Data Analysis App

A while back, I asked if anyone had a suggestion for the best physics apps that are available for mobile devices. I've been mostly using my iPad when I am away from home, ditching my travel laptop. It has worked rather well for me. The only thing that I miss is that I don't have my usual data analysis/graphing software that I often use. I use Origin on my laptop/desktop to analyze, plot, and produce publication-quality graphs. I don't intend to do such extensive work on my iPad, but I do need a quick and dirty way to enter or import data, plot it, and do some rudimentary analysis on it. At the very least, it must be able to do some simple data-fitting and produce a decent-enough graph that I can e-mail to my collaborators.

After looking around for a bit, and after trying this one out for the past month, I think I found a very nice app that does just the thing that I was looking for. The app is called "DataAnalysis". You can find it in the Apple App Store, and I don't know if it has a version on Android. I don't work for the company and get nothing for recommending this app (darn it!), so this is an unsolicited recommendation.

The app is easy enough to use, even though it has links to a couple of YouTube tutorials if you need them. You can either import ASCII text data, or create your own data in an empty data sheet. The data are in a simple, two-column format, space separated (don't you commas or it'll complain!). Once you have your data, you can easily plot it.

You then have the option of doing some simple data analysis. It has a number of already built-in mathematical expression that you can fit your data to. For an undergraduate student in science and engineering, this feature should be sufficient for most cases.

It has a limited number of customization for your graphs. I don't expect to produce a publication-quality graph using this app. But it is good enough for me to send a graph to my collaborators. Having the ability to save and/or send graphics/pdf of the data easily is an important feature that I require, and this app does that.

The one major drawback that I see with this app is the inability (at least, I couldn't find how to do it yet, if the capability exists) to plot more than just one set of data on the same graph. Right now, all I can do is give a set of x and y values. I can't do a set of x, and then a set of y1, y2, etc.. values. It will be a nice feature to have to be able to plot more than just one set of data in a single graph. It can't be that difficult of a feature to add.

Otherwise, this is a very useful app on the go and it does what I need it to do.

Zz.

For RHIC and LHC, Data Is King!

While people are very caught up with the powering up of the LHC and anticipating first collision, many people forget that there's a another monumental task ahead for many people - the handling of an believable amount of data that will be coming out of the various detectors at the LHC. No amount of real-world application comes close to matching the data-handling task that has to be carried out once the LHC is in full operation.

This article looks at the daunting task of distributing just the anticipated data coming from the ATLAS detector at the LHC.

As the sole Tier 1 computing facility for ATLAS in the United States, Brookhaven provides a large portion of the overall computing resources for U.S. collaborators and serves as the central hub for storing, processing and distributing ATLAS experimental data among scientists across the country.

This mission is possible because of the ability to build upon and receive support from the Open Science Grid project, Ernst said. Yet, even after ramping up to 8 petabytes of accessible online storage – a capacity ten times greater than existed when ATLAS joined the RACF eight years ago – the computing center’s scientists still have plenty of testing and problem-solving to conduct before the LHC begins operations this fall.

It is not an understatement that many advances in computing that we see today were driven by the needs of scientific projects such as this. What is being done here will eventually trickle down to various parts of society in a few years.

Zz.

More CP Violation

On the heels of the KEK report that I mentioned earlier, here comes the analysis of the data from the Tevatron at Fermilab that point to the same conclusion.

The amount of CP violation observed in experiments (and enshrined in the standard model), however, is far too little to explain why matter should have prevailed in its ancient war with antimatter. To get a clean look at CP symmetry, DZero and its sibling detector, CDF, focus on the BS, which consists of a bottom quark and a strange antiquark. (Quarks are components of protons and neutrons.) Working independently, the two detectors both found an extra dose of CP violation beyond what the standard model predicts.

Neither result on its own was very convincing, so a team of Italian researchers combined the data, similar to the way medical researchers cull information from independent clinical trials, to look for rare side effects. Together, the data make it 99.7 percent likely that the discrepancy is real, not due to chance, says physicist Luca Silvestrini of the National Institute for Nuclear Physics in Rome, who took part in the study submitted to Physical Review Letters.

Looks like both the CDF and D0 got similar things, which is always good.

Zz.

Statistical Evidence Consistent With Performance-Enhancing Drugs in Professional Baseball

It seems that the use of statistical analysis has been quite prominent in the controversy surrounding the use of performance-enhancing drugs in baseball. Roger Clemens had a team trying to use such statistics in his favor, but it has now been discredited by a team of Ivy League professors.

Now comes even more support for the Mitchell Report. A team from Boston University has analyzed the statistics in baseball, and applied the power law density distribution from complex systems. They arrived at the conclusion written in the title.

So far, the scientific and statistical aspects of this controversy do not support the innocence of the players involved.

Zz.

New WMAP Data Once Again Agrees With Cosmological Model

The more they test it, the more convincing it becomes.

The new report out of the new analysis of WMAP data on the neutrino background has again produced a result consistent with the Big Bang Nucleosynthesis.

As such, the cosmic microwave background provides an independent estimate of the number of neutrino “families” in nature: 4.4 ± 1.5. Despite having been inferred from a totally different cosmological epoch, this value agrees with constraints from Big-Bang nucleosynthesis, the first few minutes of the universe during which light nuclei were manufactured, and with precision measurements at particle accelerators which fix the number of families at three. The WMAP5 data also constrain the combined mass of all types of neutrino to be less than 0.61 eV.

“The discovery of the neutrino background tells us that our models are pretty much right,” says cosmologist Pedro Ferreira of the University of Oxford. “Stuff from particle physics that you’re not putting in by hand just drops out of them — that’s pretty cool if you ask me.”

Yes, very cool! :)

Zz.

Report Backing Clemens Chooses Its Facts Carefully

So, you didn't think that Roger Clemens and the alleged steroid use from the Mitchell Report would be on a physics blog, did you? Bear with me. This has everything to do with data analysis and statistics, something many of us physicists, especially experimentalists, have to do.

For those who don't know about this (especially those from outside North America or don't follow US baseball), there is a major scancal in the sports of US baseball. The recently released Mitchell report, commissioned by the Major League Baseball association has implicated many high-profile players of using steroids during their professional careers. One of them is Roger Clemens, a well-known baseball pitcher who was well on his way to the baseball Hall of Fame in a few years.

Clemens has vehemently denied such practice. He has gone on the "offensive" of trying to produce "evidence" for his innocence. One of the things he (more likely, his defense team) has done is to produce a set of statistics showing that his performance late in his career (during the period that he was accused of using human growth hormone) is not unusual. So his camp actually tried to produce some quantitative analysis to proclaim his innocence.

This is fine and dandy. Unfortunately, his "data analysis" is being disputed by no less than three Ivy League academicians. Three professors from the University of Pennsylvania has challenged the validity of the analysis and wrote an article in the New York Times to rebutt the conclusion from Clemens statistics. The most damaging conclusion they gather out of a more thorough analysis of the statistics was this statement:

Other measures suggest Clemens performed similarly to his contemporaries. But these comparisons do not provide evidence of his innocence; they simply fail to provide evidence of his guilt.

Our reading is that the available data on Clemens’s career strongly hint that some unusual factors may have been at play in producing his excellent late-career statistics.

In other words, if the Clemens camp was hoping that the statistics show his innocence, they are wrong. It certainly doesn't show that he has used any performance enhancing drugs, but it certainly also can't be used as evidence for his innocence like they had hoped.

Zz.

Google to Host Terabytes of Open-Source Science Data

This is certainly a nature progression, especially with Google. It appears that they will introduce an area where scientists can have access to terabytes of space to store open-sourced scientific data that can be shared with anyone. There are already institutions that do this, especially in astronomy and astrophysics, where data such as the CMB, etc. are available freely for the public.

I don't think, however, that high energy physics data will be among this, considering the humongous amount of data that is generated per second during a particle collision. Besides, analyzing such data sets are not trivial. :)

Zz.