Radio Astronomy with Python
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| Lizenz | CC-Namensnennung - keine kommerzielle Nutzung - Weitergabe unter gleichen Bedingungen 3.0 Unported: Sie dürfen das Werk bzw. den Inhalt zu jedem legalen und nicht-kommerziellen Zweck nutzen, verändern und in unveränderter oder veränderter Form vervielfältigen, verbreiten und öffentlich zugänglich machen, sofern Sie den Namen des Autors/Rechteinhabers in der von ihm festgelegten Weise nennen und das Werk bzw. diesen Inhalt auch in veränderter Form nur unter den Bedingungen dieser Lizenz weitergeben | |
| Identifikatoren | 10.5446/49964 (DOI) | |
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00:00
MathematikDoppler-EffektPunktspektrumVerschiebungsoperatorFrequenzQuellcodeZahlenbereichWellenlehreVersionsverwaltungGrundraumLuenberger-BeobachterRelativitätstheorieEin-AusgabeObjekt <Kategorie>TaskProjektive EbeneMultiplikationsoperatort-TestBus <Informatik>GraphfärbungKrümmungsmaßBesprechung/Interview
03:25
WellenpaketDigitale PhotographieAlgorithmusSondierungAnalysisTemperaturstrahlungSchnittmengeEinflussgrößeGraphfärbungInformationSpektralanalyse <Stochastik>DifferenteVerschiebungsoperatorPhysikalisches SystemServerGrößenordnungPunktspektrumMAPVirtuelle MaschineMathematikerinEin-AusgabeNeuronales NetzGauß-ProzessPhysikalische TheorieEntscheidungstheorieObjekt <Kategorie>QuellcodePhysikalismusStatistikElement <Gruppentheorie>BinärdatenVersionsverwaltungSpektrum <Mathematik>VarianzEntscheidungsmodellMateriewelleSoftwareTemplateFunktionalPixelSchreib-Lese-KopfSchlüsselverwaltungWellenlehreWaveletDienst <Informatik>Besprechung/Interview
10:02
SpeicherabzugComputeranimation
10:21
Besprechung/InterviewComputeranimation
10:39
Numerische StrömungssimulationDatenfeldBesprechung/InterviewComputeranimation
10:59
Doppler-EffektMathematikerinFastringSoftwaretestGruppenoperationStatistische HypotheseBesprechung/Interview
11:35
Bus <Informatik>MathematikVersionsverwaltungFormation <Mathematik>WellenlehreSoftwareFrequenzDifferenteVarietät <Mathematik>QuellcodeAnalysisFitnessfunktionEin-AusgabeTemplateVarianzVerschiebungsoperatorWellenpaketNeuronales NetzRechter WinkelGraphfärbungDatenanalyseVirtuelle MaschineGrundraumWasserdampftafelPunktspektrumPhysikalismusAlgorithmusEinflussgrößeGauß-ProzessSchnittmengeDigitale PhotographieTopologieEntscheidungstheorieMultiplikationsoperatorPhysikalische TheorieEreignishorizontCodierungResultanteKonstanteComputeranimationBesprechung/Interview
17:46
SoftwareBildgebendes VerfahrenStochastischer ProzessQuick-SortComputeranimation
18:07
ZahlenbereichBildgebendes VerfahrenGraphfärbungEinflussgrößeDienst <Informatik>Besprechung/Interview
18:33
ServerInternetworkingComputeranimationBesprechung/Interview
Transkript: Englisch(automatisch erzeugt)
00:06
restart I am I am a master student I working on radio telescope project here in Brazil and my task is to reconstruct a red shift what off far
00:29
away objects and I will tell you how I do this first you had to know what is a
00:41
redshift to understand what is redshift you have to record what is the Doppler effect the Doppler effect is the changing frequency of a wave in relation to an observer who is moving relative to the wave source one example of Doppler effect shift is the change of pitch heard when a bus
01:04
sounding a horn approach and recedes from the observer compared to the input frequency they receive the frequency is higher during the approach and we it's identical when they sent it pass it's passing by us and it's
01:27
lower during the recession redshift is the light version of this phenomenon you whisper and see it all the time and we we can we can see if
01:43
you look at the sky we we know that that that there is blue the blue stars and red stars and the the red stars and are far away from us then the
02:01
bluest ones and because we have we have this in the color spectrum spectrum this frequency is this frequency is higher than this and when
02:27
we see a celestial object that there is red it means that is moving far away from us in the expanding universe this is this is this is why we this
02:44
one main reasons we stood this thing because we want to know that if the universe is expanding or if it is contracting or if it is stationary when we the rebel constant is is a number that measures how how the universe is
03:11
expanding and the rebel number is calculated measuring the the red shift
03:32
there are two main approach to to know where the red shift of a star we can do
03:43
data driving analysis is that what I do and we can do what the plate feature analysis using physics theory I don't know physics theory because I am
04:00
research I do I use that data and use machine to machine learn methods to study this phenomenon well if you use a machine learning methods we have to have a training set and wish we we privilege know where what is head what
04:25
is the right redshift and to to and we have two kinds of data a you we use our training set we use spectroscopy redshift and photos the red shift a
04:46
spectroscopy redshift you refer to the measurement of radiation intensity as a function of a wavelet and we just use it to describe the all
05:05
the wave of the spectra of the intensity of all electromagnet key and of elements of our atom of our stars we have infrared information we have all
05:22
the other of the colors information and a bunch gave gay gamma ray information and do it it's it's a complete it's a more complete analysis of often object it could be it can be an atom or it can be a star but
05:48
photos a red shift it's and so just some colors so some we can just how is the intensity of red yellow blue and infrared in the stars there are
06:08
many system there are many system of photos a red shift it's it depends on the survey and the data you you analyze analyze and for example I use
06:24
pen stars data which I have five colors and we call this colors and make magnitudes and we we compare the this photos a red shift you have with five colors without all the beans of the spectroscopy data we have in our
06:46
training set but and frequently we have many sources ministers of uncertainty and some of they are there we have a lack of input some train to
07:08
training where because we will when we do this analysis we have the map of skies and we and it is divided in pixels but it we can okay we can have
07:27
but we are comparing and spectroscopy data to and red shift to one and there there are some missing galaxies in one or another and we we have because
07:47
we had what we compare to serve it to service to to match our training set but we have some being the uncertainty of the difference
08:06
algorithms algorithms we use and we have a completed training sets because we are dealing with a very a very sensitive data that there is a there
08:26
that could be in completely and do depends on the server you are working on and here in my work I use two of the most popular astronomical a
08:41
software's well for doing I use GPZ and a NZ to and the first and is approach that use Gaussian processes to estimate a photometric redshift and we
09:01
are foxy on the on the variance of the set because our data has met has a bunch of your a horse and we have to use statistical analysis because the
09:22
because the source of the of your research is far far away we have to we don't have a good precision well wait and the second we use is a in easy to
09:41
that is the updated version of the original algorithm which use neural networks and in boosted decision and regression trees does it does anyone have a question hello hi I'm Francesca Baskin and I am just
10:09
come so I was wondering if you could resume briefly the core of your worker and I work with reconstruction of a photometric redshift do you know what
10:29
is redshift yes in general but okay if you can explain something else do you are you are you astronomer or something um no I work in the field of
10:46
computational fluid dynamics so totally another field and I'm a beginner in Python so very basic notion I will try to explain what I do because I I'm
11:07
still learning about it I am a mathematician and I were I mean working in a cosmology group and and I'm doing this in my master thesis and to
11:23
understand what a redshift is and you have to record what is the Doppler effect and the you know and when you are there is a bus and near to you and there is a there is a change of pitch pitch heard when when this bus
11:47
sounding a hard approach and received from you and compared to the empty frequency and the empty frequency of the way the wave is higher during the
12:01
approach and when and it's lower during the recession redshift is the light version of this phenomenon and we experience it and it all the time with sound you have you can see this with this where we had here blue and we
12:27
have here red the frequency is is high of the light when we are here in the blue and we as we go and the frequency of the light is lower and reaching red
12:46
so when you see and I start that is red it's bluest then water it's more near it's more near then why why we why we start this thing because we want to
13:08
know if the universe is expanding of it is contracting off if it is doing nothing we have it we have some events that a universe is expanding
13:21
and the measure of redshift allows us to measure this using a thing called rubber constant and this is one main reason we start this this thing there
13:42
are two main approach we use when we measure a redshift we can do a data analysis that is why what I do because I don't know enough physics to do a template feature analysis and then they use how healthy a physics theory
14:07
and I I in my research I use machine learning techniques and then there are many there are many a machine learning software's that we can use in for for
14:27
it but I I used specifically to source I use a GPZ they use get the Gaussian process and I use a an easy to the there is M and use neural networks
14:44
and boosted decision trees but but to do our and but to do this we have to have our training set and because of this we have the spectroscopy redshift
15:04
and our photos a redshift what is the difference between the two the difference is that photos a redshift has have has many as I'm just a five
15:23
color six colors it's colors that we actually see accepted the infrared but spectroscopy redshift is all the it's all the bands of the spectroscopy all the bands of the spectrum and it include and codes x-rays a gamma rays
15:47
and a variety of different bands of the spectrum of them the air tone it's using at all analysis and you it's used in stellar analysis so and we have
16:09
a lot of difficult when we are doing this because we have a very uncertainty and source we have frequently we have incomplete training sets we have
16:25
the uncertainty in the algorithm we use and we have a lot of uncertainty in our imputes of training and because of this we do not use a package like
16:42
psychic learning takes or fall directly we use it use there but I we use they in some way we can get its variance because we have we we we
17:02
work with some uncertainty and of the redshift because we cannot because because we have this certain source and we cannot have the exactly result and this is why we have we work with specific softwares which are described
17:29
by academic papers in my work I use this this to a GPZL and in is to do
17:42
you have some questions yeah I have no experience about the about these two softwares but I was wondering because my work is about reconstruction in medical imaging and so do you do a sort of reconstruction process my name my
18:08
data is is it's not image it's it's a it's a number are you we it's the measure intensity of some some color it's not it's not the the image of
18:28
the stars do you do you know SSD we I work with two service they are
18:41
available on the internet and I cannot find the chat but if you if you if you search you can you can find them and it's placed ten stars with P a and n
19:01
stars and s d s s