Simeon Bird

Johns Hopkins University (& UC Riverside)

Strong Absorbers in the Lyman-alpha Forest and Primordial Black Holes

Talk Outline

1) A Lyman alpha forest emulator

2) Detecting DLAs with machine learning

3) Effects of DLAs/LLS on flux power

4) Primordial Black Holes

Lyman alpha Forest

● Statistics of hydrogen absorption ● Indirectly measures small-scale matter power

Ed Wright

Lyman alpha Forest

● Measures slightly non-linear structure● Needs simulations

Ed Wright

Interpolation

● Want >104 parameter samples● Interpolate; estimate output of simulations from

existing set without actually running them.● Need map from cosmology → observables

For DESI need < 1% level accuracy

Interpolation

Universalis Cosmographia, the Waldseemüller map dated 1507 (wikipedia)

● Early maps of the world had a similar problem

● We are mapping Panamato 1% without ever visiting!

Measurements

Quadratic Interpolation

● Current analysis● Vary one parameter at a time● Flux power interpolation quadratic polynomial

Latin Hypercube

Sample each row and column once on grid

No information in corners Good

Gaussian Processes

● Bayesian function interpolation, which computes probability distribution of f(x) conditional on input set.

● Standard python implementation

Left: Prior on function Right: Posterior conditioned on samples

Rasmussen & Williams (GPML)

Gaussian Processes

● Magic in: Kernel function● Describes how correlation between function

values depends on parameter distance.Left: Prior on function Right: Posterior

conditioned on samples

Rasmussen & Williams (GPML)

Gaussian Processes

● Magic in: Kernel function● Describes how correlation between function

values depends on parameter distance.● Dot kernel (linear interpolation) plus squared

exponential:

● L is estimated from the samples, for every parameter

Rasmussen & Williams (GPML)

Gaussian Processes

● Gaussian Process also estimates interpolation error● Include this uncertainty in likelihood.● Interpolation error just increases uncertainty

Emulator Results

● Each line is a different redshift● Ratio of prediction to extra simulation● 30 simulations, parameters are σ8 and ns

GP+hypercube Quadratic polynomial

Emulator Results

● Much better interpolation!● Increase simulation density in high confidence

region

GP+hypercube Quadratic polynomial

Talk Outline

1) A Lyman alpha forest emulator

2) Detecting DLAs with machine learning

3) Effects of DLAs/LLS on flux power

4) Primordial Black Holes

What are DLAs?

DLAs: neutral hydrogen reservoirs at z=4-2

Neutral hydrogen density: red is DLA, blue is LLS NHI > 2x1020 cm-2

Prochaska+ 2008

Teaching Computers to Detect DLAs

Roman Garnett , Shirley Ho, Me.

1605.04460, 1610.01165

Strong absorbers with NHI > 2x1020 cm-2

Galaxies: from galaxy physics, not cosmology

Foregrounds for Lyman alpha forest

Finding DLAs in Spectra

Normally done by visual inspection of spectraLook for wide dips in the spectrum below:

Finding DLAs in Spectra

Currently done by visual inspection of spectraLook for wide dips in the spectrum below:

Finding DLAs in Spectra

DESI will have > 107 spectraVisual inspection clearly impractical

Finding DLAs in Spectra

So we taught a computer to do thisLearned GP kernel for continuum + absorption

Finding DLAs in Spectra

Learn Bayesian model for quasar without DLA

Learn and test with DR9 visual catalog!

Model for DLA added using Voigt profile

Emission model

Absorption model+noise

Finding DLAs in Spectra

Likelihood of learned model vs like. of DLA.

Get DLA probability for poor detections: use all data, even if SNR < 1

Emission model

Absorption model+noise

Is this a DLA?

Raw

Is this a DLA?

Raw Smoothed

Results

DLA line density – with errorsD

LA L

ine

de

nsity

Results

Total density of neutral hydrogen (note z>4)

Conclusions

• This gives us posterior probability for DLA

• Completely automatic

• Can clean all data, even with SNR < 1

• Catalogue purity vs completeness adjustable

• Extendable to other quasar features

Talk Outline

1) A Lyman alpha forest emulator

2) Detecting DLAs with machine learning

3) Effects of DLAs/LLS on flux power

4) Primordial Black Holes

Lyman alpha Forest

● Undetected DLAs or Lyman Limit Systemsare a foreground and mess up the statistics

● Not in Lyman alpha forest simulations

Ed Wright

Characterising DLAs/LLS

Template for effect on flux power

with Keir Rogers, Peiris, Pontzen, Font-Ribera

State of the art: McDonald 05:– inserted DLAs

– computed power spectrum

– Total abundance free parameter

Characterising Residuals

1. It is easier to detect strong DLAs

Undetected population is not total population

Column density dependent template

2. DLAs in smaller halos at high redshif

Redshif dependent template

Characterising Residuals

Illustris simulation:

● 75 Mpc/h box● Galaxy physics

Contains DLAs: no need to insert themJust make spectra

Illustris Galaxy Model: Ok!

Abundances Metallicity (halo mass)

Has roughly correct DLA abundance and clustering

DLA Flux Power Effect

Flux power spectrum including dense gas

Compare flux P(k) without LLS or DLA spectra

Find multiplicative bias

Strong Absorber Flux PowerFFT of damping wings

Strong Absorber Flux Redshift Evolution

● Higher redshift → more flux power● We also made a fitting function:

Comparison to McDonald 05

Our total is similar to McDonald

Power masking strong DLAs very different !

Did LIGO detect dark matter?

Simeon Bird (JHU)

I. Cholis, J. Munoz, Y. Ali-Haimoud, M. Kamionkowski, E. Kovetz, A. Raccanelli, A. Riess

arXiv: 1603.00464 PRL 116 201301

Did LIGO Detect Dark Matter?

Are LIGO black holes from stellar evolution? Or primordial?

Form from inflationary perturbationsSmall scales, no theory constraints

(LIGO collaboration)

Primordial Black Hole Dark Matter

Black Holes are all the dark matter

Form halos

Gravitational wave emission forms binaries

Does the merger rate match LIGO?

Does the Merger Rate Match LIGO?

Make standard assumptions about dark matter density

NFW profile, Press-Schechter halo mass function, concentration-mass relation

Gravitational Wave Cross-Section

Mergers dominated by the smallest halos:relative velocity ~ halo virial velocity

Mergers are fast compared to binary formation

(Quinlan & Shapiro 1989)

Merger Rate

• Integrated:

• LIGO:

Merger Rate

Total mergers:

This number could have been 10±10

INTERESTING

Did LIGO Detect Dark Matter?

???

Are PBHs at 30 Solar MassRuled Out?

Stellar Heating

Black holes would heat & expand star clusters:

Existence of star clusters constrains PBHs

Brandt 2016

Disruption of Star Clusters

Caveats:

– Central black holes?

– Structure constraints need simulations

Detecting Primordial Black Holes

• Micro-lensing: Black hole in front of star

• Star brighter

• Strong constraints on PBH and other MACHO

More Microlensing

• No micro-lensing constraints if lens is too rare to go past

• Non-detection of lensing (OGLE, HSC):

More Microlensing

Star crossed a DM caustic and was highly magnified

Venumadhav 2017

More Microlensing

Star crossed a DM caustic and was highly magnified

PBH dark matter would disrupt the smooth caustic and reduce the max magnification

This is only one star...

Are PBHs ruled out?

All other masses are conclusively ruled out

Venumadhav 2017

Mass Function

Two populations → mass function bump

Curiously, 7/10 LIGO BHs are ~ 30 solar

Kovetz+ 2017

But Black Holes are Luminous?

• No gas disc for PBHs

• Gas density in halo is low

• Low accretion rate → low luminosity