13 research outputs found
Modeling Biased Tracers at the Field Level
In this paper we test the perturbative halo bias model at the field level.
The advantage of this approach is that any analysis can be done without sample
variance if the same initial conditions are used in simulations and
perturbation theory calculations. We write the bias expansion in terms of
modified bias operators in Eulerian space, designed such that the large bulk
flows are automatically resummed and not treated perturbatively. Using these
operators, the bias model accurately matches the Eulerian density of halos in
N-body simulations. The mean-square model error is close to the Poisson shot
noise for a wide range of halo masses and it is rather scale-independent, with
scale-dependent corrections becoming relevant at the nonlinear scale. In
contrast, for linear bias the mean-square model error can be higher than the
Poisson prediction by factors of up to a few on large scales, and it becomes
scale dependent already in the linear regime. We show that by weighting
simulated halos by their mass, the mean-square error of the model can be
further reduced by up to an order of magnitude, or by a factor of two when
including mass scatter. We also test the Standard Eulerian bias model
using the nonlinear matter field measured from simulations and show that it
leads to a larger and more scale-dependent model error than the bias expansion
based on perturbation theory. These results may be of particular relevance for
cosmological inference methods that use a likelihood of the biased tracer at
the field level, or for initial condition and BAO reconstruction that requires
a precise estimate of the large-scale potential from the biased tracer density.Comment: 61 pages, 27 figures. Minor edits and added references to match
published versio
Rewarding Chatbots for Real-World Engagement with Millions of Users
The emergence of pretrained large language models has led to the deployment
of a range of social chatbots for chitchat. Although these chatbots demonstrate
language ability and fluency, they are not guaranteed to be engaging and can
struggle to retain users. This work investigates the development of social
chatbots that prioritize user engagement to enhance retention, specifically
examining the use of human feedback to efficiently develop highly engaging
chatbots. The proposed approach uses automatic pseudo-labels collected from
user interactions to train a reward model that can be used to reject
low-scoring sample responses generated by the chatbot model at inference time.
Intuitive evaluation metrics, such as mean conversation length (MCL), are
introduced as proxies to measure the level of engagement of deployed chatbots.
A/B testing on groups of 10,000 new daily chatbot users on the Chai Research
platform shows that this approach increases the MCL by up to 70%, which
translates to a more than 30% increase in user retention for a GPT-J 6B model.
Future work aims to use the reward model to realise a data fly-wheel, where the
latest user conversations can be used to alternately fine-tune the language
model and the reward model
Testing Inflation with Large Scale Structure: Connecting Hopes with Reality
The statistics of primordial curvature fluctuations are our window into the
period of inflation, where these fluctuations were generated. To date, the
cosmic microwave background has been the dominant source of information about
these perturbations. Large scale structure is however from where drastic
improvements should originate. In this paper, we explain the theoretical
motivations for pursuing such measurements and the challenges that lie ahead.
In particular, we discuss and identify theoretical targets regarding the
measurement of primordial non-Gaussianity. We argue that when quantified in
terms of the local (equilateral) template amplitude
(), natural target levels of sensitivity are . We highlight that such levels are within
reach of future surveys by measuring 2-, 3- and 4-point statistics of the
galaxy spatial distribution. This paper summarizes a workshop held at CITA
(University of Toronto) on October 23-24, 2014.Comment: 27 pages + reference
On Soft Limits of Inflationary Correlation Functions
Soft limits of inflationary correlation functions are both observationally
relevant and theoretically robust. Various theorems can be proven about them
that are insensitive to detailed model-building assumptions. In this paper, we
re-derive several of these theorems in a universal way. Our method makes
manifest why soft limits are such an interesting probe of the spectrum of
additional light fields during inflation. We illustrate these abstract results
with a detailed case study of the soft limits of quasi-single-field inflation.Comment: 26 pages, 5 figures; V2: references added + pedagogical improvements
of Sec. 2 and App.