Friday, April 09, 2021

Bhabha scattering and the anomalous magnetic dipole moment of the muon

Just noting some observations here, likely of no significance at all. 

  •  Bhabha scattering is the electron-positron scattering process. 

  •  Per Wiki
Electron-positron colliders operating in the region of the low-lying hadronic resonances (about 1 GeV to 10 GeV), such as the Beijing Electron Synchrotron (BES) and the Belle and BaBar "B-factory" experiments, use large-angle Bhabha scattering as a luminosity monitor. To achieve the desired precision at the 0.1% level, the experimental measurements must be compared to a theoretical calculation including next-to-leading-order radiative corrections. The high-precision measurement of the total hadronic cross section at these low energies is a crucial input into the theoretical calculation of the anomalous magnetic dipole moment of the muon, which is used to constrain supersymmetry and other models of physics beyond the Standard Model.

  • One would think Bhabha scattering is extremely well understood in terms of theoretical calculations.  So I was surprised to find this paper from 2020

    Patrick Janot, Stanisław Jadach,
    Improved Bhabha cross section at LEP and the number of light neutrino species,
    Physics Letters B, Volume 803, 2020, 135319, ISSN 0370-2693, (

    Abstract: In e+e− collisions, the integrated luminosity is generally measured from the rate of low-angle Bhabha interactions e+e−→e+e−. In the published LEP results, the inferred theoretical uncertainty of ±0.061% on the predicted rate is significantly larger than the reported experimental uncertainties. We present an updated and more accurate prediction of the Bhabha cross section in this letter, which is found to reduce the Bhabha cross section by about 0.048%, and its uncertainty to ±0.037%. When accounted for, these changes modify the number of light neutrino species (and its accuracy), as determined from the LEP measurement of the hadronic cross section at the Z peak, to Nν=2.9963±0.0074. The 20-years-old 2σ tension with the Standard Model is gone. 

  • A discussion of the recent muon result is on Peter Woit's blog.  Some of the comments under that blog post are of interest.
Presumably the large-angle Bhabha scattering used to calibrate the newer experiments is already much more accurate from the get-go.


Tuesday, April 06, 2021

The Epigenetics of Poverty

 A preliminary, from Wiki: "DNA methylation is a biological process by which methyl groups are added to the DNA molecule. Methylation can change the activity of a DNA segment without changing the sequence. When located in a gene promoter, DNA methylation typically acts to repress gene transcription. In mammals, DNA methylation is essential for normal development and is associated with a number of key processes including genomic imprintingX-chromosome inactivation, repression of transposable elementsaging, and carcinogenesis."

Someone drew my attention to this: Poverty leaves a mark on our genes.  This is from 2019.

A new Northwestern University study challenges prevailing understandings of genes as immutable features of biology that are fixed at conception.

Previous research has shown that socioeconomic status (SES) is a powerful determinant of human health and disease, and social inequality is a ubiquitous stressor for human populations globally. Lower educational attainment and/or income predict increased risk for heart disease, diabetes, many cancers and infectious diseases, for example. Furthermore, lower SES is associated with physiological processes that contribute to the development of disease, including chronic inflammation, insulin resistance and cortisol dysregulation.

In this study, researchers found evidence that poverty can become embedded across wide swaths of the genome. They discovered that lower socioeconomic status is associated with levels of DNA methylation (DNAm) -- a key epigenetic mark that has the potential to shape gene expression -- at more than 2,500 sites, across more than 1,500 genes.

In other words, poverty leaves a mark on nearly 10 percent of the genes in the genome.

Lead author Thomas McDade said this is significant for two reasons.

"First, we have known for a long time that SES is a powerful determinant of health, but the underlying mechanisms through which our bodies 'remember' the experiences of poverty are not known," said McDade, professor of anthropology in the Weinberg College of Arts and Sciences at Northwestern and director of the Laboratory for Human Biology Research.

"Our findings suggest that DNA methylation may play an important role, and the wide scope of the associations between SES and DNAm is consistent with the wide range of biological systems and health outcomes we know to be shaped by SES."

Secondly, said McDade, also a faculty fellow at Northwestern's Institute for Policy Research, experiences over the course of development become embodied in the genome, to literally shape its structure and function.

"There is no nature vs. nurture," he adds.

McDade said he was surprised to find so many associations between socioeconomic status and DNA methylation, across such a large number of genes.

"This pattern highlights a potential mechanism through which poverty can have a lasting impact on a wide range of physiological systems and processes," he said.

The original paper is here.

A criticism of the field is here: Social epigenomics: Are we at an impasse?

Sunday, April 04, 2021

Benacerraf: What Numbers Could Not Be

 Without commentary, here's a link to Benacerraf's original paper and a cut-paste job from the Stanford Encyclopedia of Philosophy.

Saturday, April 03, 2021

The metaphysical status of types

 I'm reproducing a few paragraphs from chapter 7 of Peter Smith's Introduction to Formal Logic below.  The thought is that if and when we teach a machine, it will at a minimum, expose the implicit assumptions in our metaphysics.  There's more to think about than just the paragraphs below, but this much will do, I think.

7.1 Types vs tokens

We begin with two sections introducing relevant distinctions. Firstly, we want the distinction between types and tokens. This is best introduced via a simple example.

Suppose then that you and I take a piece of paper each, and boldly write ‘Logic is fun!’ a few times in the centre. So we produce a number of different physical inscriptions – perhaps yours are rather large and in blue ink, mine are smaller and in black pencil. Now we key the same encouraging motto into our laptops, and print out the results: we get more physical inscriptions, first some formed from pixels on our screens and then some formed from printer ink.

How many different sentences are there here? We can say: many, some in ink, some in pencil, some in pixels, etc. Equally, we can say: there is one sentence here, multiply instantiated. Evidently, we must distinguish the many different sentence-instances or sentence tokens – physically constituted in various ways, of different sizes, lasting for different lengths of time, etc. – from the one sentential form or sentence type which they are all instances of.

We can of course similarly distinguish word tokens from word types, and distinguish book tokens – e.g. printed copies – from book types (compare the questions ‘How many books has J. K. Rowling sold?’ and ‘How many books has J. K. Rowling written?’).

What makes a physical sentence a token of a particular type? And what exactly is the metaphysical status of types? Tough questions that we can’t answer here! But it is very widely agreed that we need some type/token distinction, however it is to be elaborated.

Types are very natural to us humans.  We train deep neural networks to distinguish between types, like recognizing cats and dogs. But if you think about it, we've already "told" the neural network that types are important.

One can imagine that the ability to come up with types is very useful from the point of view of evolution,  (e.g., identifying predator tokens as a type might be efficient), and that humans with faulty type mechanisms turn out to be evolutionary dead-ends.   The thought experiment that might help to figure out where types come from is to figure out how to get a machine to come up with types from tokens without implicitly requiring that it must do so in the first place.