The LHC is currently operating with a proton energy of 4 TeV and à functions at the ATLAS and CMS interaction points of 0.6 m. This is close to the design value at 7 TeV (à ¼ 0:55 m) and represented a challenge for various aspects of the machine operation. In particular, a huge effort was put into the optics commissioning and an unprecedented peak beating of around 7% was achieved in a high energy hadron collider.
Since 2015 the LHC has been operating at 6.5 TeV. In 2016 the β-functions at the interaction points of ATLAS and CMS were squeezed to 0.4 m. This is below the design β Ã ¼ 0.55 m at 7 TeV, and has been instrumental to surpass the design luminosity of 10 34 cm −2 s −1. Achieving a lower than nominal β Ã has been possible thanks to the extraordinary performance of the LHC, in which the control of the optics has played a fundamental role. Even though the β-beating for the virgin machine was above 100%, corrections reduced the rms β-beating below 1% at the two main experiments and below 2% rms around the ring. This guarantees a safe operation as well as providing equal amount of luminosity for the two experiments. In this article we describe the recent improvements to the measurement, correction algorithms and technical equipment which allowed this unprecedented control of the optics for a high-energy hadron collider.
Abstract. We present a novel technique for combining statistical machine learning for proof-pattern recognition with symbolic methods for lemma discovery. The resulting tool, ACL2(ml), gathers proof statistics and uses statistical pattern-recognition to pre-processes data from libraries, and then suggests auxiliary lemmas in new proofs by analogy with already seen examples. This paper presents the implementation of ACL2(ml) alongside theoretical descriptions of the proof-pattern recognition and lemma discovery methods involved in it.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.