaining a higher accuracy. We compare our best convolutional 2D neural network architecture and evaluate it versus the previous 3D architecture and Geant4 data. Our results demonstrate a high physics accuracy and further consolidate the use of GANs for fast detector simulations.

Data from High Energy Physics (HEP) experiments are collected with significant financial and human effort and are mostly unique. An inter-experimental study group on HEP data preservation and long-term analysis was convened as a panel of the International Committee for Future Accelerators (ICFA). The group was formed by large collider-based experiments and investigated the technical and organizational aspects of HEP data preservation. An intermediate report was released in November 2009 addressing the general issues of data preservation in HEP and an extended blueprint paper was published in 2012. In July 2014 the DPHEP collaboration was formed as a result of the signature of the Collaboration Agreement by seven large funding agencies (others have since joined or are in the process of acquisition) and in June 2015 the first DPHEP Collaboration Workshop and Collaboration Board meeting took place. This status report of the DPHEP collaboration details the progress during the period from 2013 to 2015 inclusive.

Merlin++ is a C++ charged-particle tracking library developed for the simulation and analysis of complex beam dynamics within high energy particle accelerators. Accurate simulation and analysis of particle dynamics is an essential part of the design of new particle accelerators, and for the optimization of existing ones. Merlin++ is a feature-full library with focus on long-term tracking studies. A user may simulate distributions of protons or electrons in either single particle or sliced macro-particle bunches. The tracking code includes both straight and curvilinear coordinate systems allowing for the simulation of either linear or circular accelerator lattice designs, and uses a fast and accurate explicit symplectic integrator. Physics processes for common design studies have been implemented, including RF cavity acceleration, synchrotron radiation damping, on-line physical aperture checks and collimation, proton scattering, wakefield simulation, and spin-tracking. Merlin++ was written using C++ object orientated design practices and has been optimized for speed using multicore processors. This article presents an account of the program, including its functionality and guidance for use. Program Title: Merlin++ CPC Library link to program files: https://doi.org/10.17632/4x4nsbhz37.1 Developer's repository link: 10.5281/zenodo.3700155 Licensing provisions: GPLv2+ Programming language: C++ Nature of problem: Complexity of particle accelerators beam dynamics over extensive tracking distances. Solution method: Long-term particle accelerator and tracking simulations utilizing explicit symplectic integrators. Additional comments including restrictions and unusual features: For further information see github.com/Merlin-Collaboration

Quantum computing offers a new paradigm for advancing high-energy physics research by enabling novel methods for representing and reasoning about fundamental quantum mechanical phenomena. Realizing these ideals will require the development of novel computational tools for modeling and simulation, detection and classification, data analysis, and forecasting of high-energy physics (HEP) experiments. While the emerging hardware, software, and applications of quantum computing are exciting opportunities, significant gaps remain in integrating such techniques into the HEP community research programs. Here we identify both the challenges and opportunities for developing quantum computing systems and software to advance HEP discovery science. We describe opportunities for the focused development of algorithms, applications, software, hardware, and infrastructure to support both practical and theoretical applications of quantum computing to HEP problems within the next 10 years.

We introduce two new loss functions designed to directly optimise the statistical significance of the expected number of signal events when training neural networks to classify events as signal or background in the scenario of a search for new physics at a particle collider. The loss functions are designed to directly maximise commonly used estimates of the statistical significance, $s/\sqrt{s+b}$, and the Asimov estimate, $Z_A$. We consider their use in a toy SUSY search with 30~fb$^{-1}$ of 14~TeV data collected at the LHC. In the case that the search for the SUSY model is dominated by systematic uncertainties, it is found that the loss function based on $Z_A$ can outperform the binary cross entropy in defining an optimal search region.

Inclusive doubly differential cross sections d 2 σ pA /dx F dp 2 as a function of Feynman-x (x F ) and transverse momentum (p T ) for the production of K 0 , Λ and $\bar{\varLambda}$ in proton-nucleus interactions at 920 GeV are presented. The measurements were performed by HERA-B in the negative x F range (−0.12<x F <0.0) and for transverse momenta up to p T =1.6 GeV/c. Results for three target materials: carbon, titanium and tungsten are given. The ratios of production cross sections are presented and discussed. The Cronin effect is clearly observed for all three V 0 species. The atomic number dependence is parameterized as σ pA =σ pN ⋅A α where σ pN is the proton-nucleon cross section. The measured values of α are all near one. The results are compared with EPOS 1.67 and PYTHIA 6.3. EPOS reproduces the data to within ≈20% except at very low transverse momentum.

Measurements of $$D^{*}(2010)$$ meson production in diffractive deep inelastic scattering $$(5<Q^{2}<100\,\mathrm{GeV}^{2})$$ are presented which are based on HERA data recorded at a centre-of-mass energy $$\sqrt{s} = 319\,\mathrm{GeV}$$ with an integrated luminosity of 287 pb $$^{-1}$$ . The reaction $$ep \rightarrow eXY$$ is studied, where the system X, containing at least one $$D^{*}(2010)$$ meson, is separated from a leading low-mass proton dissociative system Y by a large rapidity gap. The kinematics of $$D^{*}$$ candidates are reconstructed in the $$D^{*}\rightarrow K \pi \pi $$ decay channel. The measured cross sections compare favourably with next-to-leading order QCD predictions, where charm quarks are produced via boson-gluon fusion. The charm quarks are then independently fragmented to the $$D^{*}$$ mesons. The calculations rely on the collinear factorisation theorem and are based on diffractive parton densities previously obtained by H1 from fits to inclusive diffractive cross sections. The data are further used to determine the diffractive to inclusive $$D^{*}$$ production ratio in deep inelastic scattering.

In particle physics the simulation of particle transport through detectors requires an enormous amount of computational resources, utilizing more than 50% of the resources of the CERN Worldwide Large Hadron Collider Grid. This challenge has motivated the investigation of different, faster approaches for replacing the standard Monte Carlo simulations. Deep Learning Generative Adversarial Networks are among the most promising alternatives. Previous studies showed that they achieve the necessary level of accuracy while decreasing the simulation time by orders of magnitudes. In this paper we present a newly developed neural network architecture which reproduces a three-dimensional problem employing 2D convolutional layers and we compare its performance with an earlier architecture consisting of 3D convolutional layers. The performance evaluation relies on direct comparison to Monte Carlo simulations, in terms of different physics quantities usually employed to quantify the detector response. We prove that our new neural network architecture reaches a higher level of accuracy with respect to the 3D convolutional GAN while reducing the necessary computational resources. Calorimeters are among the most expensive detectors in terms of simulation time. Therefore we focus our study on an electromagnetic calorimeter prototype with a regular highly granular geometry, as an example of future calorimeters.

We report on the status of the data preservation project at DESY for the HERA experiments and present the latest design of the storage which is a central element for bit- preservation. The HEP experiments based at the HERA accelerator at DESY collected large and unique datasets during the period from 1992 to 2007. As part of the ongoing DPHEP data preservation efforts at DESY, these datasets must be transferred into storage systems that keep the data available for ongoing studies and guarantee safe long term access. To achieve a high level of reliability, we use the dCache distributed storage solution and make use of its replication capabilities and tape interfaces. We also investigate a recently introduced Small File Service that allows for the fully automatic creation of tape friendly container files.

A detailed description of an original method used to measure the luminosity accumulated by the HERA-B experiment for a data sample taken during the 2002-2003 HERA running period is reported. We show that, with this method, a total luminosity measurement can be achieved with a typical precision, including overall systematic uncertainties, at a level of 5% or better. We also report evidence for the detection of delta-rays generated in the target and comment on the possible use of such delta rays to measure luminosity.

The main challenge of quantum computing on its way to scalability is the erroneous behaviour of current devices. Understanding and predicting their impact on computations is essential to counteract these errors with methods such as quantum error mitigation. Thus, it is necessary to construct and evaluate accurate noise models. However, the evaluation of noise models does not yet follow a systematic approach, making it nearly impossible to estimate the accuracy of a model for a given application. Therefore, we developed and present a systematic approach to benchmark noise models for quantum computing applications. It compares the results of hardware experiments to predictions of noise models for a representative set of quantum circuits. We also construct a noise model and optimize its parameters with a series of training circuits. We then perform a volumetric benchmark comparing our model to other models from the literature.

Abstract The quantum angle generator (QAG) is a new full quantum machine learning model designed to generate accurate images on current noise intermediate scale quantum devices. Variational quantum circuits form the core of the QAG model, and various circuit architectures are evaluated. In combination with the so-called MERA-upsampling architecture, the QAG model achieves excellent results, which are analyzed and evaluated in detail. To our knowledge, this is the first time that a quantum model has achieved such accurate results. To explore the robustness of the model to noise, an extensive quantum noise study is performed. In this paper, it is demonstrated that the model trained on a physical quantum device learns the noise characteristics of the hardware and generates outstanding results. It is verified that even a quantum hardware machine calibration change during training of up to 8% can be well tolerated. For demonstration, the model is employed in indispensable simulations in high energy physics required to measure particle energies and, ultimately, to discover unknown particles at the large Hadron Collider at CERN.

The Quantum Angle Generator (QAG) is a cutting-edge quantum machine learning model designed to generate precise images on current Noise Intermediate Scale Quantum devices. It utilizes variational quantum circuits and incorporates the MERA-upsampling architecture, achieving exceptional accuracy. The study demonstrates the QAG model's ability to learn hardware noise behavior, with stable results in the presence of simulated quantum hardware noise up to $1.5\%$ during inference and $3\%$ during training. However, deploying the noiseless trained model on real quantum hardware reduces accuracy. Training the model directly on hardware allows it to learn the underlying noise behavior, maintaining precision comparable to the noisy simulator. The QAG model's noise robustness and accuracy make it suitable for analyzing simulated calorimeter shower images used in high-energy physics simulations at CERN's Large Hadron Collider.

We describe a hands-on introduction to deep learning in particle physics, performed during the 5th INFIERI school in Wuhan, China. We presented fundamental machine learning concepts to students from diverse backgrounds in physics and computing, and prepared them to apply these techniques to solve an example problem from particle physics (hadronic top quark tagging). We exploited the simplicity of tools like Jupyter notebooks, and the user-friendly approaches of data science libraries such as Keras with TensorFlow.

In this paper we promote the use of Support Vector Machines (SVM) as a machine learning tool for searches in high-energy physics. As an example for a new- physics search we discuss the popular case of Supersymmetry at the Large Hadron Collider. We demonstrate that the SVM is a valuable tool and show that an automated discovery- significance based optimization of the SVM hyper-parameters is a highly efficient way to prepare an SVM for such applications. A new C++ LIBSVM interface called SVM-HINT is developed and available on Github.

In this paper we promote the use of Support Vector Machines (SVM) as a machine learning tool for searches in high-energy physics. As an example for a new- physics search we discuss the popular case of Supersymmetry at the Large Hadron Collider. We demonstrate that the SVM is a valuable tool and show that an automated discovery- significance based optimization of the SVM hyper-parameters is a highly efficient way to prepare an SVM for such applications. A new C++ LIBSVM interface called SVM-HINT is developed and available on Github.

We introduce a service for dCache that allows automatic and transparent packing and extracting of file sets into large container files. This is motivated by the adoption of dCache by new communities that often work with rather small data files which causes large time overheads on reads and writes by the underlying tape systems. Our service can be attached to existing dCache instances and we were show that it is able to optimize average access times to tape access.

This report summarises studies of selected failures in the ILC and CLIC linear colliders. The focus is on simulations of failure modes in the main linacs (including, for CLIC the main drive beam deccelerator) leading to an evaluation of the potential for beam damage. The impact on the beam delivery system in particular the primary post-linac collimation systems is also studied.

The main linac is the most expensive subsystem of the proposed International Linear Collider (ILC). Even a seldom failure scenario may be worth considering. On the other hand the large iris of its cavities provides for a higher operational safety margin compared to most other ILC subsystems. Several intricate failure scenarios are conceivable. Here we will investigate two examples where component failures cause a beam deflection large enough to hit the cavities.

In particle physics the simulation of particle transport through detectors requires an enormous amount of computational resources, utilizing more than 50% of the resources of the CERN Worldwide Large Hadron Collider Grid. This challenge has motivated the investigation of different, faster approaches for replacing the standard Monte Carlo simulations. Deep Learning Generative Adversarial Networks are among the most promising alternatives. Previous studies showed that they achieve the necessary level of accuracy while decreasing the simulation time by orders of magnitudes. In this paper we present a newly developed neural network architecture which reproduces a three-dimensional problem employing 2D convolutional layers and we compare its performance with an earlier architecture consisting of 3D convolutional layers. The performance evaluation relies on direct comparison to Monte Carlo simulations, in terms of different physics quantities usually employed to quantify the detector response. We prove that our new neural network architecture reaches a higher level of accuracy with respect to the 3D convolutional GAN while reducing the necessary computational resources. Calorimeters are among the most expensive detectors in terms of simulation time. Therefore we focus our study on an electromagnetic calorimeter prototype with a regular highly granular geometry, as an example of future calorimeters.

Abstract The determination of the strong coupling constant $$\alpha _{\mathrm{s}} (m_{\mathrm{Z}})$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub> <mml:mi>α</mml:mi> <mml:mi>s</mml:mi> </mml:msub> <mml:mrow> <mml:mo>(</mml:mo> <mml:msub> <mml:mi>m</mml:mi> <mml:mi>Z</mml:mi> </mml:msub> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> </mml:math> from H1 inclusive and dijet cross section data [1] exploits perturbative QCD predictions in next-to-next-to-leading order (NNLO) [2–4]. An implementation error in the NNLO predictions was found [4] which changes the numerical values of the predictions and the resulting values of the fits. Using the corrected NNLO predictions together with inclusive jet and dijet data, the strong coupling constant is determined to be $$\alpha _{\mathrm{s}} (m_{\mathrm{Z}}) =0.1166\,(19)_{\mathrm{exp}}\,(24)_{\mathrm{th}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub> <mml:mi>α</mml:mi> <mml:mi>s</mml:mi> </mml:msub> <mml:mrow> <mml:mo>(</mml:mo> <mml:msub> <mml:mi>m</mml:mi> <mml:mi>Z</mml:mi> </mml:msub> <mml:mo>)</mml:mo> </mml:mrow> <mml:mo>=</mml:mo> <mml:mn>0.1166</mml:mn> <mml:mspace /> <mml:msub> <mml:mrow> <mml:mo>(</mml:mo> <mml:mn>19</mml:mn> <mml:mo>)</mml:mo> </mml:mrow> <mml:mi>exp</mml:mi> </mml:msub> <mml:mspace /> <mml:msub> <mml:mrow> <mml:mo>(</mml:mo> <mml:mn>24</mml:mn> <mml:mo>)</mml:mo> </mml:mrow> <mml:mi>th</mml:mi> </mml:msub> </mml:mrow> </mml:math> . Complementarily, $$\alpha _{\mathrm{s}} (m_{\mathrm{Z}})$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub> <mml:mi>α</mml:mi> <mml:mi>s</mml:mi> </mml:msub> <mml:mrow> <mml:mo>(</mml:mo> <mml:msub> <mml:mi>m</mml:mi> <mml:mi>Z</mml:mi> </mml:msub> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> </mml:math> is determined together with parton distribution functions of the proton (PDFs) from jet and inclusive DIS data measured by the H1 experiment. The value $$\alpha _{\mathrm{s}} (m_{\mathrm{Z}}) =0.1147\,(25)_{\mathrm{tot}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub> <mml:mi>α</mml:mi> <mml:mi>s</mml:mi> </mml:msub> <mml:mrow> <mml:mo>(</mml:mo> <mml:msub> <mml:mi>m</mml:mi> <mml:mi>Z</mml:mi> </mml:msub> <mml:mo>)</mml:mo> </mml:mrow> <mml:mo>=</mml:mo> <mml:mn>0.1147</mml:mn> <mml:mspace /> <mml:msub> <mml:mrow> <mml:mo>(</mml:mo> <mml:mn>25</mml:mn> <mml:mo>)</mml:mo> </mml:mrow> <mml:mi>tot</mml:mi> </mml:msub> </mml:mrow> </mml:math> obtained is consistent with the determination from jet data alone. Corrected figures and numerical results are provided and the discussion is adapted accordingly.

In these simulation studies an energy weighting method is applied to the signals of the CMS hadronic calorimeter readout with a longitudinal segmentation for a possible future upgrade. Tabulated weighting factors are used to compensate for the different response of hadronic and electromagnetic energy depositions of simulated pion showers in the hadronic calorimeter. The weighting improves the relative energy resolution: $$ (\sigma _E /E)^2 = \left[ {((92.2 \pm 0.6)\% /\sqrt E )^2 + ((6.5 \pm 0.1)\% )^2 } \right] $$ (before weighting), $$ (\sigma _{E,weight} /E)^2 = \left[ {((85.4 \pm 0.5)\% /\sqrt E )^2 + ((4.4 \pm 0.1)\% )^2 } \right] $$ (after weighting), where E in the square root has units of GeV.

Simplified models have become a widely used and important tool to cover the more diverse phenomenology beyond constrained SUSY models. However, they come with a substantial number of caveats themselves, and great care needs to be taken when drawing conclusions from limits based on the simplified approach. To illustrate this issue with a concrete example, we examine the applicability of simplified model results to a series of full SUSY model points which all feature a small stau-LSP mass difference, and are compatible with electroweak and flavor precision observables as well as current LHC results. Various channels have been studied using the Snowmass Combined LHC detector implementation in the Delphes simulation package, as well as the Letter of Intent or Technical Design Report simulations of the ILD detector concept at the ILC. We investigated both the LHC and ILC capabilities for discovery, separation and identification of all parts of the spectrum. While parts of the spectrum would be discovered at the LHC, there is substantial room for further discoveries and property determination at the ILC.

While we expect quantum computers to surpass their classical counterparts in the future, current devices are prone to high error rates and techniques to minimise the impact of these errors are indispensable. There already exists a variety of error mitigation methods addressing this quantum noise that differ in effectiveness, and scalability. But for a more systematic and comprehensible approach we propose the introduction of modelling, in particular for representing cause-effect relations as well as for evaluating methods or combinations thereof with respect to a selection of relevant criteria.

Abstract The measurement of the jet cross sections by the H1 collaboration had been compared to various predictions including the next-to-next-to-leading order (NNLO) QCD calculations which are corrected in this erratum for an implementation error in one of the components of the NNLO calculations. The jet data and the other predictions remain unchanged. Eight figures, one table and conclusions are adapted accordingly, exhibiting even better agreement between the corrected NNLO predictions and the jet data.

The first measurement of lepton-jet momentum imbalance and azimuthal correlation in lepton-proton scattering at high momentum transfer is presented. These data, taken with the H1 detector at HERA, are corrected for detector effects using an unbinned machine learning algorithm OmniFold, which considers eight observables simultaneously in this first application. The unfolded cross sections are compared to calculations performed within the context of collinear or transverse-momentum-dependent (TMD) factorization in Quantum Chromodynamics (QCD) as well as Monte Carlo event generators. The measurement probes a wide range of QCD phenomena, including TMD parton distribution functions and their evolution with energy in so far unexplored kinematic regions.

In particle physics the simulation of particle transport through detectors requires an enormous amount of computational resources, utilizing more than 50% of the resources of the CERN Worldwide Large Hadron Collider Grid. This challenge has motivated the investigation of different, faster approaches for replacing the standard Monte Carlo simulations. Deep Learning Generative Adversarial Networks are among the most promising alternatives. Previous studies showed that they achieve the necessary level of accuracy while decreasing the simulation time by orders of magnitudes. In this paper we present a newly developed neural network architecture which reproduces a three-dimensional problem employing 2D convolutional layers and we compare its performance with an earlier architecture consisting of 3D convolutional layers. The performance evaluation relies on direct comparison to Monte Carlo simulations, in terms of different physics quantities usually employed to quantify the detector response. We prove that our new neural network architecture reaches a higher level of accuracy with respect to the 3D convolutional GAN while reducing the necessary computational resources. Calorimeters are among the most expensive detectors in terms of simulation time. Therefore we focus our study on an electromagnetic calorimeter prototype with a regular highly granular geometry, as an example of future calorimeters.