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Illustratie van de werking van een Lyman-alpha blob
Lyman-alpha blobs of LABs zijn kolossale wolken van waterstofgas op grote kosmische afstanden dieĀ zich over honderdduizenden lichtjaren kunnen uitstrekken. Hun naam verwijst naar de karakteristieke ultraviolette straling die zij uitzenden: zogeheten Lyman-alfastraling1. Van meet af aan was het ontstaan van deze objecten een astronomisch raadsel. Nieuwe waarnemingen met ALMA hebben dat raadsel nu opgelost.
Reusachtige kosmische gaswolk gloeit van binnenuit
Het grootste en best bestudeerde object van dit type wordt simpelweg LAB-1 genoemd. Het bevindt zich in het hart van een enorme jonge cluster van sterrenstelsels en werd pas in het jaar 2000 ontdekt. LAB-1 is zo ver weg dat zijn licht er 11,5 miljard jaar over heeft gedaan om ons te bereiken.
Een team astronomen onder leiding van Jim Geach van het Centre for Astrophysics Research of the University of Hertfordshire (VK) heeft LAB-1 nu bekeken met de Atacama Large Millimeter/Submillimeter Array (ALMA), een instrument dat bij uitstek geschikt is om het licht van koele stofwolken in verre sterrenstelsels waar te nemen. Daarmee is het gelukt om LAB-1 op te lossen in een aantal afzonderlijke bronnen van submillimeterstraling2.
Inzoomen op een reusachtige kosmische gaswolk
Vervolgens hebben de astronomen de ALMA-beelden gecombineerd met waarnemingen van het Multi Unit Spectroscopic Explorer-instrument (MUSE) dat aan ESOās Very Large Telescope (VLT) is gekoppeld, om de Lyman-alfastraling in kaart te brengen. Op die manier kon worden aangetoond dat de ALMA-bronnen zich in het centrum van de Lyman-alpha blob bevinden en nieuwe sterren produceren in een tempo dat 100 keer zo groot is als dat van onze Melkweg.
Opnamen van de Hubble-ruimtetelescoop van NASA en ESA en spectroscopische waarnemingen door het W.M. Keck Observatory3 hebben verder laten zien dat de ALMA-bronnen zijn omringd door talrijke zwakke begeleidende sterrenstelsels die het centrale duo mogelijk aan een bombardement onderwerpen, en op die manier de vorming van nieuwe sterren bevorderen.
Overzichtsfoto van de hemel rond een reusachtige kosmische gaswolk
Vervolgens heeft het team met behulp van een geavanceerde simulatie van de vorming van sterrenstelsels aangetoond dat de Lyman-alfastraling van de reusachtige gaswolk kan worden toegeschreven aan ultraviolet licht, afkomstig van jonge sterren, dat door het omringende waterstofgas wordt verstrooid. Dat zou leiden tot de āblobā zoals we die waarnemen.
Jim Geach, eerste auteur van het artikel, legt uit: āVergelijk het maar met een straatlantaarn op een mistige avond ā je ziet dan een diffuse gloed, doordat het licht door kleine waterdruppeltjes wordt verstrooid. Hier gebeurt iets vergelijkbaars, al is de āstraatlantaarnā in dit geval een sterrenstelsel dat in hoog tempo nieuwe sterren produceert en bestaat de āmistā uit intergalactisch gas. De sterrenstelsels verlichten hun omgeving.ā
Begrijpen hoe sterrenstelsels ontstaan en evolueren is een enorme uitdaging. Astronomen denken dat LABs belangrijk zijn omdat dit de plekken lijken te zijn waar de grootste sterrenstelsels in het heelal worden geboren. De uitgebreide Lyman-alfagloed verschaft informatie over wat er gebeurt in het oergas rond jonge sterrenstelsels ā een omgeving die zich moeilijk laat onderzoeken.
Jim Geach concludeert: āWat zo spannend is aan deze gaswolken is dat ze ons een kijkje geven in wat zich rond deze jonge sterrenstelsels-in-wording afspeelt. De oorsprong van het uitgebreide Lyman-alfalicht is lange tijd controversieel geweest. Maar met deze combinatie van nieuwe waarnemingen en geavanceerde simulaties hebben we naar mijn idee een 15 jaar oud raadsel opgelost: Lyman-alpha Blob-1 is de plek waar een groot elliptisch sterrenstelsel aan het ontstaan is, dat ooit het hart van een reusachtige cluster zal vormen. We zien een 11,5 miljard jaar oude momentopname van de vorming van dat sterrenstelsel.ā Bron: ESO.
i don't get where to start on part b of the post lab. im very confused on where to begin.
Letās see if I can help!
Hereās question B:
"Use individual and class data from the excel spreadsheet of Lab 1, Table 1 in conjunction with the Simple Statistics of Lab 3 to complete the following. You should turn in your expanded Table, your graph, and the answers to the questions listed below. The graph may be drawn precisely by hand if you are uncomfortable using a graphing program (like Excel), but the table and answers should be typed.
You may recall in lab. that we computed some simple statistics: the standard deviation (SD) and the mean. For a quick refresher, the standard deviation is a way of describing the spread of your data. It tells you how far apart the data points that you gathered are. The mean is a way of describing where the majority of your data fall and where the middle of that is (though it can be thrown off if you have a few extreme values).Ā
If you forget how to compute the sample mean and SD, and you want to compute these values by hand, I have included a set of instructions at the end. You can also use a computer language or program like Excel, R, or SASS to do this for you.
1.) Using your Groupās data, expand the Table you made for arrows 5 & 6 of Lab 1 with the following columns:
Ā Standard Deviation (SD); calculate for each Treatment
Ā Average + SD, and Average āSD; calculate both for each Treatment
For question one, please turn in a table featuring your groupās data with one row for each of the treatments (Binocular, Monocular non-dominant, Monocular dominant) and columns for each of the trials (3). At the end of each row, include two new columns. One column should have the mean for that treatment, and one columns should have the sample standard deviation.
Hereās an example using sample binocular data:
Treatment Trial 1 Trial 2 Trial 3 SD Average +SD Average āSD Binocular 28 27 31 2 30 26
Note that there are only significant figures out to the ones column for the SD and mean because we can only have whole paperclips!
2.) Using the information just calculated, answer the following questions (1.5pts):
Were the SDs similar between Treatments?
What factors could have contributed to those differences/similarities?
Describe the benefits of using the SD versus the Average to compare Treatments.
Now compare the standard deviations you just computed for each Treatment group. Which has the largest SD? The smallest? Was binocular close to Monocular ND or Monocular D? Did they all look very similar?
These kinds of questions tell us about how different our treatment groups might be from each other. Try to think about what having a bigger or smaller SD might mean for the data in each treatment group.
Next, letās think about what could cause the trends you see in how spread apart your data are for each treatment. Why do you think you see differences between the standard deviations of these different treatments? If all the standard deviations are very similar, why do you think that is?
Finally, letās consider why we might want to talk about our SD instead of our mean. What kind of information does our SD convey? What kind of information does the mean tell us? How could using both the SD and the mean give us more information than just considering one of them?
Think about how talking about the spread of our data might inform our understanding of our treatments instead of considering where the majority of our data lie. Does it tell us something about how separate the groups might be?
3.) Make a graph of your Groupās data (You may find Appendix B, Line Graphs in your lab manual to be helpful for this exercise.) (2pts)
Axis labels: the X-axis should be the Trials, the Y-axis should be the number of paper clips put in the flask.
There should be three data points for each Trial (one for each Treatment within each Trial).
Each data point should have error bars representing the Standard Deviation you calculated for that Treatment (using the numbers you calculated in 1.b above).
Include a legend identifying which data points belong to which Treatment.
Now we want to present our data so other scientists can view it! How can we present it so that they know how different each of our treatments was for each trial?
Well, we can present each trial for each treatment as a point on a graph. We want our independent variable on the X-axis and our dependent variable on the Y-axis. Since we are worried about number of paperclips put in the flask for each trial, we will use Trial # on our X-axis because we got to change that on purpose. We will use # of paperclips put in the flask on our Y-axis because we want to know how that varied by trial. Because we also want to be able to see the differences between treatments for each trial, weāll also color all the points for each treatment different colors. For example, Binocular can be blue, Monocular Dominant can be orange, and Monocular Non-dominant can be black. Make sure to include a key for your reader to tell them what each color means! If you are still lost, take a look at the graph that Dr. Oswald included in the spreadsheet. Your graph should look something like that.
Now how do we show the spread of our data for each treatment?
Hello, error bars, my old friends! Itās nice to see you in my graph again. Error bars tell your reader how likely it is that you might get a different value if you were to perform your experiment again by telling them about how different your trials were for this experiment. Here, your error bars are your SD. These will appear on your graph as little Iās that surround your data points. The length of the | is determined by how big your SD is. In our Binocular example above, each of my binocular points would be surrounded by a line of length 2 units. If you are having trouble doing this in excel, here is a tutorial. You may also draw these in for this assignment (ONLY), but you will have to make them very neat and use a straight edge!
4.) Using full sentences, compare your Groupās graph to the graph of your Sectionās data that was included in the Excel Spreadsheet provided after lab:
Did other Groups have similar SDs to your groupās SDs? If not, describe the differences. (1pt)
Describe 3 possible explanations for your answer to (a). (1.5pts)ā
If you havenāt already, take a look at the graph Dr. Oswald made of section 5ās data (Wed 14:30-17:30). Now look at the graph you just made using your groupās data. Does your groupās data look like our sectionās data? If it doesnāt, how is it different? Maybe your group was very consistent and put the same number of paper clips in the flask each time, but our whole section was not very consistent, and every group put in a different number of paperclips for each treatment and trial. Is the spread of your data greater than or less than the whole sectionās? Maybe your groupās SD is exactly the same as the whole sectionās.
Now that you have noticed the differences between the spread of your groupās data and our sectionās data, think about why you might see these differences. How might having more data points (a bigger sample size) alter the SD of our data? What factors do you think affected the variability of our data? For example, the fact that each group had a different experimenter might affect the differences between groups, while using the same experimenter between trials might affect an individual groupās SD. Try to think of three reasons your data and our sectionās are more or less spread out in comparison to each other.
Notes:
Computing the sample mean for each Treatment group:
1.) Add all the samples in your Treatment group together (Ī£).
2.) Divide by the total number of samples you gathered (N).
Here is an example for binocular data:
Treatment Trial 1 Trial 2 Trial 3 Σ N Mean Binocular 28 27 31 86 3 28
You will notice that 86 does not divide evenly into 3! Because we have only whole numbers in our sample, our precision is to the ones digit, so we have to exclude that extra 2/3 we get when we divide our sum by our N. Make sure to keep track of sig figs.
Computing the sample standard deviation:
1.) Weāll need the mean we computed before and all our data. First, we take all our data and we subtract the mean from each data point (the deviation for the mean).
2.) Now we take each of those deviations from the mean and we square them.
3.) Now take the mean of all those squared deviations from the mean.Here, we take the mean with a twist: instead of dividing by N, the number of samples, we divide by N-1. This is the sample variance!
4.) Take the square root of the variance to get the SD.
Observations from ESO's Very Large Telescope have shed light on the power source of a rare vast cloud of glowing gas in the early Universe. The observations show for the first time that this giant "Lyman-alpha blob" ā one of the largest single objects known ā must be powered by galaxies embedded within it. The results appear in the 18 August issue of the journal Nature.
This video zoom sequence starts with a wide-field view of the dim constellation of Aquarius (The Water Carrier) and slowly closes in on one of the largest known single objects in the Universe, the Lyman-alpha blob LAB1. Observations with the ESO VLT show, for the first time, that the giant "blob" must be powered by galaxies embedded within the cloud.