The SM@LHC series consists actually of specialized workshops designed to bring together experienced researcher and have them discuss the open topics and points of improvement that concern Standard Model physics. Well, not only Standard Model, actually; nowadays the precision of SM measurements is so large that we expect to be able to see sizeable discrepancies from SM predictions in case there is some new physics nested into the couplings (parameters representing the strength of an interaction between a set of particles).
In the “usual” conferences about HEP physics, a talk on “multiboson measurements in ATLAS and CMS” would consist in a list of nice results with highlights about who did what with a better precision; while showcasing results is very important, one sometimes feels the need of a more critical discussion of the results, to identify possible improvements to be made and therefore inform future action.
Workshops like SM@LHC satisfy exactly this need; speakers are invited by the organizers to give talks focused more on the issues and open points than on the accomplishments. In order to prepare my overview of multiboson measurements, I have read in detail a number of ATLAS and CMS papers, following this mandate. Because of my tasks within the Collaboration (I review papers for the phrasing of statistical claims, for example), I have grown a bit picky on the topic of reporting results, and I started to notice things.
After preparing the talk I took a plane to Zürich right before Easter, spent the weekend visiting the town with my wife, and started thinking about systematizing the observations I had made to possibly abstracting some kind of guidelines.
During the four days of the workshop, I started jotting out a few ideas, and on Friday morning I submitted the result to the ArXiv.
My Reporting Results in High Energy Physics Papers: a Manifesto is now out, and I have already received feedback from the community (quite good, so far!). If you feel like reading these 10 entertaining pages, make sure you drop me a like with additional feedback; I surely missed some point and the document can be always improved.
Last but not least, the act of writing the acknowledgements section for this paper led me to investigate about my CMS membership; I realized this year (in July, actually) marks my 10th year in CMS; I am not sure this is a milestone, but somehow it feels like one.
For sure, looking back, I realize how many things I now kind of understand—things I had absolutely no clue about when I started. And that feels good 😀
Chinese Whispers is a children’s game; according to the linked Wikipedia article, it’s called Telephone Game in American English, which better resembles the Italian telefono senza fili (literally, wireless phone).
Regardless of the name, which might stir up some discussion in its British version due to stereotype, the point is it’s a game in which information gets progressively distorted at each step—or I should rather say that opportunity for distortion at each step is embedded in the rules of the game.
Information is usually distorted by the environment (i.e. by the challenge of quickly whispering words one player to each other), but there’s always the chance that a player intentionally changes the message. This makes often the game a bit less funny (the funniest realizations—at least to me—are the ones in which the changes are unintentional), but results in no big deal; the message has no real utility.
In the real world, messages are usually important in being meant to have some effect on the recipient, and intentional distortion becomes an issue because the distortion is motivated by the hidden agenda of the player (or in general actor, in this context) that distorts the message.
In science this issues can rise in the way scientific results are presented to the general public, and also in the way results are presented to a public of peers; I will discuss two recent examples that bothered me a bit.
The first example is the popular book The Order of Time by Carlo Rovelli. In the book, Rovelli argues essentially that time is a sort of emergent property rather than a fundamental entity. The book has been followed by a series of interviews and articles in the press, which helped popularize it and certainly pumped up sales.
The book—and the general attitude shown in press articles and interviews—creates huge harm, though, because the notion that sticks with the layman is precisely that time does not exist. While this is certainly an interesting theory, worth discussion and scientific exploration (if feasible), it is a theory. A fancy, interesting theory that is not supported by any evidence whatsoever, at this moment in time (pun intended).
I think that selling (because the issue here is selling) a theory as if it was a fact is seriously damaging both the public and the community, with the aggravating factor that the public is defenseless; the public just trusts whatever is written in a popular book or in a press article, regardless of the truth—as the Trump campaign taught us. Furthermore, unfortunately the general public does not go and check more informed reports such as an article from Nature which points out that the theory is just Rovelli’s theory and that the layman should not buy the theory as if it was the truth.
If you think that I am exaggerating, consider that I am one of the administrators of what is probably the major Italian Facebook group on outreach on the topic of Quantum Mechanics, Meccanica Quantistica; Gruppo Serio; every couple days we have users that keep posting their thoughts “on the fact that time does not exist”, to the point that we stopped allowing those posts to pass through our filters. When we still accepted those discussions, I have been able to experience firsthand that these people have read the book (or a press article about it) and have taken home the message that the state of the art of scientific knowledge is that time does not exist. And this is very bothering. I think Rovelli messed up very badly in this, and I have the impression (I hope the incorrect impression) that he is unwilling or not caring about correcting this mistake.
Rovelli’s book is not the only example of a book that does a disservice to outreach by projecting the theory or the biases of the author into the general public; another recent example would be the book (and blog post about FCC) by Sabine Hossenfelder in which she claims that a new particle collider would be a waste of money, but I think that others have already written extensively about the topic, so I won’t delve into the topic in this blog post (I already did on Twitter, though), and my second example won’t be Sabine’s book.
My second example will be a sneakier example I have assisted to last week in a seminar in my institution, Université catholique de Louvain. In the context of the assignment of some PhDs honoris causa to renown scientists, Neil Turok has been invited and gave a couple lectures. One lecture was to the general public, and I missed it because of other commitment; you can find the full video of it in my institution’s website. The second lecture, the one I will focus on, was to a semi-general public; not only researchers like me from the CP3 (Centre for Cosmology, Particle Physics and Phenomenology—kudos for centre, Oxford comma is missing though), but also bachelor and master students in Physics.
A seminar for specialists is pretty much an open field, where it’s assumed that the spectators will be actively engaged and will critically evaluate any bit of information transmitted by the speaker.
A lecture with bachelor and master students—who were encouraged to participate and make questions—is a more delicate scenario, in which I would argue that you want to make sure that everything will be communicated with the necessary caveats. Either well-established theories should be presented, or new, bizarre, untested theories; in the case of the latter, there should be ample warnings about the theories not being part of the scientific consensus. I am not saying that new/bizarre/untested theories should not be presented; on the contrary, it is good for the formation of the critical mind of the students that debate is stirred up and that exciting possibilites are presented to them. What I am saying is that such possibilities should be presented as such, and not as the unquestionable truth; here is where I think Turok messed up pretty badly.
The lecture was about a CPT-symmetric universe; a couple slides into the talk, he presented a slide in which he wrote an equation and outlined the different components and the scientists that solved those pieces of the puzzle. There was an almost invisible (dark violet on black) bit of the equation that I was not able to read but that turned out to be pretty crucial; he claimed that he used to put disclaimers about that piece of the equation, because it referred to dark matter, but that recently he removed the disclaimer because that part of the puzzle has been solved.
At that point, I kind of woke up, because to this day we are pretty far from being able to state that “we solved Dark Matter”.
He then went on to state that competing theories such as freeze-out and freeze-in are full of ad-hoc assumptions, whereas his theory was simple and elegant; he even threw in the middle some paternalistic comments saying that in astrophysics/cosmology lately people just produce bad papers for the sake of it, whereas he prefers simple solutions based on works from 50 years ago.
Now, it might be true that some people produce bad papers just for the sake of it, and it might be true that going back to the roots of a discipline can result in ideas with a newly found strength and solidity. But using this argument to bash at competing models seems to me a bit arrogant and uncalled for. Particularly in front of undergraduate students.
During the Q&A, a couple colleagues of mine argued on two different fronts; one argued that freeze-in mechanisms—contrary to what stated by Turok—do not assume a huge number of new fields and ad-hoc assumptions. I am no expert on astrophysics, but we had in the past weeks two or three seminars about freeze-out and freeze-in mechanisms at CP3, and I am pretty sure my colleague was right; yet, Turok dismissed him basically saying that he was sure my colleague was wrong, and the moderator in the end had to use the traditional diplomatic let’s continue discussing this during the coffee break before things went awry.
The other colleague argued that the “very simple and standard-model only” model by Turok assumed not just the Standard Model but also right-handed neutrinos, to which a small exchange followed about whether RH neutrinos can be considered practically-Standard-Model or not. The discussion dragged on a bit, and at some point Turok admitted—although very en-passant—that also his model is affected by totally ad-hoc assumptions such the Z2 symmetry that makes one and only one of the RH neutrinos stable. And yes, that assumption is totally ad-hoc and is apparently the only way in which the theory can explain why of all RH neutrinos only one should be stable and give rise to Dark Matter. Again, I think that while it’s healthy that students are exposed to debate and to new ideas, the way in which the theory has been presented before the critics has been very problematic.
Summarizing, I think our duty as scientists is to give both the public and the students the most objective picture about whatever new theory we fancy at the moment—even if we ourselves devised that theory.
It is good to expose the public to some degree of the professional debate about some topics—although it probably depends on the topic; debate about CPT has not the same impact on the layman as a debate about black holes—remember when people believed the LHC would have destroyed the Earth?—or vaccines.
However, when speaking to—or writing for—people that have not the capabilities of critically sieving through information, we should be very careful to not misrepresent the difference between the current scientific consensus and yet untested theories.
It’s grant-writing season, and I have a grant request submission deadline at the beginning of next week. On top of that, I have to finalize a paper I will present (also next week) at a statistics conference.
Last week I have been busy finalizing my latest CMS paper that is now on the ArXiv, about WZ bosons production (very nice measurement, improving the current experimental picture quite a lot, I must say—highly suggested reading), and grading programming assignments for the course I am assistant of this semester in UCLouvain.
The nice thing is that—contrary to the past—I am not scratching fingers at the deadline while feeling the pressure of urgency; I am just doing exactly what I should be doing at this stage, i.e. polishing material that is mostly final.
And this, folks, is a hugely relaxing sensation.
So relaxing that, in fact, I am writing this while going to fetch the pizzas The Wife and I ordered for take-away 🙂
I am using R for a project of mine; I had used R a few years ago in a very elementary way, but I had never gone into it seriously.
Thanks to a statistician—ESR of the AMVA4NewPhysics network, Grzegorz Kotkowski—who did an internship with my supervision at the Universidad de Oviedo last year, I got acquainted with RStudio, and decided to give it a try.
I had a few troubles at the beginning, mostly to figure out the peculiarities of R with respect to other languages, and nowadays I mostly fight with ggplot for tweaking the graphics of my plots.
However, today I was unit-testing my code for a plot I made that was highly suspicious; the variable to be plotted appeared to have a value of Inf most of the times.
Digging inside the code, I figured out that the Bayes Factor in these cases was… negative!!! Now, this is bad in a huge way, because the Bayes Factor is supposed to be positive-defined (at least in usual Bayesian statistics, that obeys the Kolmogorov axioma); it turns out that the negative sign came from the Gauss hypergeometric function that I was importing from the package Appell 0.0-4.
According to the package documentation , two methods for computing the function are used, taken from literature: the Forrey method and the Michel-Stoitsov method, the latter being the default.
Now, what I am interested in is , so I naively tried to change the default method:
As you can see, the alternative method (Forrey) doesn’t even work. However, I understand the input parameters have relatively large values, so I was not worried about that. What worried me was still the negative value for the real part as given by the Appell implementation of the Michel-Stoitsov algorithm!
Indeed, the result is positive. What to do? Should I assume Wolfram-Alpha is the correct one? I had no idea, nor I wanted to dig into the details of the two calculations, so I thought of cross-checking with Python. Why Python? Because in Python is implemented in both mpmath and scipy, two packages that are routinely used and debugged by lots of people;
OK, it definitely looks like the Appell implementation has an issue!
It might also be that Wolframalpha, mpmath, and scipy all have the wrong implementation, but they are far more under scrutiny (active developers, etc) than Appell, and the value I get from them is the one that yields a meaningful result (positive probability…); at this point I would not bet on Appell’s implementation being correct.
Now the situation forks into a practical solution and a proper solution. The practical solution is that my plot now uses the scipy implementation (the mpmath one had some issues in the R vectorization, whereas scipy works fine).
The proper solution is that I tried to file a bug report: however, the CRAN page for the Appell package does not provide any means of filing a bug. A quick google search pointed me to a CRAN read-only github repository, but it is not possible to open an issue (the interface has not been activated, apparently), and I am not really in this moment available to debug the (FORTRAN, ouch!) code and prepare a pull request.
I therefore wrote an email to Daniel, the package maintainer, and to Gábor, which if I understand correctly is the CRAN responsible for committing the package (if I interpreted correctly the Github repository and commits).
Gábor actually answered, but he is not involved with the package itself, whereas the email to Daniel bounced back, because of “inexisting email address”. I think I’ll just wait until I will have time and willingness to stick the head into the FORTRAN code, and eventually prepare a pull request, but I don’t know when this will happen, nor I know if and when I will get an answer back from the maintainers.
This post is a bit of a public bug report, and a bit of an attempt of getting in touch with the maintainer; if you know of a (more proper?) way of reporting a bug for an R package, please let me know in the comments below!
I have long debated with myself whether to write the posts in Markdown (WordPress supports that) or using the WP editor, and I have finally chosen the WP editor; the reason is that the editor features a simple way of inserting formulas. To obtain the same with Markdown, as far as I understand the WP Markdown plugin should support a Markdown plugin, which I guess is asking much of WordPress; correct me if I am wrong (@pablodecm, I am thinking about you).
Long story short, it turns out that the way of embedding formulas in the WP editor is quite simple: you just have to enclose the formula between dollar signs, and the first dollar sign must be followed without spaces by the word “latex” (the dollar signs are delimiters of an interpreted environment, and the word specifies which is the interpreter to be used). You can theb add options to the formula by adding to the end ampersands followed by the parameter=value syntax; it’s very useful for setting the colour of the text and of the background, for example (by default, the interpreter assumes white background and black text, and is blind to the actual HTML/CSS style.
One thing leaves me puzzled, though; one particular formula gets rendered in two mini-lines within a single text line, resulting in a pretty ugly effect:
As our can see, the last part gets put into an ugly mini-line that does not correspond to different main lines; as a result, the two lines of the formula are squeezed into a single text line. In case you see the formula just fine (i.e. the -8.4 in the same line as the rest of the formula), please drop me a line in the comments below: I have tried a couple different browsers both on a computer and on a phone, and the issue seems to be common, so I would exclude web browser issues). I have tried varying the spacing to no avail. I will keep digging, but if any of my two subscribers (literally; two people subscribed so far) has any suggestion, I’ll be glad to try it out!
Until then, I will go back to the main theme of the blog 😉
[Originally posted on December 31th, 2018. Revised a couple sentences on January 2nd, 2019 — sketching under pressure and without proofreading, while the wife is showering, before going to a New Year’s Eve celebration is not really the way to go 😀 ]
As promised in Active inactivity, here is a new blog post, before the end of the year!
Since New Year’s Eve is upon us, I think it is only fair to begin this introduction to De Finetti’s definition of probability with a preparatory introduction to the concept of expectation.
In Statistics, the word expectation has somehow a peculiar meaning, that to me represents an improvement on the everyday meaning of the word; the layman’s definition of the word expectation, according to the Oxford Dictionary is “A strong belief that something will happen or be the case.“. Is this enough for the statistician?
Well, yes and no.
Yes, in the sense that the act of making a statement about the future is somehow maintained, at least for a suitable realization of the abstract definition of probability. No, in the sense that we are not interested in making a generic statement about what we believe that will happen in the future; we want to make a statement that reasonably encompasses everything that could happen, resulting in a statement about the average outcome that I can expect.
Let’s make a simple example: say that you draw a card from a deck, and that you gamble such that you win 10 euros is you get a King, and you lose 10 euros if you get a black card. What do you expect to happen on average?
The relevant useful concept here is that of expectation: each outcome (King, anything but a King) has two numbers associated to it: the probability of obtaining that outcome, and the value or pay-off that you get if that particular outcome happens. For example, there are 4 Kings out of the 52 cards that make the deck, so the probability of obtaining a King is , whereas the pay-off you get if the outcome happens is , euros. By converse, the probability of obtaining not-a-King is , and the value you stand to win is , euros (negative, since you would incur in a loss).
To know what you can reasonably expect to happen on average in this situation, it is necessary to think a bit. If the situation was simpler, for example if you stand to win 10 euros regardless of the outcome of the card draw, you can expect that you will win 10 euros. By converse, if you stand to loose 10 euros regardless of the outcome, you can expect that you will loose 10 euros. But in our fictitious situation different outcomes are rewarded with different values; it is then crucial to have a way of estimating a global, average value for what you can expect. Since each pay-off will happen only its corresponding outcome happens, a natural choice is to weight each possible pay-off with the probability that its corresponding outcome will actually happen. It turns out then that, following this line of reasoning, your expectation is given by an average of the pay-offs, weighted by the probability that each outcome happen. For our concrete case, ; in words, the expected value you get out of your gamble is lose 8.4 euros.
You can already use such considerations to find the expected value of any kind of situation in which you can gamble on some well-known outcomes; this works for example for any gamble on a deck of cards, in which you can easily calculate the probability of any outcome by simply counting the cards—or combinations thereof—in the deck).
In order to interpret such statement, and to really get to the bottom of the meaning of expected value, we need to make a small step back and look into how we can define and compute an essential element of the formula for the expectation: probabilities.
However, you will have to wait for the New Year, because my wife has finished showering, and we need to get ready for this night’s party!
See what I did here? Not only have I described briefly the concept of expectation, but I have also given you a way of computing what is the value you expect to get from this blog for the first week of 2019: what is the probability you assign to me writing and publishing the next blog post before January 7th? What is the value you assign to the blog post coming out, and what is the value you assign to the blog post not coming out?
Try to get your probabilities and pay-offs figured out before midnight!
My introductory post, Welcome to This New Beginning, came on February 19th, 2018. The day of tomorrow will mark 10 months from that moment; should I feel bad about it?
Yes and no.
Let me explain. I had started this project out of frustration and of the need of having a new haven where to rant about what I really care about, but then stuff piled up and I could not put myself to produce content for this blog. The exception being 4 drafts that are still in a very crude form, and that have been expanded at the staggering rate of about 4 or 5 words every couple months.
As I have been preaching to the AMVA4NewPhysics students (in quality of Outreach Offices of the network), the key for a successful blog is building engagement, and engagement is built in perhaps equal parts by interesting, high-quality content and by a frequent and regular update pace. I would not judge quality by the introductory post (nor by the post you are reading now), so all I am left with is the frequency, which is horrendously low.
Yet this has been a very productive year, in which I found stability in a newfound balance between CMS and non-CMS research and between life and work. I have moved to Belgium in July, and am now a researcher in the Institut de recherche en matématique et physique of Université catholique de Louvain; the institute offers an amazing melting pot of experimental physicists, theoreticians, phenomenologists, and generator folks. I am very excited of being here, and am seeing about bringing to light the non-CMS fruits of this melting pot (from the CMS side, I have worked to an update to the observation paper of ttH production, and most importantly to a paper on WZ cross section measurement and search for anomalous triple gauge couplings that is being submitted to JHEP today or tomorrow).
Most importantly, thanks to the new work gig my girlfriend and I moved in together: we actually got married in September in Belgium (followed by a white-dress party with family and friends in Italy)!
So to speak, Belgium is doing great so far: it gave me an exciting job and an exciting wife!
Now that many things have converged, I hope I will really kick-off this blog with an amazing series of posts! I won’t likely follow up immediately on the four drafts I have, because I am getting excited with the idea of writing a series of posts on De Finetti’s definition of probability, so I will most likely start from those. But you never know; the only certainty so far is that I plan to release the next post before the new year, so I will leave you to calculate your posterior for me to actually release the next post in the timescale I advertized 😀
[EDIT: if you want to know what I am up to in a given moment, you can find updated biographical information in the About page. This post will NOT be updated with new affiliations and ventures.]
My name is Pietro, and I have a PhD in physics, but my current main research interest is statistics, with a focus on statistical learning techniques.
In my daily job, I am a researcher in Universidad de Oviedo, Spain, where I work as a particle physicist within the CMS experiment, and am the delegated node PI for the AdvancedMVA4NewPhysics ITN network (you can find me blogging there as well, by the way). I am active in Standard Model (ttH search, WZ cross section, ttbar cross section) and BSM physics (2HDM in the Higgs sector, and SUSY in top sector).
Drawing from my experiences in CMS data analysis, however, I grew fond of statistical techniques, both on the matter of their foundations and on the field of statistical learning. As a consequence, my research focus began to shift from the same usual physics to these more interesting, fundamental, methodological topics. I joined the CMS Statistics Committee, where I found a rich landscape of interesting use cases and a fertile field for discussion. However, there is a specific area of statistics that is rarely applied or discussed in HEP: Bayesian statistics. I felt this is really a pity, so I started roaming extensively this exciting area.
I hope to be able, in this blog, to spark in you some interest for statistical learning and for Bayesian statistics, or at least to give you a good time in reading my random thoughts about these topics.
If you are reading this post, you can safely skip reading the About page, which contains mostly a cut-and-paste of this post, unless you want to find a list of my publications, which is actually present in that page.
Oh, and the look-n-feel of the blog might change a bit in the next days: I am not entirely satisfied with the tinkering I have done so far, so I will most likely tinker with fonts and colours some more.
To conclude, and without further ado, welcome to my new small project: I hope you will enjoy it 🙂