Power limitations are expected at injection energy for the main Radio Frequency (RF) system due to the doubled bunch intensity in the High Luminosity (HL-) Large Hadron Collider (LHC) era. One way to overcome these power limitations is to reduce the capture voltage. The smaller RF bucket, however, leads to increased beam losses at the start of the ramp. In practice, these beam losses, which contain both capture and flat-bottom losses, can trigger beam dumps if any of the Beam Loss Monitor (BLM) thresholds are reached. In this contribution, the correlation between start-of-ramp beam loss and beam observables before the ramp is investigated by analysing Beam Current Transformer (BCT) measurements from physics fills. Estimates of how the maximum ratio to BLM dump threshold scales with longitudinal losses are also made. The aim is to make predictions for operation at higher bunch intensities on the basis of these correlations in view of the intensity ramp up for the HL-LHC era.

Accurate knowledge of beam parameters is essential for optimizing the performance of particle accelerators like the Large Hadron Collider (LHC). An initial machine-learning (ML) model for the reconstruction of the longitudinal distribution has been extended to extract the main parameters of multiple bunches at LHC injection. The extended model utilizes an encoder-decoder architecture to analyze sets of longitudinal profile measurements. Its development was partially driven by the need of a real-time beam energy error estimate, which was not directly available in the past. The derived beam parameters moreover include injection phase error, bunch length and intensity in the LHC, as well as the RF voltages at extraction from the Super Proton Synchrotron (SPS) and at capture in the LHC. In this paper, we compare the results of the ML model with conventional measurements of bunch length and energy error, from the beam quality monitor (BQM) and the orbit acquisition system, respectively. These benchmarks demonstrate the potential of applying the ML model for operational exploitation in LHC.

Efficient operation of the Large Hadron Collider (LHC) relies on accurate longitudinal beam measurements to diagnose beam instabilities and verify the correctness of bunch-shaping techniques. To achieve this goal, a diagnostic system was developed to perform high-resolution measurements of longitudinal bunch profiles. High-performance oscilloscopes, synchronized to precise accelerator events, are employed to carry out the measurements, acquiring data from wideband wall-current monitors installed in the machine. This paper provides details on the implementation of the system, highlighting its current and future applications that will play a key role in increasing beam intensity in the LHC.

In the Large Hadron Collider (LHC), intra-beam scattering (IBS) is one of the main drivers of longitudinal emittance growth during the long injection plateau. With the halo of the longitudinal bunch distribution being close to the separatrix, IBS consequently drives beam losses by pushing particles outside the RF bucket at the flat-bottom. As IBS and beam losses impose a requirement on the minimum RF bucket size, this mechanism has an important impact on the RF power requirements for the High Luminosity (HL-) LHC. In this contribution, the effect of IBS is introduced in the Beam Longitudinal Dynamics (BLonD) tracking code. This numerical model is then benchmarked against analytical estimates, as well as against beam measurements performed in the LHC. The impact of IBS-driven losses on the RF power requirements is discussed through the correlation between the time spent at flat-bottom and the average bunch length, which translates into start-of-ramp losses.