CMB seen through random Swiss Cheese

We consider a Swiss Cheese model with a random arrangement of LemaitreTolman-Bondi holes in Lambda CDM cheese. We study two kinds of holes with radius r(b) = 50 h(-1) Mpc, with either an underdense or an overdense centre, called the open and closed case, respectively. We calculate the effect of the holes on the temperature, angular diameter distance and, for the first time in Swiss Cheese models, shear of the CMB. We quantify the systematic shift of the mean and the statistical scatter, and calculate the power spectra. In the open case, the temperature power spectrum is three orders of magnitude below the linear ISW spectrum. It is sensitive to the details of the hole, in the closed case the amplitude is two orders of magnitude smaller. In contrast, the power spectra of the distance and shear are more robust, and agree with perturbation theory and previous Swiss Cheese results. We do not find a statistically significant mean shift in the sky average of the angular diameter distance, and obtain the 95% limit vertical bar Delta D-A/(D) over bar (A)vertical bar less than or similar to 10(-4). We consider the argument that areas of spherical surfaces are nearly unaffected by perturbations, which is often invoked in light propagation calculations. The closed case is consistent with this at 1 sigma, whereas in the open case the probability is only 1.4%.Peer reviewe

Valon kulku ja havainnot epähomogeenisissa kosmologisissa malleissa

The science of cosmology relies heavily on interpreting observations in the context of a theoretical model. If the model does not capture all of the relevant physical effects, the interpretation of observations is on shaky grounds. The concordance model in cosmology is based on the homogeneous and isotropic Friedmann-Robertson-Walker metric with small perturbations. One long standing question is whether the small-scale details of the matter distribution can modify the predictions of the concordance model, or whether the concordance model can describe the universe to a high precision. In this thesis, I discuss some potential ways in which inhomogeneities may change the interpretation of observations from the predictions of the concordance model. One possibility is that the small-scale structure affects the average expansion rate of the universe via a process called backreaction. In such a case the concordance model fails to describe the time-evolution of the universe accurately, leading to the mis-interpretation of observations. Another possibility is that the paths that light rays travel on are curved in such a way that they do not cross all regions with equal probability. If some regions are favoured and others disfavoured, the average description of the concordance model gives incorrect results. My collaborators and I investigated the effects of voids on the CMB using second order perturbation theory and the exact Lemaître-Tolman-Bondi solution. A void has been detected in the direction of the CMB Cold Spot, but we found that contrary to the claims made in the literature, it was not large and deep enough to explain the Cold Spot. The results from perturbation theory and exact calculation agreed to a high precision, which was not surprising, as the void is fairly shallow. We have studied a toy model of the universe, called the Swiss Cheese model, to see if the model can produce observational signals that deviate significantly from the predictions of the concordance model. We studied the backreaction in such models, and concluded that in physically motivated Swiss Cheese models, its impact on the expansion rate must be small. We also considered an unphysical model that was constructed to have the holes expand independently from the background. Even though the inhomogeneities change the expansion rate completely, the backreaction contribution to the total average expansion rate today was only at 1% level. We also studied weak lensing in a more realistic Swiss Cheese model to see how the structures change the brightness and shape of sources. We found that the simplest assumption, no change in the average flux, seemed to be violated with a probability of 98.6%. Our results agree on the magnitude of the effect, in that it should be very small, but the exact value is significantly different. There are many reasons why this may be the case, and one of the reasons is that the structures alter the area of the constant-redshift surface around the observer. However, to find conclusive proof of this, the calculation should be re-done with a higher resolution.Kosmologia on tieteenala joka tutkii maailmankaikkeutta kokonaisuutena. Jotta maailmankaikkeudesta saataisiin tietoa kokonaisuutena, täytyy sitä kuvata matemaattisen mallin avulla. Maailmankaikkeus on niin monimutkainen, että täysin realistisen mallin laskeminen on mahdoton tehtävä. Niinpä täytyykin tehdä joitain yksinkertaistuksia ja karkeistuksia, jotta maailmankaikkeutta voidaan kuvata matemaattisesti. Yleensä maailmankaikkeutta kuvataan olettamalla sen olevan homogeeninen ja isotrooppinen kun sitä tarkastellaan kyllin suurella mittakaavalla. Rakenteet, kuten galaksit tai galaksiryppäät, otetaan huomioon häiriöteorian avulla tai ei lainkaan. Tässä väitöskirjassa olen tutkinut sitä, voiko rakenteiden tarkemmalla mallinnuksella muuttaa havaintojen tulkintaa. On tärkeää selvittää, mikäli yleensä käytetty analyysi on riittävä, vai jääkö siinä tärkeitä ilmiöitä mallintamatta. Erityisen kiinnostava on kysymys, voivatko pienen mittakaavan rakenteet vaikuttaa mitattujen etäisyyksien keskiarvoihin. Tähän on kaksi mahdollisuutta. Rakenteet voivat muuttaa maailmankaikkeuden keskimääräistä laajenemisnopeutta takaisinkytkennäksi ('backreaction') kutsutun mekanismin välityksellä. Toisaalta rakenteet voivat muuttaa valonsäteiden kulkemia polkuja niin, että jotkut alueet ovat suositumpia kuin toiset. Myös tällöin standardianalyysi saattaa antaa virheellisiä ennusteita. Väitöskirjassa tutkitaan suuren aineen alitihentymän vaikutusta kosmisen mikroaaltotaustan lämpötilaan. On esitetty, että tällainen alitihentymä voisi selittää mikroaaltotaustassa havaitun anomalian, ns. kylmän läiskän ('Cold Spot'). Kylmän läiskän suunnassa havaittu alitihentymä ei kuitenkaan ole kyllin suuri tai syvä selittämään sitä. Lisäksi väitöskirjassa tutkitaan ns. 'Swiss Cheese' -malleja, joissa rakenteita kuvataan eksaktin ratkaisun, ei häiriöteorian avulla. Tutkimuksissani olen osoittanut että realistisissa Swiss Cheese -malleissa takaisinkytkennän vaikutuksen on oltava pieni. Näyttää kuitenkin siltä, että Swiss Cheese -malli antaa kvalitatiivisesti erilaisia tuloksia heikolle gravitaatiolinssi-ilmiölle

Average expansion rate and light propagation in a cosmological Tardis spacetime

We construct the first exact statistically homogeneous and isotropic cosmological solution in which inhomogeneity has a significant effect on the expansion rate. The universe is modelled as a Swiss Cheese, with dust FRW background and inhomogeneous holes. We show that if the holes are described by the quasispherical Szekeres solution, their average expansion rate is close to the background under certain rather general conditions. We specialise to spherically symmetric holes and violate one of these conditions. As a result, the average expansion rate at late times grows relative to the background, i.e. backreaction is significant. The holes fit smoothly into the background, but are larger on the inside than a corresponding background domain: we call them Tardis regions. We study light propagation, find the effective equations of state and consider the relation of the spatially averaged expansion rate to the redshift and the angular diameter distance.Peer reviewe

Ravinnon kasvinsuojeluainejäämät : kumulatiivinen riskinarviointi

Kasvinsuojeluaineita käytetään elintarviketuotannossa kasvitautien ehkäisemiseen sekä kasvintuhoojien vaikutusten ajoittamiseen ja kasvun säätelyyn. Käytettyjen tehoaineiden riskinarviointi yksi kerrallaan on tuottanut tärkeää perustietoa, mutta se ei ole antanut selkeää kokonaiskuvaa kuluttajien altistumisesta. Tämän vuoksi asetelmaa tarkasteltiin kokonaisvaltaisesti, ottaen kaikki elintarvikkeista havaitut tehoainejäämät mukaan arvioon kumulatiivisesti. Aikuisten lisäksi mukana on ensi kertaa myös lapsiryhmiä. Ensimmäistä kertaa Suomessa tarkastellaan sekä pitkäaikaista että akuuttia altistusta. Esitetty kumulatiivinen riskinarviointi perustuu vuosina 2002- 2008 kasvinsuojeluainejäämien valvonnassa yhteensä 10 565 elintarvikenäytteestä saatuihin tutkimustuloksiin. Lisäksi riskinarvioinnissa on käytetty Terveyden ja hyvinvoinnin laitoksen ja DIPP-konsortion tuottamia aikuisten ja lasten ruoankulutustietoja (Finravinto 2007 ja DIPP-ravintotutkimukset). Ravinnon välittämälle tehoainejäämien altistukselle on tunnusomaista matala perustaso, jossa esiintyy lyhytaikaisia altistushuippuja. Kun ravinnon välittämä pitkäaikainen altistus kasvinsuojeluaineille on hyväksyttävällä tasolla, ei lyhytaikaisen altistuksen tilanne kaikilta osin ole yhtä hyvä. Kolmivuotiailla lapsilla todennäköisyys aRfD:n ylittymiselle on organofosfaattien ja karbamaattien osalta ollut suurempi kuin 0,1 % eli enemmän kuin yksi tuhannesta, mikä ei vielä vastaa tavoitteita. Ylitykset aiheutuvat tuontituotteista,koska niiden taustalla olevia karbamaatteja ja organofosfaatteja ei enää käytetä Suomessa. Myönteistä kehitystä on tapahtunut, mutta tilannetta on aiheellista edelleen seurata.Växtskyddsmedel används i livsmedelsproduktionen för att förhindra växtsjukdomar och för att begränsa effekterna av skadegörare samt för reglering av tillväxten. Att man värderat risker av ett använt effektämne åt gången har producerat viktig grundinformation, men det har inte gett en klar helhetsbild av exponering av konsumenter. Därför granskades ärendet helhetsbetonat, så att alla i livsmedel framkomna rester av effektämnen kumulativt togs med i värderingen. För första gången var med också barngrupper utöver de vuxna. Likaså för första gången granskades i Finland både långvarig och akut exponering. Den framförda kumulativa riskvärderingen grundar sig på de forskningsresultat av 10 565 livsmedelsprov som erhållits vid övervakningen av västskyddsmedelsrester under åren 2002–2008. Dessutom har man i riskvärderingen använt de uppgifter om vuxnas och barns matkonsumtion som producerats av Institutet för hälsa och välfärd och DIPP-konsortiet (Finravinto 2007 och DIPP-näringsundersökningarna). Exponering för rester av effektämnen i kosten kännetecknas av en låg grundnivå med kortvariga exponeringstoppar. Även om långvarig exponering för växtskyddsmedel via kosten ligger på godkänd nivå, är läget för alla delar inte lika bra med kortvarig exponering. Sannolikheten för överskridande av aRfD har hos 3-åriga barn varit större än 0,1 %, dvs. fler än 1 av 1 000 när det gäller organofosfater och karbamater, vilket ännu inte motsvarar de mål som ställts. En positiv utveckling har skett, men det är skäl att vidare följa läget.Plant production products are used to prevent plant diseases, to restrict harmful organisms, and to regulate growth. The consumer risks of active substances are evaluated considering one substance at a time, which gives crucial information, but as such cannot give an overall picture of dietary exposure. The probability of dietary exposure was estimated by cumulative simulation methods. For the first time in Finland exposure estimation in acute setting and also among sensitive groups, such as young children, has been carried out. The cumulative risk assessment presented herein is based on research results gained in control of pesticides residues from a total of 10 565 foodstuff samples. Moreover, risk assessment has utilised data on food consumption of adults and children, produced by the National Institute for Health and Welfare and the DIPP Consortium (the National FINDIET 2007 Survey and DIPP Nutrition Studies). Dietary exposure to residues of plant protection products is characterized by a low chronic exposure level, on which higher acute exposure occasionally takes place. While chronic exposure to pesticide residues did not raise any concerns, the situation in acute exposure setting cannot be considered equally good. Carbamates and organophosphates resulted in aRfD exceedances among children and adults with a probability higher than 0,01%. While the probability of an aRfD exceedance in adult group in the past few years has decreased to acceptable levels, among three-year old children it has not. Although the most important underlying carbamates and organophosphates are not used in Finland any more, current situation does not in all respects fulfil the goals set for the level of protection. Therefore monitoring of sensitive subpopulations should be continued

Can a supervoid explain the Cold Spot?

Peer reviewe

Development and External Validation of a Deep Learning Algorithm to Identify and Localize Subarachnoid Hemorrhage on CT Scans

OBJECTIVE In medical imaging, a limited number of trained deep learning algorithms have been externally validated and released publicly. We hypothesized that a deep learning algorithm can be trained to identify and localize subarachnoid haemorrhage (SAH) on head computed tomography (CT) scans, and that the trained model performs satisfactorily when tested using external and real-world data. METHODS We used non-contrast head CT images of patients admitted Helsinki University Hospital between 2012 and 2017. We manually segmented (i.e. delineated) SAH on 90 head CT scans, and used the segmented CT scans together with 22 negative (no SAH) control CT scans in training an open-source convolutional neural network (U-Net) to identify and localize SAH. We then tested the performance of the trained algorithm by using external datasets (137 SAH and 1242 control cases) collected in two foreign countries, and also by creating a dataset of consecutive emergency head CT scans (8 SAH and 511 control cases) performed during on call hours in 5 different domestic hospitals in September 2021. We assessed the algorithm's capability to identify SAH by calculating patient- and slice-level performance metrics, such as sensitivity and specificity. RESULTS In the external validation set of 1379 cases, the algorithm identified 136 out of 137 SAH cases correctly (sensitivity 99.3%, specificity 63.2%). Of the 49064 axial head CT slices, the algorithm identified and localized SAH in 1845 out of 2110 slices with SAH (sensitivity 87.4%, specificity 95.3%). Of 519 consecutive emergency head CT scans imaged in September 2021, the algorithm identified all 8 SAH cases correctly (sensitivity 100.0%, specificity 75.3%). The slice-level (27167 axial slices in total) sensitivity and specificity were 87.3% and 98.8%, as the algorithm identified and localized SAH in 58 out of 77 slices with SAH. The performance of the algorithm can be tested on through a webservice. CONCLUSIONS We show that the shared algorithm identifies SAH cases with a high sensitivity, and that the slice-level specificity is high. In addition to openly sharing a high-performing deep learning algorithm, our work presents infrequently used approaches in designing, training, testing and reporting deep learning algorithms developed for medical imaging diagnostics. CLASSIFICATION OF EVIDENCE This study provides Class III evidence a deep learning algorithm correctly identifies the presence of subarachnoid hemorrhage on CT scan

Suosittelu Ylen palveluissa

Kirjastoverkkopäivien 2021 TP5 Suosittelutyöpajan esitys: "YLE:n tavoitteet suosittelujärjestelmille sekä kokemuksia niiden toteuttamisesta", jonka pitivät Kati Erkkilä ja Mikko Lavinto YLE:lt

Development and External Validation of a Deep Learning Algorithm to Identify and Localize Subarachnoid Hemorrhage on CT Scans

Background and ObjectivesIn medical imaging, a limited number of trained deep learning algorithms have been externally validated and released publicly. We hypothesized that a deep learning algorithm can be trained to identify and localize subarachnoid hemorrhage (SAH) on head computed tomography (CT) scans and that the trained model performs satisfactorily when tested using external and real-world data.MethodsWe used noncontrast head CT images of patients admitted to Helsinki University Hospital between 2012 and 2017. We manually segmented (i.e., delineated) SAH on 90 head CT scans and used the segmented CT scans together with 22 negative (no SAH) control CT scans in training an open-source convolutional neural network (U-Net) to identify and localize SAH. We then tested the performance of the trained algorithm by using external data sets (137 SAH and 1,242 control cases) collected in 2 foreign countries and also by creating a data set of consecutive emergency head CT scans (8 SAH and 511 control cases) performed during on-call hours in 5 different domestic hospitals in September 2021. We assessed the algorithm's capability to identify SAH by calculating patient- and slice-level performance metrics, such as sensitivity and specificity.ResultsIn the external validation set of 1,379 cases, the algorithm identified 136 of 137 SAH cases correctly (sensitivity 99.3% and specificity 63.2%). Of the 49,064 axial head CT slices, the algorithm identified and localized SAH in 1845 of 2,110 slices with SAH (sensitivity 87.4% and specificity 95.3%). Of 519 consecutive emergency head CT scans imaged in September 2021, the algorithm identified all 8 SAH cases correctly (sensitivity 100.0% and specificity 75.3%). The slice-level (27,167 axial slices in total) sensitivity and specificity were 87.3% and 98.8%, respectively, as the algorithm identified and localized SAH in 58 of 77 slices with SAH. The performance of the algorithm can be tested on through a web service.DiscussionWe show that the shared algorithm identifies SAH cases with a high sensitivity and that the slice-level specificity is high. In addition to openly sharing a high-performing deep learning algorithm, our work presents infrequently used approaches in designing, training, testing, and reporting deep learning algorithms developed for medical imaging diagnostics.Classification of EvidenceThis study provides Class III evidence that a deep learning algorithm correctly identifies the presence of subarachnoid hemorrhage on CT scan.Peer reviewe