Cern News

ATLAS provides first measurement of the W-boson width at the LHC View of an ATLAS collision event in which a candidate W boson decays into a muon and a neutrino. The reconstructed tracks of the charged particles in the inner part of the ATLAS detector are shown as orange lines. The energy deposits in the detector’s calorimeters are shown as yellow boxes. The identified muon is shown as a red line. The missing transverse momentum associated with the neutrino is shown as a green dashed line. (Image: ATLAS/CERN)

The discovery of the Higgs boson in 2012 slotted in the final missing piece of the Standard Model puzzle. Yet, it left lingering questions. What lies beyond this framework? Where are the new phenomena that would solve the Universe's remaining mysteries, such as the nature of dark matter and the origin of matter–antimatter asymmetry?

One parameter that may hold clues about new physics phenomena is the “width” of the W boson, the electrically charged carrier of the weak force. A particle’s width is directly related to its lifetime and describes how it decays to other particles. If the W boson decays in unexpected ways, such as into yet-to-be-discovered new particles, these would influence the measured width. As its value is precisely predicted by the Standard Model based on the strength of the charged weak force and the mass of the W boson (along with smaller quantum effects), any significant deviation from the prediction would indicate the presence of unaccounted phenomena.

In a new study, the ATLAS collaboration measured the W-boson width at the Large Hadron Collider (LHC) for the first time. The W-boson width had previously been measured at CERN’s Large Electron–Positron (LEP) collider and Fermilab’s Tevatron collider, yielding an average value of 2085 ± 42 million electronvolts (MeV), consistent with the Standard-Model prediction of 2088 ± 1 MeV. Using proton–proton collision data at an energy of 7 TeV collected during Run 1 of the LHC, ATLAS measured the W-boson width as 2202 ± 47 MeV. This is the most precise measurement to date made by a single experiment, and — while a bit larger — it is consistent with the Standard-Model prediction to within 2.5 standard deviations (see figure below).

This remarkable result was achieved by performing a detailed particle-momentum analysis of decays of the W boson into an electron or a muon and their corresponding neutrino, which goes undetected but leaves a signature of missing energy in the collision event (see image above). This required physicists to precisely calibrate the ATLAS detector’s response to these particles in terms of efficiency, energy and momentum, taking contributions from background processes into account.

However, achieving such high precision also requires the confluence of several high-precision results. For instance, an accurate understanding of W-boson production in proton–proton collisions was essential, and researchers relied on a combination of theoretical predictions validated by various measurements of W and Z boson properties. Also crucial to this measurement is the knowledge of the inner structure of the proton, which is described in parton distribution functions. ATLAS physicists incorporated and tested parton distribution functions derived by global research groups from fits to data from a wide range of particle physics experiments.

The ATLAS collaboration measured the W-boson width simultaneously with the W-boson mass using a statistical method that allowed part of the parameters quantifying uncertainties to be directly constrained from the measured data, thus improving the measurement’s precision. The updated measurement of the W-boson mass is 80367 ± 16 MeV, which improves on and supersedes the previous ATLAS measurement using the same dataset. The measured values of both the mass and the width are consistent with the Standard-Model predictions.

Future measurements of the W-boson width and mass using larger ATLAS datasets are expected to reduce the statistical and experimental uncertainties. Concurrently, advancements in theoretical predictions and a more refined understanding of parton distribution functions will help to reduce the theoretical uncertainties. As their measurements become ever more precise, physicists will be able to conduct yet more stringent tests of the Standard Model and probe for new particles and forces.

Comparison of the measured W-boson width with the Standard-Model prediction and with measurements from the LEP and Tevatron colliders. The vertical grey band illustrates the Standard-Model prediction, while the black dots and the associated horizontal bands represent the published experimental results. (Image: ATLAS/CERN) abelchio Wed, 04/10/2024 - 12:03 Byline ATLAS collaboration Publication Date Wed, 04/10/2024 - 11:57

CMS releases Higgs boson discovery data to the public CMS event display of a candidate Higgs boson decaying into two photons, one of the two decay channels that were key to the discovery of the particle. (Image: CERN)

As part of its continued commitment to making its science fully open, the CMS collaboration has just publicly released, in electronic format, the combination of CMS measurements that contributed to establishing the discovery of the Higgs boson in 2012. This release coincides with the publication of the Combine software – the statistical analysis tool that CMS developed during the first run of the Large Hadron Collider (LHC) to search for the unique particle, which has since been adopted throughout the collaboration.

Physics measurements based on data from the LHC are usually reported as a central value and its corresponding uncertainty. For instance, soon after observing the Higgs boson in LHC proton–proton collision data, CMS measured its mass as 125.3 plus or minus 0.6 GeV (the proton mass being about 1 GeV). But this figure is just a brief summary of the measurement outcome, a bit like the title of a book.

In a measurement, the full information extracted from the data is encoded in a mathematical function, known as the likelihood function, that includes the measured value of a quantity as well as its dependence on external factors. In the case of a CMS measurement, these factors encompass the calibration of the CMS detector, the accuracy of the CMS detector simulation used to facilitate the measurement and other systematic effects.

A likelihood function of a measurement based on LHC data can be complex, as many aspects need to be pinned down to fully understand the messy collisions that take place at the LHC. For example, the likelihood function of the combination of CMS Higgs boson discovery measurements, which CMS just released in electronic format, has nearly 700 parameters for a fixed value of the Higgs boson mass. Among these, only one – the number of Higgs bosons found in the data – is the physics parameter of interest, while the rest model systematic uncertainties.

Each of these parameters corresponds to a dimension of a multi-dimensional abstract space, in which the likelihood function can be drawn. It is hard for humans to visualise a space with more than a few dimensions, let alone one with many. The new release of the likelihood function of the CMS Higgs boson discovery measurements – the first likelihood function to be made publicly available by the collaboration – allows researchers to get around this problem. With a publicly accessible likelihood function, physicists outside the CMS collaboration can now precisely factor in the CMS Higgs boson discovery measurements in their studies.

The release of this likelihood function, as well as that of the Combine software, which is used to model the likelihood and fit the data, marks a new milestone in CMS’s decade-long commitment to fully open science. It joins hundreds of open-access publications, the release of almost five petabytes of CMS data on the CERN open-data portal and the publication of its entire software framework on GitHub.

Find out more on the CMS website.

abelchio Tue, 04/09/2024 - 14:29 Byline CMS collaboration Publication Date Tue, 04/16/2024 - 10:50

Computer Security: Swipes vs PINs vs passwords vs you

What kind of person are you? An artist, like a painter? A credit card fanatic or just “in numbers”? Cerebral, a memoriser or even a genius? An influencer, like a peacock, or just prettily self-confident? A security buff or sufficiently security aware? Or just ignorant about security and your privacy? Let’s assume for a moment that the way you unlock your smartphone tells us which.

There are many different ways to unlock your smartphone: swiping patterns, PIN numbers, passwords, biometric fingerprints or face recognition. Some are more secure, some less so. But all are better than nothing. So, let’s look at some of them.

Swiping patterns: The obvious choice on Android phones. Your favourite pattern on a 3x3 matrix. But as it should be a continuous swipe, the number of actual possibilities are quite limited, boiling down to about 20 most-used swipes. If yours is listed there, it may be time to move to another, more secure swipe. In any case, your swiping can be spied on and then tried once your smartphone is stolen.

Worse ─ although it’s probably still academic ─ a small basic sonar system combining a local loudspeaker to emit acoustic signals inaudible to humans and a microphone to record them coming back again allowed researchers to use “the echo signal […] to profile user interaction with the device”, i.e. the way your finger swipes over and interacts with the screen. They’ve shown how this sonar can be employed to help identify the swipe pattern to unlock an Android phone – reducing the number of trials to be performed by an attacker by 70%. And that’s only their proof of concept… Maybe PINs and passwords are better?

PINs vs passwords: A common paradigm of computer security is linked to password complexity. Four-digit PIN numbers are no longer state of the art. And even six digits are not necessarily sufficient. While guessing and brute-forcing is difficult, as your smartphone should have a lock-out procedure only allowing a small number of tries before introducing timeouts or even wiping your phone completely(!), PINs can be easily spied on and replayed once your smartphone has been stolen*. Or do you shield your screen as you type your smartphone PIN as you do for your credit card at an ATM? Of course, a better choice is a long and complex password or even passphrase (unless you use one of the top 10 most-used passwords). Admittedly, typing such long and complex passwords can be tedious. Enter: biometrics.

Biometrics: Still our favourite – using your fingerprint sensor or a capture of your face to unlock your phone. Your smartphone (and laptop) manufacturers went to extreme lengths to ensure that your biometric signature cannot be tampered with by your fingerprint on a piece of tape, your face in a photo or your sleeping self. And they also ensured that your biometric information is properly and securely stored using a special-purpose hardware chip (TPM: “trusted platform module”). Still, fingerprint authentication in particular has been broken into in the past for Android and Windows devices, making face recognition our favourite choice to protect access to your smartphone and all the personal (and professional!) data you store and access with it.

 

*Actually, Apple’s latest security patch also fixed some issues with this.

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Do you want to learn more about computer security incidents and issues at CERN? Follow our Monthly Report. For further information, questions or help, check our website or contact us at Computer.Security@cern.ch.

anschaef Tue, 04/09/2024 - 14:03 Byline Computer Security team Publication Date Tue, 04/09/2024 - 14:01