The hunt for new physics is back. The world’s most powerful chemical crushing machine, the Large Hadron Collider (LHC), has emerged after being closed for more than three years. Proton beams have once again circled its 27-mile loop CERN, a European particle-physics laboratory near Geneva. By July, physicists will be able to change their experiments and watch more particles collide.In its first two phases, in 2009-13 and 2015-18, the LHC explored the world-famous physics world. All that work – including the discovery of the 2012 Higgs boson victory – has confirmed the current definition of particle physics and the forces that make up the Universe: a common model. But scientists who study billions of high-resolution detritus have not found evidence of any amazing new particles or anything else that is completely unknown.
This time may vary. LHC has so far cost US $ 9.2 billion to build, including the latest upgrades: the third version comes with more data, better technology and new ways to search new physics. In addition, scientists start with an impressive shopping list of amazing results – more than at the beginning of the last game – that shows where to look for particles other than the standard model.
“We really start with an increase in adrenaline,” said Isabel Pedraza, a physicist at Meritorious Autonomous University of Puebla (BUAP) in Mexico.”I’m sure we’ll see something in run 3.”
High power and additional data
After the adjustment of the particle accelerator, the third version of the LHC will collide with the protons at 13.6 trillion electron volts (TeV) – slightly higher than run 2, reaching 13 TeV. A powerful crash should increase the likelihood that a collision would create particles in dynamic environments where other theories suggest that new physics could be lying, said RendeSteerenberg, leading beam operations at CERN. Machine beams will also produce more compacted particles, which increases the risk of collisions. This will allow the LHC to maintain its high level of collision for a long time, eventually allowing the test to record as much data as in the first two integrated combinations.
To deal with floods, mechanical tunnels – layers of sensors that capture explosive particles
A major challenge for LHC researchers has been that very small collision data could be stored. The machine collides 40 million times per second, and each proton-proton collision, or ‘event’, could spill hundreds of particles. ‘Trigger’ systems must deliver the most interesting of these events and discard a lot of data. For example, in CMS – one of the four main LHC tests – the hardware-based trigger performs complex cuts of about 100,000 events per second on the basis of structural particle-like tests, before the software selects approximately 1,000. fully reconstructed for analysis.
With additional data, surveillance systems should check for additional events. One development comes from a test of chips originally designed for video games, called GPUs (graphics processing units). These can regenerate particle histories much faster than conventional processors, so the software will be able to scan faster and break additional parameters per second. That will allow it to detect unusual bumps that may have been missed earlier.
In particular, the LHCb experiment rearranged the detector’s power to use only software to scan exciting physics events. Improvements in all research mean that it has to collect four times as much data in run 3 as it did in run 2. “It’s almost like a brand new detector,” said Yasmine Amhis, a physicist at the Irène-Joliot Curie Physics Laboratory. Two Infinities Labs in Orsay, France, and a member of the LHCb partnership
ATLAS testing & results
Run 3 will also give physicists more precision in their measurements of known particles, such as the Higgs boson, says Ludovico Pontecorvo, a physicist with ATLAS testing. This alone can produce results that are in conflict with known physics – for example, when measuring it minimizes error bars enough to place it outside the normal model predictions.
But physicists also want to know if the vast majority of the latest strange results are a real mystery, which could help fill some gaps in our understanding of the Universe. The standard model is incomplete: it cannot account for things like dark matter, for example.
That does not match LHC data: ratings at ATLAS and LHCb do not match CDF data, although they are less accurate. Physicians at CMS are now working on their own measurements, using data from a second machine run. Data from run 3 can provide a straightforward response, though not immediately, as the W boson weight is very difficult to measure.
B-meson confusion
LHC data revealed some confusion. In particular, the evidence has formed nearly a decade of abnormal behavior in particles called B mesons. These transient particles, which decompose quickly into others, are so named because they contain pairs of basic particles that comprise the ‘low’ or ‘beauty’ quark. LHCb analysis suggests that B-meson decomposition tends to produce more electrons more often than to produce their heaviest cousins, muons2. A typical model predicts that the environment should not discriminate against one another, says Tara Shears, a particle physicist at the University of Liverpool, UK, and a member of the LHCb partnership. “Muons are produced about 15% less than electrons, and it’s weird,” he said.
The result differs from the predictions of a standard model with a value of about 3 sigma, or a standard deviation of 3 from what is expected – which translates to 3 out of 1,000 chances that random noise may produce obvious bias. Only additional data can confirm the result in reality or statistical variability. Testers may not understand something in their data or machine, but now that many of the relevant LHCb detectors have been changed, the next phase of data collection should provide the opposite check, Shears said. “We will be pressured if [unusual] goes. But that is life as a scientist, that is possible. ”
Search for leptoquark
The CMS and ATLAS will also do what LHCb can do: combine collision data to directly target rare particles that theorists suggest could create problems that have not yet been confirmed. Such hypothetical particles have been called leptoquark, because, with great force, they can take particle structures of two very different families – leptones, such as electrons and muons, and quarks (see ‘Code decay’). These hybrid particles come from ideas that seek to combine basic electrical energy, which are weak and strong as characteristics of the same energy, and can explain the effects of LHCb. The leptoquark – or its sophisticated version – also fits in some surprisingly confusing; average last year3, from the Muon g – 2 study at Fermilab,
Machine learning helps with search
Run 3 will also see a completely new test. FASER, half a mile from ATLAS, will hunt for light and weak particles including neutrinos and new shapes that can explain black matter. (These particles cannot be detected by ATLAS, as they can fly out of place at the intersection of the LHC line and block the detectors). Meanwhile, ATLAS testing and CMS now have advanced detectors but will not receive major hardware upgrades until long-term closure, by 2026. At this point, the LHC will be redesigned to create more ‘high light’ beams, which will begin. increased by 2029 (see ‘LHC timeline’). This will allow scientists in the next race to collect 10 times as much collision data as in the 1 to 3 runs combined. Currently, CMS and ATLAS have prototype technology to help them prepare.
Scientists thought that this would be a fruitful strategy, for they had good guidance on where to look. Many expect to find new hard particles, such as those predicted by a group of ideas known as supersymmetry, soon after the start of the LHC. That they see nothing that brings it all out but the most controversial versions of supersymmetry. Today, a few theoretical extensions of a conventional model seem to be more realistic than others.Experimentalists are now switching to search strategies that are less pressured by what is expected. Both ATLAS and CMS will search for long-lived particles that may survive in two collisions, for example. New search techniques often mean analytics software that refutes common assumptions, Siral said.
Machine learning can also help. Many LHC testers are already using this process to distinguish certain desired conflicts from background sound. This is a ‘supervised’ reading: the algorithm is given a pattern to hunt. But researchers are increasingly using ‘unattended’ machine learning algorithms that can scan extensively for confusing, unexpected results. For example, the neural network can compare events compared to the studied simulations of a normal model. If impersonation can re-create an event, that is a mystery. Although this type of method has not yet been systematically used, “I think this is the way people will get in,” said Sascha Caron of Radboud University Nijmegen in the Netherlands, who is working to implement these methods in ATLAS data.
Researchers plan to train and test the algorithm in CMS
In making search unbiased, the factors that determine which exciting events should be considered are important, and thus help new GPUs to investigate candidate events on a broader scale. CMS will also use a method called ‘testing’: to analyze the reconstruction of all 100,000 or more selected events initially b. The causes themselves may also soon rely on machine learning to make their own decisions. Katya Govorkova, a particle physics specialist at CERN, and her colleagues have come up with a high-level proof-based algorithm that uses machine learning to choose which 40 million collider event per second will save, in line with its typical standard model5. In run 3, the researchers plan to train and test the algorithm in CMS collisions, next to the normal test tray. The challenge will be to be able to analyze the events the label algorithm labels as bizarre, because it cannot pinpoint exactly why the event is bizarre, Govorkova said.
Physicists need to keep an open mind about where they can find the rope that will lead them to a more realistic model, Amhis said. While the current confusing harvest is exciting, even the most unusual previous findings in many experiments have turned into statistical variables that have disappeared when additional data is collected. “It is important that we continue to push the whole physics program,” he said. “It’s a matter of not putting all your eggs in one basket.”
Source Journal Reference: Elizabeth Gibney, How the revamped Large Hadron Collider will hunt for new physics, Nature News Feature, Nature 605, 604-607 (2022) doi: https://doi.org/10.1038/d41586-022-01388-6
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