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Scott Dodelson - Carnegie Mellon University. Pittsburgh, PA, US

Scott Dodelson

Professor and Department Head | Carnegie Mellon University

Pittsburgh, PA, UNITED STATES

Scott Dodelson is interested in learning about fundamental physics by analyzing data from cosmic surveys.

Biography

Scott Dodelson conducts research at the interface between particle physics and cosmology, examining the phenomena of dark energy, dark matter, inflation and cosmological neutrinos. He serves as co-chair of the Science Committee for the Dark Energy Survey, is actively involved in the LSST Dark Energy Science Collaboration and works with data from the South Pole Telescope.

Areas of Expertise (5)

Dark Matter

Physics

Space

Cosmology

Astrophysics

Media Appearances (5)

Carnegie Mellon named NSF planning institute for artificial intelligence in physics

EurekAlert!  online

2020-08-26

"So much of physics is data rich, whether we are collecting data from the entire night sky, advanced particle detectors like the Large Hadron Collider or modeling individual proteins in a cell," said Scott Dodelson, professor and head of the Department of Physics and principal investigator of the NSF grant. "The partnership between AI and physics will be synergistic. AI will accelerate physics discovery for the future. In turn, what we learn can be fed back into foundational AI research."

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Students Log On for First Days of Remote Instruction at CMU

Carnegie Mellon University News  online

2020-03-25

At the start of Scott Dodelson's Quantum Physics class, he polls his students to find out where they are physically, and what they're experiencing in different parts of the world. The stories are all similar. The streets are quiet. People are trying to stay inside.

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Evidence for a new fundamental particle thrills and baffles physicists

The Washington Post  online

2018-06-18

Physicists are both thrilled and baffled by a new report from a neutrino experiment at the Fermi National Accelerator Laboratory near Chicago. The MiniBooNE experiment has detected far more neutrinos of a particular type than expected, a finding that is most easily explained by the existence of a new elementary particle: a “sterile” neutrino that’s even stranger and more reclusive than the three known neutrino types.

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Discussing dark matter

Astronomy Magazine  online

2017-11-08

For this dark matter roundtable discussion, The Kavli Foundation brought together cosmologist Scott Dodelson, physicist Risa Wechsler, and astrophysicist George Efstathiou. Each is affiliated with the Dark Energy Survey, an international collaboration focused on shedding light on the dark energy that is responsible for speeding up the expansion of the universe.

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Speed limit found for sluggish dark matter

New Scientist  online

2013-10-07

If particles of dark matter had never formed the clumps they are in today, they would scurry around space at no more than a sluggish 54 metres per second. The finding is one of the few known values for a characteristic of “cold dark matter”, thought to be the most common type of the stuff in the universe.

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Media

Publications:

Scott Dodelson Publication Scott Dodelson Publication

Documents:

Photos:

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Videos:

Scott Dodelson Lecture 1 on Large Scale Structure Scott Dodelson Bogazici Public Lecture Astrophysics at Fermilab by Dr. Scott Dodelson

Audio/Podcasts:

Social

Industry Expertise (2)

Research

Education/Learning

Accomplishments (2)

Sigma Pi Sigma (Physics Honor Society) (professional)

1983

Tau Beta Pi (National Engineering Honor Society) (professional)

1982

Education (1)

Columbia University: Ph.D., Physics 1988

Affiliations (2)

  • American Physical Society : Fellow
  • Fermilab : Physics Advisory Committee

Articles (5)

Dark Energy Survey Year 3 results: magnification modelling and impact on cosmological constraints from galaxy clustering and galaxy–galaxy lensing

Monthly Notices of the Royal Astronomical Society

2023 We study the effect of magnification in the Dark Energy Survey Year 3 analysis of galaxy clustering and galaxy–galaxy lensing, using two different lens samples: a sample of luminous red galaxies, redMaGiC, and a sample with a redshift-dependent magnitude limit, MagLim. We account for the effect of magnification on both the flux and size selection of galaxies, accounting for systematic effects using the Balrog image simulations. We estimate the impact of magnification on the galaxy clustering and galaxy–galaxy lensing cosmology analysis, finding it to be a significant systematic for the MagLim sample.

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Non-local contribution from small scales in galaxy–galaxy lensing: comparison of mitigation schemes

Monthly Notices of the Royal Astronomical Society

2023 Recent cosmological analyses with large-scale structure and weak lensing measurements, usually referred to as 3 × 2pt, had to discard a lot of signal to noise from small scales due to our inability to accurately model non-linearities and baryonic effects. Galaxy–galaxy lensing, or the position–shear correlation between lens and source galaxies, is one of the three two-point correlation functions that are included in such analyses, usually estimated with the mean tangential shear.

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Dark Energy Survey Year 3 results: Constraints on extensions to Λ CDM with weak lensing and galaxy clustering

Physical Review D

2023 We constrain six possible extensions to the Λ cold dark matter (CDM) model using measurements from the Dark Energy Survey’s first three years of observations, alone and in combination with external cosmological probes. The DES data are the two-point correlation functions of weak gravitational lensing, galaxy clustering, and their cross-correlation. We use simulated data vectors and blind analyses of real data to validate the robustness of our results to astrophysical and modeling systematic errors.

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Lessons learned from the two largest Galaxy morphological classification catalogues built by convolutional neural networks

Monthly Notices of the Royal Astronomical Society

2022 We compare the two largest galaxy morphology catalogues, which separate early- and late-type galaxies at intermediate redshift. The two catalogues were built by applying supervised deep learning (convolutional neural networks, CNNs) to the Dark Energy Survey data down to a magnitude limit of ∼21 mag. The methodologies used for the construction of the catalogues include differences such as the cutout sizes, the labels used for training, and the input to the CNN – monochromatic images versus gri-band normalized images.

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Dark Energy Survey Year 3 results: Cosmological constraints from galaxy clustering and galaxy-galaxy lensing using the MagLim lens sample

Physical Review D

2022 The cosmological information extracted from photometric surveys is most robust when multiple probes of the large scale structure of the Universe are used. Two of the most sensitive probes are the clustering of galaxies and the tangential shear of background galaxy shapes produced by those foreground galaxies, so-called galaxy-galaxy lensing. Combining the measurements of these two two-point functions leads to cosmological constraints that are independent of the way galaxies trace matter (the galaxy bias factor).

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